data_overview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='top'></a>\n",
    "# How to slice and dice the data\n",
    "Below are a series of examples on how to slice and dice the data that is stored in the *.sqlite* file generated by the [MorningStar.com](https://www.morningstar.com) web scraper. \n",
    "\n",
    "##### NOTE: \n",
    "- The data used in the code below come from the *.sqlite* file that is automatically generated by the web scraper once it has been installed and ran locally on your machine. See [README]() for instructions on how to run install and run the scraper.\n",
    "- Navigation links only when using [Jupyter notebook](https://jupyter.org/).\n",
    "\n",
    "\n",
    "**Content** \n",
    "\n",
    "1. [Required modules and matplotlib backend](#modules)\n",
    "1. [Creating a master (bridge table) DataFrame instance using the DataFrames class](#master)\n",
    "1. [Methods for creating DataFrame instances](#methods)\n",
    "    1. `quoteheader` - [MorningStar (MS) Quote Header](#quote)\n",
    "    1. `valuation` - [MS Valuation table with Price Ratios (P/E, P/S, P/B, P/C) for the past 10 yrs](#val)\n",
    "    1. `keyratios` - [MS Ratio - Key Financial Ratios & Values](#keyratios)\n",
    "    1. `finhealth` - [MS Ratio - Financial Health](#finhealth)\n",
    "    1. `profitability` - [MS Ratio - Profitability](#prof)\n",
    "    1. `growth` - [MS Ratio - Growth](#growth)\n",
    "    1. `cfhealth` - [MS Ratio - Cash Flow Health](#cfh)\n",
    "    1. `efficiency` - [MS Ratio - Efficiency](#eff)\n",
    "    1. `annualIS` - [MS Annual Income Statements](#isa)\n",
    "    1. `quarterlyIS` - [MS Quarterly Income Statements](#isq)\n",
    "    1. `annualBS` - [MS Annual Balance Sheets](#bsa)\n",
    "    1. `quarterlyBS` - [MS Quarterly Balance Sheets](#bsq)\n",
    "    1. `annualCF` - [MS Annual Cash Flow Statements](#cfa)\n",
    "    1. `quarterlyCF` - [MS Quarterly Cash Flow Statements](#cfq)\n",
    "    1. `insider_trades` - [Insider Transactions](#it)\n",
    "1. [Performing statistical analysis](#stats)\n",
    "    1. [Count of database records](#stats)\n",
    "    1. [Last updated dates](#lastupdate)\n",
    "    1. [Number of records by security type](#type)\n",
    "    1. [Number of records by country, based on the location of exchanges](#country)\n",
    "    1. [Number of records per exchange](#exchange)\n",
    "    1. [Number of stocks by sector](#sector)\n",
    "    1. [Number of stocks by industry](#industry)\n",
    "    1. [Mean price ratios (P/E, P/S, P/B, P/CF) of stocks by sectors](#meanpr)\n",
    "1. [Applying various criteria to filter common stocks](#value) *(in progress)*\n",
    "1. [Additional sample / test code](#additional) *(in progress)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"modules\"></a>\n",
    "# Required modules and matplotlib backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import dataframes #module containing class used to create DataFrame objects from SQLite database file\n",
    "import datetime as DT\n",
    "import re\n",
    "\n",
    "# Reload in case changes have been made to module file\n",
    "from importlib import reload\n",
    "reload(dataframes);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to the top](#top)\n",
    "<a id=\"master\"></a>\n",
    "# Creating the master DataFrame instance\n",
    "The DataFrames class is part of the [dataframes module](dataframes.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating initial DataFrames objects from file db/mstables.sqlite...\n",
      "\n",
      "\t- DataFrame 'df.colheaders' ...\n",
      "\t- DataFrame 'df.timerefs' ...\n",
      "\t- DataFrame 'df.urls' ...\n",
      "\t- DataFrame 'df.securitytypes' ...\n",
      "\t- DataFrame 'df.tickers' ...\n",
      "\t- DataFrame 'df.sectors' ...\n",
      "\t- DataFrame 'df.industries' ...\n",
      "\t- DataFrame 'df.stockstyles' ...\n",
      "\t- DataFrame 'df.exchanges' ...\n",
      "\t- DataFrame 'df.countries' ...\n",
      "\t- DataFrame 'df.companies' ...\n",
      "\t- DataFrame 'df.currencies' ...\n",
      "\t- DataFrame 'df.stocktypes' ...\n",
      "\t- DataFrame 'df.master' ...\n",
      "\n",
      "Initial DataFrames created successfully.\n"
     ]
    }
   ],
   "source": [
    "db_file_name = 'mstables' # SQLite database file that contains the data to be analyzed\n",
    "df = dataframes.DataFrames('db/{}.sqlite'.format(db_file_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Master DataFrame instance from reference tables\n",
    "Merge `df.master` (*Master* bridge table) with other reference tables (e.g. `df.tickers`, `df.exchanges`, etc.) and filter out inactive / invalid records.\n",
    "### DataFrame Instance\n",
    "Create `df_master`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_master = df.master.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the following filter:\n",
    "<br>\n",
    "- $lastdate < cutoff\\_date$\n",
    "<br>\n",
    "- $cutoff\\_date = date one\\ week\\ prior\\ to\\ last\\ date\\ the\\ database\\ was\\ updated$\n",
    "<br>\n",
    "<br>\n",
    "This filter ensure that only active records are included in the master dataframe, excluding inactive MorningStar records which are no longer being updated on a regular basis. These inactive records are typically symbols that are no longer active in their exchange."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff_days = 10\n",
    "df_updated_ct = df_master[['update_date', 'ticker']].groupby('update_date').count().sort_index()\n",
    "cutoff_date = df_updated_ct[df_updated_ct['ticker'] > 100].index[0] - DT.timedelta(days=cutoff_days)\n",
    "\n",
    "df_master = df_master.where(df_master['lastdate'] >= cutoff_date).dropna(axis=0, how='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DataFrame Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame df_master contains 100,115 records and 33 columns.\n"
     ]
    }
   ],
   "source": [
    "msg = 'DataFrame df_master contains {:,.0f} records and {:,.0f} columns.'\n",
    "print(msg.format(df_master.shape[0], df_master.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ticker_id', 'exchange_id', 'update_date', 'ticker', 'company',\n",
       "       'exchange', 'exchange_sym', 'industry', 'sector', 'country',\n",
       "       'country_c2', 'country_c3', 'security_type_code', 'security_type',\n",
       "       'stock_type', 'style', 'openprice', 'lastprice', 'day_hi',\n",
       "       'day_lo', '_52wk_hi', '_52wk_lo', 'yield', 'lastvol', 'avevol',\n",
       "       'PE_Forward', 'pb', 'ps', 'pc', 'lastdate', 'currency',\n",
       "       'currency_code', 'fy_end'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br></br>\n",
    "[return to the top](#top)\n",
    "<a id='methods'></a>\n",
    "# Creating DataFrame instances with dataframes methods\n",
    "Class DataFrames from [dataframe.py](dataframe.py) contains the following methods that return a pd.DataFrame object for the specified database table:\n",
    "\n",
    "- `quoteheader` - [MorningStar (MS) Quote Header](#quote)\n",
    "- `valuation` - [MS Valuation table with Price Ratios (P/E, P/S, P/B, P/C) for the past 10 yrs](#val)\n",
    "- `keyratios` - [MS Ratio - Key Financial Ratios & Values](#keyratios)\n",
    "- `finhealth` - [MS Ratio - Financial Health](#finhealth)\n",
    "- `profitability` - [MS Ratio - Profitability](#prof)\n",
    "- `growth` - [MS Ratio - Growth](#growth)\n",
    "- `cfhealth` - [MS Ratio - Cash Flow Health](#cfh)\n",
    "- `efficiency` - [MS Ratio - Efficiency](#eff)\n",
    "- `annualIS` - [MS Annual Income Statements](#isa)\n",
    "- `quarterlyIS` - [MS Quarterly Income Statements](#isq)\n",
    "- `annualBS` - [MS Annual Balance Sheets](#bsa)\n",
    "- `quarterlyBS` - [MS Quarterly Balance Sheets](#bsq)\n",
    "- `annualCF` - [MS Annual Cash Flow Statements](#cfa)\n",
    "- `quarterlyCF` - [MS Quarterly Cash Flow Statements](#cfq)\n",
    "- `insider_trades` - [Insider Transactions](#it)\n",
    "\n",
    "<a id='quote'></a>\n",
    "### Quote Header \n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_quote = df.quoteheader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker_id</th>\n",
       "      <th>exchange_id</th>\n",
       "      <th>openprice</th>\n",
       "      <th>lastprice</th>\n",
       "      <th>day_hi</th>\n",
       "      <th>day_lo</th>\n",
       "      <th>_52wk_hi</th>\n",
       "      <th>_52wk_lo</th>\n",
       "      <th>yield</th>\n",
       "      <th>lastvol</th>\n",
       "      <th>avevol</th>\n",
       "      <th>fpe</th>\n",
       "      <th>pb</th>\n",
       "      <th>ps</th>\n",
       "      <th>pc</th>\n",
       "      <th>currency_id</th>\n",
       "      <th>lastdate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>374</td>\n",
       "      <td>15.65</td>\n",
       "      <td>15.68</td>\n",
       "      <td>15.69</td>\n",
       "      <td>15.64</td>\n",
       "      <td>17.72</td>\n",
       "      <td>12.16</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2747.0</td>\n",
       "      <td>2983.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>16.2</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2019-05-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>374</td>\n",
       "      <td>15.65</td>\n",
       "      <td>15.52</td>\n",
       "      <td>15.69</td>\n",
       "      <td>15.52</td>\n",
       "      <td>17.68</td>\n",
       "      <td>13.68</td>\n",
       "      <td>2.03</td>\n",
       "      <td>7800.0</td>\n",
       "      <td>2280.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.4</td>\n",
       "      <td>6.3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2019-05-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>374</td>\n",
       "      <td>15.61</td>\n",
       "      <td>15.31</td>\n",
       "      <td>15.61</td>\n",
       "      <td>15.31</td>\n",
       "      <td>17.79</td>\n",
       "      <td>11.99</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>11124.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.4</td>\n",
       "      <td>6.4</td>\n",
       "      <td>16.2</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2019-05-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>482</td>\n",
       "      <td>102.97</td>\n",
       "      <td>101.78</td>\n",
       "      <td>103.94</td>\n",
       "      <td>101.38</td>\n",
       "      <td>115.11</td>\n",
       "      <td>87.87</td>\n",
       "      <td>1.16</td>\n",
       "      <td>971891.0</td>\n",
       "      <td>758551.0</td>\n",
       "      <td>22.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>23.6</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2019-05-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2019-04-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker_id  exchange_id  openprice lastprice  day_hi  day_lo _52wk_hi  \\\n",
       "0          1          374      15.65     15.68   15.69   15.64    17.72   \n",
       "1          2          374      15.65     15.52   15.69   15.52    17.68   \n",
       "2          3          374      15.61     15.31   15.61   15.31    17.79   \n",
       "3          4          482     102.97    101.78  103.94  101.38   115.11   \n",
       "4          5            1       0.00         0       0       0        0   \n",
       "\n",
       "  _52wk_lo  yield   lastvol    avevol   fpe   pb   ps    pc  currency_id  \\\n",
       "0    12.16   2.01    2747.0    2983.0   NaN  2.4  6.4  16.2        104.0   \n",
       "1    13.68   2.03    7800.0    2280.0   NaN  2.4  6.3  16.0        104.0   \n",
       "2    11.99   2.00    1013.0   11124.0   NaN  2.4  6.4  16.2        104.0   \n",
       "3    87.87   1.16  971891.0  758551.0  22.9  3.5  4.4  23.6        104.0   \n",
       "4        0    NaN     500.0     342.0   NaN  NaN  NaN   NaN        104.0   \n",
       "\n",
       "     lastdate  \n",
       "0  2019-05-07  \n",
       "1  2019-05-06  \n",
       "2  2019-05-07  \n",
       "3  2019-05-07  \n",
       "4  2019-04-25  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_quote.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 119,684 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_quote)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='val'></a>\n",
    "[return to the top](#top)\n",
    "### Price Ratios (P/E, P/S, P/B, P/C)\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_vals = df.valuation().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 81,772 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_vals)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['exchange_id', 'ticker_id', 'PE_2009', 'PE_2010', 'PE_2011', 'PE_2012',\n",
       "       'PE_2013', 'PE_2014', 'PE_2015', 'PE_2016', 'PE_2017', 'PE_2018',\n",
       "       'PE_TTM', 'PS_2009', 'PS_2010', 'PS_2011', 'PS_2012', 'PS_2013',\n",
       "       'PS_2014', 'PS_2015', 'PS_2016', 'PS_2017', 'PS_2018', 'PS_TTM',\n",
       "       'PB_2009', 'PB_2010', 'PB_2011', 'PB_2012', 'PB_2013', 'PB_2014',\n",
       "       'PB_2015', 'PB_2016', 'PB_2017', 'PB_2018', 'PB_TTM', 'PC_2009',\n",
       "       'PC_2010', 'PC_2011', 'PC_2012', 'PC_2013', 'PC_2014', 'PC_2015',\n",
       "       'PC_2016', 'PC_2017', 'PC_2018', 'PC_TTM'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vals.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='keyratios'></a>\n",
    "[return to the top](#top)\n",
    "### Key Ratios\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_keyratios = df.keyratios()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 79,103 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_keyratios)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "i0                    Revenue USD Mil\n",
       "i1                     Gross Margin %\n",
       "i2           Operating Income USD Mil\n",
       "i3                 Operating Margin %\n",
       "i4                 Net Income USD Mil\n",
       "i5             Earnings Per Share USD\n",
       "i6                      Dividends USD\n",
       "i91                  Payout Ratio % *\n",
       "i7                         Shares Mil\n",
       "i8         Book Value Per Share * USD\n",
       "i9        Operating Cash Flow USD Mil\n",
       "i10              Cap Spending USD Mil\n",
       "i11            Free Cash Flow USD Mil\n",
       "i90    Free Cash Flow Per Share * USD\n",
       "i80           Working Capital USD Mil\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels_keyratios = (df_keyratios\n",
    "                     .loc[0, [col for col in df_keyratios.columns if 'Y' not in col and col.startswith('i')]]\n",
    "                     .replace(df.colheaders['header']))\n",
    "df_labels_keyratios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='finhealth'></a>\n",
    "[return to the top](#top)\n",
    "### Financial Health\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_finhealth = df.finhealth()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 79,103 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_finhealth)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fh_balsheet         Balance Sheet Items (in %)\n",
       "i45              Cash & Short-Term Investments\n",
       "i46                        Accounts Receivable\n",
       "i47                                  Inventory\n",
       "i48                       Other Current Assets\n",
       "i49                       Total Current Assets\n",
       "i50                                   Net PP&E\n",
       "i51                                Intangibles\n",
       "i52                     Other Long-Term Assets\n",
       "i53                               Total Assets\n",
       "i54                           Accounts Payable\n",
       "i55                            Short-Term Debt\n",
       "i56                              Taxes Payable\n",
       "i57                        Accrued Liabilities\n",
       "i58               Other Short-Term Liabilities\n",
       "i59                  Total Current Liabilities\n",
       "i60                             Long-Term Debt\n",
       "i61                Other Long-Term Liabilities\n",
       "i62                          Total Liabilities\n",
       "i63                  Total Stockholders Equity\n",
       "i64                 Total Liabilities & Equity\n",
       "lfh_liquidity       Liquidity/Financial Health\n",
       "i65                              Current Ratio\n",
       "i66                                Quick Ratio\n",
       "i67                         Financial Leverage\n",
       "i68                                Debt/Equity\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels_finhealth = (df_finhealth.loc[0, [col for col in df_finhealth.columns \n",
    "                                          if 'Y' not in col and '_id' not in col]]\n",
    "                     .replace(df.colheaders['header']))\n",
    "df_labels_finhealth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='prof'></a>\n",
    "[return to the top](#top)\n",
    "### Profitability\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_profitab = df.profitability()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker_id</th>\n",
       "      <th>exchange_id</th>\n",
       "      <th>pr_margins</th>\n",
       "      <th>i12</th>\n",
       "      <th>i12_pr_Y0</th>\n",
       "      <th>i12_pr_Y1</th>\n",
       "      <th>i12_pr_Y2</th>\n",
       "      <th>i12_pr_Y3</th>\n",
       "      <th>i12_pr_Y4</th>\n",
       "      <th>i12_pr_Y5</th>\n",
       "      <th>...</th>\n",
       "      <th>pr_Y1</th>\n",
       "      <th>pr_Y2</th>\n",
       "      <th>pr_Y3</th>\n",
       "      <th>pr_Y4</th>\n",
       "      <th>pr_Y5</th>\n",
       "      <th>pr_Y6</th>\n",
       "      <th>pr_Y7</th>\n",
       "      <th>pr_Y8</th>\n",
       "      <th>pr_Y9</th>\n",
       "      <th>pr_Y10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>374</td>\n",
       "      <td>279</td>\n",
       "      <td>202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>374</td>\n",
       "      <td>279</td>\n",
       "      <td>202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>374</td>\n",
       "      <td>279</td>\n",
       "      <td>202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>482</td>\n",
       "      <td>279</td>\n",
       "      <td>202</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>279</td>\n",
       "      <td>202</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 230 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker_id  exchange_id  pr_margins  i12  i12_pr_Y0  i12_pr_Y1  i12_pr_Y2  \\\n",
       "0          1          374         279  202        NaN        NaN      100.0   \n",
       "1          2          374         279  202        NaN        NaN      100.0   \n",
       "2          3          374         279  202        NaN        NaN      100.0   \n",
       "3          4          482         279  202      100.0      100.0      100.0   \n",
       "4         23            1         279  202      100.0      100.0        NaN   \n",
       "\n",
       "   i12_pr_Y3  i12_pr_Y4  i12_pr_Y5   ...        pr_Y1      pr_Y2      pr_Y3  \\\n",
       "0      100.0      100.0      100.0   ...   2010-12-01 2011-12-01 2012-12-01   \n",
       "1      100.0      100.0      100.0   ...   2010-12-01 2011-12-01 2012-12-01   \n",
       "2      100.0      100.0      100.0   ...   2010-12-01 2011-12-01 2012-12-01   \n",
       "3      100.0      100.0      100.0   ...   2010-12-01 2011-12-01 2012-12-01   \n",
       "4      100.0        NaN      100.0   ...   2010-12-01 2011-12-01 2012-12-01   \n",
       "\n",
       "       pr_Y4      pr_Y5      pr_Y6      pr_Y7      pr_Y8      pr_Y9  pr_Y10  \n",
       "0 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01 2018-12-01     TTM  \n",
       "1 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01 2018-12-01     TTM  \n",
       "2 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01 2018-12-01     TTM  \n",
       "3 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01 2018-12-01     TTM  \n",
       "4 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01 2018-12-01     TTM  \n",
       "\n",
       "[5 rows x 230 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_profitab.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 79,103 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_profitab)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pr_margins              Margins % of Sales\n",
       "i12                                Revenue\n",
       "i13                                   COGS\n",
       "i14                           Gross Margin\n",
       "i15                                   SG&A\n",
       "i16                                    R&D\n",
       "i17                                  Other\n",
       "i18                       Operating Margin\n",
       "i19                    Net Int Inc & Other\n",
       "i20                             EBT Margin\n",
       "pr_profit                    Profitability\n",
       "i21                             Tax Rate %\n",
       "i22                           Net Margin %\n",
       "i23               Asset Turnover (Average)\n",
       "i24                     Return on Assets %\n",
       "i25           Financial Leverage (Average)\n",
       "i26                     Return on Equity %\n",
       "i27           Return on Invested Capital %\n",
       "i95                      Interest Coverage\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels_profitab = (df_profitab.loc[0, [col for col in df_profitab.columns if 'Y' not in col and '_id' not in col]]\n",
    "                    .replace(df.colheaders['header']))\n",
    "df_labels_profitab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='growth'></a>\n",
    "[return to the top](#top)\n",
    "### Growth\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_growth = df.growth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker_id</th>\n",
       "      <th>exchange_id</th>\n",
       "      <th>gr_revenue</th>\n",
       "      <th>i28</th>\n",
       "      <th>i28_gr_Y0</th>\n",
       "      <th>i28_gr_Y1</th>\n",
       "      <th>i28_gr_Y2</th>\n",
       "      <th>i28_gr_Y3</th>\n",
       "      <th>i28_gr_Y4</th>\n",
       "      <th>i28_gr_Y5</th>\n",
       "      <th>...</th>\n",
       "      <th>gr_Y1</th>\n",
       "      <th>gr_Y2</th>\n",
       "      <th>gr_Y3</th>\n",
       "      <th>gr_Y4</th>\n",
       "      <th>gr_Y5</th>\n",
       "      <th>gr_Y6</th>\n",
       "      <th>gr_Y7</th>\n",
       "      <th>gr_Y8</th>\n",
       "      <th>gr_Y9</th>\n",
       "      <th>gr_Y10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>374</td>\n",
       "      <td>298</td>\n",
       "      <td>299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.70</td>\n",
       "      <td>19.81</td>\n",
       "      <td>103.73</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Latest Qtr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>374</td>\n",
       "      <td>298</td>\n",
       "      <td>299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.70</td>\n",
       "      <td>19.81</td>\n",
       "      <td>103.73</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Latest Qtr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>374</td>\n",
       "      <td>298</td>\n",
       "      <td>299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.70</td>\n",
       "      <td>19.81</td>\n",
       "      <td>103.73</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Latest Qtr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>482</td>\n",
       "      <td>298</td>\n",
       "      <td>299</td>\n",
       "      <td>2.23</td>\n",
       "      <td>2.59</td>\n",
       "      <td>16.25</td>\n",
       "      <td>0.83</td>\n",
       "      <td>11.65</td>\n",
       "      <td>7.90</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Latest Qtr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>298</td>\n",
       "      <td>299</td>\n",
       "      <td>238.49</td>\n",
       "      <td>16.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>Latest Qtr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 209 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker_id  exchange_id  gr_revenue  i28  i28_gr_Y0  i28_gr_Y1  i28_gr_Y2  \\\n",
       "0          1          374         298  299        NaN        NaN        NaN   \n",
       "1          2          374         298  299        NaN        NaN        NaN   \n",
       "2          3          374         298  299        NaN        NaN        NaN   \n",
       "3          4          482         298  299       2.23       2.59      16.25   \n",
       "4         23            1         298  299     238.49      16.43        NaN   \n",
       "\n",
       "   i28_gr_Y3  i28_gr_Y4  i28_gr_Y5     ...          gr_Y1      gr_Y2  \\\n",
       "0     -11.70      19.81     103.73     ...     2010-12-01 2011-12-01   \n",
       "1     -11.70      19.81     103.73     ...     2010-12-01 2011-12-01   \n",
       "2     -11.70      19.81     103.73     ...     2010-12-01 2011-12-01   \n",
       "3       0.83      11.65       7.90     ...     2010-12-01 2011-12-01   \n",
       "4        NaN        NaN        NaN     ...     2010-12-01 2011-12-01   \n",
       "\n",
       "       gr_Y3      gr_Y4      gr_Y5      gr_Y6      gr_Y7      gr_Y8  \\\n",
       "0 2012-12-01 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01   \n",
       "1 2012-12-01 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01   \n",
       "2 2012-12-01 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01   \n",
       "3 2012-12-01 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01   \n",
       "4 2012-12-01 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01   \n",
       "\n",
       "       gr_Y9      gr_Y10  \n",
       "0 2018-12-01  Latest Qtr  \n",
       "1 2018-12-01  Latest Qtr  \n",
       "2 2018-12-01  Latest Qtr  \n",
       "3 2018-12-01  Latest Qtr  \n",
       "4 2018-12-01  Latest Qtr  \n",
       "\n",
       "[5 rows x 209 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_growth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 79,103 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_growth)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gr_revenue               Revenue %\n",
       "i28                 Year over Year\n",
       "i29                 3-Year Average\n",
       "i30                 5-Year Average\n",
       "i31                10-Year Average\n",
       "gr_operating    Operating Income %\n",
       "i32                 Year over Year\n",
       "i33                 3-Year Average\n",
       "i34                 5-Year Average\n",
       "i35                10-Year Average\n",
       "gr_ni                 Net Income %\n",
       "i81                 Year over Year\n",
       "i82                 3-Year Average\n",
       "i83                 5-Year Average\n",
       "i84                10-Year Average\n",
       "gr_eps                       EPS %\n",
       "i36                 Year over Year\n",
       "i37                 3-Year Average\n",
       "i38                 5-Year Average\n",
       "i39                10-Year Average\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels_growth = (df_growth.loc[0, [col for col in df_growth.columns \n",
    "                                      if 'Y' not in col and '_id' not in col]].replace(df.colheaders['header']))\n",
    "df_labels_growth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='cfh'></a>\n",
    "[return to the top](#top)\n",
    "### Cash Flow Health\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cfhealth = df.cfhealth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker_id</th>\n",
       "      <th>exchange_id</th>\n",
       "      <th>cf_cashflow</th>\n",
       "      <th>i40</th>\n",
       "      <th>i40_cf_Y0</th>\n",
       "      <th>i40_cf_Y1</th>\n",
       "      <th>i40_cf_Y2</th>\n",
       "      <th>i40_cf_Y3</th>\n",
       "      <th>i40_cf_Y4</th>\n",
       "      <th>i40_cf_Y5</th>\n",
       "      <th>...</th>\n",
       "      <th>cf_Y1</th>\n",
       "      <th>cf_Y2</th>\n",
       "      <th>cf_Y3</th>\n",
       "      <th>cf_Y4</th>\n",
       "      <th>cf_Y5</th>\n",
       "      <th>cf_Y6</th>\n",
       "      <th>cf_Y7</th>\n",
       "      <th>cf_Y8</th>\n",
       "      <th>cf_Y9</th>\n",
       "      <th>cf_Y10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>374</td>\n",
       "      <td>318</td>\n",
       "      <td>319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>374</td>\n",
       "      <td>318</td>\n",
       "      <td>319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>374</td>\n",
       "      <td>318</td>\n",
       "      <td>319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>482</td>\n",
       "      <td>318</td>\n",
       "      <td>319</td>\n",
       "      <td>-31.63</td>\n",
       "      <td>19.64</td>\n",
       "      <td>50.56</td>\n",
       "      <td>-1.28</td>\n",
       "      <td>11.89</td>\n",
       "      <td>17.06</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>318</td>\n",
       "      <td>319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker_id  exchange_id  cf_cashflow  i40  i40_cf_Y0  i40_cf_Y1  i40_cf_Y2  \\\n",
       "0          1          374          318  319        NaN        NaN        NaN   \n",
       "1          2          374          318  319        NaN        NaN        NaN   \n",
       "2          3          374          318  319        NaN        NaN        NaN   \n",
       "3          4          482          318  319     -31.63      19.64      50.56   \n",
       "4         23            1          318  319        NaN      51.78        NaN   \n",
       "\n",
       "   i40_cf_Y3  i40_cf_Y4  i40_cf_Y5   ...        cf_Y1      cf_Y2      cf_Y3  \\\n",
       "0      97.88        NaN        NaN   ...   2010-12-01 2011-12-01 2012-12-01   \n",
       "1      97.88        NaN        NaN   ...   2010-12-01 2011-12-01 2012-12-01   \n",
       "2      97.88        NaN        NaN   ...   2010-12-01 2011-12-01 2012-12-01   \n",
       "3      -1.28      11.89      17.06   ...   2010-12-01 2011-12-01 2012-12-01   \n",
       "4        NaN        NaN        NaN   ...   2010-12-01 2011-12-01 2012-12-01   \n",
       "\n",
       "       cf_Y4      cf_Y5      cf_Y6      cf_Y7      cf_Y8      cf_Y9  cf_Y10  \n",
       "0 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01 2018-12-01     TTM  \n",
       "1 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01 2018-12-01     TTM  \n",
       "2 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01 2018-12-01     TTM  \n",
       "3 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01 2018-12-01     TTM  \n",
       "4 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01 2018-12-01     TTM  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cfhealth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 79,103 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_cfhealth)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cf_cashflow                    Cash Flow Ratios\n",
       "i40            Operating Cash Flow Growth % YOY\n",
       "i41                 Free Cash Flow Growth % YOY\n",
       "i42                      Cap Ex as a % of Sales\n",
       "i43                      Free Cash Flow/Sales %\n",
       "i44                   Free Cash Flow/Net Income\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels_cfhealth = df_cfhealth.loc[0, [col for col in df_cfhealth.columns if 'Y' not in col \n",
    "                                         and '_id' not in col]].replace(df.colheaders['header'])\n",
    "df_labels_cfhealth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eff'></a>\n",
    "[return to the top](#top)\n",
    "### Efficiency\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_efficiency = df.efficiency()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker_id</th>\n",
       "      <th>exchange_id</th>\n",
       "      <th>ef_efficiency</th>\n",
       "      <th>i69</th>\n",
       "      <th>i69_ef_Y0</th>\n",
       "      <th>i69_ef_Y1</th>\n",
       "      <th>i69_ef_Y2</th>\n",
       "      <th>i69_ef_Y3</th>\n",
       "      <th>i69_ef_Y4</th>\n",
       "      <th>i69_ef_Y5</th>\n",
       "      <th>...</th>\n",
       "      <th>ef_Y1</th>\n",
       "      <th>ef_Y2</th>\n",
       "      <th>ef_Y3</th>\n",
       "      <th>ef_Y4</th>\n",
       "      <th>ef_Y5</th>\n",
       "      <th>ef_Y6</th>\n",
       "      <th>ef_Y7</th>\n",
       "      <th>ef_Y8</th>\n",
       "      <th>ef_Y9</th>\n",
       "      <th>ef_Y10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>374</td>\n",
       "      <td>350</td>\n",
       "      <td>351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.34</td>\n",
       "      <td>85.86</td>\n",
       "      <td>61.96</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>374</td>\n",
       "      <td>350</td>\n",
       "      <td>351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.34</td>\n",
       "      <td>85.86</td>\n",
       "      <td>61.96</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>374</td>\n",
       "      <td>350</td>\n",
       "      <td>351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.34</td>\n",
       "      <td>85.86</td>\n",
       "      <td>61.96</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>482</td>\n",
       "      <td>350</td>\n",
       "      <td>351</td>\n",
       "      <td>25.66</td>\n",
       "      <td>28.47</td>\n",
       "      <td>27.05</td>\n",
       "      <td>29.65</td>\n",
       "      <td>30.48</td>\n",
       "      <td>32.01</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>350</td>\n",
       "      <td>351</td>\n",
       "      <td>93.87</td>\n",
       "      <td>58.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.13</td>\n",
       "      <td>...</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>TTM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ticker_id  exchange_id  ef_efficiency  i69  i69_ef_Y0  i69_ef_Y1  \\\n",
       "0          1          374            350  351        NaN        NaN   \n",
       "1          2          374            350  351        NaN        NaN   \n",
       "2          3          374            350  351        NaN        NaN   \n",
       "3          4          482            350  351      25.66      28.47   \n",
       "4         23            1            350  351      93.87      58.87   \n",
       "\n",
       "   i69_ef_Y2  i69_ef_Y3  i69_ef_Y4  i69_ef_Y5   ...        ef_Y1      ef_Y2  \\\n",
       "0        NaN      82.34      85.86      61.96   ...   2010-12-01 2011-12-01   \n",
       "1        NaN      82.34      85.86      61.96   ...   2010-12-01 2011-12-01   \n",
       "2        NaN      82.34      85.86      61.96   ...   2010-12-01 2011-12-01   \n",
       "3      27.05      29.65      30.48      32.01   ...   2010-12-01 2011-12-01   \n",
       "4        NaN      11.21        NaN      84.13   ...   2010-12-01 2011-12-01   \n",
       "\n",
       "       ef_Y3      ef_Y4      ef_Y5      ef_Y6      ef_Y7      ef_Y8  \\\n",
       "0 2012-12-01 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01   \n",
       "1 2012-12-01 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01   \n",
       "2 2012-12-01 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01   \n",
       "3 2012-12-01 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01   \n",
       "4 2012-12-01 2013-12-01 2014-12-01 2015-12-01 2016-12-01 2017-12-01   \n",
       "\n",
       "       ef_Y9  ef_Y10  \n",
       "0 2018-12-01     TTM  \n",
       "1 2018-12-01     TTM  \n",
       "2 2018-12-01     TTM  \n",
       "3 2018-12-01     TTM  \n",
       "4 2018-12-01     TTM  \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_efficiency.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 79,103 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_efficiency)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ef_efficiency                Efficiency\n",
       "i69              Days Sales Outstanding\n",
       "i70                      Days Inventory\n",
       "i71                     Payables Period\n",
       "i72               Cash Conversion Cycle\n",
       "i73                Receivables Turnover\n",
       "i74                  Inventory Turnover\n",
       "i75               Fixed Assets Turnover\n",
       "i76                      Asset Turnover\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Financial Health DataFrame Columns\n",
    "(df_efficiency.loc[0, [col for col in df_efficiency.columns if 'Y' not in col and '_id' not in col]]\n",
    " .replace(df.colheaders['header']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='isa'></a>\n",
    "[return to the top](#top)\n",
    "### Annual Income Statement\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_annualIS = df.annualIS()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 79,100 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_annualIS)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(['Advertising and market...'], 'label_i36'),\n",
       " (['Advertising and market...'], 'label_i46'),\n",
       " (['Advertising and promot...'], 'label_i15'),\n",
       " (['Amortization of intang...'], 'label_i47'),\n",
       " (['Asset impairment'], 'label_i24'),\n",
       " (['Asset mgmt and securit...'], 'label_i4'),\n",
       " (['Basic'], 'label_i83'),\n",
       " (['Basic'], 'label_i85'),\n",
       " (['Benefits, claims and e...'], 'label_s2'),\n",
       " (['Borrowed funds'], 'label_i14'),\n",
       " (['Commissions and fees'], 'label_i22'),\n",
       " (['Compensation and benef...'], 'label_i13'),\n",
       " (['Compensation and benef...'], 'label_i32'),\n",
       " (['Compensation and benef...'], 'label_i42'),\n",
       " (['Cost of revenue'], 'label_i6'),\n",
       " (['Costs and expenses'], 'label_g3'),\n",
       " (['Credit card income'], 'label_i27'),\n",
       " (['Cumulative effect of a...'], 'label_i43'),\n",
       " (['Cumulative effect of a...'], 'label_i66'),\n",
       " (['Cumulative effect of a...'], 'label_i73'),\n",
       " (['Deposits'], 'label_i12'),\n",
       " (['Deposits with banks'], 'label_i3'),\n",
       " (['Depreciation and amort...'], 'label_i12'),\n",
       " (['Depreciation and amort...'], 'label_i13'),\n",
       " (['Depreciation and amort...'], 'label_i16'),\n",
       " (['Depreciation and amort...'], 'label_i18'),\n",
       " (['Depreciation and amort...'], 'label_i25'),\n",
       " (['Depreciation and amort...'], 'label_i26'),\n",
       " (['Depreciation and amort...'], 'label_i37'),\n",
       " (['Diluted'], 'label_i84'),\n",
       " (['Diluted'], 'label_i86'),\n",
       " (['Dividend income'], 'label_i7'),\n",
       " (['EBITDA'], 'label_i90'),\n",
       " (['Earnings per share'], 'label_g9'),\n",
       " (['Equity investment inco...'], 'label_i24'),\n",
       " (['Expenses'], 'label_g2'),\n",
       " (['Extraordinary items'], 'label_i42'),\n",
       " (['Extraordinary items'], 'label_i65'),\n",
       " (['Extraordinary items'], 'label_i72'),\n",
       " (['Federal funds purchase...'], 'label_i13'),\n",
       " (['Federal funds sold'], 'label_i4'),\n",
       " (['Gain on sale of equity...'], 'label_i25'),\n",
       " (['Gross profit'], 'label_i10'),\n",
       " (['Income (loss) from con...'], 'label_i61'),\n",
       " (['Income before income t...'], 'label_i30'),\n",
       " (['Income before income t...'], 'label_i41'),\n",
       " (['Income before income t...'], 'label_i60'),\n",
       " (['Income before income t...'], 'label_i61'),\n",
       " (['Income before taxes'], 'label_i60'),\n",
       " (['Income before taxes'], 'label_i61'),\n",
       " (['Income from discontinu...'], 'label_i64'),\n",
       " (['Income tax (expense) b...'], 'label_i62'),\n",
       " (['Income taxes'], 'label_i62'),\n",
       " (['Insurance commissions'], 'label_i28'),\n",
       " (['Insurance premium'], 'label_i28'),\n",
       " (['Interest Expense'], 'label_i31'),\n",
       " (['Interest Expense'], 'label_i51'),\n",
       " (['Interest and dividend ...'], 'label_s1'),\n",
       " (['Interest credited to p...'], 'label_i16'),\n",
       " (['Interest expense'], 'label_g2'),\n",
       " (['Interest expense'], 'label_i15'),\n",
       " (['Interest expense'], 'label_i24'),\n",
       " (['Interest expense'], 'label_i28'),\n",
       " (['Interest expense'], 'label_s2'),\n",
       " (['Interest expenses'], 'label_i23'),\n",
       " (['Interest income'], 'label_g1'),\n",
       " (['Interest income'], 'label_i11'),\n",
       " (['Interest income'], 'label_i22'),\n",
       " (['Investment banking'], 'label_i2'),\n",
       " (['Investment income, net'], 'label_i4'),\n",
       " (['Lending and deposit-re...'], 'label_i25'),\n",
       " (['Loans and Leases'], 'label_i2'),\n",
       " (['Long-term debt'], 'label_i15'),\n",
       " (['Merger, acquisition an...'], 'label_i19'),\n",
       " (['Merger, acquisition an...'], 'label_i39'),\n",
       " (['Merger, acquisition an...'], 'label_i49'),\n",
       " (['Minority interest'], 'label_i62'),\n",
       " (['Net income'], 'label_i50'),\n",
       " (['Net income'], 'label_i70'),\n",
       " (['Net income'], 'label_i80'),\n",
       " (['Net income available t...'], 'label_i82'),\n",
       " (['Net income from contin...'], 'label_i40'),\n",
       " (['Net income from contin...'], 'label_i70'),\n",
       " (['Net income from discon...'], 'label_i41'),\n",
       " (['Net income from discon...'], 'label_i71'),\n",
       " (['Net interest inc after...'], 'label_i37'),\n",
       " (['Net interest income'], 'label_i20'),\n",
       " (['Noninterest expenses'], 'label_s3'),\n",
       " (['Noninterest expenses'], 'label_s5'),\n",
       " (['Noninterest revenue'], 'label_g3'),\n",
       " (['Noninterest revenue'], 'label_s4'),\n",
       " (['Nonoperating income'], 'label_s4'),\n",
       " (['Nonrecurring expense'], 'label_i19'),\n",
       " (['Occupancy expense'], 'label_i33'),\n",
       " (['Occupancy expense'], 'label_i43'),\n",
       " (['Operating expenses'], 'label_g3'),\n",
       " (['Operating expenses'], 'label_i15'),\n",
       " (['Operating expenses'], 'label_s2'),\n",
       " (['Operating expenses'], 'label_s3'),\n",
       " (['Operating income'], 'label_i20'),\n",
       " (['Operating income'], 'label_i21'),\n",
       " (['Operating income'], 'label_i30'),\n",
       " (['Operation and maintena...'], 'label_i11'),\n",
       " (['Other'], 'label_i44'),\n",
       " (['Other'], 'label_i74'),\n",
       " (['Other assets'], 'label_i9'),\n",
       " (['Other distributions'], 'label_i63'),\n",
       " (['Other expense'], 'label_i19'),\n",
       " (['Other expenses'], 'label_i20'),\n",
       " (['Other expenses'], 'label_i41'),\n",
       " (['Other expenses'], 'label_i51'),\n",
       " (['Other income'], 'label_i12'),\n",
       " (['Other income'], 'label_i35'),\n",
       " (['Other income'], 'label_i39'),\n",
       " (['Other income'], 'label_i69'),\n",
       " (['Other income (expense)'], 'label_i23'),\n",
       " (['Other income (expense)'], 'label_i29'),\n",
       " (['Other income (expense)'], 'label_i32'),\n",
       " (['Other income (expense)'], 'label_i52'),\n",
       " (['Other income (expense)'], 'label_i63'),\n",
       " (['Other income (loss)'], 'label_i10'),\n",
       " (['Other operating expens...'], 'label_i29'),\n",
       " (['Other special charges'], 'label_i40'),\n",
       " (['Other special charges'], 'label_i50'),\n",
       " (['Policy acquisition and...'], 'label_i13'),\n",
       " (['Policyholder benefits ...'], 'label_i12'),\n",
       " (['Policyholder dividends'], 'label_i17'),\n",
       " (['Preferred distribution...'], 'label_i62'),\n",
       " (['Preferred dividend'], 'label_i81'),\n",
       " (['Preferred dividends'], 'label_i81'),\n",
       " (['Premiums'], 'label_i2'),\n",
       " (['Principal transactions'], 'label_i23'),\n",
       " (['Professional and outsi...'], 'label_i35'),\n",
       " (['Professional and outsi...'], 'label_i45'),\n",
       " (['Provision (benefit) fo...'], 'label_i62'),\n",
       " (['Provision for income t...'], 'label_i38'),\n",
       " (['Provision for income t...'], 'label_i61'),\n",
       " (['Provision for income t...'], 'label_i62'),\n",
       " (['Provision for loan los...'], 'label_i36'),\n",
       " (['Provisions for credit ...'], 'label_i36'),\n",
       " (['Realized capital gains...'], 'label_i5'),\n",
       " (['Research and developme...'], 'label_i11'),\n",
       " (['Restructuring, merger ...'], 'label_i25'),\n",
       " (['Restructuring, merger ...'], 'label_i26'),\n",
       " (['Restructuring, merger ...'], 'label_i27'),\n",
       " (['Revenue'], 'label_i1'),\n",
       " (['Revenue'], 'label_s1'),\n",
       " (['Revenues'], 'label_s1'),\n",
       " (['Revenues, net of inter...'], 'label_i20'),\n",
       " (['Sales, General and adm...'], 'label_i12'),\n",
       " (['Sales, General and adm...'], 'label_i14'),\n",
       " (['Securities'], 'label_i3'),\n",
       " (['Securities'], 'label_i5'),\n",
       " (['Securities gains (loss...'], 'label_i26'),\n",
       " (['Selling, general and a...'], 'label_i14'),\n",
       " (['Service fees and commi...'], 'label_i3'),\n",
       " (['Short-term borrowing'], 'label_i14'),\n",
       " (['Tech, communication an...'], 'label_i34'),\n",
       " (['Tech, communication an...'], 'label_i44'),\n",
       " (['Technology and occupan...'], 'label_i14'),\n",
       " (['Total benefits, claims...'], 'label_tts2'),\n",
       " (['Total costs and expens...'], 'label_ttg3'),\n",
       " (['Total expenses'], 'label_ttg2'),\n",
       " (['Total interest and div...'], 'label_tts1'),\n",
       " (['Total interest expense'], 'label_ttg2'),\n",
       " (['Total interest expense'], 'label_tts2'),\n",
       " (['Total interest income'], 'label_ttg1'),\n",
       " (['Total interest income'], 'label_tts1'),\n",
       " (['Total net revenue'], 'label_tts1'),\n",
       " (['Total noninterest expe...'], 'label_tts3'),\n",
       " (['Total noninterest expe...'], 'label_tts5'),\n",
       " (['Total noninterest reve...'], 'label_ttg3'),\n",
       " (['Total noninterest reve...'], 'label_tts4'),\n",
       " (['Total nonoperating inc...'], 'label_tts4'),\n",
       " (['Total operating expens...'], 'label_ttg3'),\n",
       " (['Total operating expens...'], 'label_tts2'),\n",
       " (['Total revenues'], 'label_tts1'),\n",
       " (['Trading assets'], 'label_i6'),\n",
       " (['Underwriting and distr...'], 'label_i12'),\n",
       " (['Weighted average share...'], 'label_g10')]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [col for col in df_annualIS if 'label' in col]\n",
    "labels = [[label, header] for label in labels \n",
    "          for header in df_annualIS[label].unique().tolist() if pd.notna(header)]\n",
    "\n",
    "df_labels_aIS = (pd.DataFrame(labels, columns=['header', 'value']).set_index('header').astype('int'))\n",
    "df_labels_aIS['value'] = df_labels_aIS['value'].replace(df.colheaders['header'])\n",
    "df_labels_aIS[df_labels_aIS['value'].astype('str').str.contains('ncome')].sort_values(by='value')\n",
    "\n",
    "sorted(list(zip(df_labels_aIS.values.tolist(), df_labels_aIS.index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='isq'></a>\n",
    "[return to the top](#top)\n",
    "### Quarterly Income Statements\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_quarterlyIS = df.quarterlyIS()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 58,716 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_quarterlyIS)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(['Advertising and market...'], 'label_i36'),\n",
       " (['Advertising and market...'], 'label_i46'),\n",
       " (['Advertising and promot...'], 'label_i15'),\n",
       " (['Amortization of intang...'], 'label_i47'),\n",
       " (['Asset impairment'], 'label_i24'),\n",
       " (['Asset mgmt and securit...'], 'label_i4'),\n",
       " (['Basic'], 'label_i83'),\n",
       " (['Basic'], 'label_i85'),\n",
       " (['Benefits, claims and e...'], 'label_s2'),\n",
       " (['Borrowed funds'], 'label_i14'),\n",
       " (['Commissions and fees'], 'label_i22'),\n",
       " (['Compensation and benef...'], 'label_i13'),\n",
       " (['Compensation and benef...'], 'label_i32'),\n",
       " (['Compensation and benef...'], 'label_i42'),\n",
       " (['Cost of revenue'], 'label_i6'),\n",
       " (['Costs and expenses'], 'label_g3'),\n",
       " (['Credit card income'], 'label_i27'),\n",
       " (['Cumulative effect of a...'], 'label_i43'),\n",
       " (['Cumulative effect of a...'], 'label_i66'),\n",
       " (['Cumulative effect of a...'], 'label_i73'),\n",
       " (['Deposits'], 'label_i12'),\n",
       " (['Deposits with banks'], 'label_i3'),\n",
       " (['Depreciation and amort...'], 'label_i12'),\n",
       " (['Depreciation and amort...'], 'label_i13'),\n",
       " (['Depreciation and amort...'], 'label_i16'),\n",
       " (['Depreciation and amort...'], 'label_i18'),\n",
       " (['Depreciation and amort...'], 'label_i25'),\n",
       " (['Depreciation and amort...'], 'label_i26'),\n",
       " (['Depreciation and amort...'], 'label_i37'),\n",
       " (['Diluted'], 'label_i84'),\n",
       " (['Diluted'], 'label_i86'),\n",
       " (['Dividend income'], 'label_i7'),\n",
       " (['EBITDA'], 'label_i90'),\n",
       " (['Earnings per share'], 'label_g9'),\n",
       " (['Equity investment inco...'], 'label_i24'),\n",
       " (['Expenses'], 'label_g2'),\n",
       " (['Extraordinary items'], 'label_i42'),\n",
       " (['Extraordinary items'], 'label_i65'),\n",
       " (['Extraordinary items'], 'label_i72'),\n",
       " (['Federal funds purchase...'], 'label_i13'),\n",
       " (['Federal funds sold'], 'label_i4'),\n",
       " (['Gain on sale of equity...'], 'label_i25'),\n",
       " (['Gross profit'], 'label_i10'),\n",
       " (['Income (loss) from con...'], 'label_i61'),\n",
       " (['Income before income t...'], 'label_i30'),\n",
       " (['Income before income t...'], 'label_i41'),\n",
       " (['Income before income t...'], 'label_i60'),\n",
       " (['Income before income t...'], 'label_i61'),\n",
       " (['Income before taxes'], 'label_i60'),\n",
       " (['Income before taxes'], 'label_i61'),\n",
       " (['Income from discontinu...'], 'label_i64'),\n",
       " (['Income tax (expense) b...'], 'label_i62'),\n",
       " (['Income taxes'], 'label_i62'),\n",
       " (['Insurance commissions'], 'label_i28'),\n",
       " (['Insurance premium'], 'label_i28'),\n",
       " (['Interest Expense'], 'label_i31'),\n",
       " (['Interest Expense'], 'label_i51'),\n",
       " (['Interest and dividend ...'], 'label_s1'),\n",
       " (['Interest credited to p...'], 'label_i16'),\n",
       " (['Interest expense'], 'label_g2'),\n",
       " (['Interest expense'], 'label_i15'),\n",
       " (['Interest expense'], 'label_i24'),\n",
       " (['Interest expense'], 'label_i28'),\n",
       " (['Interest expense'], 'label_s2'),\n",
       " (['Interest expenses'], 'label_i23'),\n",
       " (['Interest income'], 'label_g1'),\n",
       " (['Interest income'], 'label_i11'),\n",
       " (['Interest income'], 'label_i22'),\n",
       " (['Investment banking'], 'label_i2'),\n",
       " (['Investment income, net'], 'label_i4'),\n",
       " (['Lending and deposit-re...'], 'label_i25'),\n",
       " (['Loans and Leases'], 'label_i2'),\n",
       " (['Long-term debt'], 'label_i15'),\n",
       " (['Merger, acquisition an...'], 'label_i19'),\n",
       " (['Merger, acquisition an...'], 'label_i39'),\n",
       " (['Merger, acquisition an...'], 'label_i49'),\n",
       " (['Minority interest'], 'label_i62'),\n",
       " (['Net income'], 'label_i50'),\n",
       " (['Net income'], 'label_i70'),\n",
       " (['Net income'], 'label_i80'),\n",
       " (['Net income available t...'], 'label_i82'),\n",
       " (['Net income from contin...'], 'label_i40'),\n",
       " (['Net income from contin...'], 'label_i70'),\n",
       " (['Net income from discon...'], 'label_i41'),\n",
       " (['Net income from discon...'], 'label_i71'),\n",
       " (['Net interest inc after...'], 'label_i37'),\n",
       " (['Net interest income'], 'label_i20'),\n",
       " (['Noninterest expenses'], 'label_s3'),\n",
       " (['Noninterest expenses'], 'label_s5'),\n",
       " (['Noninterest revenue'], 'label_g3'),\n",
       " (['Noninterest revenue'], 'label_s4'),\n",
       " (['Nonoperating income'], 'label_s4'),\n",
       " (['Nonrecurring expense'], 'label_i19'),\n",
       " (['Occupancy expense'], 'label_i33'),\n",
       " (['Occupancy expense'], 'label_i43'),\n",
       " (['Operating expenses'], 'label_g3'),\n",
       " (['Operating expenses'], 'label_i15'),\n",
       " (['Operating expenses'], 'label_s2'),\n",
       " (['Operating expenses'], 'label_s3'),\n",
       " (['Operating income'], 'label_i20'),\n",
       " (['Operating income'], 'label_i21'),\n",
       " (['Operating income'], 'label_i30'),\n",
       " (['Operation and maintena...'], 'label_i11'),\n",
       " (['Other'], 'label_i44'),\n",
       " (['Other'], 'label_i74'),\n",
       " (['Other assets'], 'label_i9'),\n",
       " (['Other distributions'], 'label_i63'),\n",
       " (['Other expense'], 'label_i19'),\n",
       " (['Other expenses'], 'label_i20'),\n",
       " (['Other expenses'], 'label_i41'),\n",
       " (['Other expenses'], 'label_i51'),\n",
       " (['Other income'], 'label_i12'),\n",
       " (['Other income'], 'label_i35'),\n",
       " (['Other income'], 'label_i39'),\n",
       " (['Other income'], 'label_i69'),\n",
       " (['Other income (expense)'], 'label_i23'),\n",
       " (['Other income (expense)'], 'label_i29'),\n",
       " (['Other income (expense)'], 'label_i32'),\n",
       " (['Other income (expense)'], 'label_i52'),\n",
       " (['Other income (expense)'], 'label_i63'),\n",
       " (['Other income (loss)'], 'label_i10'),\n",
       " (['Other operating expens...'], 'label_i29'),\n",
       " (['Other special charges'], 'label_i40'),\n",
       " (['Other special charges'], 'label_i50'),\n",
       " (['Policy acquisition and...'], 'label_i13'),\n",
       " (['Policyholder benefits ...'], 'label_i12'),\n",
       " (['Policyholder dividends'], 'label_i17'),\n",
       " (['Preferred distribution...'], 'label_i62'),\n",
       " (['Preferred dividend'], 'label_i81'),\n",
       " (['Preferred dividends'], 'label_i81'),\n",
       " (['Premiums'], 'label_i2'),\n",
       " (['Principal transactions'], 'label_i23'),\n",
       " (['Professional and outsi...'], 'label_i35'),\n",
       " (['Professional and outsi...'], 'label_i45'),\n",
       " (['Provision (benefit) fo...'], 'label_i62'),\n",
       " (['Provision for income t...'], 'label_i38'),\n",
       " (['Provision for income t...'], 'label_i61'),\n",
       " (['Provision for income t...'], 'label_i62'),\n",
       " (['Provision for loan los...'], 'label_i36'),\n",
       " (['Provisions for credit ...'], 'label_i36'),\n",
       " (['Realized capital gains...'], 'label_i5'),\n",
       " (['Research and developme...'], 'label_i11'),\n",
       " (['Restructuring, merger ...'], 'label_i25'),\n",
       " (['Restructuring, merger ...'], 'label_i26'),\n",
       " (['Restructuring, merger ...'], 'label_i27'),\n",
       " (['Revenue'], 'label_i1'),\n",
       " (['Revenue'], 'label_s1'),\n",
       " (['Revenues'], 'label_s1'),\n",
       " (['Revenues, net of inter...'], 'label_i20'),\n",
       " (['Sales, General and adm...'], 'label_i12'),\n",
       " (['Sales, General and adm...'], 'label_i14'),\n",
       " (['Securities'], 'label_i3'),\n",
       " (['Securities'], 'label_i5'),\n",
       " (['Securities gains (loss...'], 'label_i26'),\n",
       " (['Selling, general and a...'], 'label_i14'),\n",
       " (['Service fees and commi...'], 'label_i3'),\n",
       " (['Short-term borrowing'], 'label_i14'),\n",
       " (['Tech, communication an...'], 'label_i34'),\n",
       " (['Tech, communication an...'], 'label_i44'),\n",
       " (['Technology and occupan...'], 'label_i14'),\n",
       " (['Total benefits, claims...'], 'label_tts2'),\n",
       " (['Total costs and expens...'], 'label_ttg3'),\n",
       " (['Total expenses'], 'label_ttg2'),\n",
       " (['Total interest and div...'], 'label_tts1'),\n",
       " (['Total interest expense'], 'label_ttg2'),\n",
       " (['Total interest expense'], 'label_tts2'),\n",
       " (['Total interest income'], 'label_ttg1'),\n",
       " (['Total interest income'], 'label_tts1'),\n",
       " (['Total net revenue'], 'label_tts1'),\n",
       " (['Total noninterest expe...'], 'label_tts3'),\n",
       " (['Total noninterest expe...'], 'label_tts5'),\n",
       " (['Total noninterest reve...'], 'label_ttg3'),\n",
       " (['Total noninterest reve...'], 'label_tts4'),\n",
       " (['Total nonoperating inc...'], 'label_tts4'),\n",
       " (['Total operating expens...'], 'label_ttg3'),\n",
       " (['Total operating expens...'], 'label_tts2'),\n",
       " (['Total revenues'], 'label_tts1'),\n",
       " (['Trading assets'], 'label_i6'),\n",
       " (['Underwriting and distr...'], 'label_i12'),\n",
       " (['Weighted average share...'], 'label_g10')]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [col for col in df_annualIS if 'label' in col]\n",
    "labels = [[label, header] for label in labels \n",
    "          for header in df_annualIS[label].unique().tolist() if pd.notna(header)]\n",
    "\n",
    "df_labels_aIS = (pd.DataFrame(labels, columns=['header', 'value']).set_index('header').astype('int'))\n",
    "df_labels_aIS['value'] = df_labels_aIS['value'].replace(df.colheaders['header'])\n",
    "df_labels_aIS[df_labels_aIS['value'].astype('str').str.contains('ncome')].sort_values(by='value')\n",
    "\n",
    "sorted(list(zip(df_labels_aIS.values.tolist(), df_labels_aIS.index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='bsa'></a>\n",
    "[return to the top](#top)\n",
    "### Annual Balance Sheet\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_annualBS = df.annualBS()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 79,273 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_annualBS)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(['Accounts payable'], 'label_i41'),\n",
       " (['Accounts payable'], 'label_i42'),\n",
       " (['Accounts payable'], 'label_i43'),\n",
       " (['Accrued expenses and l...'], 'label_i46'),\n",
       " (['Accrued expenses and l...'], 'label_i47'),\n",
       " (['Accrued investment inc...'], 'label_i8'),\n",
       " (['Accrued liabilities'], 'label_i45'),\n",
       " (['Accrued liabilities'], 'label_i46'),\n",
       " (['Accrued liabilities'], 'label_i53'),\n",
       " (['Accumulated Depreciati...'], 'label_i10'),\n",
       " (['Accumulated Depreciati...'], 'label_i14'),\n",
       " (['Accumulated depreciati...'], 'label_i2'),\n",
       " (['Accumulated other comp...'], 'label_i89'),\n",
       " (['Additional paid-in cap...'], 'label_i84'),\n",
       " (['Allowance for loan los...'], 'label_i12'),\n",
       " (['Allowance for loan los...'], 'label_i9'),\n",
       " (['Assets'], 'label_g1'),\n",
       " (['Assets'], 'label_s1'),\n",
       " (['Buildings and improvem...'], 'label_i10'),\n",
       " (['Capital leases'], 'label_i42'),\n",
       " (['Capital leases'], 'label_i43'),\n",
       " (['Capital leases'], 'label_i51'),\n",
       " (['Cash'], 'label_gg1'),\n",
       " (['Cash and cash equivale...'], 'label_i1'),\n",
       " (['Cash and cash equivale...'], 'label_i4'),\n",
       " (['Cash and cash equivale...'], 'label_i7'),\n",
       " (['Cash and due from bank...'], 'label_i1'),\n",
       " (['Commercial loans'], 'label_i8'),\n",
       " (['Common stock'], 'label_i82'),\n",
       " (['Consumer loans'], 'label_i10'),\n",
       " (['Current assets'], 'label_g1'),\n",
       " (['Current liabilities'], 'label_gg5'),\n",
       " (['Debt securities'], 'label_i7'),\n",
       " (['Deferred income tax as...'], 'label_i11'),\n",
       " (['Deferred income taxes'], 'label_i14'),\n",
       " (['Deferred income taxes'], 'label_i15'),\n",
       " (['Deferred income taxes'], 'label_i18'),\n",
       " (['Deferred income taxes'], 'label_i43'),\n",
       " (['Deferred income taxes'], 'label_i44'),\n",
       " (['Deferred income taxes'], 'label_i46'),\n",
       " (['Deferred income taxes'], 'label_i5'),\n",
       " (['Deferred income taxes'], 'label_i6'),\n",
       " (['Deferred policy acquis...'], 'label_i10'),\n",
       " (['Deferred revenues'], 'label_i46'),\n",
       " (['Deferred revenues'], 'label_i47'),\n",
       " (['Deferred revenues'], 'label_i54'),\n",
       " (['Deferred tax liabiliti...'], 'label_i52'),\n",
       " (['Deferred taxes'], 'label_i46'),\n",
       " (['Deferred taxes liabili...'], 'label_i52'),\n",
       " (['Deposits'], 'label_gg5'),\n",
       " (['Deposits'], 'label_i41'),\n",
       " (['Deposits'], 'label_i45'),\n",
       " (['Deposits with banks'], 'label_i2'),\n",
       " (['Derivative assets'], 'label_i6'),\n",
       " (['Derivative liabilities'], 'label_i44'),\n",
       " (['Dividends payable'], 'label_i47'),\n",
       " (['Equity and other inves...'], 'label_i11'),\n",
       " (['Equity and other inves...'], 'label_i12'),\n",
       " (['Equity and other inves...'], 'label_i15'),\n",
       " (['Equity securities'], 'label_i2'),\n",
       " (['Federal Home Loan Bank...'], 'label_i6'),\n",
       " (['Federal funds purchase...'], 'label_i42'),\n",
       " (['Federal funds sold'], 'label_i10'),\n",
       " (['Federal funds sold'], 'label_i3'),\n",
       " (['Fixed maturity securit...'], 'label_i1'),\n",
       " (['Fixtures and equipment'], 'label_i11'),\n",
       " (['Foreclosed real estate...'], 'label_i30'),\n",
       " (['Future policy benefits'], 'label_i41'),\n",
       " (['General Partner'], 'label_i87'),\n",
       " (['Goodwill'], 'label_i12'),\n",
       " (['Goodwill'], 'label_i13'),\n",
       " (['Goodwill'], 'label_i16'),\n",
       " (['Goodwill'], 'label_i7'),\n",
       " (['Goodwill'], 'label_i8'),\n",
       " (['Gross property, plant ...'], 'label_i9'),\n",
       " (['Income taxes payable'], 'label_i45'),\n",
       " (['Intangible assets'], 'label_i13'),\n",
       " (['Intangible assets'], 'label_i14'),\n",
       " (['Intangible assets'], 'label_i17'),\n",
       " (['Intangible assets'], 'label_i9'),\n",
       " (['Interest receivable'], 'label_i14'),\n",
       " (['Interest-bearing'], 'label_i42'),\n",
       " (['Inventories'], 'label_i4'),\n",
       " (['Investments'], 'label_i3'),\n",
       " (['Land'], 'label_i9'),\n",
       " (['Liabilities'], 'label_g5'),\n",
       " (['Liabilities and stockh...'], 'label_s2'),\n",
       " (['Loans'], 'label_gg3'),\n",
       " (['Loans'], 'label_i30'),\n",
       " (['Loans'], 'label_i4'),\n",
       " (['Loans'], 'label_i8'),\n",
       " (['Loans, total'], 'label_i4'),\n",
       " (['Long-term debt'], 'label_i42'),\n",
       " (['Long-term debt'], 'label_i44'),\n",
       " (['Long-term debt'], 'label_i45'),\n",
       " (['Long-term debt'], 'label_i46'),\n",
       " (['Long-term debt'], 'label_i47'),\n",
       " (['Long-term debt'], 'label_i50'),\n",
       " (['Minority Interest'], 'label_i47'),\n",
       " (['Minority interest'], 'label_i56'),\n",
       " (['Net loans'], 'label_i10'),\n",
       " (['Net property, plant an...'], 'label_ttgg2'),\n",
       " (['Non-current assets'], 'label_g2'),\n",
       " (['Non-current liabilitie...'], 'label_gg6'),\n",
       " (['Non-interest-bearing'], 'label_i41'),\n",
       " (['Other Equity'], 'label_i83'),\n",
       " (['Other assets'], 'label_i11'),\n",
       " (['Other assets'], 'label_i16'),\n",
       " (['Other assets'], 'label_i19'),\n",
       " (['Other assets'], 'label_i40'),\n",
       " (['Other assets'], 'label_i8'),\n",
       " (['Other current assets'], 'label_i8'),\n",
       " (['Other current liabilit...'], 'label_i49'),\n",
       " (['Other equity'], 'label_i83'),\n",
       " (['Other intangible asset...'], 'label_i14'),\n",
       " (['Other intangible asset...'], 'label_i8'),\n",
       " (['Other intangibles'], 'label_i17'),\n",
       " (['Other liabilities'], 'label_i49'),\n",
       " (['Other liabilities'], 'label_i50'),\n",
       " (['Other liabilities'], 'label_i80'),\n",
       " (['Other liabilities and ...'], 'label_i95'),\n",
       " (['Other long-term assets'], 'label_i17'),\n",
       " (['Other long-term assets'], 'label_i18'),\n",
       " (['Other long-term assets'], 'label_i21'),\n",
       " (['Other long-term liabil...'], 'label_i58'),\n",
       " (['Other properties'], 'label_i13'),\n",
       " (['Payables'], 'label_i43'),\n",
       " (['Payables'], 'label_i45'),\n",
       " (['Payables and accrued e...'], 'label_i43'),\n",
       " (['Payables and accrued e...'], 'label_i44'),\n",
       " (['Pensions and other ben...'], 'label_i55'),\n",
       " (['Pensions and other pos...'], 'label_i55'),\n",
       " (['Policyholder funds'], 'label_i42'),\n",
       " (['Preferred stock'], 'label_i81'),\n",
       " (['Premises and equipment'], 'label_i12'),\n",
       " (['Premises and equipment'], 'label_i7'),\n",
       " (['Premises and equipment...'], 'label_i15'),\n",
       " (['Premiums and other rec...'], 'label_i9'),\n",
       " (['Prepaid expenses'], 'label_i6'),\n",
       " (['Prepaid pension benefi...'], 'label_i15'),\n",
       " (['Prepaid pension costs'], 'label_i16'),\n",
       " (['Prepaid pension costs'], 'label_i19'),\n",
       " (['Property and equipment'], 'label_i12'),\n",
       " (['Property and equipment'], 'label_i5'),\n",
       " (['Property and equipment...'], 'label_i12'),\n",
       " (['Property, plant and eq...'], 'label_gg2'),\n",
       " (['Property, plant and eq...'], 'label_ttgg2'),\n",
       " (['Real estate'], 'label_i5'),\n",
       " (['Real estate properties'], 'label_i1'),\n",
       " (['Real estate properties...'], 'label_i3'),\n",
       " (['Receivables'], 'label_i11'),\n",
       " (['Receivables'], 'label_i2'),\n",
       " (['Receivables'], 'label_i3'),\n",
       " (['Receivables'], 'label_i6'),\n",
       " (['Regulatory assets'], 'label_i30'),\n",
       " (['Regulatory liabilities'], 'label_i70'),\n",
       " (['Repurchase agreement'], 'label_i43'),\n",
       " (['Residential loans'], 'label_i9'),\n",
       " (['Restricted cash'], 'label_i30'),\n",
       " (['Restricted cash'], 'label_i5'),\n",
       " (['Restricted cash'], 'label_i9'),\n",
       " (['Restricted cash and ca...'], 'label_i2'),\n",
       " (['Retained earnings'], 'label_i85'),\n",
       " (['Securities'], 'label_gg2'),\n",
       " (['Securities and investm...'], 'label_i4'),\n",
       " (['Securities available f...'], 'label_i4'),\n",
       " (['Securities borrowed'], 'label_i4'),\n",
       " (['Securities borrowed'], 'label_i5'),\n",
       " (['Securities held to mat...'], 'label_i5'),\n",
       " (['Securities purchased u...'], 'label_i7'),\n",
       " (['Separate account asset...'], 'label_i14'),\n",
       " (['Separate account liabi...'], 'label_i48'),\n",
       " (['Short-term borrowing'], 'label_i41'),\n",
       " (['Short-term borrowing'], 'label_i42'),\n",
       " (['Short-term borrowing'], 'label_i45'),\n",
       " (['Short-term borrowing'], 'label_i46'),\n",
       " (['Short-term debt'], 'label_i41'),\n",
       " (['Short-term debt'], 'label_i42'),\n",
       " (['Short-term debt'], 'label_i44'),\n",
       " (['Short-term investments'], 'label_i2'),\n",
       " (['Short-term investments'], 'label_i6'),\n",
       " (['Stockholders equity'], 'label_g8'),\n",
       " (['Taxes payable'], 'label_i44'),\n",
       " (['Taxes payable'], 'label_i45'),\n",
       " (['Taxes payable'], 'label_i47'),\n",
       " (['Total Stockholders eq...'], 'label_ttg8'),\n",
       " (['Total assets'], 'label_ttg1'),\n",
       " (['Total assets'], 'label_tts1'),\n",
       " (['Total cash'], 'label_ttgg1'),\n",
       " (['Total cash and cash eq...'], 'label_ttgg1'),\n",
       " (['Total current assets'], 'label_ttg1'),\n",
       " (['Total current liabilit...'], 'label_ttgg5'),\n",
       " (['Total deposits'], 'label_ttgg5'),\n",
       " (['Total liabilities'], 'label_ttg5'),\n",
       " (['Total liabilities and ...'], 'label_tts2'),\n",
       " (['Total loans'], 'label_i11'),\n",
       " (['Total loans, net'], 'label_ttgg3'),\n",
       " (['Total non-current asse...'], 'label_ttg2'),\n",
       " (['Total non-current liab...'], 'label_ttgg6'),\n",
       " (['Total securities'], 'label_ttgg2'),\n",
       " (['Total stockholders eq...'], 'label_ttg8'),\n",
       " (['Trading account securi...'], 'label_i3'),\n",
       " (['Trading assets'], 'label_i5'),\n",
       " (['Trading liabilities'], 'label_i43'),\n",
       " (['Trading liabilities'], 'label_i44'),\n",
       " (['Trading securities'], 'label_i3'),\n",
       " (['Treasury stock'], 'label_i86'),\n",
       " (['Unearned premiums'], 'label_i43'),\n",
       " (['stockholders equity'], 'label_g8')]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [col for col in df_annualBS if 'label' in col]\n",
    "labels = [[label, header] for label in labels \n",
    "          for header in df_annualBS[label].unique().tolist() if pd.notna(header)]\n",
    "\n",
    "df_labels_aBS = (pd.DataFrame(labels, columns=['header', 'value']).set_index('header').astype('int'))\n",
    "df_labels_aBS['value'] = df_labels_aBS['value'].replace(df.colheaders['header'])\n",
    "df_labels_aBS[df_labels_aBS['value'].astype('str').str.contains('ncome')].sort_values(by='value')\n",
    "\n",
    "sorted(list(zip(df_labels_aBS.values.tolist(), df_labels_aBS.index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='bsq'></a>\n",
    "[return to the top](#top)\n",
    "### Quarterly Balance Sheet\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_quarterlyBS = df.quarterlyBS()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 78,869 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_quarterlyBS)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(['Accounts payable'], 'label_i41'),\n",
       " (['Accounts payable'], 'label_i42'),\n",
       " (['Accounts payable'], 'label_i43'),\n",
       " (['Accrued expenses and l...'], 'label_i46'),\n",
       " (['Accrued expenses and l...'], 'label_i47'),\n",
       " (['Accrued investment inc...'], 'label_i8'),\n",
       " (['Accrued liabilities'], 'label_i45'),\n",
       " (['Accrued liabilities'], 'label_i46'),\n",
       " (['Accrued liabilities'], 'label_i53'),\n",
       " (['Accumulated Depreciati...'], 'label_i10'),\n",
       " (['Accumulated Depreciati...'], 'label_i14'),\n",
       " (['Accumulated depreciati...'], 'label_i2'),\n",
       " (['Accumulated other comp...'], 'label_i89'),\n",
       " (['Additional paid-in cap...'], 'label_i84'),\n",
       " (['Allowance for loan los...'], 'label_i12'),\n",
       " (['Allowance for loan los...'], 'label_i9'),\n",
       " (['Assets'], 'label_g1'),\n",
       " (['Assets'], 'label_s1'),\n",
       " (['Buildings and improvem...'], 'label_i10'),\n",
       " (['Capital leases'], 'label_i42'),\n",
       " (['Capital leases'], 'label_i43'),\n",
       " (['Capital leases'], 'label_i51'),\n",
       " (['Cash'], 'label_gg1'),\n",
       " (['Cash and cash equivale...'], 'label_i1'),\n",
       " (['Cash and cash equivale...'], 'label_i4'),\n",
       " (['Cash and cash equivale...'], 'label_i7'),\n",
       " (['Cash and due from bank...'], 'label_i1'),\n",
       " (['Commercial loans'], 'label_i8'),\n",
       " (['Common stock'], 'label_i82'),\n",
       " (['Consumer loans'], 'label_i10'),\n",
       " (['Current assets'], 'label_g1'),\n",
       " (['Current liabilities'], 'label_gg5'),\n",
       " (['Debt securities'], 'label_i7'),\n",
       " (['Deferred income tax as...'], 'label_i11'),\n",
       " (['Deferred income taxes'], 'label_i14'),\n",
       " (['Deferred income taxes'], 'label_i15'),\n",
       " (['Deferred income taxes'], 'label_i18'),\n",
       " (['Deferred income taxes'], 'label_i43'),\n",
       " (['Deferred income taxes'], 'label_i44'),\n",
       " (['Deferred income taxes'], 'label_i46'),\n",
       " (['Deferred income taxes'], 'label_i5'),\n",
       " (['Deferred income taxes'], 'label_i6'),\n",
       " (['Deferred policy acquis...'], 'label_i10'),\n",
       " (['Deferred revenues'], 'label_i46'),\n",
       " (['Deferred revenues'], 'label_i47'),\n",
       " (['Deferred revenues'], 'label_i54'),\n",
       " (['Deferred tax liabiliti...'], 'label_i52'),\n",
       " (['Deferred taxes'], 'label_i46'),\n",
       " (['Deferred taxes liabili...'], 'label_i52'),\n",
       " (['Deposits'], 'label_gg5'),\n",
       " (['Deposits'], 'label_i41'),\n",
       " (['Deposits'], 'label_i45'),\n",
       " (['Deposits with banks'], 'label_i2'),\n",
       " (['Derivative assets'], 'label_i6'),\n",
       " (['Derivative liabilities'], 'label_i44'),\n",
       " (['Dividends payable'], 'label_i47'),\n",
       " (['Equity and other inves...'], 'label_i11'),\n",
       " (['Equity and other inves...'], 'label_i12'),\n",
       " (['Equity and other inves...'], 'label_i15'),\n",
       " (['Equity securities'], 'label_i2'),\n",
       " (['Federal Home Loan Bank...'], 'label_i6'),\n",
       " (['Federal funds purchase...'], 'label_i42'),\n",
       " (['Federal funds sold'], 'label_i10'),\n",
       " (['Federal funds sold'], 'label_i3'),\n",
       " (['Fixed maturity securit...'], 'label_i1'),\n",
       " (['Fixtures and equipment'], 'label_i11'),\n",
       " (['Foreclosed real estate...'], 'label_i30'),\n",
       " (['Future policy benefits'], 'label_i41'),\n",
       " (['General Partner'], 'label_i87'),\n",
       " (['Goodwill'], 'label_i12'),\n",
       " (['Goodwill'], 'label_i13'),\n",
       " (['Goodwill'], 'label_i16'),\n",
       " (['Goodwill'], 'label_i7'),\n",
       " (['Goodwill'], 'label_i8'),\n",
       " (['Gross property, plant ...'], 'label_i9'),\n",
       " (['Income taxes payable'], 'label_i45'),\n",
       " (['Intangible assets'], 'label_i13'),\n",
       " (['Intangible assets'], 'label_i14'),\n",
       " (['Intangible assets'], 'label_i17'),\n",
       " (['Intangible assets'], 'label_i9'),\n",
       " (['Interest receivable'], 'label_i14'),\n",
       " (['Interest-bearing'], 'label_i42'),\n",
       " (['Inventories'], 'label_i4'),\n",
       " (['Investments'], 'label_i3'),\n",
       " (['Land'], 'label_i9'),\n",
       " (['Liabilities'], 'label_g5'),\n",
       " (['Liabilities and stockh...'], 'label_s2'),\n",
       " (['Loans'], 'label_gg3'),\n",
       " (['Loans'], 'label_i30'),\n",
       " (['Loans'], 'label_i4'),\n",
       " (['Loans'], 'label_i8'),\n",
       " (['Loans, total'], 'label_i4'),\n",
       " (['Long-term debt'], 'label_i42'),\n",
       " (['Long-term debt'], 'label_i44'),\n",
       " (['Long-term debt'], 'label_i45'),\n",
       " (['Long-term debt'], 'label_i46'),\n",
       " (['Long-term debt'], 'label_i47'),\n",
       " (['Long-term debt'], 'label_i50'),\n",
       " (['Minority Interest'], 'label_i47'),\n",
       " (['Minority interest'], 'label_i56'),\n",
       " (['Net loans'], 'label_i10'),\n",
       " (['Net property, plant an...'], 'label_ttgg2'),\n",
       " (['Non-current assets'], 'label_g2'),\n",
       " (['Non-current liabilitie...'], 'label_gg6'),\n",
       " (['Non-interest-bearing'], 'label_i41'),\n",
       " (['Other Equity'], 'label_i83'),\n",
       " (['Other assets'], 'label_i11'),\n",
       " (['Other assets'], 'label_i16'),\n",
       " (['Other assets'], 'label_i19'),\n",
       " (['Other assets'], 'label_i40'),\n",
       " (['Other assets'], 'label_i8'),\n",
       " (['Other current assets'], 'label_i8'),\n",
       " (['Other current liabilit...'], 'label_i49'),\n",
       " (['Other equity'], 'label_i83'),\n",
       " (['Other intangible asset...'], 'label_i14'),\n",
       " (['Other intangible asset...'], 'label_i8'),\n",
       " (['Other intangibles'], 'label_i17'),\n",
       " (['Other liabilities'], 'label_i49'),\n",
       " (['Other liabilities'], 'label_i50'),\n",
       " (['Other liabilities'], 'label_i80'),\n",
       " (['Other liabilities and ...'], 'label_i95'),\n",
       " (['Other long-term assets'], 'label_i17'),\n",
       " (['Other long-term assets'], 'label_i18'),\n",
       " (['Other long-term assets'], 'label_i21'),\n",
       " (['Other long-term liabil...'], 'label_i58'),\n",
       " (['Other properties'], 'label_i13'),\n",
       " (['Payables'], 'label_i43'),\n",
       " (['Payables'], 'label_i45'),\n",
       " (['Payables and accrued e...'], 'label_i43'),\n",
       " (['Payables and accrued e...'], 'label_i44'),\n",
       " (['Pensions and other ben...'], 'label_i55'),\n",
       " (['Pensions and other pos...'], 'label_i55'),\n",
       " (['Policyholder funds'], 'label_i42'),\n",
       " (['Preferred stock'], 'label_i81'),\n",
       " (['Premises and equipment'], 'label_i12'),\n",
       " (['Premises and equipment'], 'label_i7'),\n",
       " (['Premises and equipment...'], 'label_i15'),\n",
       " (['Premiums and other rec...'], 'label_i9'),\n",
       " (['Prepaid expenses'], 'label_i6'),\n",
       " (['Prepaid pension benefi...'], 'label_i15'),\n",
       " (['Prepaid pension costs'], 'label_i16'),\n",
       " (['Prepaid pension costs'], 'label_i19'),\n",
       " (['Property and equipment'], 'label_i12'),\n",
       " (['Property and equipment'], 'label_i5'),\n",
       " (['Property and equipment...'], 'label_i12'),\n",
       " (['Property, plant and eq...'], 'label_gg2'),\n",
       " (['Property, plant and eq...'], 'label_ttgg2'),\n",
       " (['Real estate'], 'label_i5'),\n",
       " (['Real estate properties'], 'label_i1'),\n",
       " (['Real estate properties...'], 'label_i3'),\n",
       " (['Receivables'], 'label_i11'),\n",
       " (['Receivables'], 'label_i2'),\n",
       " (['Receivables'], 'label_i3'),\n",
       " (['Receivables'], 'label_i6'),\n",
       " (['Regulatory assets'], 'label_i30'),\n",
       " (['Regulatory liabilities'], 'label_i70'),\n",
       " (['Repurchase agreement'], 'label_i43'),\n",
       " (['Residential loans'], 'label_i9'),\n",
       " (['Restricted cash'], 'label_i30'),\n",
       " (['Restricted cash'], 'label_i5'),\n",
       " (['Restricted cash'], 'label_i9'),\n",
       " (['Restricted cash and ca...'], 'label_i2'),\n",
       " (['Retained earnings'], 'label_i85'),\n",
       " (['Securities'], 'label_gg2'),\n",
       " (['Securities and investm...'], 'label_i4'),\n",
       " (['Securities available f...'], 'label_i4'),\n",
       " (['Securities borrowed'], 'label_i4'),\n",
       " (['Securities borrowed'], 'label_i5'),\n",
       " (['Securities held to mat...'], 'label_i5'),\n",
       " (['Securities purchased u...'], 'label_i7'),\n",
       " (['Separate account asset...'], 'label_i14'),\n",
       " (['Separate account liabi...'], 'label_i48'),\n",
       " (['Short-term borrowing'], 'label_i41'),\n",
       " (['Short-term borrowing'], 'label_i42'),\n",
       " (['Short-term borrowing'], 'label_i45'),\n",
       " (['Short-term borrowing'], 'label_i46'),\n",
       " (['Short-term debt'], 'label_i41'),\n",
       " (['Short-term debt'], 'label_i42'),\n",
       " (['Short-term debt'], 'label_i44'),\n",
       " (['Short-term investments'], 'label_i2'),\n",
       " (['Short-term investments'], 'label_i6'),\n",
       " (['Stockholders equity'], 'label_g8'),\n",
       " (['Taxes payable'], 'label_i44'),\n",
       " (['Taxes payable'], 'label_i45'),\n",
       " (['Taxes payable'], 'label_i47'),\n",
       " (['Total Stockholders eq...'], 'label_ttg8'),\n",
       " (['Total assets'], 'label_ttg1'),\n",
       " (['Total assets'], 'label_tts1'),\n",
       " (['Total cash'], 'label_ttgg1'),\n",
       " (['Total cash and cash eq...'], 'label_ttgg1'),\n",
       " (['Total current assets'], 'label_ttg1'),\n",
       " (['Total current liabilit...'], 'label_ttgg5'),\n",
       " (['Total deposits'], 'label_ttgg5'),\n",
       " (['Total liabilities'], 'label_ttg5'),\n",
       " (['Total liabilities and ...'], 'label_tts2'),\n",
       " (['Total loans'], 'label_i11'),\n",
       " (['Total loans, net'], 'label_ttgg3'),\n",
       " (['Total non-current asse...'], 'label_ttg2'),\n",
       " (['Total non-current liab...'], 'label_ttgg6'),\n",
       " (['Total securities'], 'label_ttgg2'),\n",
       " (['Total stockholders eq...'], 'label_ttg8'),\n",
       " (['Trading account securi...'], 'label_i3'),\n",
       " (['Trading assets'], 'label_i5'),\n",
       " (['Trading liabilities'], 'label_i43'),\n",
       " (['Trading liabilities'], 'label_i44'),\n",
       " (['Trading securities'], 'label_i3'),\n",
       " (['Treasury stock'], 'label_i86'),\n",
       " (['Unearned premiums'], 'label_i43'),\n",
       " (['stockholders equity'], 'label_g8')]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [col for col in df_quarterlyBS if 'label' in col]\n",
    "labels = [[label, header] for label in labels \n",
    "          for header in df_quarterlyBS[label].unique().tolist() if pd.notna(header)]\n",
    "\n",
    "df_labels_qBS = (pd.DataFrame(labels, columns=['header', 'value']).set_index('header').astype('int'))\n",
    "df_labels_qBS['value'] = df_labels_qBS['value'].replace(df.colheaders['header'])\n",
    "df_labels_qBS[df_labels_qBS['value'].astype('str').str.contains('ncome')].sort_values(by='value')\n",
    "\n",
    "sorted(list(zip(df_labels_qBS.values.tolist(), df_labels_qBS.index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='cfa'></a>\n",
    "[return to the top](#top)\n",
    "### Annual Cash Flow Statement\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_annualCF = df.annualCF()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 78,693 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_annualCF)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(['(Gain) Loss from disco...'], 'label_i3'),\n",
       " (['(Gain) Loss from disco...'], 'label_i7'),\n",
       " (['(Gains) loss on dispos...'], 'label_i11'),\n",
       " (['(Gains) loss on dispos...'], 'label_i12'),\n",
       " (['Accounts payable'], 'label_i19'),\n",
       " (['Accounts payable'], 'label_i20'),\n",
       " (['Accounts receivable'], 'label_i16'),\n",
       " (['Accrued liabilities'], 'label_i20'),\n",
       " (['Accrued liabilities'], 'label_i21'),\n",
       " (['Accrued liabilities'], 'label_i30'),\n",
       " (['Acquisitions and dispo...'], 'label_i33'),\n",
       " (['Acquisitions and dispo...'], 'label_i36'),\n",
       " (['Acquisitions and dispo...'], 'label_i43'),\n",
       " (['Acquisitions, net'], 'label_i33'),\n",
       " (['Amortization of debt a...'], 'label_i7'),\n",
       " (['Amortization of debt d...'], 'label_i10'),\n",
       " (['Amortization of debt d...'], 'label_i3'),\n",
       " (['Amortization of debt d...'], 'label_i7'),\n",
       " (['Capital expenditure'], 'label_i96'),\n",
       " (['Capitalization of defe...'], 'label_i24'),\n",
       " (['Cash Flows From Financ...'], 'label_s3'),\n",
       " (['Cash Flows From Invest...'], 'label_s2'),\n",
       " (['Cash Flows From Operat...'], 'label_s1'),\n",
       " (['Cash at beginning of p...'], 'label_i94'),\n",
       " (['Cash at end of period'], 'label_i95'),\n",
       " (['Cash dividends paid'], 'label_i69'),\n",
       " (['Cash dividends paid'], 'label_i70'),\n",
       " (['Cash dividends paid'], 'label_i72'),\n",
       " (['Cash paid for income t...'], 'label_i98'),\n",
       " (['Cash paid for interest'], 'label_i99'),\n",
       " (['Change in deposits'], 'label_i61'),\n",
       " (['Change in deposits wit...'], 'label_i31'),\n",
       " (['Change in federal fund...'], 'label_i32'),\n",
       " (['Change in federal fund...'], 'label_i62'),\n",
       " (['Change in short-term b...'], 'label_i61'),\n",
       " (['Change in short-term b...'], 'label_i63'),\n",
       " (['Change in working capi...'], 'label_i15'),\n",
       " (['Changes in loans, net'], 'label_i35'),\n",
       " (['Common stock issued'], 'label_i64'),\n",
       " (['Common stock issued'], 'label_i65'),\n",
       " (['Common stock issued'], 'label_i66'),\n",
       " (['Common stock issued'], 'label_i67'),\n",
       " (['Common stock repurchas...'], 'label_i67'),\n",
       " (['Cumulative effect of a...'], 'label_i5'),\n",
       " (['Cumulative effect of a...'], 'label_i9'),\n",
       " (['Debt issued'], 'label_i61'),\n",
       " (['Debt issued'], 'label_i63'),\n",
       " (['Debt repayment'], 'label_i62'),\n",
       " (['Debt repayment'], 'label_i64'),\n",
       " (['Deferred charges'], 'label_i23'),\n",
       " (['Deferred income taxes'], 'label_i6'),\n",
       " (['Deferred tax (benefit)...'], 'label_i11'),\n",
       " (['Deferred tax (benefit)...'], 'label_i9'),\n",
       " (['Deferred tax(benefit) ...'], 'label_i6'),\n",
       " (['Depreciation & amortiz...'], 'label_i2'),\n",
       " (['Depreciation & amortiz...'], 'label_i5'),\n",
       " (['Depreciation & amortiz...'], 'label_i6'),\n",
       " (['Dividend paid'], 'label_i69'),\n",
       " (['Dividend paid'], 'label_i71'),\n",
       " (['Dividend payable'], 'label_i70'),\n",
       " (['Dividend payable'], 'label_i71'),\n",
       " (['Dividend payable'], 'label_i72'),\n",
       " (['Dividend payable'], 'label_i73'),\n",
       " (['Effect of exchange rat...'], 'label_i91'),\n",
       " (['Equity in (income) los...'], 'label_i4'),\n",
       " (['Excess tax benefit fro...'], 'label_i63'),\n",
       " (['Excess tax benefit fro...'], 'label_i64'),\n",
       " (['Excess tax benefit fro...'], 'label_i65'),\n",
       " (['Excess tax benefit fro...'], 'label_i66'),\n",
       " (['Excess tax benefit fro...'], 'label_i68'),\n",
       " (['Extraordinary items'], 'label_i4'),\n",
       " (['Extraordinary items'], 'label_i8'),\n",
       " (['Free Cash Flow'], 'label_g7'),\n",
       " (['Free cash flow'], 'label_i97'),\n",
       " (['Fund held under reinsu...'], 'label_i26'),\n",
       " (['Income taxes payable'], 'label_i22'),\n",
       " (['Income taxes payable'], 'label_i23'),\n",
       " (['Income taxes payable'], 'label_i32'),\n",
       " (['Interest payable'], 'label_i21'),\n",
       " (['Interest payable'], 'label_i22'),\n",
       " (['Interest payable'], 'label_i31'),\n",
       " (['Inventory'], 'label_i17'),\n",
       " (['Investment income due ...'], 'label_i25'),\n",
       " (['Investment/asset impai...'], 'label_i4'),\n",
       " (['Investment/asset impai...'], 'label_i6'),\n",
       " (['Investment/asset impai...'], 'label_i8'),\n",
       " (['Investments (gains) lo...'], 'label_i10'),\n",
       " (['Investments (gains) lo...'], 'label_i3'),\n",
       " (['Investments (gains) lo...'], 'label_i5'),\n",
       " (['Investments in propert...'], 'label_i31'),\n",
       " (['Investments in technol...'], 'label_i36'),\n",
       " (['Investments losses (ga...'], 'label_i5'),\n",
       " (['Loans'], 'label_i17'),\n",
       " (['Loans issued'], 'label_i45'),\n",
       " (['Long-term debt issued'], 'label_i62'),\n",
       " (['Long-term debt issued'], 'label_i64'),\n",
       " (['Long-term debt repayme...'], 'label_i63'),\n",
       " (['Long-term debt repayme...'], 'label_i65'),\n",
       " (['Net cash provided by (...'], 'label_i92'),\n",
       " (['Net cash provided by (...'], 'label_tts3'),\n",
       " (['Net cash provided by o...'], 'label_tts1'),\n",
       " (['Net cash used for inve...'], 'label_tts2'),\n",
       " (['Net change in cash'], 'label_i93'),\n",
       " (['Net income'], 'label_i1'),\n",
       " (['Operating cash flow'], 'label_i100'),\n",
       " (['Other assets and liabi...'], 'label_i24'),\n",
       " (['Other assets and liabi...'], 'label_i34'),\n",
       " (['Other current assets'], 'label_i19'),\n",
       " (['Other current liabilit...'], 'label_i24'),\n",
       " (['Other financing activi...'], 'label_i90'),\n",
       " (['Other investing activi...'], 'label_i60'),\n",
       " (['Other investing charge...'], 'label_i60'),\n",
       " (['Other non-cash items'], 'label_i30'),\n",
       " (['Other operating activi...'], 'label_i30'),\n",
       " (['Other operating activi...'], 'label_i40'),\n",
       " (['Other operating gain (...'], 'label_i12'),\n",
       " (['Other working capital'], 'label_i23'),\n",
       " (['Other working capital'], 'label_i25'),\n",
       " (['Payables'], 'label_i19'),\n",
       " (['Payables'], 'label_i29'),\n",
       " (['Payments from loans'], 'label_i42'),\n",
       " (['Preferred stock issued'], 'label_i63'),\n",
       " (['Preferred stock issued'], 'label_i65'),\n",
       " (['Preferred stock issued'], 'label_i66'),\n",
       " (['Preferred stock issued'], 'label_i67'),\n",
       " (['Preferred stock issued'], 'label_i68'),\n",
       " (['Preferred stock repaid'], 'label_i64'),\n",
       " (['Premiums and insurance...'], 'label_i23'),\n",
       " (['Prepaid expenses'], 'label_i18'),\n",
       " (['Prepaid expenses'], 'label_i28'),\n",
       " (['Property, and equipmen...'], 'label_i35'),\n",
       " (['Property, and equipmen...'], 'label_i38'),\n",
       " (['Property, and equipmen...'], 'label_i46'),\n",
       " (['Property, plant, and e...'], 'label_i32'),\n",
       " (['Provision for credit l...'], 'label_i2'),\n",
       " (['Purchases of intangibl...'], 'label_i37'),\n",
       " (['Purchases of intangibl...'], 'label_i38'),\n",
       " (['Purchases of intangibl...'], 'label_i47'),\n",
       " (['Purchases of investmen...'], 'label_i34'),\n",
       " (['Purchases of investmen...'], 'label_i44'),\n",
       " (['Receivable'], 'label_i16'),\n",
       " (['Receivable'], 'label_i27'),\n",
       " (['Redemption of preferre...'], 'label_i67'),\n",
       " (['Redemption of preferre...'], 'label_i68'),\n",
       " (['Redemption of preferre...'], 'label_i69'),\n",
       " (['Redemption of preferre...'], 'label_i70'),\n",
       " (['Repurchases of treasur...'], 'label_i68'),\n",
       " (['Repurchases of treasur...'], 'label_i69'),\n",
       " (['Repurchases of treasur...'], 'label_i70'),\n",
       " (['Repurchases of treasur...'], 'label_i71'),\n",
       " (['Reserves for claims an...'], 'label_i22'),\n",
       " (['Sales of intangibles'], 'label_i39'),\n",
       " (['Sales of technologies'], 'label_i37'),\n",
       " (['Sales/Maturities of in...'], 'label_i35'),\n",
       " (['Sales/maturities of fi...'], 'label_i41'),\n",
       " (['Sales/maturity of inve...'], 'label_i33'),\n",
       " (['Short-term borrowing'], 'label_i61'),\n",
       " (['Stock based compensati...'], 'label_i10'),\n",
       " (['Stock based compensati...'], 'label_i12'),\n",
       " (['Stock based compensati...'], 'label_i13'),\n",
       " (['Supplemental schedule ...'], 'label_g8'),\n",
       " (['Trading securities'], 'label_i33'),\n",
       " (['Unrealized (gains) los...'], 'label_i2'),\n",
       " (['Warrant issued'], 'label_i65'),\n",
       " (['Warrant issued'], 'label_i66'),\n",
       " (['Warrant issued'], 'label_i67'),\n",
       " (['Warrant issued'], 'label_i68'),\n",
       " (['Warrant issued'], 'label_i69')]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [col for col in df_annualCF if 'label' in col]\n",
    "labels = [[label, header] for label in labels \n",
    "          for header in df_annualCF[label].unique().tolist() if pd.notna(header)]\n",
    "\n",
    "df_labels_aCF = (pd.DataFrame(labels, columns=['header', 'value']).set_index('header').astype('int'))\n",
    "df_labels_aCF['value'] = df_labels_aCF['value'].replace(df.colheaders['header'])\n",
    "df_labels_aCF[df_labels_aCF['value'].astype('str').str.contains('ncome')].sort_values(by='value')\n",
    "\n",
    "sorted(list(zip(df_labels_aCF.values.tolist(), df_labels_aCF.index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='cfq'></a>\n",
    "[return to the top](#top)\n",
    "### Quarterly Cash Flow Statement\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_quarterlyCF = df.quarterlyCF()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 79,134 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_quarterlyCF)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(['(Gain) Loss from disco...'], 'label_i3'),\n",
       " (['(Gain) Loss from disco...'], 'label_i7'),\n",
       " (['(Gains) loss on dispos...'], 'label_i11'),\n",
       " (['(Gains) loss on dispos...'], 'label_i12'),\n",
       " (['Accounts payable'], 'label_i19'),\n",
       " (['Accounts payable'], 'label_i20'),\n",
       " (['Accounts receivable'], 'label_i16'),\n",
       " (['Accrued liabilities'], 'label_i20'),\n",
       " (['Accrued liabilities'], 'label_i21'),\n",
       " (['Accrued liabilities'], 'label_i30'),\n",
       " (['Acquisitions and dispo...'], 'label_i33'),\n",
       " (['Acquisitions and dispo...'], 'label_i36'),\n",
       " (['Acquisitions and dispo...'], 'label_i43'),\n",
       " (['Acquisitions, net'], 'label_i33'),\n",
       " (['Amortization of debt a...'], 'label_i7'),\n",
       " (['Amortization of debt d...'], 'label_i10'),\n",
       " (['Amortization of debt d...'], 'label_i3'),\n",
       " (['Amortization of debt d...'], 'label_i7'),\n",
       " (['Capital expenditure'], 'label_i96'),\n",
       " (['Capitalization of defe...'], 'label_i24'),\n",
       " (['Cash Flows From Financ...'], 'label_s3'),\n",
       " (['Cash Flows From Invest...'], 'label_s2'),\n",
       " (['Cash Flows From Operat...'], 'label_s1'),\n",
       " (['Cash at beginning of p...'], 'label_i94'),\n",
       " (['Cash at end of period'], 'label_i95'),\n",
       " (['Cash dividends paid'], 'label_i69'),\n",
       " (['Cash dividends paid'], 'label_i70'),\n",
       " (['Cash dividends paid'], 'label_i72'),\n",
       " (['Cash paid for income t...'], 'label_i98'),\n",
       " (['Cash paid for interest'], 'label_i99'),\n",
       " (['Change in deposits'], 'label_i61'),\n",
       " (['Change in deposits wit...'], 'label_i31'),\n",
       " (['Change in federal fund...'], 'label_i32'),\n",
       " (['Change in federal fund...'], 'label_i62'),\n",
       " (['Change in short-term b...'], 'label_i61'),\n",
       " (['Change in short-term b...'], 'label_i63'),\n",
       " (['Change in working capi...'], 'label_i15'),\n",
       " (['Changes in loans, net'], 'label_i35'),\n",
       " (['Common stock issued'], 'label_i64'),\n",
       " (['Common stock issued'], 'label_i65'),\n",
       " (['Common stock issued'], 'label_i66'),\n",
       " (['Common stock issued'], 'label_i67'),\n",
       " (['Common stock repurchas...'], 'label_i67'),\n",
       " (['Cumulative effect of a...'], 'label_i5'),\n",
       " (['Cumulative effect of a...'], 'label_i9'),\n",
       " (['Debt issued'], 'label_i61'),\n",
       " (['Debt issued'], 'label_i63'),\n",
       " (['Debt repayment'], 'label_i62'),\n",
       " (['Debt repayment'], 'label_i64'),\n",
       " (['Deferred charges'], 'label_i23'),\n",
       " (['Deferred income taxes'], 'label_i6'),\n",
       " (['Deferred tax (benefit)...'], 'label_i11'),\n",
       " (['Deferred tax (benefit)...'], 'label_i9'),\n",
       " (['Deferred tax(benefit) ...'], 'label_i6'),\n",
       " (['Depreciation & amortiz...'], 'label_i2'),\n",
       " (['Depreciation & amortiz...'], 'label_i5'),\n",
       " (['Depreciation & amortiz...'], 'label_i6'),\n",
       " (['Dividend paid'], 'label_i69'),\n",
       " (['Dividend paid'], 'label_i71'),\n",
       " (['Dividend payable'], 'label_i70'),\n",
       " (['Dividend payable'], 'label_i71'),\n",
       " (['Dividend payable'], 'label_i72'),\n",
       " (['Dividend payable'], 'label_i73'),\n",
       " (['Effect of exchange rat...'], 'label_i91'),\n",
       " (['Equity in (income) los...'], 'label_i4'),\n",
       " (['Excess tax benefit fro...'], 'label_i63'),\n",
       " (['Excess tax benefit fro...'], 'label_i64'),\n",
       " (['Excess tax benefit fro...'], 'label_i65'),\n",
       " (['Excess tax benefit fro...'], 'label_i66'),\n",
       " (['Excess tax benefit fro...'], 'label_i68'),\n",
       " (['Extraordinary items'], 'label_i4'),\n",
       " (['Extraordinary items'], 'label_i8'),\n",
       " (['Free Cash Flow'], 'label_g7'),\n",
       " (['Free cash flow'], 'label_i97'),\n",
       " (['Fund held under reinsu...'], 'label_i26'),\n",
       " (['Income taxes payable'], 'label_i22'),\n",
       " (['Income taxes payable'], 'label_i23'),\n",
       " (['Income taxes payable'], 'label_i32'),\n",
       " (['Interest payable'], 'label_i21'),\n",
       " (['Interest payable'], 'label_i22'),\n",
       " (['Interest payable'], 'label_i31'),\n",
       " (['Inventory'], 'label_i17'),\n",
       " (['Investment income due ...'], 'label_i25'),\n",
       " (['Investment/asset impai...'], 'label_i4'),\n",
       " (['Investment/asset impai...'], 'label_i6'),\n",
       " (['Investment/asset impai...'], 'label_i8'),\n",
       " (['Investments (gains) lo...'], 'label_i10'),\n",
       " (['Investments (gains) lo...'], 'label_i3'),\n",
       " (['Investments (gains) lo...'], 'label_i5'),\n",
       " (['Investments in propert...'], 'label_i31'),\n",
       " (['Investments in technol...'], 'label_i36'),\n",
       " (['Investments losses (ga...'], 'label_i5'),\n",
       " (['Loans'], 'label_i17'),\n",
       " (['Loans issued'], 'label_i45'),\n",
       " (['Long-term debt issued'], 'label_i62'),\n",
       " (['Long-term debt issued'], 'label_i64'),\n",
       " (['Long-term debt repayme...'], 'label_i63'),\n",
       " (['Long-term debt repayme...'], 'label_i65'),\n",
       " (['Net cash provided by (...'], 'label_i92'),\n",
       " (['Net cash provided by (...'], 'label_tts3'),\n",
       " (['Net cash provided by o...'], 'label_tts1'),\n",
       " (['Net cash used for inve...'], 'label_tts2'),\n",
       " (['Net change in cash'], 'label_i93'),\n",
       " (['Net income'], 'label_i1'),\n",
       " (['Operating cash flow'], 'label_i100'),\n",
       " (['Other assets and liabi...'], 'label_i24'),\n",
       " (['Other assets and liabi...'], 'label_i34'),\n",
       " (['Other current assets'], 'label_i19'),\n",
       " (['Other current liabilit...'], 'label_i24'),\n",
       " (['Other financing activi...'], 'label_i90'),\n",
       " (['Other investing activi...'], 'label_i60'),\n",
       " (['Other investing charge...'], 'label_i60'),\n",
       " (['Other non-cash items'], 'label_i30'),\n",
       " (['Other operating activi...'], 'label_i30'),\n",
       " (['Other operating activi...'], 'label_i40'),\n",
       " (['Other operating gain (...'], 'label_i12'),\n",
       " (['Other working capital'], 'label_i23'),\n",
       " (['Other working capital'], 'label_i25'),\n",
       " (['Payables'], 'label_i19'),\n",
       " (['Payables'], 'label_i29'),\n",
       " (['Payments from loans'], 'label_i42'),\n",
       " (['Preferred stock issued'], 'label_i63'),\n",
       " (['Preferred stock issued'], 'label_i65'),\n",
       " (['Preferred stock issued'], 'label_i66'),\n",
       " (['Preferred stock issued'], 'label_i67'),\n",
       " (['Preferred stock issued'], 'label_i68'),\n",
       " (['Preferred stock repaid'], 'label_i64'),\n",
       " (['Premiums and insurance...'], 'label_i23'),\n",
       " (['Prepaid expenses'], 'label_i18'),\n",
       " (['Prepaid expenses'], 'label_i28'),\n",
       " (['Property, and equipmen...'], 'label_i35'),\n",
       " (['Property, and equipmen...'], 'label_i38'),\n",
       " (['Property, and equipmen...'], 'label_i46'),\n",
       " (['Property, plant, and e...'], 'label_i32'),\n",
       " (['Provision for credit l...'], 'label_i2'),\n",
       " (['Purchases of intangibl...'], 'label_i37'),\n",
       " (['Purchases of intangibl...'], 'label_i38'),\n",
       " (['Purchases of intangibl...'], 'label_i47'),\n",
       " (['Purchases of investmen...'], 'label_i34'),\n",
       " (['Purchases of investmen...'], 'label_i44'),\n",
       " (['Receivable'], 'label_i16'),\n",
       " (['Receivable'], 'label_i27'),\n",
       " (['Redemption of preferre...'], 'label_i67'),\n",
       " (['Redemption of preferre...'], 'label_i68'),\n",
       " (['Redemption of preferre...'], 'label_i69'),\n",
       " (['Redemption of preferre...'], 'label_i70'),\n",
       " (['Repurchases of treasur...'], 'label_i68'),\n",
       " (['Repurchases of treasur...'], 'label_i69'),\n",
       " (['Repurchases of treasur...'], 'label_i70'),\n",
       " (['Repurchases of treasur...'], 'label_i71'),\n",
       " (['Reserves for claims an...'], 'label_i22'),\n",
       " (['Sales of intangibles'], 'label_i39'),\n",
       " (['Sales of technologies'], 'label_i37'),\n",
       " (['Sales/Maturities of in...'], 'label_i35'),\n",
       " (['Sales/maturities of fi...'], 'label_i41'),\n",
       " (['Sales/maturity of inve...'], 'label_i33'),\n",
       " (['Short-term borrowing'], 'label_i61'),\n",
       " (['Stock based compensati...'], 'label_i10'),\n",
       " (['Stock based compensati...'], 'label_i12'),\n",
       " (['Stock based compensati...'], 'label_i13'),\n",
       " (['Supplemental schedule ...'], 'label_g8'),\n",
       " (['Trading securities'], 'label_i33'),\n",
       " (['Unrealized (gains) los...'], 'label_i2'),\n",
       " (['Warrant issued'], 'label_i65'),\n",
       " (['Warrant issued'], 'label_i66'),\n",
       " (['Warrant issued'], 'label_i67'),\n",
       " (['Warrant issued'], 'label_i68'),\n",
       " (['Warrant issued'], 'label_i69')]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [col for col in df_quarterlyCF if 'label' in col]\n",
    "labels = [[label, header] for label in labels \n",
    "          for header in df_quarterlyCF[label].unique().tolist() if pd.notna(header)]\n",
    "\n",
    "df_labels_qCF = (pd.DataFrame(labels, columns=['header', 'value']).set_index('header').astype('int'))\n",
    "df_labels_qCF['value'] = df_labels_qCF['value'].replace(df.colheaders['header'])\n",
    "df_labels_qCF[df_labels_qCF['value'].astype('str').str.contains('ncome')].sort_values(by='value')\n",
    "\n",
    "sorted(list(zip(df_labels_qCF.values.tolist(), df_labels_qCF.index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='it'></a>\n",
    "[return to the top](#top)\n",
    "### Insider Transactions\n",
    "##### DataFrame Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_insidertrades = df.insider_trades()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DataFrame Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame contains 701,466 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame contains {:,.0f} records.'.format(len(df_insidertrades)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"stats\"></a>\n",
    "[return to the top](#top)\n",
    "# Performing statistical analysis\n",
    "### Count of database records\n",
    "**1.** Total number of records **before** merging reference tables (length of `df.master0`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame df.master contains 119,345 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame df.master contains {:,.0f} records.'.format(len(df.master0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.** Total number of records **after** merging reference tables (length of `df.master`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame df_master0 contains 112,156 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame df_master0 contains {:,.0f} records.'.format(len(df.master)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.** Total number of records **after** filtering out inactive records (length of `df_master`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame df_master contains 100,115 records.\n"
     ]
    }
   ],
   "source": [
    "print('DataFrame df_master contains {:,.0f} records.'.format(len(df_master)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"lastupdate\"></a>\n",
    "[return to the top](#top)\n",
    "### Last updated dates\n",
    "List of dates (as a pd.Series object) of when the database records were last updated. \n",
    "The values indicate the number of records updated on each date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>update_date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-08</th>\n",
       "      <td>1385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-07</th>\n",
       "      <td>98650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-27</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ticker_count\n",
       "update_date              \n",
       "2019-05-08           1385\n",
       "2019-05-07          98650\n",
       "2019-05-01             34\n",
       "2019-04-30             36\n",
       "2019-04-27              2"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_master[['update_date', 'ticker']].groupby(by='update_date').count().sort_index(ascending=False)\n",
    " .rename(columns={'ticker':'ticker_count'}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"type\"></a>\n",
    "[return to the top](#top)\n",
    "### Number of records by Security Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>security_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Closed-End Fund</th>\n",
       "      <td>1181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exchange-Traded Fund</th>\n",
       "      <td>6204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Money Market Fund</th>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open-End Fund</th>\n",
       "      <td>25646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stock</th>\n",
       "      <td>66390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ticker_count\n",
       "security_type                     \n",
       "Closed-End Fund               1181\n",
       "Exchange-Traded Fund          6204\n",
       "Index                          498\n",
       "Money Market Fund              196\n",
       "Open-End Fund                25646\n",
       "Stock                        66390"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_master[['security_type', 'ticker']].groupby(by='security_type').count()\n",
    " .rename(columns={'ticker':'ticker_count'}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"country\"></a>\n",
    "[return to the top](#top)\n",
    "### Number of records by Country, based on the location of exchanges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ticker_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>country_c3</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <th>AUS</th>\n",
       "      <td>2028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <th>BEL</th>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <th>CAN</th>\n",
       "      <td>4135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <th>CHN</th>\n",
       "      <td>3792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <th>FIN</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <th>FRA</th>\n",
       "      <td>1193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <th>DEU</th>\n",
       "      <td>35746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <th>HKG</th>\n",
       "      <td>2363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <th>IRL</th>\n",
       "      <td>1634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <th>ITA</th>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <th>LUX</th>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <th>NLD</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <th>NOR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <th>PRT</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <th>SWE</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>GBR</th>\n",
       "      <td>5443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>USA</th>\n",
       "      <td>42755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ticker_count\n",
       "country        country_c3              \n",
       "Australia      AUS                 2028\n",
       "Belgium        BEL                  162\n",
       "Canada         CAN                 4135\n",
       "China          CHN                 3792\n",
       "Finland        FIN                    2\n",
       "France         FRA                 1193\n",
       "Germany        DEU                35746\n",
       "Hong Kong      HKG                 2363\n",
       "Ireland        IRL                 1634\n",
       "Italy          ITA                  624\n",
       "Luxembourg     LUX                  220\n",
       "Netherlands    NLD                   23\n",
       "Norway         NOR                    1\n",
       "Portugal       PRT                   57\n",
       "Sweden         SWE                    8\n",
       "United Kingdom GBR                 5443\n",
       "United States  USA                42755"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_master[['country', 'country_c3', 'ticker']]\n",
    " .groupby(by=['country', 'country_c3']).count().rename(columns={'ticker':'ticker_count'})\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"exchange\"></a>\n",
    "[return to the top](#top)\n",
    "### Number of records per exchange\n",
    "Where $ticker\\_count > 100$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ticker_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>country_c3</th>\n",
       "      <th>exchange</th>\n",
       "      <th>exchange_sym</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>USA</th>\n",
       "      <th>NASDAQ</th>\n",
       "      <th>XNAS</th>\n",
       "      <td>29080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Germany</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">DEU</th>\n",
       "      <th>DEUTSCHE BOERSE AG</th>\n",
       "      <th>XFRA</th>\n",
       "      <td>10920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOERSE STUTTGART</th>\n",
       "      <th>XSTU</th>\n",
       "      <td>8224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOERSE BERLIN</th>\n",
       "      <th>XBER</th>\n",
       "      <td>7891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>USA</th>\n",
       "      <th>OTC MARKETS</th>\n",
       "      <th>PINX</th>\n",
       "      <td>6913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <th>DEU</th>\n",
       "      <th>BOERSE MUENCHEN</th>\n",
       "      <th>XMUN</th>\n",
       "      <td>4870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>GBR</th>\n",
       "      <th>LONDON STOCK EXCHANGE</th>\n",
       "      <th>XLON</th>\n",
       "      <td>3224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>USA</th>\n",
       "      <th>NEW YORK STOCK EXCHANGE, INC.</th>\n",
       "      <th>XNYS</th>\n",
       "      <td>2875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <th>HKG</th>\n",
       "      <th>HONG KONG EXCHANGES AND CLEARING LTD</th>\n",
       "      <th>XHKG</th>\n",
       "      <td>2363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>GBR</th>\n",
       "      <th>LSE International Trading Service</th>\n",
       "      <th>LTS</th>\n",
       "      <td>2219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <th>CHN</th>\n",
       "      <th>SHENZHEN STOCK EXCHANGE</th>\n",
       "      <th>XSHE</th>\n",
       "      <td>2203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <th>DEU</th>\n",
       "      <th>BOERSE DUESSELDORF</th>\n",
       "      <th>XDUS</th>\n",
       "      <td>2192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <th>AUS</th>\n",
       "      <th>ASX - ALL MARKETS</th>\n",
       "      <th>XASX</th>\n",
       "      <td>2028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Canada</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">CAN</th>\n",
       "      <th>TORONTO STOCK EXCHANGE</th>\n",
       "      <th>XTSE</th>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSX VENTURE EXCHANGE</th>\n",
       "      <th>XTSX</th>\n",
       "      <td>1633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>USA</th>\n",
       "      <th>NYSE ARCA</th>\n",
       "      <th>ARCX</th>\n",
       "      <td>1592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <th>CHN</th>\n",
       "      <th>SHANGHAI STOCK EXCHANGE</th>\n",
       "      <th>XSHG</th>\n",
       "      <td>1589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <th>IRL</th>\n",
       "      <th>XETRA</th>\n",
       "      <th>XETR</th>\n",
       "      <td>1347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>USA</th>\n",
       "      <th>OTC GREY MARKET</th>\n",
       "      <th>GREY</th>\n",
       "      <td>1324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <th>FRA</th>\n",
       "      <th>EURONEXT - EURONEXT PARIS</th>\n",
       "      <th>XPAR</th>\n",
       "      <td>1193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <th>DEU</th>\n",
       "      <th>BOERSE HAMBURG</th>\n",
       "      <th>XHAM</th>\n",
       "      <td>1188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <th>ITA</th>\n",
       "      <th>BORSA ITALIANA S.P.A.</th>\n",
       "      <th>XMIL</th>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <th>DEU</th>\n",
       "      <th>BOERSE HANNOVER</th>\n",
       "      <th>XHAN</th>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <th>CAN</th>\n",
       "      <th>CANADIAN NATIONAL STOCK EXCHANGE</th>\n",
       "      <th>XCNQ</th>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">United States</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">USA</th>\n",
       "      <th>BATS Z-EXCHANGE</th>\n",
       "      <th>BATS</th>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NYSE MKT LLC</th>\n",
       "      <th>XASE</th>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <th>IRL</th>\n",
       "      <th>SIX SWISS EXCHANGE</th>\n",
       "      <th>XSWX</th>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <th>LUX</th>\n",
       "      <th>EURONEXT - EURONEXT AMSTERDAM</th>\n",
       "      <th>XAMS</th>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>USA</th>\n",
       "      <th>S&amp;P Indices</th>\n",
       "      <th>SPI</th>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <th>BEL</th>\n",
       "      <th>EURONEXT - EURONEXT BRUSSELS</th>\n",
       "      <th>XBRU</th>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>USA</th>\n",
       "      <th>Dow Jones Indices</th>\n",
       "      <th>DJI</th>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                             ticker_count\n",
       "country        country_c3 exchange                             exchange_sym              \n",
       "United States  USA        NASDAQ                               XNAS                 29080\n",
       "Germany        DEU        DEUTSCHE BOERSE AG                   XFRA                 10920\n",
       "                          BOERSE STUTTGART                     XSTU                  8224\n",
       "                          BOERSE BERLIN                        XBER                  7891\n",
       "United States  USA        OTC MARKETS                          PINX                  6913\n",
       "Germany        DEU        BOERSE MUENCHEN                      XMUN                  4870\n",
       "United Kingdom GBR        LONDON STOCK EXCHANGE                XLON                  3224\n",
       "United States  USA        NEW YORK STOCK EXCHANGE, INC.        XNYS                  2875\n",
       "Hong Kong      HKG        HONG KONG EXCHANGES AND CLEARING LTD XHKG                  2363\n",
       "United Kingdom GBR        LSE International Trading Service    LTS                   2219\n",
       "China          CHN        SHENZHEN STOCK EXCHANGE              XSHE                  2203\n",
       "Germany        DEU        BOERSE DUESSELDORF                   XDUS                  2192\n",
       "Australia      AUS        ASX - ALL MARKETS                    XASX                  2028\n",
       "Canada         CAN        TORONTO STOCK EXCHANGE               XTSE                  2018\n",
       "                          TSX VENTURE EXCHANGE                 XTSX                  1633\n",
       "United States  USA        NYSE ARCA                            ARCX                  1592\n",
       "China          CHN        SHANGHAI STOCK EXCHANGE              XSHG                  1589\n",
       "Ireland        IRL        XETRA                                XETR                  1347\n",
       "United States  USA        OTC GREY MARKET                      GREY                  1324\n",
       "France         FRA        EURONEXT - EURONEXT PARIS            XPAR                  1193\n",
       "Germany        DEU        BOERSE HAMBURG                       XHAM                  1188\n",
       "Italy          ITA        BORSA ITALIANA S.P.A.                XMIL                   624\n",
       "Germany        DEU        BOERSE HANNOVER                      XHAN                   461\n",
       "Canada         CAN        CANADIAN NATIONAL STOCK EXCHANGE     XCNQ                   429\n",
       "United States  USA        BATS Z-EXCHANGE                      BATS                   299\n",
       "                          NYSE MKT LLC                         XASE                   296\n",
       "Ireland        IRL        SIX SWISS EXCHANGE                   XSWX                   287\n",
       "Luxembourg     LUX        EURONEXT - EURONEXT AMSTERDAM        XAMS                   199\n",
       "United States  USA        S&P Indices                          SPI                    169\n",
       "Belgium        BEL        EURONEXT - EURONEXT BRUSSELS         XBRU                   162\n",
       "United States  USA        Dow Jones Indices                    DJI                    130"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['country', 'country_c3', 'exchange', 'exchange_sym', 'ticker']\n",
    "df_exchanges = df_master[cols].groupby(by=cols[:-1]).count().rename(columns={'ticker':'ticker_count'})\n",
    "df_exchanges[df_exchanges['ticker_count'] > 100].sort_values(by='ticker_count', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "[return to the top](#top)\n",
    "### Number of Stocks by Country of Exchange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ticker_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>country_c3</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <th>DEU</th>\n",
       "      <td>35716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <th>USA</th>\n",
       "      <td>13693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>GBR</th>\n",
       "      <td>3845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <th>CHN</th>\n",
       "      <td>3592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <th>CAN</th>\n",
       "      <td>3322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hong Kong</th>\n",
       "      <th>HKG</th>\n",
       "      <td>2268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <th>AUS</th>\n",
       "      <td>1730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <th>FRA</th>\n",
       "      <td>831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <th>IRL</th>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <th>ITA</th>\n",
       "      <td>446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <th>BEL</th>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luxembourg</th>\n",
       "      <th>LUX</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <th>PRT</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ticker_count\n",
       "country        country_c3              \n",
       "Germany        DEU                35716\n",
       "United States  USA                13693\n",
       "United Kingdom GBR                 3845\n",
       "China          CHN                 3592\n",
       "Canada         CAN                 3322\n",
       "Hong Kong      HKG                 2268\n",
       "Australia      AUS                 1730\n",
       "France         FRA                  831\n",
       "Ireland        IRL                  740\n",
       "Italy          ITA                  446\n",
       "Belgium        BEL                  161\n",
       "Luxembourg     LUX                  118\n",
       "Portugal       PRT                   53"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_master\n",
    " .where(df_master['security_type'] == 'Stock').dropna(axis=0, how='all')[['country', 'country_c3', 'ticker']]\n",
    " .groupby(by=['country', 'country_c3']).count().rename(columns={'ticker':'ticker_count'})\n",
    " .sort_values(by='ticker_count', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"sector\"></a>\n",
    "[return to the top](#top)\n",
    "### Number of stocks by sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sector\n",
       "Basic Materials           11072\n",
       "Technology                 9300\n",
       "Industrials                9192\n",
       "Consumer Cyclical          8322\n",
       "Healthcare                 7476\n",
       "Financial Services         7375\n",
       "Energy                     3736\n",
       "Consumer Defensive         3515\n",
       "Real Estate                3325\n",
       "Utilities                  1804\n",
       "Communication Services     1397\n",
       "Name: stock_count, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_master\n",
    " .where((df_master['security_type'] == 'Stock') & (df_master['sector'] != '—')).dropna(axis=0, how='all')\n",
    " .groupby(by='sector').count()\n",
    " .rename(columns={'ticker':'stock_count'}))['stock_count'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"industry\"></a>\n",
    "[return to the top](#top)\n",
    "### Number of stocks by industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>stock_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sector</th>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">Basic Materials</th>\n",
       "      <th>Agricultural Inputs</th>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aluminum</th>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Building Materials</th>\n",
       "      <td>721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chemicals</th>\n",
       "      <td>657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coal</th>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copper</th>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold</th>\n",
       "      <td>2118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrial Metals &amp; Minerals</th>\n",
       "      <td>4756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lumber &amp; Wood Production</th>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paper &amp; Paper Products</th>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Silver</th>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specialty Chemicals</th>\n",
       "      <td>676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steel</th>\n",
       "      <td>494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Communication Services</th>\n",
       "      <th>Pay TV</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telecom Services</th>\n",
       "      <td>1299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">Consumer Cyclical</th>\n",
       "      <th>Advertising Agencies</th>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apparel Manufacturing</th>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apparel Stores</th>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto &amp; Truck Dealerships</th>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto Manufacturers</th>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto Parts</th>\n",
       "      <td>721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broadcasting - Radio</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Broadcasting - TV</th>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department Stores</th>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Footwear &amp; Accessories</th>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gambling</th>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Home Furnishings &amp; Fixtures</th>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Home Improvement Stores</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leisure</th>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lodging</th>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Real Estate</th>\n",
       "      <th>REIT - Industrial</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REIT - Office</th>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REIT - Residential</th>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REIT - Retail</th>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Estate - General</th>\n",
       "      <td>901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Estate Services</th>\n",
       "      <td>1044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"19\" valign=\"top\">Technology</th>\n",
       "      <th>Communication Equipment</th>\n",
       "      <td>834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Computer Distribution</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Computer Systems</th>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumer Electronics</th>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract Manufacturers</th>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Storage</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic Components</th>\n",
       "      <td>977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronic Gaming &amp; Multimedia</th>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronics Distribution</th>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health Information Services</th>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Information Technology Services</th>\n",
       "      <td>860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Internet Content &amp; Information</th>\n",
       "      <td>751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scientific &amp; Technical Instruments</th>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semiconductor Equipment &amp; Materials</th>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semiconductor Memory</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semiconductors</th>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Software - Application</th>\n",
       "      <td>1892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Software - Infrastructure</th>\n",
       "      <td>696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar</th>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Utilities</th>\n",
       "      <th>Utilities - Diversified</th>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities - Independent Power Producers</th>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities - Regulated Electric</th>\n",
       "      <td>605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities - Regulated Gas</th>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities - Regulated Water</th>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>148 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                stock_count\n",
       "sector                 industry                                            \n",
       "Basic Materials        Agricultural Inputs                              279\n",
       "                       Aluminum                                         134\n",
       "                       Building Materials                               721\n",
       "                       Chemicals                                        657\n",
       "                       Coal                                             363\n",
       "                       Copper                                           286\n",
       "                       Gold                                            2118\n",
       "                       Industrial Metals & Minerals                    4756\n",
       "                       Lumber & Wood Production                         146\n",
       "                       Paper & Paper Products                           263\n",
       "                       Silver                                           179\n",
       "                       Specialty Chemicals                              676\n",
       "                       Steel                                            494\n",
       "Communication Services Pay TV                                            98\n",
       "                       Telecom Services                                1299\n",
       "Consumer Cyclical      Advertising Agencies                             199\n",
       "                       Apparel Manufacturing                            410\n",
       "                       Apparel Stores                                   282\n",
       "                       Auto & Truck Dealerships                         151\n",
       "                       Auto Manufacturers                               429\n",
       "                       Auto Parts                                       721\n",
       "                       Broadcasting - Radio                              38\n",
       "                       Broadcasting - TV                                254\n",
       "                       Department Stores                                292\n",
       "                       Footwear & Accessories                           198\n",
       "                       Gambling                                         230\n",
       "                       Home Furnishings & Fixtures                      326\n",
       "                       Home Improvement Stores                           65\n",
       "                       Leisure                                          642\n",
       "                       Lodging                                          233\n",
       "...                                                                     ...\n",
       "Real Estate            REIT - Industrial                                150\n",
       "                       REIT - Office                                    185\n",
       "                       REIT - Residential                               163\n",
       "                       REIT - Retail                                    284\n",
       "                       Real Estate - General                            901\n",
       "                       Real Estate Services                            1044\n",
       "Technology             Communication Equipment                          834\n",
       "                       Computer Distribution                             34\n",
       "                       Computer Systems                                 310\n",
       "                       Consumer Electronics                             441\n",
       "                       Contract Manufacturers                           113\n",
       "                       Data Storage                                      56\n",
       "                       Electronic Components                            977\n",
       "                       Electronic Gaming & Multimedia                   394\n",
       "                       Electronics Distribution                         128\n",
       "                       Health Information Services                      297\n",
       "                       Information Technology Services                  860\n",
       "                       Internet Content & Information                   751\n",
       "                       Scientific & Technical Instruments               382\n",
       "                       Semiconductor Equipment & Materials              361\n",
       "                       Semiconductor Memory                              54\n",
       "                       Semiconductors                                   499\n",
       "                       Software - Application                          1892\n",
       "                       Software - Infrastructure                        696\n",
       "                       Solar                                            221\n",
       "Utilities              Utilities - Diversified                          405\n",
       "                       Utilities - Independent Power Producers          421\n",
       "                       Utilities - Regulated Electric                   605\n",
       "                       Utilities - Regulated Gas                        212\n",
       "                       Utilities - Regulated Water                      161\n",
       "\n",
       "[148 rows x 1 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_master[['sector', 'industry', 'ticker']]\n",
    " .where((df_master['security_type'] == 'Stock') & (df_master['industry'] != '—')).dropna(axis=0, how='all')\n",
    " .groupby(by=['sector', 'industry']).count().rename(columns={'ticker':'stock_count'}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"meanpr\"></a>\n",
    "[return to the top](#top)\n",
    "### Mean price ratios (P/E, P/S, P/B, P/CF) of stocks by sectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, merge `df_master` and `df_vals` and remove outliers where Price Ratio > 10,000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 66,490 Stock records that fit this criteria.\n"
     ]
    }
   ],
   "source": [
    "df_valuation = (df_master\n",
    "                .where((df_master['security_type'] == 'Stock') & (df_master['sector'] != '—'))\n",
    "                .dropna(axis=0, how='all')\n",
    "                .merge(df_vals, on=['ticker_id', 'exchange_id'])\n",
    "                .drop(['ticker_id', 'exchange_id'], axis=1))\n",
    "\n",
    "cols = list(filter(lambda col: col.startswith('P'), df_valuation))\n",
    "df0 = df_valuation.copy()\n",
    "for col in cols:\n",
    "    df0 = df[(df[col] < 10000) | df[col].isna()]\n",
    "print('There are {:,.0f} Stock records that fit this criteria.'.format(len(df)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean TTM Price Ratios for all stocks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>PE_Forward</th>\n",
       "      <th>PE_TTM</th>\n",
       "      <th>PB_TTM</th>\n",
       "      <th>PS_TTM</th>\n",
       "      <th>PC_TTM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>7379</td>\n",
       "      <td>38.7278</td>\n",
       "      <td>70.1173</td>\n",
       "      <td>10.6114</td>\n",
       "      <td>86.6804</td>\n",
       "      <td>48.3582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>9279</td>\n",
       "      <td>51.4025</td>\n",
       "      <td>62.5899</td>\n",
       "      <td>9.8119</td>\n",
       "      <td>17.0798</td>\n",
       "      <td>63.5401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Communication Services</th>\n",
       "      <td>1410</td>\n",
       "      <td>36.0375</td>\n",
       "      <td>55.0353</td>\n",
       "      <td>4.3192</td>\n",
       "      <td>7.8809</td>\n",
       "      <td>15.5794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumer Cyclical</th>\n",
       "      <td>8339</td>\n",
       "      <td>28.9007</td>\n",
       "      <td>46.9821</td>\n",
       "      <td>5.6921</td>\n",
       "      <td>8.2603</td>\n",
       "      <td>31.6819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrials</th>\n",
       "      <td>9248</td>\n",
       "      <td>23.2270</td>\n",
       "      <td>41.2598</td>\n",
       "      <td>9.7552</td>\n",
       "      <td>5.7658</td>\n",
       "      <td>32.9585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Estate</th>\n",
       "      <td>3328</td>\n",
       "      <td>27.4631</td>\n",
       "      <td>37.4330</td>\n",
       "      <td>1.9365</td>\n",
       "      <td>10.2436</td>\n",
       "      <td>34.9050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumer Defensive</th>\n",
       "      <td>3527</td>\n",
       "      <td>23.3037</td>\n",
       "      <td>35.7422</td>\n",
       "      <td>7.0997</td>\n",
       "      <td>13.1392</td>\n",
       "      <td>30.0404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basic Materials</th>\n",
       "      <td>10992</td>\n",
       "      <td>19.4836</td>\n",
       "      <td>32.4849</td>\n",
       "      <td>5.6546</td>\n",
       "      <td>47.1302</td>\n",
       "      <td>26.2566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>1799</td>\n",
       "      <td>18.8048</td>\n",
       "      <td>32.0523</td>\n",
       "      <td>3.1856</td>\n",
       "      <td>12.0561</td>\n",
       "      <td>18.3065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>3758</td>\n",
       "      <td>25.3304</td>\n",
       "      <td>32.0346</td>\n",
       "      <td>4.0467</td>\n",
       "      <td>30.7673</td>\n",
       "      <td>16.7114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial Services</th>\n",
       "      <td>7431</td>\n",
       "      <td>15.6996</td>\n",
       "      <td>29.5859</td>\n",
       "      <td>2.7732</td>\n",
       "      <td>13.6718</td>\n",
       "      <td>31.8589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count  PE_Forward   PE_TTM   PB_TTM   PS_TTM   PC_TTM\n",
       "sector                                                                       \n",
       "Healthcare               7379     38.7278  70.1173  10.6114  86.6804  48.3582\n",
       "Technology               9279     51.4025  62.5899   9.8119  17.0798  63.5401\n",
       "Communication Services   1410     36.0375  55.0353   4.3192   7.8809  15.5794\n",
       "Consumer Cyclical        8339     28.9007  46.9821   5.6921   8.2603  31.6819\n",
       "Industrials              9248     23.2270  41.2598   9.7552   5.7658  32.9585\n",
       "Real Estate              3328     27.4631  37.4330   1.9365  10.2436  34.9050\n",
       "Consumer Defensive       3527     23.3037  35.7422   7.0997  13.1392  30.0404\n",
       "Basic Materials         10992     19.4836  32.4849   5.6546  47.1302  26.2566\n",
       "Utilities                1799     18.8048  32.0523   3.1856  12.0561  18.3065\n",
       "Energy                   3758     25.3304  32.0346   4.0467  30.7673  16.7114\n",
       "Financial Services       7431     15.6996  29.5859   2.7732  13.6718  31.8589"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_val_mean = (df[['sector', 'company']].groupby('sector').count()\n",
    "               .rename(columns={'company':'count'})\n",
    "               .merge(df.groupby('sector').mean().round(4), on='sector')\n",
    "               .sort_values(by='PE_TTM', ascending=False))\n",
    "\n",
    "df_val_mean[['count', 'PE_Forward', 'PE_TTM', 'PB_TTM', 'PS_TTM', 'PC_TTM']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean TTM Price Ratios for USA stocks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 13,928 Stock records that fit this criteria.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>PE_Forward</th>\n",
       "      <th>PE_TTM</th>\n",
       "      <th>PB_TTM</th>\n",
       "      <th>PS_TTM</th>\n",
       "      <th>PC_TTM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>1840</td>\n",
       "      <td>52.6292</td>\n",
       "      <td>77.2917</td>\n",
       "      <td>20.4686</td>\n",
       "      <td>39.7942</td>\n",
       "      <td>55.7550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>1830</td>\n",
       "      <td>47.1120</td>\n",
       "      <td>67.7186</td>\n",
       "      <td>17.6418</td>\n",
       "      <td>88.6630</td>\n",
       "      <td>42.7329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Communication Services</th>\n",
       "      <td>272</td>\n",
       "      <td>43.8836</td>\n",
       "      <td>56.7124</td>\n",
       "      <td>7.6704</td>\n",
       "      <td>21.2787</td>\n",
       "      <td>14.5135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Estate</th>\n",
       "      <td>720</td>\n",
       "      <td>33.6794</td>\n",
       "      <td>53.8995</td>\n",
       "      <td>2.4658</td>\n",
       "      <td>11.5156</td>\n",
       "      <td>34.3973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumer Defensive</th>\n",
       "      <td>757</td>\n",
       "      <td>22.9971</td>\n",
       "      <td>42.8492</td>\n",
       "      <td>23.6390</td>\n",
       "      <td>38.9571</td>\n",
       "      <td>29.4465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrials</th>\n",
       "      <td>1876</td>\n",
       "      <td>24.0752</td>\n",
       "      <td>39.3345</td>\n",
       "      <td>20.8514</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>30.0759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumer Cyclical</th>\n",
       "      <td>1504</td>\n",
       "      <td>27.1595</td>\n",
       "      <td>38.8393</td>\n",
       "      <td>7.5907</td>\n",
       "      <td>7.7895</td>\n",
       "      <td>19.8953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>882</td>\n",
       "      <td>25.0200</td>\n",
       "      <td>38.0208</td>\n",
       "      <td>6.2017</td>\n",
       "      <td>19.9507</td>\n",
       "      <td>16.3427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>360</td>\n",
       "      <td>20.1373</td>\n",
       "      <td>29.9965</td>\n",
       "      <td>6.8532</td>\n",
       "      <td>17.3556</td>\n",
       "      <td>11.7945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial Services</th>\n",
       "      <td>2076</td>\n",
       "      <td>19.2023</td>\n",
       "      <td>29.7624</td>\n",
       "      <td>3.0778</td>\n",
       "      <td>17.0589</td>\n",
       "      <td>24.3544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basic Materials</th>\n",
       "      <td>1811</td>\n",
       "      <td>23.1804</td>\n",
       "      <td>23.9269</td>\n",
       "      <td>7.0965</td>\n",
       "      <td>55.4644</td>\n",
       "      <td>21.0118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count  PE_Forward   PE_TTM   PB_TTM   PS_TTM   PC_TTM\n",
       "sector                                                                       \n",
       "Technology               1840     52.6292  77.2917  20.4686  39.7942  55.7550\n",
       "Healthcare               1830     47.1120  67.7186  17.6418  88.6630  42.7329\n",
       "Communication Services    272     43.8836  56.7124   7.6704  21.2787  14.5135\n",
       "Real Estate               720     33.6794  53.8995   2.4658  11.5156  34.3973\n",
       "Consumer Defensive        757     22.9971  42.8492  23.6390  38.9571  29.4465\n",
       "Industrials              1876     24.0752  39.3345  20.8514  12.0000  30.0759\n",
       "Consumer Cyclical        1504     27.1595  38.8393   7.5907   7.7895  19.8953\n",
       "Energy                    882     25.0200  38.0208   6.2017  19.9507  16.3427\n",
       "Utilities                 360     20.1373  29.9965   6.8532  17.3556  11.7945\n",
       "Financial Services       2076     19.2023  29.7624   3.0778  17.0589  24.3544\n",
       "Basic Materials          1811     23.1804  23.9269   7.0965  55.4644  21.0118"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_valuation_USA = df[df['country_c3'] == 'USA']\n",
    "print('There are {:,.0f} Stock records that fit this criteria.'.format(len(df_valuation_USA)))\n",
    "\n",
    "df_val_mean_USA = (df_valuation_USA[['sector', 'company']].groupby('sector').count()\n",
    "                   .rename(columns={'company':'count'})\n",
    "                   .merge(df_valuation_USA.groupby('sector').mean().round(4), on='sector')\n",
    "                   .sort_values(by='PE_TTM', ascending=False))\n",
    "\n",
    "df_val_mean_USA[['count', 'PE_Forward', 'PE_TTM', 'PB_TTM', 'PS_TTM', 'PC_TTM']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean TTM Price Ratios for DEU (Germany) stocks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 35,508 Stock records that fit this criteria.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>PE_Forward</th>\n",
       "      <th>PE_TTM</th>\n",
       "      <th>PB_TTM</th>\n",
       "      <th>PS_TTM</th>\n",
       "      <th>PC_TTM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>4149</td>\n",
       "      <td>38.0205</td>\n",
       "      <td>73.3655</td>\n",
       "      <td>7.9922</td>\n",
       "      <td>93.7931</td>\n",
       "      <td>42.3822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>5049</td>\n",
       "      <td>51.2918</td>\n",
       "      <td>54.5387</td>\n",
       "      <td>7.1046</td>\n",
       "      <td>11.4675</td>\n",
       "      <td>56.5138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Communication Services</th>\n",
       "      <td>876</td>\n",
       "      <td>31.9593</td>\n",
       "      <td>53.9995</td>\n",
       "      <td>3.7028</td>\n",
       "      <td>5.8847</td>\n",
       "      <td>15.2744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumer Cyclical</th>\n",
       "      <td>4471</td>\n",
       "      <td>29.9099</td>\n",
       "      <td>43.0560</td>\n",
       "      <td>5.0866</td>\n",
       "      <td>10.6949</td>\n",
       "      <td>22.6453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrials</th>\n",
       "      <td>4828</td>\n",
       "      <td>22.0153</td>\n",
       "      <td>34.1633</td>\n",
       "      <td>8.1829</td>\n",
       "      <td>4.6972</td>\n",
       "      <td>22.2574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Estate</th>\n",
       "      <td>1655</td>\n",
       "      <td>28.0527</td>\n",
       "      <td>34.0643</td>\n",
       "      <td>1.6142</td>\n",
       "      <td>8.4974</td>\n",
       "      <td>29.9444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial Services</th>\n",
       "      <td>3616</td>\n",
       "      <td>14.4595</td>\n",
       "      <td>29.8283</td>\n",
       "      <td>2.6123</td>\n",
       "      <td>10.0668</td>\n",
       "      <td>36.7102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumer Defensive</th>\n",
       "      <td>1901</td>\n",
       "      <td>23.5722</td>\n",
       "      <td>29.4245</td>\n",
       "      <td>3.7208</td>\n",
       "      <td>7.4142</td>\n",
       "      <td>26.3159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>992</td>\n",
       "      <td>18.4580</td>\n",
       "      <td>28.6574</td>\n",
       "      <td>2.2723</td>\n",
       "      <td>7.7665</td>\n",
       "      <td>19.3081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>2066</td>\n",
       "      <td>24.9557</td>\n",
       "      <td>27.7079</td>\n",
       "      <td>3.4324</td>\n",
       "      <td>26.7135</td>\n",
       "      <td>12.8232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basic Materials</th>\n",
       "      <td>5905</td>\n",
       "      <td>18.3551</td>\n",
       "      <td>24.0647</td>\n",
       "      <td>5.0153</td>\n",
       "      <td>40.6715</td>\n",
       "      <td>18.5288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count  PE_Forward   PE_TTM  PB_TTM   PS_TTM   PC_TTM\n",
       "sector                                                                      \n",
       "Healthcare               4149     38.0205  73.3655  7.9922  93.7931  42.3822\n",
       "Technology               5049     51.2918  54.5387  7.1046  11.4675  56.5138\n",
       "Communication Services    876     31.9593  53.9995  3.7028   5.8847  15.2744\n",
       "Consumer Cyclical        4471     29.9099  43.0560  5.0866  10.6949  22.6453\n",
       "Industrials              4828     22.0153  34.1633  8.1829   4.6972  22.2574\n",
       "Real Estate              1655     28.0527  34.0643  1.6142   8.4974  29.9444\n",
       "Financial Services       3616     14.4595  29.8283  2.6123  10.0668  36.7102\n",
       "Consumer Defensive       1901     23.5722  29.4245  3.7208   7.4142  26.3159\n",
       "Utilities                 992     18.4580  28.6574  2.2723   7.7665  19.3081\n",
       "Energy                   2066     24.9557  27.7079  3.4324  26.7135  12.8232\n",
       "Basic Materials          5905     18.3551  24.0647  5.0153  40.6715  18.5288"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_valuation_DEU = df[df['country_c3'] == 'DEU']\n",
    "print('There are {:,.0f} Stock records that fit this criteria.'.format(len(df_valuation_DEU)))\n",
    "\n",
    "df_val_mean_DEU = (df_valuation_DEU[['sector', 'company']].groupby('sector').count()\n",
    "                   .rename(columns={'company':'count'})\n",
    "                   .merge(df_valuation_DEU.groupby('sector').mean().round(4), on='sector')\n",
    "                   .sort_values(by='PE_TTM', ascending=False))\n",
    "\n",
    "df_val_mean_DEU[['count', 'PE_Forward', 'PE_TTM', 'PB_TTM', 'PS_TTM', 'PC_TTM']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean TTM Price Ratios for S&P 500 stocks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = r'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'\n",
    "tables = pd.read_html(url)\n",
    "df_sp500 = tables[0]\n",
    "df_sp500.columns = df_sp500.iloc[0]\n",
    "df_sp500 = df_sp500.drop(0, axis=0).set_index('Symbol').join(df.set_index('ticker'))\n",
    "df_sp500 = df_sp500[df_sp500['country_c3'] == 'USA'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 495 Stock records that fit this criteria.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>PE_Forward</th>\n",
       "      <th>PE_TTM</th>\n",
       "      <th>PB_TTM</th>\n",
       "      <th>PS_TTM</th>\n",
       "      <th>PC_TTM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sector</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Real Estate</th>\n",
       "      <td>28</td>\n",
       "      <td>53.0464</td>\n",
       "      <td>153.8536</td>\n",
       "      <td>3.6214</td>\n",
       "      <td>9.1321</td>\n",
       "      <td>19.1643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthcare</th>\n",
       "      <td>59</td>\n",
       "      <td>20.1644</td>\n",
       "      <td>112.7847</td>\n",
       "      <td>13.4881</td>\n",
       "      <td>4.7000</td>\n",
       "      <td>24.2390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Communication Services</th>\n",
       "      <td>8</td>\n",
       "      <td>42.0875</td>\n",
       "      <td>93.8000</td>\n",
       "      <td>3.6125</td>\n",
       "      <td>4.1375</td>\n",
       "      <td>10.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumer Cyclical</th>\n",
       "      <td>75</td>\n",
       "      <td>19.3227</td>\n",
       "      <td>51.1600</td>\n",
       "      <td>11.7853</td>\n",
       "      <td>2.0213</td>\n",
       "      <td>15.5813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial Services</th>\n",
       "      <td>73</td>\n",
       "      <td>14.2877</td>\n",
       "      <td>36.1479</td>\n",
       "      <td>8.1041</td>\n",
       "      <td>3.7041</td>\n",
       "      <td>22.2110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>62</td>\n",
       "      <td>22.4581</td>\n",
       "      <td>33.4371</td>\n",
       "      <td>6.7887</td>\n",
       "      <td>5.4629</td>\n",
       "      <td>34.1161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>29</td>\n",
       "      <td>73.5448</td>\n",
       "      <td>30.0034</td>\n",
       "      <td>1.9103</td>\n",
       "      <td>2.1586</td>\n",
       "      <td>9.9966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Basic Materials</th>\n",
       "      <td>22</td>\n",
       "      <td>19.5045</td>\n",
       "      <td>28.1818</td>\n",
       "      <td>3.7864</td>\n",
       "      <td>2.2636</td>\n",
       "      <td>14.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Industrials</th>\n",
       "      <td>74</td>\n",
       "      <td>18.9257</td>\n",
       "      <td>23.8878</td>\n",
       "      <td>28.9595</td>\n",
       "      <td>2.8432</td>\n",
       "      <td>18.0554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>28</td>\n",
       "      <td>19.2857</td>\n",
       "      <td>22.0357</td>\n",
       "      <td>2.2214</td>\n",
       "      <td>2.6036</td>\n",
       "      <td>10.0821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consumer Defensive</th>\n",
       "      <td>37</td>\n",
       "      <td>18.9027</td>\n",
       "      <td>19.5189</td>\n",
       "      <td>5.9568</td>\n",
       "      <td>2.6162</td>\n",
       "      <td>16.7973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count  PE_Forward    PE_TTM   PB_TTM  PS_TTM   PC_TTM\n",
       "sector                                                                       \n",
       "Real Estate                28     53.0464  153.8536   3.6214  9.1321  19.1643\n",
       "Healthcare                 59     20.1644  112.7847  13.4881  4.7000  24.2390\n",
       "Communication Services      8     42.0875   93.8000   3.6125  4.1375  10.7000\n",
       "Consumer Cyclical          75     19.3227   51.1600  11.7853  2.0213  15.5813\n",
       "Financial Services         73     14.2877   36.1479   8.1041  3.7041  22.2110\n",
       "Technology                 62     22.4581   33.4371   6.7887  5.4629  34.1161\n",
       "Energy                     29     73.5448   30.0034   1.9103  2.1586   9.9966\n",
       "Basic Materials            22     19.5045   28.1818   3.7864  2.2636  14.9000\n",
       "Industrials                74     18.9257   23.8878  28.9595  2.8432  18.0554\n",
       "Utilities                  28     19.2857   22.0357   2.2214  2.6036  10.0821\n",
       "Consumer Defensive         37     18.9027   19.5189   5.9568  2.6162  16.7973"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('There are {:,.0f} Stock records that fit this criteria.'.format(len(df_sp500)))\n",
    "\n",
    "df_val_mean_sp500 = (df_sp500[['sector', 'company']].groupby('sector').count()\n",
    "                     .rename(columns={'company':'count'})\n",
    "                     .merge(df_sp500.groupby('sector').mean().round(4), on='sector')\n",
    "                     .sort_values(by='PE_TTM', ascending=False))\n",
    "\n",
    "df_val_mean_sp500[['count', 'PE_Forward', 'PE_TTM', 'PB_TTM', 'PS_TTM', 'PC_TTM']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to the top](#top)\n",
    "#### Plot of TTM P/E by Sectors\n",
    "*All Stocks*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_pe, (ax_pe, ax_pe_usa, ax_pe_deu) = plt.subplots(3, 1, sharex=True, sharey=True, figsize=(8, 6))\n",
    "\n",
    "# All Stocks\n",
    "pe = df_val_mean['PE_TTM']\n",
    "x = [x*3 for x in range(len(pe))]\n",
    "y = pe\n",
    "bars = ax_pe.bar(x, y, width=2)\n",
    "for bar in bars:\n",
    "    ax_pe.text(bar.get_x()+1, bar.get_height()+1.5, '{:.1f}'.format(bar.get_height()), \n",
    "               color='black', ha='center', fontsize=9)\n",
    "ax_pe.get_children()[22].set_color(None)\n",
    "ax_pe.get_children()[23].set_color(None)\n",
    "ax_pe.get_children()[25].set_color(None)\n",
    "ax_pe.set_title('All Stocks', loc='left', fontsize=9, fontweight='bold')\n",
    "\n",
    "# USA\n",
    "pe_usa = df_val_mean_USA['PE_TTM']\n",
    "x = [x*3 for x in range(len(pe_usa))]\n",
    "y = pe_usa\n",
    "bars = ax_pe_usa.bar(x, y, width=2)\n",
    "for bar in bars:\n",
    "    ax_pe_usa.text(bar.get_x()+1, bar.get_height()+1.5, '{:.1f}'.format(bar.get_height()), \n",
    "                   color='black', ha='center', fontsize=9)\n",
    "ax_pe_usa.get_children()[22].set_color(None)\n",
    "ax_pe_usa.get_children()[23].set_color(None)\n",
    "ax_pe_usa.get_children()[25].set_color(None)\n",
    "ax_pe_usa.set_title('USA', loc='left', fontsize=9, fontweight='bold')\n",
    "\n",
    "# DEU\n",
    "pe_deu = df_val_mean_DEU['PE_TTM']\n",
    "x = [x*3 for x in range(len(pe_deu))]\n",
    "y = pe_deu\n",
    "bars = ax_pe_deu.bar(x, y, width=2)\n",
    "for bar in bars:\n",
    "    ax_pe_deu.text(bar.get_x()+1, bar.get_height()+1.5, '{:.1f}'.format(bar.get_height()), \n",
    "                   color='black', ha='center', fontsize=9)\n",
    "ax_pe_deu.get_children()[22].set_color(None)\n",
    "ax_pe_deu.get_children()[23].set_color(None)\n",
    "ax_pe_deu.get_children()[25].set_color(None)\n",
    "ax_pe_deu.set_title('DEU', loc='left', fontsize=9, fontweight='bold')\n",
    "\n",
    "# Plot adjustments\n",
    "plt.xticks(ticks=x, labels=y.index.tolist(), fontsize=9)\n",
    "plt.axis([-3, len(x)*3, 0, 100])\n",
    "plt.suptitle('Average TTM P/E of Stocks by Sector for key regions', fontsize=11, fontweight='bold')\n",
    "plt.yticks([])\n",
    "plt.subplots_adjust(bottom=0.3, hspace=1)\n",
    "\n",
    "for tick in ax_pe_deu.xaxis.get_ticklabels():\n",
    "    tick.set_rotation(90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to the top](#top)\n",
    "#### P/E by Sector for past 10 yrs for US Stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"700\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = list(filter(lambda col: col.startswith('PE_'), df.columns))[1:]\n",
    "\n",
    "fig, axs = plt.subplots(11, 1, figsize=(7, 6), sharex=True)\n",
    "sectors = df_val_mean_USA.index.values\n",
    "x = list(range(11))\n",
    "\n",
    "for sector, ax in zip(sectors, axs):\n",
    "    y = df_val_mean_USA[cols].loc[sector].values\n",
    "    p0 = ax.bar(x, y, width=0.5)\n",
    "    #ax.set_title(sector, loc='left', fontsize=9, fontweight='bold')\n",
    "    ax.spines['left'].set_color(None)\n",
    "    ax.spines['right'].set_color(None)\n",
    "    ax.spines['top'].set_color(None)\n",
    "    for pt in list(zip(x, y)):\n",
    "        if pt[1] > 0:\n",
    "            ax.text(pt[0], pt[1] + max(y)*0.05, '{:.0f}'.format(pt[1]), ha='center', fontsize=8)\n",
    "    ax.set_yticks([])\n",
    "    ax.axis([-5.5, 11, 0, max(y)+10])\n",
    "    ax.set_xlim(-5.5, 11)\n",
    "    ax.text(-5.5, max(y)+10, sector, fontweight='bold', fontsize=8)\n",
    "    ax0 = ax\n",
    "    \n",
    "plt.suptitle('US Stocks P/E by Sector for past 10 yrs', fontweight='bold', fontsize=11)\n",
    "plt.subplots_adjust(top=0.91, bottom=0.08, hspace=0.4)\n",
    "_ = plt.xticks(ticks=x, labels=list(map(lambda col: col[3:], cols)), fontsize=8, fontweight='bold')\n",
    "for label in ax.xaxis.get_ticklabels():\n",
    "    label.set_rotation(45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to the top](#top)\n",
    "#### P/S by Sector for past 10 yrs for US Stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"700\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = list(filter(lambda col: col.startswith('PS_'), df.columns))\n",
    "\n",
    "fig, axs = plt.subplots(11, 1, figsize=(7, 6), sharex=True)\n",
    "sectors = df_val_mean_USA.index.values\n",
    "x = list(range(11))\n",
    "\n",
    "for sector, ax in zip(sectors, axs):\n",
    "    y = df_val_mean_USA[cols].loc[sector].values\n",
    "    p0 = ax.bar(x, y, width=0.5)\n",
    "    #ax.set_title(sector, loc='left', fontsize=9, fontweight='bold')\n",
    "    ax.spines['left'].set_color(None)\n",
    "    ax.spines['right'].set_color(None)\n",
    "    ax.spines['top'].set_color(None)\n",
    "    for pt in list(zip(x, y)):\n",
    "        if pt[1] > 0:\n",
    "            ax.text(pt[0], pt[1] + max(y)*0.05, '{:.0f}'.format(pt[1]), ha='center', fontsize=8)\n",
    "    ax.set_yticks([])\n",
    "    ax.axis([-5.5, 11, 0, max(y)+10])\n",
    "    ax.set_xlim(-5.5, 11)\n",
    "    ax.text(-5.5, max(y)+10, sector, fontweight='bold', fontsize=8)\n",
    "    ax0 = ax\n",
    "    \n",
    "plt.suptitle('US Stocks P/S by Sector for past 10 yrs', fontweight='bold', fontsize=11)\n",
    "plt.subplots_adjust(top=0.91, bottom=0.08, hspace=0.4)\n",
    "_ = plt.xticks(ticks=x, labels=list(map(lambda col: col[3:], cols)), fontsize=8, fontweight='bold')\n",
    "for label in ax.xaxis.get_ticklabels():\n",
    "    label.set_rotation(45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to the top](#top)\n",
    "### Stocks in the Cannabis Industry\n",
    "Using stocks listed on [marijuanaindex.com](http://marijuanaindex.com/stock-quotes/north-american-marijuana-index/) under North America"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('input/pot_stocks.json') as file:\n",
    "    pot_symbols = json.loads(file.read())\n",
    "    \n",
    "pot_stocks = (pd.DataFrame(pot_symbols, columns=['ticker', 'country_c3'])\n",
    "               .merge(df_master, how='left', on=['ticker', 'country_c3']).drop('country', axis=1)\n",
    "               .rename(columns={'country_c3':'country', 'exchange_sym':'exch'}))\n",
    "\n",
    "pot_stocks = (pot_stocks.where(((pot_stocks['country'] == 'USA') | \n",
    "                                (pot_stocks['country'] == 'CAN')) &\n",
    "                               (pot_stocks['sector'] != '—'))\n",
    "              .dropna(axis=0, how='all').sort_values(by='company'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Below are the 46 stocks listed on marijuanaindex.com for North America.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>ticker</th>\n",
       "      <th>exch</th>\n",
       "      <th>company</th>\n",
       "      <th>sector</th>\n",
       "      <th>industry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>CAN</td>\n",
       "      <td>TGIF</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>1933 Industries Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USA</td>\n",
       "      <td>ACRGF</td>\n",
       "      <td>PINX</td>\n",
       "      <td>Acreage Holdings Inc Ordinary Shares (Sub Voting)</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAN</td>\n",
       "      <td>ACRG.U</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Acreage Holdings Inc Ordinary Shares (Sub Voting)</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>USA</td>\n",
       "      <td>APHA</td>\n",
       "      <td>XNYS</td>\n",
       "      <td>Aphria Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CAN</td>\n",
       "      <td>ACB</td>\n",
       "      <td>XTSE</td>\n",
       "      <td>Aurora Cannabis Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>CAN</td>\n",
       "      <td>XLY</td>\n",
       "      <td>XTSX</td>\n",
       "      <td>Auxly Cannabis Group Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>USA</td>\n",
       "      <td>CVSI</td>\n",
       "      <td>PINX</td>\n",
       "      <td>CV Sciences Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>CAN</td>\n",
       "      <td>TRST</td>\n",
       "      <td>XTSE</td>\n",
       "      <td>CannTrust Holdings Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAN</td>\n",
       "      <td>CNNX</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Cannex Capital Holdings Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>USA</td>\n",
       "      <td>CGC</td>\n",
       "      <td>XNYS</td>\n",
       "      <td>Canopy Growth Corp</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CAN</td>\n",
       "      <td>RIV</td>\n",
       "      <td>XTSX</td>\n",
       "      <td>Canopy Rivers Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CAN</td>\n",
       "      <td>CWEB</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Charlottes Web Holdings Inc Ordinary Shares</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAN</td>\n",
       "      <td>CL</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Cresco Labs Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>USA</td>\n",
       "      <td>CRON</td>\n",
       "      <td>XNAS</td>\n",
       "      <td>Cronos Group Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CAN</td>\n",
       "      <td>CURA</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Curaleaf Holdings Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CAN</td>\n",
       "      <td>EMH</td>\n",
       "      <td>XTSX</td>\n",
       "      <td>Emerald Health Therapeutics Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Biotechnology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>USA</td>\n",
       "      <td>GWPH</td>\n",
       "      <td>XNAS</td>\n",
       "      <td>GW Pharmaceuticals PLC ADR</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Major</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CAN</td>\n",
       "      <td>GLH</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Golden Leaf Holdings Ltd</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAN</td>\n",
       "      <td>GGB</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Green Growth Brands Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CAN</td>\n",
       "      <td>GTII</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Green Thumb Industries Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>USA</td>\n",
       "      <td>GRWG</td>\n",
       "      <td>PINX</td>\n",
       "      <td>GrowGeneration Corp</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Distribution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CAN</td>\n",
       "      <td>HEXO</td>\n",
       "      <td>XTSE</td>\n",
       "      <td>HEXO Corp</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CAN</td>\n",
       "      <td>HARV</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Harvest Health &amp; Recreation Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>USA</td>\n",
       "      <td>IIPR</td>\n",
       "      <td>XNYS</td>\n",
       "      <td>Innovative Industrial Properties Inc Registere...</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>REIT - Industrial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>CAN</td>\n",
       "      <td>ISOL</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Isodiol International Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>USA</td>\n",
       "      <td>KSHB</td>\n",
       "      <td>PINX</td>\n",
       "      <td>KushCo Holdings Inc</td>\n",
       "      <td>Consumer Cyclical</td>\n",
       "      <td>Packaging &amp; Containers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAN</td>\n",
       "      <td>LHS</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Liberty Health Sciences Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CAN</td>\n",
       "      <td>MJAR</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>MJardin Group Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>USA</td>\n",
       "      <td>MPXOF</td>\n",
       "      <td>PINX</td>\n",
       "      <td>MPX International Corp</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CAN</td>\n",
       "      <td>MPXI</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>MPX International Corp</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>USA</td>\n",
       "      <td>MRMD</td>\n",
       "      <td>PINX</td>\n",
       "      <td>MariMed Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Biotechnology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CAN</td>\n",
       "      <td>MMEN</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>MedMen Enterprises Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CAN</td>\n",
       "      <td>N</td>\n",
       "      <td>XTSX</td>\n",
       "      <td>Namaste Technologies Inc</td>\n",
       "      <td>Consumer Cyclical</td>\n",
       "      <td>Specialty Retail</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CAN</td>\n",
       "      <td>HIP</td>\n",
       "      <td>XTSX</td>\n",
       "      <td>Newstrike Brands Ltd</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CAN</td>\n",
       "      <td>OGI</td>\n",
       "      <td>XTSX</td>\n",
       "      <td>OrganiGram Holdings Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CAN</td>\n",
       "      <td>OH</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Origin House</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CAN</td>\n",
       "      <td>PLTH</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Planet 13 Holdings Inc Ordinary Shares</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CAN</td>\n",
       "      <td>SNN</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Sunniva Inc Ordinary Shares</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>CAN</td>\n",
       "      <td>TILT</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>TILT Holdings Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>CAN</td>\n",
       "      <td>TER</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>TerrAscend Corp</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>USA</td>\n",
       "      <td>TRTC</td>\n",
       "      <td>PINX</td>\n",
       "      <td>Terra Tech Corp</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Farm &amp; Construction Equipment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>CAN</td>\n",
       "      <td>TGOD</td>\n",
       "      <td>XTSE</td>\n",
       "      <td>The Green Organic Dutchman Holdings Ltd Ordina...</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CAN</td>\n",
       "      <td>FIRE</td>\n",
       "      <td>XTSE</td>\n",
       "      <td>The Supreme Cannabis Co Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>USA</td>\n",
       "      <td>TLRY</td>\n",
       "      <td>XNAS</td>\n",
       "      <td>Tilray Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>CAN</td>\n",
       "      <td>TRUL</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Trulieve Cannabis Corp Ordinary Shares (Sub Vo...</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CAN</td>\n",
       "      <td>VIVO</td>\n",
       "      <td>XTSX</td>\n",
       "      <td>VIVO Cannabis Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>CAN</td>\n",
       "      <td>WAYL</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>Wayland Group Corp</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CAN</td>\n",
       "      <td>IAN</td>\n",
       "      <td>XCNQ</td>\n",
       "      <td>iAnthus Capital Holdings Inc</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Drug Manufacturers - Specialty &amp; Generic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  ticker  exch                                            company  \\\n",
       "29     CAN    TGIF  XCNQ                                1933 Industries Inc   \n",
       "1      USA   ACRGF  PINX  Acreage Holdings Inc Ordinary Shares (Sub Voting)   \n",
       "2      CAN  ACRG.U  XCNQ  Acreage Holdings Inc Ordinary Shares (Sub Voting)   \n",
       "37     USA    APHA  XNYS                                         Aphria Inc   \n",
       "0      CAN     ACB  XTSE                                Aurora Cannabis Inc   \n",
       "36     CAN     XLY  XTSX                           Auxly Cannabis Group Inc   \n",
       "40     USA    CVSI  PINX                                    CV Sciences Inc   \n",
       "32     CAN    TRST  XTSE                             CannTrust Holdings Inc   \n",
       "4      CAN    CNNX  XCNQ                        Cannex Capital Holdings Inc   \n",
       "38     USA     CGC  XNYS                                 Canopy Growth Corp   \n",
       "26     CAN     RIV  XTSX                                  Canopy Rivers Inc   \n",
       "6      CAN    CWEB  XCNQ        Charlottes Web Holdings Inc Ordinary Shares   \n",
       "3      CAN      CL  XCNQ                                    Cresco Labs Inc   \n",
       "39     USA    CRON  XNAS                                   Cronos Group Inc   \n",
       "5      CAN    CURA  XCNQ                              Curaleaf Holdings Inc   \n",
       "7      CAN     EMH  XTSX                    Emerald Health Therapeutics Inc   \n",
       "42     USA    GWPH  XNAS                         GW Pharmaceuticals PLC ADR   \n",
       "10     CAN     GLH  XCNQ                           Golden Leaf Holdings Ltd   \n",
       "9      CAN     GGB  XCNQ                            Green Growth Brands Inc   \n",
       "11     CAN    GTII  XCNQ                         Green Thumb Industries Inc   \n",
       "41     USA    GRWG  PINX                                GrowGeneration Corp   \n",
       "13     CAN    HEXO  XTSE                                          HEXO Corp   \n",
       "12     CAN    HARV  XCNQ                    Harvest Health & Recreation Inc   \n",
       "43     USA    IIPR  XNYS  Innovative Industrial Properties Inc Registere...   \n",
       "16     CAN    ISOL  XCNQ                          Isodiol International Inc   \n",
       "44     USA    KSHB  PINX                                KushCo Holdings Inc   \n",
       "17     CAN     LHS  XCNQ                        Liberty Health Sciences Inc   \n",
       "18     CAN    MJAR  XCNQ                                  MJardin Group Inc   \n",
       "21     USA   MPXOF  PINX                             MPX International Corp   \n",
       "20     CAN    MPXI  XCNQ                             MPX International Corp   \n",
       "45     USA    MRMD  PINX                                        MariMed Inc   \n",
       "19     CAN    MMEN  XCNQ                             MedMen Enterprises Inc   \n",
       "22     CAN       N  XTSX                           Namaste Technologies Inc   \n",
       "14     CAN     HIP  XTSX                               Newstrike Brands Ltd   \n",
       "23     CAN     OGI  XTSX                            OrganiGram Holdings Inc   \n",
       "24     CAN      OH  XCNQ                                       Origin House   \n",
       "25     CAN    PLTH  XCNQ             Planet 13 Holdings Inc Ordinary Shares   \n",
       "27     CAN     SNN  XCNQ                        Sunniva Inc Ordinary Shares   \n",
       "31     CAN    TILT  XCNQ                                  TILT Holdings Inc   \n",
       "28     CAN     TER  XCNQ                                    TerrAscend Corp   \n",
       "47     USA    TRTC  PINX                                    Terra Tech Corp   \n",
       "30     CAN    TGOD  XTSE  The Green Organic Dutchman Holdings Ltd Ordina...   \n",
       "8      CAN    FIRE  XTSE                        The Supreme Cannabis Co Inc   \n",
       "46     USA    TLRY  XNAS                                         Tilray Inc   \n",
       "33     CAN    TRUL  XCNQ  Trulieve Cannabis Corp Ordinary Shares (Sub Vo...   \n",
       "34     CAN    VIVO  XTSX                                  VIVO Cannabis Inc   \n",
       "35     CAN    WAYL  XCNQ                                 Wayland Group Corp   \n",
       "15     CAN     IAN  XCNQ                       iAnthus Capital Holdings Inc   \n",
       "\n",
       "               sector                                  industry  \n",
       "29         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "1          Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "2          Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "37         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "0          Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "36         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "40         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "32         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "4          Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "38         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "26         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "6          Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "3          Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "39         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "5          Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "7          Healthcare                             Biotechnology  \n",
       "42         Healthcare                Drug Manufacturers - Major  \n",
       "10         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "9          Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "11         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "41        Industrials                   Industrial Distribution  \n",
       "13         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "12         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "43        Real Estate                         REIT - Industrial  \n",
       "16         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "44  Consumer Cyclical                    Packaging & Containers  \n",
       "17         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "18         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "21         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "20         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "45         Healthcare                             Biotechnology  \n",
       "19         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "22  Consumer Cyclical                          Specialty Retail  \n",
       "14         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "23         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "24         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "25         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "27         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "31         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "28         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "47        Industrials             Farm & Construction Equipment  \n",
       "30         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "8          Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "46         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "33         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "34         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "35         Healthcare  Drug Manufacturers - Specialty & Generic  \n",
       "15         Healthcare  Drug Manufacturers - Specialty & Generic  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msg = 'Below are the {} stocks listed on marijuanaindex.com for North America.'\n",
    "print(msg.format(len(pot_stocks['company'].unique())))\n",
    "\n",
    "pot_stocks[['country', 'ticker', 'exch', 'company', 'sector', 'industry']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"value\"></a>\n",
    "[return to the top](#top)\n",
    "\n",
    "# Applying various criteria to filter common stocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is a list of different rules that can be applied to the data to screen stocks (development of *italicized rules* is still in progress)\n",
    "\n",
    "- **[Rule 0](#rule99): CAGR > 7% for past 7 years**\n",
    "- **[Rule 1](#rule1): No earnings deficit (loss) for past 5 or 7 years**\n",
    "- **[Rule 2](#rule2): Uniterrupted and increasing Dividends for past 5 yrs**\n",
    "- **[Rule 3](#rule3): P/E Ratio of 25 or less for the past 7 yrs and less then 20 for TTM**\n",
    "- **[Rule 4](#rule4): Growth for the past year**\n",
    "- **[Rule 5](#rule5): Current Ratio > 1.2**\n",
    "- **[Rule 6](#rule6): Debt/Equity < 1.0**\n",
    "- **[Rule 7](#rule7): Return on Equity > 10%**\n",
    "- **[Rule X](#rulex): Stocks with insider buys in the past 3 months**\n",
    "\n",
    "[Merge DataFrames](#mergerules) to screen stocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to the top](#top)\n",
    "<a id=\"rule99\"></a>\n",
    "## Rule 0. CAGR > 7% for past 5 years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Column labels in `df_keyratios`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "i0                    Revenue USD Mil\n",
       "i1                     Gross Margin %\n",
       "i2           Operating Income USD Mil\n",
       "i3                 Operating Margin %\n",
       "i4                 Net Income USD Mil\n",
       "i5             Earnings Per Share USD\n",
       "i6                      Dividends USD\n",
       "i91                  Payout Ratio % *\n",
       "i7                         Shares Mil\n",
       "i8         Book Value Per Share * USD\n",
       "i9        Operating Cash Flow USD Mil\n",
       "i10              Cap Spending USD Mil\n",
       "i11            Free Cash Flow USD Mil\n",
       "i90    Free Cash Flow Per Share * USD\n",
       "i80           Working Capital USD Mil\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels_keyratios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Revenue CAGR:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 19,769 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "iid = 'i0'\n",
    "field = 'Rev'\n",
    "label = 'CAGR_{}'.format(field)\n",
    "\n",
    "df_rule0_Rev = (df_keyratios.where(df_keyratios['Y9'] > pd.to_datetime('2018-04-01')).dropna(axis=0, how='all'))\n",
    "\n",
    "df_rule0_Rev[label] = 100 * ((df_rule0_Rev['{}_Y9'.format(iid)] / df_rule0_Rev['{}_Y4'.format(iid)]) ** (1/5) - 1)\n",
    "\n",
    "cols = ['ticker_id', 'exchange_id', label] #, '{}_Y4'.format(iid), '{}_Y9'.format(iid)]\n",
    "df_rule0_Rev = (df_rule0_Rev.where(df_rule0_Rev[label] >= 7).dropna(axis=0, how='all')\n",
    "                .sort_values(by=label, ascending=False))[cols]\n",
    "\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule0_Rev)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operating Income CAGR:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 19,897 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "iid = 'i2'\n",
    "field = 'OpeInc'\n",
    "label = 'CAGR_{}'.format(field)\n",
    "\n",
    "df_rule0_OpeInc = (df_keyratios.where(df_keyratios['Y9'] > pd.to_datetime('2018-04-01')).dropna(axis=0, how='all'))\n",
    "\n",
    "df_rule0_OpeInc[label] = 100 * (\n",
    "    (df_rule0_OpeInc['{}_Y9'.format(iid)] / df_rule0_OpeInc['{}_Y4'.format(iid)]) ** (1/5) - 1)\n",
    "\n",
    "cols = ['ticker_id', 'exchange_id', label]\n",
    "df_rule0_OpeInc = (df_rule0_OpeInc.where(df_rule0_OpeInc[label] >= 7).dropna(axis=0, how='all')\n",
    "                .sort_values(by=label, ascending=False))[cols]\n",
    "\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule0_OpeInc)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operating Cash Flow CAGR:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 18,506 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "iid = 'i9'\n",
    "field = 'OpeCF'\n",
    "label = 'CAGR_{}'.format(field)\n",
    "\n",
    "df_rule0_OpeCF = (df_keyratios.where(df_keyratios['Y9'] > pd.to_datetime('2018-04-01')).dropna(axis=0, how='all'))\n",
    "\n",
    "df_rule0_OpeCF[label] = 100 * (\n",
    "    (df_rule0_OpeCF['{}_Y9'.format(iid)] / df_rule0_OpeCF['{}_Y4'.format(iid)]) ** (1/5) - 1)\n",
    "\n",
    "cols = ['ticker_id', 'exchange_id', label]\n",
    "df_rule0_OpeCF = (df_rule0_OpeCF.where(df_rule0_OpeCF[label] >= 7).dropna(axis=0, how='all')\n",
    "                .sort_values(by=label, ascending=False))[cols]\n",
    "\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule0_OpeCF)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Free Cash Flow CAGR:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 18,360 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "iid = 'i11'\n",
    "field = 'FreeCF'\n",
    "label = 'CAGR_{}'.format(field)\n",
    "\n",
    "df_rule0_FreeCF = (df_keyratios.where(df_keyratios['Y9'] > pd.to_datetime('2018-04-01')).dropna(axis=0, how='all'))\n",
    "df_rule0_FreeCF[label] = 100 * (\n",
    "    (df_rule0_FreeCF['{}_Y9'.format(iid)] / df_rule0_FreeCF['{}_Y4'.format(iid)]) ** (1/5) - 1)\n",
    "\n",
    "cols = ['ticker_id', 'exchange_id', label]\n",
    "df_rule0_FreeCF = (df_rule0_FreeCF.where(df_rule0_FreeCF[label] >= 7).dropna(axis=0, how='all')\n",
    "                .sort_values(by=label, ascending=False))[cols]\n",
    "\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule0_FreeCF)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to top of this section](#value),\n",
    "[return to the top](#top)\n",
    "<a id=\"rule1\"></a>\n",
    "### Rule 1. No earnings deficit (loss) for past 5 or 7 years\n",
    "Criteria: *\"Find companies with positive earnings per share growth during the past five years with no earnings deficits. Earnings need to be higher in the most recent year than five years ago. Avoiding companies with earnings deficits during the past five years will help you stay clear of high-risk companies.\"* [(Source)](https://cabotwealth.com/daily/value-investing/benjamin-grahams-value-stock-criteria/)\n",
    "\n",
    "#### 5 Years: (PENDING CORRECTION OF CODE)\n",
    "*a. Identify Net Income column labels in* `df_annualIS`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>header</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>label_i50</th>\n",
       "      <td>Net income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label_i70</th>\n",
       "      <td>Net income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label_i80</th>\n",
       "      <td>Net income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                value\n",
       "header               \n",
       "label_i50  Net income\n",
       "label_i70  Net income\n",
       "label_i80  Net income"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ilabel = 'Net income'\n",
    "df_labels = df_labels_aIS[df_labels_aIS['value'] == ilabel].sort_values(by='value')\n",
    "df_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*b. Get column headers for 'Net income' values for the past 5 yrs*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i_ids = [(label[-3:] + '_') for label in df_labels.index]\n",
    "\n",
    "# def get_icols(col):\n",
    "#     for i_id in i_ids:\n",
    "#         if i_id in col:\n",
    "#             return True\n",
    "#     return False\n",
    "\n",
    "# main_cols = ['ticker_id', 'exchange_id', 'country', 'exchange_sym', 'ticker', 'company', 'sector', 'industry',\n",
    "#              'stock_type', 'style', 'Year_Y_6', 'Year_Y_5', 'Year_Y_4', 'Year_Y_3', 'Year_Y_2', 'Year_Y_1']\n",
    "# data_cols = sorted(list(filter(get_icols, df_annualIS.columns)), key=lambda r: (r[-1], r[5:8]), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*c. Create 'Net Income' DataFrame*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_annualIS1 = df_master.merge(df_annualIS, on=['ticker_id', 'exchange_id'])\n",
    "\n",
    "# df_netinc5 = (df_annualIS1\n",
    "#               .where((df_annualIS1['security_type'] == 'Stock') & \n",
    "#                      (df_annualIS1['Year_Y_5'] > pd.to_datetime('2018-01')))\n",
    "#               .dropna(axis=0, how='all')\n",
    "#               .drop(['country'], axis=1)\n",
    "#               .rename(columns={'country_c3':'country'})\n",
    "#              )[main_cols + data_cols]\n",
    "\n",
    "# np_netinc = df_netinc5[data_cols].values\n",
    "# netinc_cols = [('NetIncome_Y' + data_cols[i * 3][-1], (i * 3, i * 3 + 1, i * 3 + 2))\n",
    "#                for i in range(int(len(data_cols)/3))]\n",
    "\n",
    "# vals = []\n",
    "# for row in np_netinc:\n",
    "#     row_vals = []\n",
    "#     for i in range(len(netinc_cols)):\n",
    "#         val = None\n",
    "#         for col in netinc_cols[i][1]:\n",
    "#             if not np.isnan(row[col]):\n",
    "#                 val = row[col]\n",
    "#                 break\n",
    "#         row_vals.append(val)\n",
    "#     vals.append(row_vals)\n",
    "    \n",
    "# df_netinc_vals = pd.DataFrame(vals, columns=list(zip(*netinc_cols))[0])\n",
    "# df_netinc5 = df_netinc5[main_cols].join(df_netinc_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_rule1_5 = df_netinc5.where((df_netinc5['NetIncome_Y6'] > 0) & \n",
    "#                             ((df_netinc5['NetIncome_Y5'] > 0) | (df_netinc5['NetIncome_Y5'].isna() & df_netinc5['NetIncome_Y4'].isna() & df_netinc5['NetIncome_Y3'].isna() & df_netinc5['NetIncome_Y2'].isna() & df_netinc5['NetIncome_Y1'].isna())) & \n",
    "#                             ((df_netinc5['NetIncome_Y4'] > 0) | (df_netinc5['NetIncome_Y4'].isna() & df_netinc5['NetIncome_Y3'].isna() & df_netinc5['NetIncome_Y2'].isna() & df_netinc5['NetIncome_Y1'].isna())) & \n",
    "#                             ((df_netinc5['NetIncome_Y3'] > 0) | (df_netinc5['NetIncome_Y3'].isna() & df_netinc5['NetIncome_Y2'].isna() & df_netinc5['NetIncome_Y1'].isna())) & \n",
    "#                             ((df_netinc5['NetIncome_Y2'] > 0) | (df_netinc5['NetIncome_Y2'].isna() & df_netinc5['NetIncome_Y1'].isna())) & \n",
    "#                             ((df_netinc5['NetIncome_Y1'] > 0) | (df_netinc5['NetIncome_Y1'].isna()))\n",
    "#                            ).dropna(axis=0, how='all')\n",
    "\n",
    "# df_rule1_5 = df_rule1_5[['ticker_id', 'exchange_id'] + df_rule1_5.columns.values.tolist()[-12:]]\n",
    "# df_rule1_5.columns = [re.sub('Year_Y_', 'r1_Y', col) for col in df_rule1_5.columns]\n",
    "\n",
    "# print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule1_5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5 Years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 28,146 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "cols = ['ticker_id', 'exchange_id'] + \\\n",
    "        [col for col in df_keyratios.columns if col.startswith('i4_') or col.startswith('Y')]\n",
    "\n",
    "df_rule1_5 = (df_keyratios\n",
    "              .where((df_keyratios['Y9'] >= pd.to_datetime('2018-04-01')) & \n",
    "                     (df_keyratios['i4_Y10'] > 0) & \n",
    "                     ((df_keyratios['i4_Y9'] > 0) | (df_keyratios['i4_Y9'].isna() & df_keyratios['i4_Y8'].isna() & df_keyratios['i4_Y7'].isna() & df_keyratios['i4_Y6'].isna() & df_keyratios['i4_Y5'].isna())) & \n",
    "                     ((df_keyratios['i4_Y8'] > 0) | (df_keyratios['i4_Y8'].isna() & df_keyratios['i4_Y7'].isna() & df_keyratios['i4_Y6'].isna() & df_keyratios['i4_Y5'].isna())) & \n",
    "                     ((df_keyratios['i4_Y7'] > 0) | (df_keyratios['i4_Y7'].isna() & df_keyratios['i4_Y6'].isna() & df_keyratios['i4_Y5'].isna())) & \n",
    "                     ((df_keyratios['i4_Y6'] > 0) | (df_keyratios['i4_Y6'].isna() & df_keyratios['i4_Y5'].isna())) & \n",
    "                     ((df_keyratios['i4_Y5'] > 0) | (df_keyratios['i4_Y5'].isna())))\n",
    "              .dropna(axis=0, how='all'))[cols]\n",
    "\n",
    "df_rule1_5.columns = [re.sub('i4_', 'NetIncome_', col) for col in df_rule1_5.columns]\n",
    "df_rule1_5.columns = [re.sub('^Y', 'r1_Y', col) for col in df_rule1_5.columns]\n",
    "\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule1_5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7 Years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 25,731 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "cols = ['ticker_id', 'exchange_id'] + \\\n",
    "        [col for col in df_keyratios.columns if col.startswith('i4_') or col.startswith('Y')]\n",
    "\n",
    "df_rule1_7 = (df_keyratios\n",
    "              .where((df_keyratios['Y9'] >= pd.to_datetime('2018-04-01')) & \n",
    "                     (df_keyratios['i4_Y10'] > 0) & \n",
    "                     ((df_keyratios['i4_Y9'] > 0) | (df_keyratios['i4_Y9'].isna() & df_keyratios['i4_Y8'].isna() & df_keyratios['i4_Y7'].isna() & df_keyratios['i4_Y6'].isna() & df_keyratios['i4_Y5'].isna() & df_keyratios['i4_Y4'].isna() & df_keyratios['i4_Y3'].isna())) & \n",
    "                     ((df_keyratios['i4_Y8'] > 0) | (df_keyratios['i4_Y8'].isna() & df_keyratios['i4_Y7'].isna() & df_keyratios['i4_Y6'].isna() & df_keyratios['i4_Y5'].isna() & df_keyratios['i4_Y4'].isna() & df_keyratios['i4_Y3'].isna())) & \n",
    "                     ((df_keyratios['i4_Y7'] > 0) | (df_keyratios['i4_Y7'].isna() & df_keyratios['i4_Y6'].isna() & df_keyratios['i4_Y5'].isna() & df_keyratios['i4_Y4'].isna() & df_keyratios['i4_Y3'].isna())) & \n",
    "                     ((df_keyratios['i4_Y6'] > 0) | (df_keyratios['i4_Y6'].isna() & df_keyratios['i4_Y5'].isna() & df_keyratios['i4_Y4'].isna() & df_keyratios['i4_Y3'].isna())) & \n",
    "                     ((df_keyratios['i4_Y5'] > 0) | (df_keyratios['i4_Y5'].isna() & df_keyratios['i4_Y4'].isna() & df_keyratios['i4_Y3'].isna())) & \n",
    "                     ((df_keyratios['i4_Y4'] > 0) | (df_keyratios['i4_Y4'].isna() & df_keyratios['i4_Y3'].isna())) & \n",
    "                     ((df_keyratios['i4_Y3'] > 0) | (df_keyratios['i4_Y3'].isna())))\n",
    "              .dropna(axis=0, how='all'))[cols]\n",
    "\n",
    "df_rule1_7.columns = [re.sub('i4_', 'NetIncome_', col) for col in df_rule1_7.columns]\n",
    "df_rule1_7.columns = [re.sub('^Y', 'r1_Y', col) for col in df_rule1_7.columns]\n",
    "\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule1_7)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to top of this section](#value),\n",
    "[return to the top](#top)\n",
    "<a id=\"rule2\"></a>\n",
    "### Rule 2. Uniterrupted and increasing *Dividends* for past 7 yrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "i0                    Revenue USD Mil\n",
       "i1                     Gross Margin %\n",
       "i2           Operating Income USD Mil\n",
       "i3                 Operating Margin %\n",
       "i4                 Net Income USD Mil\n",
       "i5             Earnings Per Share USD\n",
       "i6                      Dividends USD\n",
       "i91                  Payout Ratio % *\n",
       "i7                         Shares Mil\n",
       "i8         Book Value Per Share * USD\n",
       "i9        Operating Cash Flow USD Mil\n",
       "i10              Cap Spending USD Mil\n",
       "i11            Free Cash Flow USD Mil\n",
       "i90    Free Cash Flow Per Share * USD\n",
       "i80           Working Capital USD Mil\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels_keyratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'i6'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "icol = df_labels_keyratios[df_labels_keyratios.str.contains('Dividends')].index[0]\n",
    "icol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i6_Y10', 'i6_Y9', 'i6_Y8', 'i6_Y7', 'i6_Y6', 'i6_Y5', 'i6_Y4', 'i6_Y3']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_cols = ['ticker_id', 'exchange_id', \n",
    "             #'country_c3', 'exchange_sym', 'ticker', 'company', \n",
    "             #'sector', 'industry', 'stock_type', 'style', \n",
    "             'Y10', 'Y9', 'Y8', 'Y7', 'Y6', 'Y5']\n",
    "icols = sorted([col for col in df_keyratios.columns if icol + '_' in col], \n",
    "               key=lambda col: int(col[4:]), reverse=True)[:8]\n",
    "icols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 10,320 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "df_rule2 = (df_keyratios\n",
    "            .where((df_keyratios['Y9'] > pd.to_datetime('2018-04-01')) & \n",
    "                   (df_keyratios['i6_Y10'] >= df_keyratios['i6_Y9']) &\n",
    "                   ((df_keyratios['i6_Y9'] >= df_keyratios['i6_Y8']) | (df_keyratios['i6_Y2'].isna() & df_keyratios['i6_Y3'].isna() & df_keyratios['i6_Y4'].isna() & df_keyratios['i6_Y5'].isna() & df_keyratios['i6_Y6'].isna() & df_keyratios['i6_Y7'].isna() & df_keyratios['i6_Y8'].isna())) & \n",
    "                   ((df_keyratios['i6_Y8'] >= df_keyratios['i6_Y7']) | (df_keyratios['i6_Y2'].isna() & df_keyratios['i6_Y3'].isna() & df_keyratios['i6_Y4'].isna() & df_keyratios['i6_Y5'].isna() & df_keyratios['i6_Y6'].isna() & df_keyratios['i6_Y7'].isna())) & \n",
    "                   ((df_keyratios['i6_Y7'] >= df_keyratios['i6_Y6']) | (df_keyratios['i6_Y2'].isna() & df_keyratios['i6_Y3'].isna() & df_keyratios['i6_Y4'].isna() & df_keyratios['i6_Y5'].isna() & df_keyratios['i6_Y6'].isna())) &\n",
    "                   ((df_keyratios['i6_Y6'] >= df_keyratios['i6_Y5']) | (df_keyratios['i6_Y2'].isna() & df_keyratios['i6_Y3'].isna() & df_keyratios['i6_Y4'].isna() & df_keyratios['i6_Y5'].isna())) &\n",
    "                   ((df_keyratios['i6_Y5'] >= df_keyratios['i6_Y4']) | (df_keyratios['i6_Y2'].isna() & df_keyratios['i6_Y3'].isna() & df_keyratios['i6_Y4'].isna())) &\n",
    "                   ((df_keyratios['i6_Y4'] >= df_keyratios['i6_Y3']) | (df_keyratios['i6_Y2'].isna() & df_keyratios['i6_Y3'].isna())) &\n",
    "                   ((df_keyratios['i6_Y3'] >= df_keyratios['i6_Y2']) | (df_keyratios['i6_Y2'].isna())))\n",
    "            .dropna(axis=0, how='all').sort_values(by='Y9', ascending=False))[main_cols + icols]\n",
    "\n",
    "df_rule2.columns = main_cols + [col.replace('i6', 'Dividend') for col in icols]\n",
    "df_rule2.columns = [re.sub('^Y', 'r2_Y', col) for col in df_rule2.columns]\n",
    "\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to top of this section](#value),\n",
    "[return to the top](#top)\n",
    "<a id=\"rule3\"></a>\n",
    "### Rule 3. P/E Ratio of 25 or less for the past 7 yrs and less then 20 for TTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ticker_id',\n",
       " 'exchange_id',\n",
       " 'PE_TTM',\n",
       " 'PE_2018',\n",
       " 'PE_2017',\n",
       " 'PE_2016',\n",
       " 'PE_2015',\n",
       " 'PE_2014',\n",
       " 'PE_2013',\n",
       " 'PE_2012']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pe_cols = [col for col in df_vals.columns if 'PE_' in col]\n",
    "pe_cols = ['ticker_id', 'exchange_id'] + [pe_cols[len(pe_cols)-i-1] for i in range(len(pe_cols))][:8]\n",
    "pe_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 4,749 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "df_rule3 = (df_vals[pe_cols]\n",
    "            .where((df_vals['PE_TTM'] <= 10) &\n",
    "                   (df_vals['PE_2018'] <= 25) &\n",
    "                   ((df_vals['PE_2017'] <= 25) | (df_vals['PE_2012'].isna() & df_vals['PE_2013'].isna() & df_vals['PE_2014'].isna() & df_vals['PE_2015'].isna() & df_vals['PE_2016'].isna() & df_vals['PE_2017'].isna())) &\n",
    "                   ((df_vals['PE_2016'] <= 25) | (df_vals['PE_2012'].isna() & df_vals['PE_2013'].isna() & df_vals['PE_2014'].isna() & df_vals['PE_2015'].isna() & df_vals['PE_2016'].isna())) &\n",
    "                   ((df_vals['PE_2015'] <= 25) | (df_vals['PE_2012'].isna() & df_vals['PE_2013'].isna() & df_vals['PE_2014'].isna() & df_vals['PE_2015'].isna())) &\n",
    "                   ((df_vals['PE_2014'] <= 25) | (df_vals['PE_2012'].isna() & df_vals['PE_2013'].isna() & df_vals['PE_2014'].isna())) &\n",
    "                   ((df_vals['PE_2013'] <= 25) | (df_vals['PE_2012'].isna() & df_vals['PE_2013'].isna())) & \n",
    "                   ((df_vals['PE_2012'] <= 25) | (df_vals['PE_2012'].isna())))\n",
    "            .dropna(axis=0, how='all').sort_values(by='PE_TTM'))\n",
    "\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to top of this section](#value),\n",
    "[return to the top](#top)\n",
    "<a id=\"rule4\"></a>\n",
    "## Rule 4. Growth for the past year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gr_revenue               Revenue %\n",
       "i28                 Year over Year\n",
       "i29                 3-Year Average\n",
       "i30                 5-Year Average\n",
       "i31                10-Year Average\n",
       "gr_operating    Operating Income %\n",
       "i32                 Year over Year\n",
       "i33                 3-Year Average\n",
       "i34                 5-Year Average\n",
       "i35                10-Year Average\n",
       "gr_ni                 Net Income %\n",
       "i81                 Year over Year\n",
       "i82                 3-Year Average\n",
       "i83                 5-Year Average\n",
       "i84                10-Year Average\n",
       "gr_eps                       EPS %\n",
       "i36                 Year over Year\n",
       "i37                 3-Year Average\n",
       "i38                 5-Year Average\n",
       "i39                10-Year Average\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels_growth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 38,295 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "iid = 'i28'; label = '{}_gr_Y9'.format(iid); col = 'Rev';\n",
    "cols = ['ticker_id', 'exchange_id', 'gr_Y9', label]\n",
    "df_rule4_Rev = (df_growth[cols]\n",
    "                .where((df_growth[label] > 0) & (df_growth['gr_Y9'] > pd.to_datetime('2018-04-01')))\n",
    "                .dropna(axis=0, how='all').sort_values(by='gr_Y9')\n",
    "                .rename(columns={label:'{}_Growth_Y9'.format(col)}))\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule4_Rev)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operating Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 22,238 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "iid = 'i32'; label = '{}_gr_Y9'.format(iid); col = 'OpeInc';\n",
    "cols = ['ticker_id', 'exchange_id', 'gr_Y9', label]\n",
    "df_rule4_OpeInc = (df_growth[cols]\n",
    "                .where((df_growth[label] > 0) & (df_growth['gr_Y9'] > pd.to_datetime('2018-04-01')))\n",
    "                .dropna(axis=0, how='all').sort_values(by='gr_Y9')\n",
    "                .rename(columns={label:'{}_Growth_Y9'.format(col)}))\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule4_OpeInc)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Net Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 21,938 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "iid = 'i81'; label = '{}_gr_Y9'.format(iid); col = 'NetInc';\n",
    "cols = ['ticker_id', 'exchange_id', 'gr_Y9', label]\n",
    "df_rule4_NetInc = (df_growth[cols]\n",
    "                .where((df_growth[label] > 0) & (df_growth['gr_Y9'] > pd.to_datetime('2018-04-01')))\n",
    "                .dropna(axis=0, how='all').sort_values(by='gr_Y9')\n",
    "                .rename(columns={label:'{}_Growth_Y9'.format(col)}))\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule4_NetInc)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to top of this section](#value),\n",
    "[return to the top](#top)\n",
    "<a id=\"rule5\"></a>\n",
    "### Rule 5. Current Ratio > 1.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lfh_liquidity    Liquidity/Financial Health\n",
       "i65                           Current Ratio\n",
       "i66                             Quick Ratio\n",
       "i67                      Financial Leverage\n",
       "i68                             Debt/Equity\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels_finhealth[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 53,182 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "col = 'i65_lfh_Y10'\n",
    "df_rule5 = (df_finhealth[['ticker_id', 'exchange_id', col]]\n",
    "            .where((df_finhealth[col] > 1.2) | (df_finhealth[col].isna()))\n",
    "            .dropna(axis=0, how='all')\n",
    "            .rename(columns={col:'current_ratio'}))\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to top of this section](#value),\n",
    "[return to the top](#top)\n",
    "<a id=\"rule6\"></a>\n",
    "### Rule 6. Debt/Equity < 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 71,935 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "col = 'i68_lfh_Y10'\n",
    "df_rule6 = (df_finhealth[['ticker_id', 'exchange_id', col]]\n",
    "            .where((df_finhealth[col] < 1.5) | (df_finhealth[col].isna()))\n",
    "            .dropna(axis=0, how='all')\n",
    "            .rename(columns={col:'debt2equity'}))\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule6)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to top of this section](#value),\n",
    "[return to the top](#top)\n",
    "<a id=\"rule7\"></a>\n",
    "### Rule 7. Return on Equity > 10%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pr_profit                   Profitability\n",
       "i21                            Tax Rate %\n",
       "i22                          Net Margin %\n",
       "i23              Asset Turnover (Average)\n",
       "i24                    Return on Assets %\n",
       "i25          Financial Leverage (Average)\n",
       "i26                    Return on Equity %\n",
       "i27          Return on Invested Capital %\n",
       "i95                     Interest Coverage\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_labels_profitab[-9:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 34,293 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "col = 'i26_pr_pro_Y10'\n",
    "df_rule7 = (df_profitab[['ticker_id', 'exchange_id', col]]\n",
    "            .where((df_profitab[col] > 10) | (df_profitab[col].isna()))\n",
    "            .dropna(axis=0, how='all')\n",
    "            .rename(columns={col:'return_on_equity'}))\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule7)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to top of this section](#value),\n",
    "[return to the top](#top)\n",
    "<a id=\"rule7\"></a>\n",
    "### Rule 8. P/B < 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 19,638 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "df_rule8 = df_vals.where(df_vals['PB_TTM'] <= 1).dropna(axis=0, how='all')\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_rule8)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to top of this section](#value),\n",
    "[return to the top](#top)\n",
    "<a id=\"rulex\"></a>\n",
    "### Rule X. Stocks with insider buys in the past 3 months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 689 records meet this criterium.\n"
     ]
    }
   ],
   "source": [
    "datefilter = pd.to_datetime(DT.date.today()-DT.timedelta(days=90))\n",
    "\n",
    "df_insiderbuys0 = (df_insidertrades\n",
    "                  .where((df_insidertrades['type'] == 'Buy') & (df_insidertrades['date'] >= datefilter))\n",
    "                  .dropna(axis=0, how='all').groupby(['ticker_id', 'exchange_id']).sum())\n",
    "\n",
    "\n",
    "df_insiderbuys = (df_master.set_index(['ticker_id', 'exchange_id'])\n",
    "                  .join(df_insiderbuys0, how='inner').reset_index()\n",
    "                  .groupby(['company', 'sector', 'industry']).mean().round(1)\n",
    "                  .sort_values(by='value', ascending=False)\n",
    "                 )[['openprice', 'yield', 'quantity', 'value']]\n",
    "\n",
    "print('Total of {:,.0f} records meet this criterium.'.format(len(df_insiderbuys)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('doc/df_insiderbuys.csv', 'w') as file:\n",
    "    file.write(df_insiderbuys.to_csv())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[return to top of this section](#value),\n",
    "[return to the top](#top)\n",
    "<a id=\"mergerules\"></a>\n",
    "### Merging DataFrames "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ticker_id', 'exchange_id', 'update_date', 'ticker', 'company',\n",
       "       'exchange', 'exchange_sym', 'industry', 'sector', 'country',\n",
       "       'country_c2', 'country_c3', 'security_type_code', 'security_type',\n",
       "       'stock_type', 'style', 'openprice', 'lastprice', 'day_hi',\n",
       "       'day_lo', '_52wk_hi', '_52wk_lo', 'yield', 'lastvol', 'avevol',\n",
       "       'PE_Forward', 'pb', 'ps', 'pc', 'lastdate', 'currency',\n",
       "       'currency_code', 'fy_end'], dtype=object)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_master.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A total of 67 stocks meet these criteria out of 66,390 (as of 2019-05-08)\n"
     ]
    }
   ],
   "source": [
    "df_rules = (df_master[df_master['security_type'] == 'Stock']\n",
    "            #.merge(df_rule0_Rev, on=['ticker_id', 'exchange_id'])    # CAGR > 7% for past 5 yrs - Revenue\n",
    "            #.merge(df_rule0_OpeInc, on=['ticker_id', 'exchange_id']) # CAGR > 7% for past 5 yrs - Ope. Income\n",
    "            #.merge(df_rule0_OpeCF, on=['ticker_id', 'exchange_id'])  # CAGR > 7% for past 5 yrs - Ope. Cash Flow\n",
    "            #.merge(df_rule0_FreeCF, on=['ticker_id', 'exchange_id']) # CAGR > 7% for past 5 yrs - Free Cash Flow\n",
    "            .merge(df_rule1_5, on=['ticker_id', 'exchange_id'])      # No earnings deficit for past 5 yrs\n",
    "            .merge(df_rule2, on=['ticker_id', 'exchange_id'])        # Uniterrupted Dividends for past 7 yrs\n",
    "            .merge(df_rule3, on=['ticker_id', 'exchange_id'])        # P/E Ratio of 10 or less for past 7 yrs\n",
    "            .merge(df_rule4_Rev, on=['ticker_id', 'exchange_id'])    # Growth for the past year - Revenue\n",
    "            .merge(df_rule4_OpeInc, on=['ticker_id', 'exchange_id']) # Growth for the past year - Ope. Income\n",
    "            .merge(df_rule4_NetInc, on=['ticker_id', 'exchange_id']) # Growth for the past year - Net Income\n",
    "            .merge(df_rule5, on=['ticker_id', 'exchange_id'])        # Current Ratio > 1.2\n",
    "            .merge(df_rule6, on=['ticker_id', 'exchange_id'])        # Debt/Equity < 1.0\n",
    "            .merge(df_rule7, on=['ticker_id', 'exchange_id'])        # Return on Equity > 10%\n",
    "            .merge(df_rule8, on=['ticker_id', 'exchange_id'])        # P/B < 1.0\n",
    "            #.merge(df_insiderbuys0, on=['ticker_id', 'exchange_id'])  # Insider buys in the past 3 months\n",
    "            #.merge(df_vals[['ticker_id', 'exchange_id', 'PB_TTM', 'PS_TTM', 'PC_TTM']], \n",
    "            #       on=['ticker_id', 'exchange_id'])\n",
    "            \n",
    "            #.groupby(['company', 'exchange_sym', 'ticker', 'sector', 'industry']).mean().round(1)\n",
    "           )[['company', 'exchange_sym', 'ticker', 'sector', 'industry',\n",
    "              'lastprice', 'yield', '_52wk_hi', '_52wk_lo',\n",
    "              'PE_TTM_x', 'PB_TTM', 'PS_TTM', 'PC_TTM', 'current_ratio', 'debt2equity', 'return_on_equity',\n",
    "              #'CAGR_Rev', 'CAGR_OpeInc', 'CAGR_OpeCF', 'CAGR_FreeCF', \n",
    "              'Rev_Growth_Y9', 'OpeInc_Growth_Y9', 'NetInc_Growth_Y9'#, 'value'\n",
    "              ]]\n",
    "\n",
    "total_companies = df_master[df_master['security_type'] == 'Stock']#.groupby('company').count()\n",
    "msg = 'A total of {:,.0f} stocks meet these criteria out of {:,.0f} (as of {})'\n",
    "print(msg.format(len(df_rules), len(total_companies), DT.date.today()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>exchange_sym</th>\n",
       "      <th>ticker</th>\n",
       "      <th>sector</th>\n",
       "      <th>industry</th>\n",
       "      <th>lastprice</th>\n",
       "      <th>yield</th>\n",
       "      <th>_52wk_hi</th>\n",
       "      <th>_52wk_lo</th>\n",
       "      <th>PE_TTM_x</th>\n",
       "      <th>PB_TTM</th>\n",
       "      <th>PS_TTM</th>\n",
       "      <th>PC_TTM</th>\n",
       "      <th>current_ratio</th>\n",
       "      <th>debt2equity</th>\n",
       "      <th>return_on_equity</th>\n",
       "      <th>Rev_Growth_Y9</th>\n",
       "      <th>OpeInc_Growth_Y9</th>\n",
       "      <th>NetInc_Growth_Y9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fidus Investment Corp</td>\n",
       "      <td>XNAS</td>\n",
       "      <td>FDUS</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Asset Management</td>\n",
       "      <td>16.20</td>\n",
       "      <td>9.54</td>\n",
       "      <td>16.46</td>\n",
       "      <td>11.54</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>42.5</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.70</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.38</td>\n",
       "      <td>10.61</td>\n",
       "      <td>12.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PT Indah Kiat Pulp &amp; Paper Corp Tbk</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>IKP</td>\n",
       "      <td>Basic Materials</td>\n",
       "      <td>Paper &amp; Paper Products</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.26</td>\n",
       "      <td>0.72</td>\n",
       "      <td>14.02</td>\n",
       "      <td>6.63</td>\n",
       "      <td>48.07</td>\n",
       "      <td>42.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PT Indah Kiat Pulp &amp; Paper Corp Tbk</td>\n",
       "      <td>XBER</td>\n",
       "      <td>IKP</td>\n",
       "      <td>Basic Materials</td>\n",
       "      <td>Paper &amp; Paper Products</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.26</td>\n",
       "      <td>0.72</td>\n",
       "      <td>14.02</td>\n",
       "      <td>6.63</td>\n",
       "      <td>48.07</td>\n",
       "      <td>42.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PT Indah Kiat Pulp &amp; Paper Corp Tbk</td>\n",
       "      <td>XMUN</td>\n",
       "      <td>IKP</td>\n",
       "      <td>Basic Materials</td>\n",
       "      <td>Paper &amp; Paper Products</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.26</td>\n",
       "      <td>0.72</td>\n",
       "      <td>14.02</td>\n",
       "      <td>6.63</td>\n",
       "      <td>48.07</td>\n",
       "      <td>42.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Network-1 Technologies Inc</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>6LK</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Communication Equipment</td>\n",
       "      <td>2.13</td>\n",
       "      <td>4.08</td>\n",
       "      <td>4.72</td>\n",
       "      <td>1.82</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>26.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.69</td>\n",
       "      <td>34.37</td>\n",
       "      <td>52.43</td>\n",
       "      <td>86.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cementir Holding SpA</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>TI7</td>\n",
       "      <td>Basic Materials</td>\n",
       "      <td>Building Materials</td>\n",
       "      <td>6.20</td>\n",
       "      <td>1.56</td>\n",
       "      <td>8.01</td>\n",
       "      <td>3.28</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.52</td>\n",
       "      <td>13.02</td>\n",
       "      <td>4.93</td>\n",
       "      <td>6.22</td>\n",
       "      <td>77.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Beijing Urban Construction Design &amp; Developmen...</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>BDG</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Engineering &amp; Construction</td>\n",
       "      <td>0.29</td>\n",
       "      <td>4.63</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.25</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.95</td>\n",
       "      <td>13.65</td>\n",
       "      <td>3.06</td>\n",
       "      <td>30.26</td>\n",
       "      <td>13.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Chinney Alliance Group Ltd</td>\n",
       "      <td>XBER</td>\n",
       "      <td>CJJ2</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Engineering &amp; Construction</td>\n",
       "      <td>0.16</td>\n",
       "      <td>4.13</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.09</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.35</td>\n",
       "      <td>8.08</td>\n",
       "      <td>7.09</td>\n",
       "      <td>10.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PT Enseval Putera Megatrading Tbk</td>\n",
       "      <td>XSTU</td>\n",
       "      <td>FEK</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Medical Distribution</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.01</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.7</td>\n",
       "      <td>2.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.00</td>\n",
       "      <td>4.76</td>\n",
       "      <td>31.74</td>\n",
       "      <td>26.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Yuzhou Properties Co Ltd</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>YP9</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>0.43</td>\n",
       "      <td>8.43</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.16</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.40</td>\n",
       "      <td>18.95</td>\n",
       "      <td>12.01</td>\n",
       "      <td>0.20</td>\n",
       "      <td>25.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AP (Thailand) PCL DR</td>\n",
       "      <td>XBER</td>\n",
       "      <td>NYVA</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>0.22</td>\n",
       "      <td>4.19</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.12</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.54</td>\n",
       "      <td>16.80</td>\n",
       "      <td>24.91</td>\n",
       "      <td>6.09</td>\n",
       "      <td>22.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AP (Thailand) PCL DR</td>\n",
       "      <td>XMUN</td>\n",
       "      <td>NYVA</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>0.21</td>\n",
       "      <td>4.44</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.12</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.54</td>\n",
       "      <td>16.80</td>\n",
       "      <td>24.91</td>\n",
       "      <td>6.09</td>\n",
       "      <td>22.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>AP (Thailand) PCL DR</td>\n",
       "      <td>XSTU</td>\n",
       "      <td>NYVA</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>0.20</td>\n",
       "      <td>4.63</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.10</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.54</td>\n",
       "      <td>16.80</td>\n",
       "      <td>24.91</td>\n",
       "      <td>6.09</td>\n",
       "      <td>22.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Yuzhou Properties Co Ltd</td>\n",
       "      <td>XSTU</td>\n",
       "      <td>YP9</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>0.43</td>\n",
       "      <td>8.39</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.15</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.40</td>\n",
       "      <td>18.95</td>\n",
       "      <td>12.01</td>\n",
       "      <td>0.20</td>\n",
       "      <td>25.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>China Aviation Oil (Singapore) Corp Ltd</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>VZ8</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Oil &amp; Gas Refining &amp; Marketing</td>\n",
       "      <td>0.82</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.28</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.00</td>\n",
       "      <td>26.71</td>\n",
       "      <td>47.31</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>China Aviation Oil (Singapore) Corp Ltd</td>\n",
       "      <td>XBER</td>\n",
       "      <td>VZ8</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Oil &amp; Gas Refining &amp; Marketing</td>\n",
       "      <td>0.82</td>\n",
       "      <td>3.66</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.29</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.00</td>\n",
       "      <td>26.71</td>\n",
       "      <td>47.31</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>China Aviation Oil (Singapore) Corp Ltd</td>\n",
       "      <td>XSTU</td>\n",
       "      <td>VZ8</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Oil &amp; Gas Refining &amp; Marketing</td>\n",
       "      <td>0.82</td>\n",
       "      <td>3.67</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.29</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.00</td>\n",
       "      <td>26.71</td>\n",
       "      <td>47.31</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>China Aviation Oil (Singapore) Corp Ltd</td>\n",
       "      <td>XHAM</td>\n",
       "      <td>VZ8</td>\n",
       "      <td>Energy</td>\n",
       "      <td>Oil &amp; Gas Refining &amp; Marketing</td>\n",
       "      <td>0.82</td>\n",
       "      <td>3.67</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.28</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.00</td>\n",
       "      <td>26.71</td>\n",
       "      <td>47.31</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Aroundtown SA</td>\n",
       "      <td>XBER</td>\n",
       "      <td>AT1</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>7.34</td>\n",
       "      <td>3.25</td>\n",
       "      <td>7.91</td>\n",
       "      <td>5.90</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>12.3</td>\n",
       "      <td>16.5</td>\n",
       "      <td>3.47</td>\n",
       "      <td>0.79</td>\n",
       "      <td>20.31</td>\n",
       "      <td>20.09</td>\n",
       "      <td>38.38</td>\n",
       "      <td>27.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Aroundtown SA</td>\n",
       "      <td>XSTU</td>\n",
       "      <td>AT1</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>7.38</td>\n",
       "      <td>3.29</td>\n",
       "      <td>7.90</td>\n",
       "      <td>5.70</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16.3</td>\n",
       "      <td>3.47</td>\n",
       "      <td>0.79</td>\n",
       "      <td>20.31</td>\n",
       "      <td>20.09</td>\n",
       "      <td>38.38</td>\n",
       "      <td>27.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Aroundtown SA</td>\n",
       "      <td>XETR</td>\n",
       "      <td>AT1</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>7.34</td>\n",
       "      <td>3.25</td>\n",
       "      <td>7.92</td>\n",
       "      <td>6.76</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>12.3</td>\n",
       "      <td>16.5</td>\n",
       "      <td>3.47</td>\n",
       "      <td>0.79</td>\n",
       "      <td>20.31</td>\n",
       "      <td>20.09</td>\n",
       "      <td>38.38</td>\n",
       "      <td>27.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Aroundtown SA</td>\n",
       "      <td>XHAM</td>\n",
       "      <td>AT1</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>7.26</td>\n",
       "      <td>3.22</td>\n",
       "      <td>7.87</td>\n",
       "      <td>5.88</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>12.4</td>\n",
       "      <td>16.6</td>\n",
       "      <td>3.47</td>\n",
       "      <td>0.79</td>\n",
       "      <td>20.31</td>\n",
       "      <td>20.09</td>\n",
       "      <td>38.38</td>\n",
       "      <td>27.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Aroundtown SA</td>\n",
       "      <td>XHAN</td>\n",
       "      <td>AT1</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>7.21</td>\n",
       "      <td>3.25</td>\n",
       "      <td>7.87</td>\n",
       "      <td>5.93</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>12.3</td>\n",
       "      <td>16.5</td>\n",
       "      <td>3.47</td>\n",
       "      <td>0.79</td>\n",
       "      <td>20.31</td>\n",
       "      <td>20.09</td>\n",
       "      <td>38.38</td>\n",
       "      <td>27.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Ado Properties SA</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>ADJ</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate Services</td>\n",
       "      <td>46.88</td>\n",
       "      <td>1.28</td>\n",
       "      <td>55.65</td>\n",
       "      <td>19.01</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>19.8</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.74</td>\n",
       "      <td>19.61</td>\n",
       "      <td>20.18</td>\n",
       "      <td>17.40</td>\n",
       "      <td>8.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>COIMA RES SpA</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>7CO</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate Services</td>\n",
       "      <td>7.56</td>\n",
       "      <td>3.93</td>\n",
       "      <td>9.17</td>\n",
       "      <td>6.34</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.6</td>\n",
       "      <td>12.7</td>\n",
       "      <td>8.62</td>\n",
       "      <td>0.70</td>\n",
       "      <td>11.54</td>\n",
       "      <td>22.11</td>\n",
       "      <td>21.10</td>\n",
       "      <td>60.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Ado Properties SA</td>\n",
       "      <td>XBER</td>\n",
       "      <td>ADJ</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate Services</td>\n",
       "      <td>46.42</td>\n",
       "      <td>1.31</td>\n",
       "      <td>55.20</td>\n",
       "      <td>22.80</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13.1</td>\n",
       "      <td>19.5</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.74</td>\n",
       "      <td>19.61</td>\n",
       "      <td>20.18</td>\n",
       "      <td>17.40</td>\n",
       "      <td>8.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>COIMA RES SpA</td>\n",
       "      <td>XBER</td>\n",
       "      <td>7CO</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate Services</td>\n",
       "      <td>7.68</td>\n",
       "      <td>3.87</td>\n",
       "      <td>9.19</td>\n",
       "      <td>6.42</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>12.9</td>\n",
       "      <td>8.62</td>\n",
       "      <td>0.70</td>\n",
       "      <td>11.54</td>\n",
       "      <td>22.11</td>\n",
       "      <td>21.10</td>\n",
       "      <td>60.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Ado Properties SA</td>\n",
       "      <td>XETR</td>\n",
       "      <td>ADJ</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate Services</td>\n",
       "      <td>47.12</td>\n",
       "      <td>1.29</td>\n",
       "      <td>55.75</td>\n",
       "      <td>44.28</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.74</td>\n",
       "      <td>19.61</td>\n",
       "      <td>20.18</td>\n",
       "      <td>17.40</td>\n",
       "      <td>8.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ado Properties SA</td>\n",
       "      <td>XHAM</td>\n",
       "      <td>ADJ</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate Services</td>\n",
       "      <td>46.26</td>\n",
       "      <td>1.30</td>\n",
       "      <td>55.05</td>\n",
       "      <td>27.94</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.74</td>\n",
       "      <td>19.61</td>\n",
       "      <td>20.18</td>\n",
       "      <td>17.40</td>\n",
       "      <td>8.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Ado Properties SA</td>\n",
       "      <td>XHAN</td>\n",
       "      <td>ADJ</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate Services</td>\n",
       "      <td>46.26</td>\n",
       "      <td>1.30</td>\n",
       "      <td>55.05</td>\n",
       "      <td>27.94</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>13.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.74</td>\n",
       "      <td>19.61</td>\n",
       "      <td>20.18</td>\n",
       "      <td>17.40</td>\n",
       "      <td>8.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Henan Jinma Energy Company Ltd Shs H Reg S</td>\n",
       "      <td>XHKG</td>\n",
       "      <td>06885</td>\n",
       "      <td>Basic Materials</td>\n",
       "      <td>Coal</td>\n",
       "      <td>4.36</td>\n",
       "      <td>6.88</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.64</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.10</td>\n",
       "      <td>42.54</td>\n",
       "      <td>45.04</td>\n",
       "      <td>50.17</td>\n",
       "      <td>56.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Chinney Alliance Group Ltd</td>\n",
       "      <td>XHKG</td>\n",
       "      <td>00385</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Engineering &amp; Construction</td>\n",
       "      <td>1.44</td>\n",
       "      <td>4.17</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.01</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.35</td>\n",
       "      <td>8.08</td>\n",
       "      <td>7.09</td>\n",
       "      <td>10.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Beijing Urban Construction Design &amp; Developmen...</td>\n",
       "      <td>XHKG</td>\n",
       "      <td>01599</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Engineering &amp; Construction</td>\n",
       "      <td>2.66</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.04</td>\n",
       "      <td>2.36</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.95</td>\n",
       "      <td>13.65</td>\n",
       "      <td>3.06</td>\n",
       "      <td>30.26</td>\n",
       "      <td>13.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Changhong Jiahua Holding Ltd</td>\n",
       "      <td>XHKG</td>\n",
       "      <td>08016</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Consumer Electronics</td>\n",
       "      <td>0.54</td>\n",
       "      <td>5.56</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.50</td>\n",
       "      <td>4.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.11</td>\n",
       "      <td>8.61</td>\n",
       "      <td>10.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Shanghai Pharmaceuticals Holding Co Ltd Class H</td>\n",
       "      <td>XHKG</td>\n",
       "      <td>02607</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Medical Distribution</td>\n",
       "      <td>16.00</td>\n",
       "      <td>2.93</td>\n",
       "      <td>24.55</td>\n",
       "      <td>15.12</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.24</td>\n",
       "      <td>10.27</td>\n",
       "      <td>21.58</td>\n",
       "      <td>46.61</td>\n",
       "      <td>10.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Sunlight Real Estate Investment Trust</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>Q9R</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>REIT - Diversified</td>\n",
       "      <td>0.63</td>\n",
       "      <td>4.79</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.36</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>10.8</td>\n",
       "      <td>16.3</td>\n",
       "      <td>1.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1.88</td>\n",
       "      <td>93.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Sunlight Real Estate Investment Trust</td>\n",
       "      <td>XBER</td>\n",
       "      <td>Q9R</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>REIT - Diversified</td>\n",
       "      <td>0.66</td>\n",
       "      <td>4.62</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.2</td>\n",
       "      <td>16.9</td>\n",
       "      <td>1.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1.88</td>\n",
       "      <td>93.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Galliford Try PLC</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>0GF</td>\n",
       "      <td>Consumer Cyclical</td>\n",
       "      <td>Residential Construction</td>\n",
       "      <td>6.42</td>\n",
       "      <td>12.12</td>\n",
       "      <td>12.40</td>\n",
       "      <td>5.95</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.26</td>\n",
       "      <td>17.36</td>\n",
       "      <td>10.12</td>\n",
       "      <td>127.13</td>\n",
       "      <td>142.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Time Watch Investments Ltd</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>0TW</td>\n",
       "      <td>Consumer Cyclical</td>\n",
       "      <td>Luxury Goods</td>\n",
       "      <td>0.11</td>\n",
       "      <td>5.50</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.13</td>\n",
       "      <td>7.22</td>\n",
       "      <td>25.42</td>\n",
       "      <td>23.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Sunlight Real Estate Investment Trust</td>\n",
       "      <td>XHKG</td>\n",
       "      <td>00435</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>REIT - Diversified</td>\n",
       "      <td>5.83</td>\n",
       "      <td>4.70</td>\n",
       "      <td>5.97</td>\n",
       "      <td>4.69</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>11.3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1.88</td>\n",
       "      <td>93.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>China Motor Bus Co Ltd</td>\n",
       "      <td>XHKG</td>\n",
       "      <td>00026</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate Services</td>\n",
       "      <td>101.30</td>\n",
       "      <td>0.20</td>\n",
       "      <td>107.10</td>\n",
       "      <td>94.10</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>52.2</td>\n",
       "      <td>104.4</td>\n",
       "      <td>11.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.35</td>\n",
       "      <td>4.52</td>\n",
       "      <td>10.17</td>\n",
       "      <td>50.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>New World Development Co Ltd</td>\n",
       "      <td>XHKG</td>\n",
       "      <td>00017</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>12.96</td>\n",
       "      <td>3.76</td>\n",
       "      <td>13.88</td>\n",
       "      <td>9.67</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>16.2</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.52</td>\n",
       "      <td>10.54</td>\n",
       "      <td>7.17</td>\n",
       "      <td>20.23</td>\n",
       "      <td>195.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Lennar Corp Class B</td>\n",
       "      <td>XNYS</td>\n",
       "      <td>LEN.B</td>\n",
       "      <td>Consumer Cyclical</td>\n",
       "      <td>Residential Construction</td>\n",
       "      <td>41.44</td>\n",
       "      <td>0.38</td>\n",
       "      <td>45.45</td>\n",
       "      <td>29.80</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>10.1</td>\n",
       "      <td>14.34</td>\n",
       "      <td>0.70</td>\n",
       "      <td>12.81</td>\n",
       "      <td>62.67</td>\n",
       "      <td>48.64</td>\n",
       "      <td>109.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Linamar Corp</td>\n",
       "      <td>XSTU</td>\n",
       "      <td>LNR</td>\n",
       "      <td>Consumer Cyclical</td>\n",
       "      <td>Auto Parts</td>\n",
       "      <td>30.78</td>\n",
       "      <td>1.00</td>\n",
       "      <td>54.18</td>\n",
       "      <td>28.07</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.65</td>\n",
       "      <td>15.62</td>\n",
       "      <td>16.41</td>\n",
       "      <td>9.36</td>\n",
       "      <td>7.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>STRABAG SE</td>\n",
       "      <td>XHAN</td>\n",
       "      <td>XD4</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Engineering &amp; Construction</td>\n",
       "      <td>29.10</td>\n",
       "      <td>4.52</td>\n",
       "      <td>38.66</td>\n",
       "      <td>20.54</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.30</td>\n",
       "      <td>10.11</td>\n",
       "      <td>12.68</td>\n",
       "      <td>79.39</td>\n",
       "      <td>26.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Linamar Corp</td>\n",
       "      <td>XTSE</td>\n",
       "      <td>LNR</td>\n",
       "      <td>Consumer Cyclical</td>\n",
       "      <td>Auto Parts</td>\n",
       "      <td>46.70</td>\n",
       "      <td>1.00</td>\n",
       "      <td>76.13</td>\n",
       "      <td>42.91</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.65</td>\n",
       "      <td>15.62</td>\n",
       "      <td>16.41</td>\n",
       "      <td>9.36</td>\n",
       "      <td>7.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Premier Fishing and Brands Ltd</td>\n",
       "      <td>XBER</td>\n",
       "      <td>9PF</td>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>Packaged Foods</td>\n",
       "      <td>0.18</td>\n",
       "      <td>14.44</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.13</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.59</td>\n",
       "      <td>0.01</td>\n",
       "      <td>11.39</td>\n",
       "      <td>16.06</td>\n",
       "      <td>18.80</td>\n",
       "      <td>20.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>CDL Investments New Zealand Ltd</td>\n",
       "      <td>XBER</td>\n",
       "      <td>BXU</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>0.44</td>\n",
       "      <td>4.74</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.35</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.96</td>\n",
       "      <td>8.04</td>\n",
       "      <td>5.89</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Beijing North Star Co Ltd Class H</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>BJ3</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>0.34</td>\n",
       "      <td>4.51</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.20</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.27</td>\n",
       "      <td>12.50</td>\n",
       "      <td>15.58</td>\n",
       "      <td>48.72</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Beijing North Star Co Ltd Class H</td>\n",
       "      <td>XBER</td>\n",
       "      <td>BJ3</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>0.34</td>\n",
       "      <td>4.54</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.20</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.27</td>\n",
       "      <td>12.50</td>\n",
       "      <td>15.58</td>\n",
       "      <td>48.72</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Beijing North Star Co Ltd Class H</td>\n",
       "      <td>XSTU</td>\n",
       "      <td>BJ3</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>0.34</td>\n",
       "      <td>4.53</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.20</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.27</td>\n",
       "      <td>12.50</td>\n",
       "      <td>15.58</td>\n",
       "      <td>48.72</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Beijing North Star Co Ltd Class H</td>\n",
       "      <td>XHKG</td>\n",
       "      <td>00588</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate - General</td>\n",
       "      <td>3.19</td>\n",
       "      <td>4.48</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1.93</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.69</td>\n",
       "      <td>1.27</td>\n",
       "      <td>12.50</td>\n",
       "      <td>15.58</td>\n",
       "      <td>48.72</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Cinda Real Estate Co Ltd</td>\n",
       "      <td>XSHG</td>\n",
       "      <td>600657</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate Services</td>\n",
       "      <td>4.38</td>\n",
       "      <td>2.76</td>\n",
       "      <td>5.90</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.04</td>\n",
       "      <td>14.46</td>\n",
       "      <td>23.25</td>\n",
       "      <td>32.53</td>\n",
       "      <td>112.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>China State Construction Engineering Corp Ltd</td>\n",
       "      <td>XSHG</td>\n",
       "      <td>601668</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Engineering &amp; Construction</td>\n",
       "      <td>5.84</td>\n",
       "      <td>2.65</td>\n",
       "      <td>6.72</td>\n",
       "      <td>4.91</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>179.5</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.46</td>\n",
       "      <td>17.24</td>\n",
       "      <td>13.78</td>\n",
       "      <td>37.26</td>\n",
       "      <td>16.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>FAWER Automotive Parts Ltd Co</td>\n",
       "      <td>XSHE</td>\n",
       "      <td>200030</td>\n",
       "      <td>Consumer Cyclical</td>\n",
       "      <td>Auto Parts</td>\n",
       "      <td>3.68</td>\n",
       "      <td>4.67</td>\n",
       "      <td>4.58</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>155.4</td>\n",
       "      <td>1.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.79</td>\n",
       "      <td>9.17</td>\n",
       "      <td>5.29</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Landmark White Ltd</td>\n",
       "      <td>XASX</td>\n",
       "      <td>LMW</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>Real Estate Services</td>\n",
       "      <td>0.28</td>\n",
       "      <td>7.14</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.20</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>16.6</td>\n",
       "      <td>1.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.72</td>\n",
       "      <td>66.37</td>\n",
       "      <td>76.81</td>\n",
       "      <td>154.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Transcontinental Inc Class A</td>\n",
       "      <td>XFRA</td>\n",
       "      <td>53L</td>\n",
       "      <td>Consumer Cyclical</td>\n",
       "      <td>Publishing</td>\n",
       "      <td>10.33</td>\n",
       "      <td>5.41</td>\n",
       "      <td>21.25</td>\n",
       "      <td>10.33</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.74</td>\n",
       "      <td>12.69</td>\n",
       "      <td>30.70</td>\n",
       "      <td>28.26</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Transcontinental Inc Class A</td>\n",
       "      <td>XBER</td>\n",
       "      <td>53L</td>\n",
       "      <td>Consumer Cyclical</td>\n",
       "      <td>Publishing</td>\n",
       "      <td>10.33</td>\n",
       "      <td>5.41</td>\n",
       "      <td>21.25</td>\n",
       "      <td>10.33</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.74</td>\n",
       "      <td>12.69</td>\n",
       "      <td>30.70</td>\n",
       "      <td>28.26</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Transcontinental Inc</td>\n",
       "      <td>XTSE</td>\n",
       "      <td>TCL.B</td>\n",
       "      <td>Consumer Cyclical</td>\n",
       "      <td>Publishing</td>\n",
       "      <td>15.21</td>\n",
       "      <td>5.39</td>\n",
       "      <td>32.79</td>\n",
       "      <td>15.21</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.74</td>\n",
       "      <td>12.69</td>\n",
       "      <td>30.70</td>\n",
       "      <td>28.26</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Transcontinental Inc Class A</td>\n",
       "      <td>XTSE</td>\n",
       "      <td>TCL.A</td>\n",
       "      <td>Consumer Cyclical</td>\n",
       "      <td>Publishing</td>\n",
       "      <td>15.20</td>\n",
       "      <td>5.41</td>\n",
       "      <td>32.89</td>\n",
       "      <td>15.11</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.74</td>\n",
       "      <td>12.69</td>\n",
       "      <td>30.70</td>\n",
       "      <td>28.26</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              company exchange_sym  ticker  \\\n",
       "0                               Fidus Investment Corp         XNAS    FDUS   \n",
       "1                 PT Indah Kiat Pulp & Paper Corp Tbk         XFRA     IKP   \n",
       "2                 PT Indah Kiat Pulp & Paper Corp Tbk         XBER     IKP   \n",
       "3                 PT Indah Kiat Pulp & Paper Corp Tbk         XMUN     IKP   \n",
       "4                          Network-1 Technologies Inc         XFRA     6LK   \n",
       "5                                Cementir Holding SpA         XFRA     TI7   \n",
       "6   Beijing Urban Construction Design & Developmen...         XFRA     BDG   \n",
       "7                          Chinney Alliance Group Ltd         XBER    CJJ2   \n",
       "8                   PT Enseval Putera Megatrading Tbk         XSTU     FEK   \n",
       "9                            Yuzhou Properties Co Ltd         XFRA     YP9   \n",
       "10                               AP (Thailand) PCL DR         XBER    NYVA   \n",
       "11                               AP (Thailand) PCL DR         XMUN    NYVA   \n",
       "12                               AP (Thailand) PCL DR         XSTU    NYVA   \n",
       "13                           Yuzhou Properties Co Ltd         XSTU     YP9   \n",
       "14            China Aviation Oil (Singapore) Corp Ltd         XFRA     VZ8   \n",
       "15            China Aviation Oil (Singapore) Corp Ltd         XBER     VZ8   \n",
       "16            China Aviation Oil (Singapore) Corp Ltd         XSTU     VZ8   \n",
       "17            China Aviation Oil (Singapore) Corp Ltd         XHAM     VZ8   \n",
       "18                                      Aroundtown SA         XBER     AT1   \n",
       "19                                      Aroundtown SA         XSTU     AT1   \n",
       "20                                      Aroundtown SA         XETR     AT1   \n",
       "21                                      Aroundtown SA         XHAM     AT1   \n",
       "22                                      Aroundtown SA         XHAN     AT1   \n",
       "23                                  Ado Properties SA         XFRA     ADJ   \n",
       "24                                      COIMA RES SpA         XFRA     7CO   \n",
       "25                                  Ado Properties SA         XBER     ADJ   \n",
       "26                                      COIMA RES SpA         XBER     7CO   \n",
       "27                                  Ado Properties SA         XETR     ADJ   \n",
       "28                                  Ado Properties SA         XHAM     ADJ   \n",
       "29                                  Ado Properties SA         XHAN     ADJ   \n",
       "..                                                ...          ...     ...   \n",
       "37         Henan Jinma Energy Company Ltd Shs H Reg S         XHKG   06885   \n",
       "38                         Chinney Alliance Group Ltd         XHKG   00385   \n",
       "39  Beijing Urban Construction Design & Developmen...         XHKG   01599   \n",
       "40                       Changhong Jiahua Holding Ltd         XHKG   08016   \n",
       "41    Shanghai Pharmaceuticals Holding Co Ltd Class H         XHKG   02607   \n",
       "42              Sunlight Real Estate Investment Trust         XFRA     Q9R   \n",
       "43              Sunlight Real Estate Investment Trust         XBER     Q9R   \n",
       "44                                  Galliford Try PLC         XFRA     0GF   \n",
       "45                         Time Watch Investments Ltd         XFRA     0TW   \n",
       "46              Sunlight Real Estate Investment Trust         XHKG   00435   \n",
       "47                             China Motor Bus Co Ltd         XHKG   00026   \n",
       "48                       New World Development Co Ltd         XHKG   00017   \n",
       "49                                Lennar Corp Class B         XNYS   LEN.B   \n",
       "50                                       Linamar Corp         XSTU     LNR   \n",
       "51                                         STRABAG SE         XHAN     XD4   \n",
       "52                                       Linamar Corp         XTSE     LNR   \n",
       "53                     Premier Fishing and Brands Ltd         XBER     9PF   \n",
       "54                    CDL Investments New Zealand Ltd         XBER     BXU   \n",
       "55                  Beijing North Star Co Ltd Class H         XFRA     BJ3   \n",
       "56                  Beijing North Star Co Ltd Class H         XBER     BJ3   \n",
       "57                  Beijing North Star Co Ltd Class H         XSTU     BJ3   \n",
       "58                  Beijing North Star Co Ltd Class H         XHKG   00588   \n",
       "59                           Cinda Real Estate Co Ltd         XSHG  600657   \n",
       "60      China State Construction Engineering Corp Ltd         XSHG  601668   \n",
       "61                      FAWER Automotive Parts Ltd Co         XSHE  200030   \n",
       "62                                 Landmark White Ltd         XASX     LMW   \n",
       "63                       Transcontinental Inc Class A         XFRA     53L   \n",
       "64                       Transcontinental Inc Class A         XBER     53L   \n",
       "65                               Transcontinental Inc         XTSE   TCL.B   \n",
       "66                       Transcontinental Inc Class A         XTSE   TCL.A   \n",
       "\n",
       "                sector                        industry  lastprice  yield  \\\n",
       "0   Financial Services                Asset Management      16.20   9.54   \n",
       "1      Basic Materials          Paper & Paper Products       0.42   1.45   \n",
       "2      Basic Materials          Paper & Paper Products       0.42   1.45   \n",
       "3      Basic Materials          Paper & Paper Products       0.42   1.40   \n",
       "4           Technology         Communication Equipment       2.13   4.08   \n",
       "5      Basic Materials              Building Materials       6.20   1.56   \n",
       "6          Industrials      Engineering & Construction       0.29   4.63   \n",
       "7          Industrials      Engineering & Construction       0.16   4.13   \n",
       "8           Healthcare            Medical Distribution       0.13   0.25   \n",
       "9          Real Estate           Real Estate - General       0.43   8.43   \n",
       "10         Real Estate           Real Estate - General       0.22   4.19   \n",
       "11         Real Estate           Real Estate - General       0.21   4.44   \n",
       "12         Real Estate           Real Estate - General       0.20   4.63   \n",
       "13         Real Estate           Real Estate - General       0.43   8.39   \n",
       "14              Energy  Oil & Gas Refining & Marketing       0.82   3.70   \n",
       "15              Energy  Oil & Gas Refining & Marketing       0.82   3.66   \n",
       "16              Energy  Oil & Gas Refining & Marketing       0.82   3.67   \n",
       "17              Energy  Oil & Gas Refining & Marketing       0.82   3.67   \n",
       "18         Real Estate           Real Estate - General       7.34   3.25   \n",
       "19         Real Estate           Real Estate - General       7.38   3.29   \n",
       "20         Real Estate           Real Estate - General       7.34   3.25   \n",
       "21         Real Estate           Real Estate - General       7.26   3.22   \n",
       "22         Real Estate           Real Estate - General       7.21   3.25   \n",
       "23         Real Estate            Real Estate Services      46.88   1.28   \n",
       "24         Real Estate            Real Estate Services       7.56   3.93   \n",
       "25         Real Estate            Real Estate Services      46.42   1.31   \n",
       "26         Real Estate            Real Estate Services       7.68   3.87   \n",
       "27         Real Estate            Real Estate Services      47.12   1.29   \n",
       "28         Real Estate            Real Estate Services      46.26   1.30   \n",
       "29         Real Estate            Real Estate Services      46.26   1.30   \n",
       "..                 ...                             ...        ...    ...   \n",
       "37     Basic Materials                            Coal       4.36   6.88   \n",
       "38         Industrials      Engineering & Construction       1.44   4.17   \n",
       "39         Industrials      Engineering & Construction       2.66   4.64   \n",
       "40          Technology            Consumer Electronics       0.54   5.56   \n",
       "41          Healthcare            Medical Distribution      16.00   2.93   \n",
       "42         Real Estate              REIT - Diversified       0.63   4.79   \n",
       "43         Real Estate              REIT - Diversified       0.66   4.62   \n",
       "44   Consumer Cyclical        Residential Construction       6.42  12.12   \n",
       "45   Consumer Cyclical                    Luxury Goods       0.11   5.50   \n",
       "46         Real Estate              REIT - Diversified       5.83   4.70   \n",
       "47         Real Estate            Real Estate Services     101.30   0.20   \n",
       "48         Real Estate           Real Estate - General      12.96   3.76   \n",
       "49   Consumer Cyclical        Residential Construction      41.44   0.38   \n",
       "50   Consumer Cyclical                      Auto Parts      30.78   1.00   \n",
       "51         Industrials      Engineering & Construction      29.10   4.52   \n",
       "52   Consumer Cyclical                      Auto Parts      46.70   1.00   \n",
       "53  Consumer Defensive                  Packaged Foods       0.18  14.44   \n",
       "54         Real Estate           Real Estate - General       0.44   4.74   \n",
       "55         Real Estate           Real Estate - General       0.34   4.51   \n",
       "56         Real Estate           Real Estate - General       0.34   4.54   \n",
       "57         Real Estate           Real Estate - General       0.34   4.53   \n",
       "58         Real Estate           Real Estate - General       3.19   4.48   \n",
       "59         Real Estate            Real Estate Services       4.38   2.76   \n",
       "60         Industrials      Engineering & Construction       5.84   2.65   \n",
       "61   Consumer Cyclical                      Auto Parts       3.68   4.67   \n",
       "62         Real Estate            Real Estate Services       0.28   7.14   \n",
       "63   Consumer Cyclical                      Publishing      10.33   5.41   \n",
       "64   Consumer Cyclical                      Publishing      10.33   5.41   \n",
       "65   Consumer Cyclical                      Publishing      15.21   5.39   \n",
       "66   Consumer Cyclical                      Publishing      15.20   5.41   \n",
       "\n",
       "    _52wk_hi  _52wk_lo  PE_TTM_x  PB_TTM  PS_TTM  PC_TTM  current_ratio  \\\n",
       "0      16.46     11.54       8.7     1.0     5.1    42.5           1.89   \n",
       "1       1.31      0.04       4.9     0.6     0.8     5.5           2.26   \n",
       "2       1.22      0.04       4.9     0.6     0.8     5.5           2.26   \n",
       "3       1.27      0.05       5.1     0.7     0.8     5.7           2.26   \n",
       "4       4.72      1.82       8.0     1.0     2.7     7.5          26.95   \n",
       "5       8.01      3.28       7.3     1.0     0.9     6.5           1.73   \n",
       "6       0.86      0.25       5.1     0.7     0.4     7.5           1.45   \n",
       "7       0.19      0.09       4.3     0.5     0.1     NaN           1.74   \n",
       "8       0.22      0.01       8.1     0.9     0.3     9.7           2.70   \n",
       "9       0.64      0.16       4.1     0.8     0.6     4.4           1.40   \n",
       "10      0.25      0.12       6.5     1.0     0.9     NaN           3.85   \n",
       "11      0.24      0.12       6.1     1.0     0.9     NaN           3.85   \n",
       "12      0.26      0.10       5.9     0.9     0.8     NaN           3.85   \n",
       "13      0.63      0.15       4.1     0.8     0.6     4.5           1.40   \n",
       "14      1.11      0.28       8.2     1.0     0.0     4.2           1.35   \n",
       "15      1.12      0.29       8.3     1.0     0.0     4.2           1.35   \n",
       "16      1.12      0.29       8.3     1.0     0.0     4.2           1.35   \n",
       "17      1.11      0.28       8.3     1.0     0.0     4.2           1.35   \n",
       "18      7.91      5.90       4.8     0.9    12.3    16.5           3.47   \n",
       "19      7.90      5.70       4.8     0.9    12.2    16.3           3.47   \n",
       "20      7.92      6.76       4.8     0.9    12.3    16.5           3.47   \n",
       "21      7.87      5.88       4.9     0.9    12.4    16.6           3.47   \n",
       "22      7.87      5.93       4.8     0.9    12.3    16.5           3.47   \n",
       "23     55.65     19.01       5.3     1.0    13.3    19.8           1.39   \n",
       "24      9.17      6.34       6.0     0.7     6.6    12.7           8.62   \n",
       "25     55.20     22.80       5.2     0.9    13.1    19.5           1.39   \n",
       "26      9.19      6.42       6.1     0.7     6.7    12.9           8.62   \n",
       "27     55.75     44.28       5.3     1.0    13.2    19.7           1.39   \n",
       "28     55.05     27.94       5.3     0.9    13.2    19.6           1.39   \n",
       "29     55.05     27.94       5.3     0.9    13.2    19.6           1.39   \n",
       "..       ...       ...       ...     ...     ...     ...            ...   \n",
       "37      5.00      3.64       2.4     0.9     0.3     3.8           1.68   \n",
       "38      1.50      1.01       4.4     0.5     0.1     NaN           1.74   \n",
       "39      4.04      2.36       5.4     0.7     0.4     7.9           1.45   \n",
       "40      0.80      0.50       4.7     0.5     0.1     NaN           1.38   \n",
       "41     24.55     15.12       9.6     1.0     0.2    12.0           1.35   \n",
       "42      0.64      0.36       6.7     0.6    10.8    16.3           1.69   \n",
       "43      0.67      0.40       6.9     0.6    11.2    16.9           1.69   \n",
       "44     12.40      5.95       5.2     0.8     0.2     NaN           1.21   \n",
       "45      0.19      0.06       8.1     1.0     0.8     5.1           2.60   \n",
       "46      5.97      4.69       7.0     0.6    11.3    17.0           1.69   \n",
       "47    107.10     94.10       4.6     0.5    52.2   104.4          11.85   \n",
       "48     13.88      9.67       5.6     0.6     1.6    16.2           1.52   \n",
       "49     45.45     29.80       7.4     0.9     0.6    10.1          14.34   \n",
       "50     54.18     28.07       5.6     0.8     0.4     4.0           2.01   \n",
       "51     38.66     20.54       8.3     0.8     0.2     4.0           1.22   \n",
       "52     76.13     42.91       5.6     0.8     0.4     4.1           2.01   \n",
       "53      0.30      0.13       7.7     0.9     1.2     5.5           3.59   \n",
       "54      0.60      0.35       6.1     1.0     2.4     NaN          13.38   \n",
       "55      0.46      0.20       4.6     0.5     0.4     3.3           1.69   \n",
       "56      0.48      0.20       4.5     0.5     0.4     3.3           1.69   \n",
       "57      0.46      0.20       4.6     0.5     0.4     3.3           1.69   \n",
       "58      3.30      1.93       4.8     0.5     0.4     3.4           1.69   \n",
       "59      5.90      3.05       3.9     0.6     0.5     1.4           1.63   \n",
       "60      6.72      4.91       6.7     1.0     0.2   179.5           1.29   \n",
       "61      4.58      3.00       6.5     0.9     0.7   155.4           1.54   \n",
       "62      0.61      0.20       7.0     0.9     0.7    16.6           1.89   \n",
       "63     21.25     10.33       7.3     0.8     0.5     4.3           1.39   \n",
       "64     21.25     10.33       7.3     0.8     0.5     4.3           1.39   \n",
       "65     32.79     15.21       7.3     0.8     0.5     4.4           1.39   \n",
       "66     32.89     15.11       7.3     0.8     0.5     4.4           1.39   \n",
       "\n",
       "    debt2equity  return_on_equity  Rev_Growth_Y9  OpeInc_Growth_Y9  \\\n",
       "0          0.70             11.41          11.38             10.61   \n",
       "1          0.72             14.02           6.63             48.07   \n",
       "2          0.72             14.02           6.63             48.07   \n",
       "3          0.72             14.02           6.63             48.07   \n",
       "4           NaN             13.69          34.37             52.43   \n",
       "5          0.52             13.02           4.93              6.22   \n",
       "6          0.95             13.65           3.06             30.26   \n",
       "7           NaN             11.35           8.08              7.09   \n",
       "8           NaN             12.00           4.76             31.74   \n",
       "9          1.40             18.95          12.01              0.20   \n",
       "10         0.54             16.80          24.91              6.09   \n",
       "11         0.54             16.80          24.91              6.09   \n",
       "12         0.54             16.80          24.91              6.09   \n",
       "13         1.40             18.95          12.01              0.20   \n",
       "14          NaN             12.00          26.71             47.31   \n",
       "15          NaN             12.00          26.71             47.31   \n",
       "16          NaN             12.00          26.71             47.31   \n",
       "17          NaN             12.00          26.71             47.31   \n",
       "18         0.79             20.31          20.09             38.38   \n",
       "19         0.79             20.31          20.09             38.38   \n",
       "20         0.79             20.31          20.09             38.38   \n",
       "21         0.79             20.31          20.09             38.38   \n",
       "22         0.79             20.31          20.09             38.38   \n",
       "23         0.74             19.61          20.18             17.40   \n",
       "24         0.70             11.54          22.11             21.10   \n",
       "25         0.74             19.61          20.18             17.40   \n",
       "26         0.70             11.54          22.11             21.10   \n",
       "27         0.74             19.61          20.18             17.40   \n",
       "28         0.74             19.61          20.18             17.40   \n",
       "29         0.74             19.61          20.18             17.40   \n",
       "..          ...               ...            ...               ...   \n",
       "37         0.10             42.54          45.04             50.17   \n",
       "38          NaN             11.35           8.08              7.09   \n",
       "39         0.95             13.65           3.06             30.26   \n",
       "40          NaN               NaN           5.11              8.61   \n",
       "41         0.24             10.27          21.58             46.61   \n",
       "42          NaN               NaN           3.71              1.88   \n",
       "43          NaN               NaN           3.71              1.88   \n",
       "44         0.26             17.36          10.12            127.13   \n",
       "45          NaN             13.13           7.22             25.42   \n",
       "46          NaN               NaN           3.71              1.88   \n",
       "47          NaN             11.35           4.52             10.17   \n",
       "48         0.52             10.54           7.17             20.23   \n",
       "49         0.70             12.81          62.67             48.64   \n",
       "50         0.65             15.62          16.41              9.36   \n",
       "51         0.30             10.11          12.68             79.39   \n",
       "52         0.65             15.62          16.41              9.36   \n",
       "53         0.01             11.39          16.06             18.80   \n",
       "54          NaN             16.96           8.04              5.89   \n",
       "55         1.27             12.50          15.58             48.72   \n",
       "56         1.27             12.50          15.58             48.72   \n",
       "57         1.27             12.50          15.58             48.72   \n",
       "58         1.27             12.50          15.58             48.72   \n",
       "59         1.04             14.46          23.25             32.53   \n",
       "60         1.46             17.24          13.78             37.26   \n",
       "61          NaN             13.79           9.17              5.29   \n",
       "62          NaN             11.72          66.37             76.81   \n",
       "63         0.74             12.69          30.70             28.26   \n",
       "64         0.74             12.69          30.70             28.26   \n",
       "65         0.74             12.69          30.70             28.26   \n",
       "66         0.74             12.69          30.70             28.26   \n",
       "\n",
       "    NetInc_Growth_Y9  \n",
       "0              12.52  \n",
       "1              42.33  \n",
       "2              42.33  \n",
       "3              42.33  \n",
       "4              86.45  \n",
       "5              77.97  \n",
       "6              13.40  \n",
       "7              10.80  \n",
       "8              26.15  \n",
       "9              25.62  \n",
       "10             22.44  \n",
       "11             22.44  \n",
       "12             22.44  \n",
       "13             25.62  \n",
       "14             10.00  \n",
       "15             10.00  \n",
       "16             10.00  \n",
       "17             10.00  \n",
       "18             27.08  \n",
       "19             27.08  \n",
       "20             27.08  \n",
       "21             27.08  \n",
       "22             27.08  \n",
       "23              8.71  \n",
       "24             60.15  \n",
       "25              8.71  \n",
       "26             60.15  \n",
       "27              8.71  \n",
       "28              8.71  \n",
       "29              8.71  \n",
       "..               ...  \n",
       "37             56.37  \n",
       "38             10.80  \n",
       "39             13.40  \n",
       "40             10.48  \n",
       "41             10.24  \n",
       "42             93.99  \n",
       "43             93.99  \n",
       "44            142.92  \n",
       "45             23.63  \n",
       "46             93.99  \n",
       "47             50.11  \n",
       "48            195.79  \n",
       "49            109.24  \n",
       "50              7.67  \n",
       "51             26.75  \n",
       "52              7.67  \n",
       "53             20.20  \n",
       "54              4.60  \n",
       "55              4.31  \n",
       "56              4.31  \n",
       "57              4.31  \n",
       "58              4.31  \n",
       "59            112.70  \n",
       "60             16.09  \n",
       "61              6.02  \n",
       "62            154.61  \n",
       "63              0.90  \n",
       "64              0.90  \n",
       "65              0.90  \n",
       "66              0.90  \n",
       "\n",
       "[67 rows x 19 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('doc/df_rules.csv', 'w') as file:\n",
    "    file.write(df_rules.to_csv())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"additional\"></a>\n",
    "[return to the top](#top)\n",
    "\n",
    "## Additional sample / test code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from io import StringIO\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'http://performance.morningstar.com/perform/Performance/stock/exportStockPrice.action?t={}:{}&pd=1yr&freq=d&pg=0&culture=en-US'\n",
    "req = requests.get(url.format('xnas', 'aaoi'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}