02_Resampling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SimFin Tutorial 02 - Resampling\n",
    "\n",
    "[Original repository on GitHub](https://github.com/simfin/simfin-tutorials)\n",
    "\n",
    "This tutorial was originally written by [Hvass Labs](https://github.com/Hvass-Labs)\n",
    "\n",
    "----\n",
    "\n",
    "\"I believe this is going to be a very difficult *croning* job. *Cloning* you idiot, not *croning!*\" &ndash; [The Sleeper](https://www.youtube.com/watch?v=27PGmG0k2Kk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Resampling is a very common operation on financial data. The fundamental data in Balance Sheets, Income Statements and Cash-Flow Statements is only provided at quarterly or annual intervals, corresponding to the financial reports of the companies. If we want to use this data to calculate daily valuation ratios such as P/E or P/Sales then we first need to upsample the fundamental data.\n",
    "\n",
    "Broadly speaking, Pandas provides three methods for resampling data. They can all up- and downsample the data, but their features are slightly different:\n",
    "\n",
    "- **AsFreq** is the simplest resampling method, which only allows filling in the missing values backwards or forwards.\n",
    "\n",
    "- **Resample** is a more flexible resampling method, which allows more advanced interpolation when filling in missing values for upsampling, and also allows more advanced summarization methods for downsampling.\n",
    "\n",
    "- **Reindex** conforms a DataFrame to the index of another DataFrame, which will be explained in more detail below.\n",
    "\n",
    "The syntax for these resampling methods is complex and confusing, so after explaining how to use the Pandas methods on SimFin data, we also show some helper-functions from the simfin package, that you may want to use instead of Pandas' native methods.\n",
    "\n",
    "It is assumed you are already familiar with Tutorial 01 on the basics of SimFin before you read this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# Import the main functionality from the SimFin Python API.\n",
    "import simfin as sf\n",
    "\n",
    "# Import names used for easy access to SimFin's data-columns.\n",
    "from simfin.names import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.2.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Version of the SimFin Python API.\n",
    "sf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SimFin data-directory.\n",
    "sf.set_data_dir('~/simfin_data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SimFin load API key or use free data.\n",
    "sf.load_api_key(path='~/simfin_api_key.txt', default_key='free')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seaborn set plotting style.\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Datasets\n",
    "\n",
    "We will use the datasets for annual Income Statements and daily Share-Prices in these examples. In reality you would typically want to use the TTM-data instead, to get 4 yearly data-points instead of just 1 yearly data-point when using annual reports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-income-annual\" on disk (0 days old), loading.\n",
      "Dataset \"us-shareprices-daily\" on disk (1 days old), loading.\n",
      "Dataset \"us-shareprices-latest\" on disk (1 days old), loading.\n",
      "CPU times: user 15.5 s, sys: 1.26 s, total: 16.8 s\n",
      "Wall time: 15.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "market = 'us'\n",
    "df_income = sf.load_income(variant='annual', market=market)\n",
    "df_prices = sf.load_shareprices(variant='daily', market=market)\n",
    "df_prices_latest = sf.load_shareprices(variant='latest', market=market)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will only use a sub-set of the datasets for tickers AAPL, AMZN and MSFT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = ['AAPL', 'AMZN', 'MSFT']\n",
    "df_income = df_income.loc[tickers, [REVENUE, NET_INCOME]].copy()\n",
    "df_prices = df_prices.loc[tickers, [CLOSE, ADJ_CLOSE]].copy()\n",
    "df_prices_latest = df_prices_latest.loc[tickers, [CLOSE, ADJ_CLOSE]].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Report Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"13\" valign=\"top\">AAPL</td>\n",
       "      <td>2007-09-30</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-09-30</td>\n",
       "      <td>3.749100e+10</td>\n",
       "      <td>6119000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-09-30</td>\n",
       "      <td>4.290500e+10</td>\n",
       "      <td>8235000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-09-30</td>\n",
       "      <td>6.522500e+10</td>\n",
       "      <td>14013000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>1.082490e+11</td>\n",
       "      <td>25922000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-09-30</td>\n",
       "      <td>1.565080e+11</td>\n",
       "      <td>41733000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-09-30</td>\n",
       "      <td>1.709100e+11</td>\n",
       "      <td>37037000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>1.827950e+11</td>\n",
       "      <td>39510000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>2.337150e+11</td>\n",
       "      <td>53394000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>2.156390e+11</td>\n",
       "      <td>45687000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>2.292340e+11</td>\n",
       "      <td>48351000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>2.655950e+11</td>\n",
       "      <td>59531000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2.601740e+11</td>\n",
       "      <td>55256000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"12\" valign=\"top\">AMZN</td>\n",
       "      <td>2007-12-31</td>\n",
       "      <td>1.483500e+10</td>\n",
       "      <td>476000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>1.916600e+10</td>\n",
       "      <td>645000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-12-31</td>\n",
       "      <td>2.450900e+10</td>\n",
       "      <td>902000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>3.420400e+10</td>\n",
       "      <td>1152000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-12-31</td>\n",
       "      <td>4.807700e+10</td>\n",
       "      <td>631000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>6.109300e+10</td>\n",
       "      <td>-39000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>7.445200e+10</td>\n",
       "      <td>274000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>8.898800e+10</td>\n",
       "      <td>-241000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>1.070060e+11</td>\n",
       "      <td>596000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>1.359870e+11</td>\n",
       "      <td>2371000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>1.778660e+11</td>\n",
       "      <td>3033000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2.328870e+11</td>\n",
       "      <td>10073000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"12\" valign=\"top\">MSFT</td>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>5.843700e+10</td>\n",
       "      <td>14569000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>6.248400e+10</td>\n",
       "      <td>18760000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-06-30</td>\n",
       "      <td>6.994300e+10</td>\n",
       "      <td>23150000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-06-30</td>\n",
       "      <td>7.372300e+10</td>\n",
       "      <td>16978000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>7.784900e+10</td>\n",
       "      <td>21863000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>8.683300e+10</td>\n",
       "      <td>22074000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>9.358000e+10</td>\n",
       "      <td>12193000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>9.115400e+10</td>\n",
       "      <td>20539000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>9.657100e+10</td>\n",
       "      <td>25489000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>39240000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                         Revenue   Net Income\n",
       "Ticker Report Date                           \n",
       "AAPL   2007-09-30   2.457800e+10   3495000000\n",
       "       2008-09-30   3.749100e+10   6119000000\n",
       "       2009-09-30   4.290500e+10   8235000000\n",
       "       2010-09-30   6.522500e+10  14013000000\n",
       "       2011-09-30   1.082490e+11  25922000000\n",
       "       2012-09-30   1.565080e+11  41733000000\n",
       "       2013-09-30   1.709100e+11  37037000000\n",
       "       2014-09-30   1.827950e+11  39510000000\n",
       "       2015-09-30   2.337150e+11  53394000000\n",
       "       2016-09-30   2.156390e+11  45687000000\n",
       "       2017-09-30   2.292340e+11  48351000000\n",
       "       2018-09-30   2.655950e+11  59531000000\n",
       "       2019-09-30   2.601740e+11  55256000000\n",
       "AMZN   2007-12-31   1.483500e+10    476000000\n",
       "       2008-12-31   1.916600e+10    645000000\n",
       "       2009-12-31   2.450900e+10    902000000\n",
       "       2010-12-31   3.420400e+10   1152000000\n",
       "       2011-12-31   4.807700e+10    631000000\n",
       "       2012-12-31   6.109300e+10    -39000000\n",
       "       2013-12-31   7.445200e+10    274000000\n",
       "       2014-12-31   8.898800e+10   -241000000\n",
       "       2015-12-31   1.070060e+11    596000000\n",
       "       2016-12-31   1.359870e+11   2371000000\n",
       "       2017-12-31   1.778660e+11   3033000000\n",
       "       2018-12-31   2.328870e+11  10073000000\n",
       "MSFT   2008-06-30   6.042000e+10  17681000000\n",
       "       2009-06-30   5.843700e+10  14569000000\n",
       "       2010-06-30   6.248400e+10  18760000000\n",
       "       2011-06-30   6.994300e+10  23150000000\n",
       "       2012-06-30   7.372300e+10  16978000000\n",
       "       2013-06-30   7.784900e+10  21863000000\n",
       "       2014-06-30   8.683300e+10  22074000000\n",
       "       2015-06-30   9.358000e+10  12193000000\n",
       "       2016-06-30   9.115400e+10  20539000000\n",
       "       2017-06-30   9.657100e+10  25489000000\n",
       "       2018-06-30   1.103600e+11  16571000000\n",
       "       2019-06-30   1.258430e+11  39240000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Close</th>\n",
       "      <th>Adj. Close</th>\n",
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       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
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       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">AAPL</td>\n",
       "      <td>2007-01-03</td>\n",
       "      <td>11.9714</td>\n",
       "      <td>10.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-01-04</td>\n",
       "      <td>12.2371</td>\n",
       "      <td>10.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-01-05</td>\n",
       "      <td>12.1500</td>\n",
       "      <td>10.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-01-08</td>\n",
       "      <td>12.2100</td>\n",
       "      <td>10.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-01-09</td>\n",
       "      <td>13.2243</td>\n",
       "      <td>11.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                     Close  Adj. Close\n",
       "Ticker Date                           \n",
       "AAPL   2007-01-03  11.9714       10.45\n",
       "       2007-01-04  12.2371       10.68\n",
       "       2007-01-05  12.1500       10.60\n",
       "       2007-01-08  12.2100       10.66\n",
       "       2007-01-09  13.2243       11.54"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj. Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>AAPL</td>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>257.13</td>\n",
       "      <td>257.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AMZN</td>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>1801.71</td>\n",
       "      <td>1801.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MSFT</td>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>144.46</td>\n",
       "      <td>144.46</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                     Close  Adj. Close\n",
       "Ticker Date                           \n",
       "AAPL   2019-11-05   257.13      257.13\n",
       "AMZN   2019-11-05  1801.71     1801.71\n",
       "MSFT   2019-11-05   144.46      144.46"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prices_latest.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AsFreq\n",
    "\n",
    "The simplest form of resampling in Pandas is called `asfreq` (presumably short for 'as frequency'). It can fill in the missing-values using either 'ffill' (forward fill) or 'bfill' (backward fill).\n",
    "\n",
    "For example, we can upsample the annual Income Statements to daily data for the ticker MSFT, by forward-filling the missing values from the last-known values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Report Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-01</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-02</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-03</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-04</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-29</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>39240000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4018 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Revenue   Net Income\n",
       "Report Date                           \n",
       "2008-06-30   6.042000e+10  17681000000\n",
       "2008-07-01   6.042000e+10  17681000000\n",
       "2008-07-02   6.042000e+10  17681000000\n",
       "2008-07-03   6.042000e+10  17681000000\n",
       "2008-07-04   6.042000e+10  17681000000\n",
       "...                   ...          ...\n",
       "2019-06-26   1.103600e+11  16571000000\n",
       "2019-06-27   1.103600e+11  16571000000\n",
       "2019-06-28   1.103600e+11  16571000000\n",
       "2019-06-29   1.103600e+11  16571000000\n",
       "2019-06-30   1.258430e+11  39240000000\n",
       "\n",
       "[4018 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income.loc['MSFT'].asfreq(freq='D', method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot the upsampled data, it will show as \"staircase\" patterns because the forward-filling simply copies the last-known values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f79274a6d30>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_income.loc['MSFT'].asfreq(freq='D', method='ffill').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AsFreq on MultiIndex\n",
    "\n",
    "If we try to do `asfreq` resampling on a DataFrame with multiple tickers, then it raises an exception because `asfreq` does not support DataFrames with MultiIndex:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cannot convert input [('AAPL', Timestamp('2007-09-30 00:00:00'))] of type <class 'tuple'> to Timestamp\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    df_income.asfreq('D', method='ffill')\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to split the DataFrame into sub-groups for each of the tickers, and perform the `asfreq` operation on each of those sub-groups, and then glue the results back together into a single DataFrame. Remember from Tutorial 01 that the `groupby` operation does exactly this.\n",
    "\n",
    "There are many ways of using `groupby` but the simplest I have been able to come up with so far, is the following. You can make the notation a little more compact (but probably also harder to read and understand), by using a lambda-function and chained calls instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Report Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">AAPL</td>\n",
       "      <td>2007-09-30</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-01</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-02</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-03</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-04</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">MSFT</td>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-29</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>39240000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12421 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Revenue   Net Income\n",
       "Ticker Report Date                           \n",
       "AAPL   2007-09-30   2.457800e+10   3495000000\n",
       "       2007-10-01   2.457800e+10   3495000000\n",
       "       2007-10-02   2.457800e+10   3495000000\n",
       "       2007-10-03   2.457800e+10   3495000000\n",
       "       2007-10-04   2.457800e+10   3495000000\n",
       "...                          ...          ...\n",
       "MSFT   2019-06-26   1.103600e+11  16571000000\n",
       "       2019-06-27   1.103600e+11  16571000000\n",
       "       2019-06-28   1.103600e+11  16571000000\n",
       "       2019-06-29   1.103600e+11  16571000000\n",
       "       2019-06-30   1.258430e+11  39240000000\n",
       "\n",
       "[12421 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _asfreq(df_grp):\n",
    "    # Remove TICKER from the MultiIndex.\n",
    "    df_grp = df_grp.reset_index(TICKER, drop=True)\n",
    "    \n",
    "    # Perform the operation on this group.\n",
    "    df_result = df_grp.asfreq(freq='D', method='ffill')\n",
    "\n",
    "    return df_result\n",
    "\n",
    "# Split the DataFrame into sub-groups and apply the _asfreq()\n",
    "# function on each of those sub-groups, and then glue the\n",
    "# results back together into a single DataFrame.\n",
    "df_income.groupby(TICKER).apply(_asfreq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AsFreq Helper-Function\n",
    "\n",
    "It is hard to remember the above syntax for doing `asfreq` on a DataFrame with a MultiIndex, so the simfin package provides a helper-function that can take both Pandas DataFrames and Series, either having a single DatatimeIndex, or a MultiIndex with two indices, one of which is a DatetimeIndex and the other is e.g. the Ticker or SimFin_Id.\n",
    "\n",
    "For example, this is how you would do `asfreq` on a DataFrame for a single stock:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Report Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-01</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-02</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-03</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-04</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-29</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>39240000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4018 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Revenue   Net Income\n",
       "Report Date                           \n",
       "2008-06-30   6.042000e+10  17681000000\n",
       "2008-07-01   6.042000e+10  17681000000\n",
       "2008-07-02   6.042000e+10  17681000000\n",
       "2008-07-03   6.042000e+10  17681000000\n",
       "2008-07-04   6.042000e+10  17681000000\n",
       "...                   ...          ...\n",
       "2019-06-26   1.103600e+11  16571000000\n",
       "2019-06-27   1.103600e+11  16571000000\n",
       "2019-06-28   1.103600e+11  16571000000\n",
       "2019-06-29   1.103600e+11  16571000000\n",
       "2019-06-30   1.258430e+11  39240000000\n",
       "\n",
       "[4018 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.asfreq(df=df_income.loc['MSFT'], freq='D', method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how you would do `asfreq` on a DataFrame with multiple stocks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Report Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">AAPL</td>\n",
       "      <td>2007-09-30</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-01</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-02</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-03</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-04</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">MSFT</td>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-29</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>39240000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12421 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Revenue   Net Income\n",
       "Ticker Report Date                           \n",
       "AAPL   2007-09-30   2.457800e+10   3495000000\n",
       "       2007-10-01   2.457800e+10   3495000000\n",
       "       2007-10-02   2.457800e+10   3495000000\n",
       "       2007-10-03   2.457800e+10   3495000000\n",
       "       2007-10-04   2.457800e+10   3495000000\n",
       "...                          ...          ...\n",
       "MSFT   2019-06-26   1.103600e+11  16571000000\n",
       "       2019-06-27   1.103600e+11  16571000000\n",
       "       2019-06-28   1.103600e+11  16571000000\n",
       "       2019-06-29   1.103600e+11  16571000000\n",
       "       2019-06-30   1.258430e+11  39240000000\n",
       "\n",
       "[12421 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.asfreq(df=df_income, freq='D', method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, a function is provided for `asfreq` with daily frequency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Report Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">AAPL</td>\n",
       "      <td>2007-09-30</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-01</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-02</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-03</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-04</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">MSFT</td>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-29</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>39240000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12421 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Revenue   Net Income\n",
       "Ticker Report Date                           \n",
       "AAPL   2007-09-30   2.457800e+10   3495000000\n",
       "       2007-10-01   2.457800e+10   3495000000\n",
       "       2007-10-02   2.457800e+10   3495000000\n",
       "       2007-10-03   2.457800e+10   3495000000\n",
       "       2007-10-04   2.457800e+10   3495000000\n",
       "...                          ...          ...\n",
       "MSFT   2019-06-26   1.103600e+11  16571000000\n",
       "       2019-06-27   1.103600e+11  16571000000\n",
       "       2019-06-28   1.103600e+11  16571000000\n",
       "       2019-06-29   1.103600e+11  16571000000\n",
       "       2019-06-30   1.258430e+11  39240000000\n",
       "\n",
       "[12421 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.asfreq_daily(df=df_income, method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily plot the resampled data using the Seaborn plotting package. Note that the index is first reset on the DataFrame, because that is how Seaborn wants the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f79273ce400>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Upsample annual Income Statements for several tickers.\n",
    "df_daily = sf.asfreq_daily(df_income, method='ffill')\n",
    "\n",
    "# Plot.\n",
    "sns.lineplot(x=REPORT_DATE, y=REVENUE, hue=TICKER,\n",
    "             data=df_daily.reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resample\n",
    "\n",
    "If a simple forward/backward-fill is not sufficient for you, or if you want to downsample data e.g. by taking the average of many data-points, then Pandas provides the `resample` function.\n",
    "\n",
    "For example, we can use the `resample` function to upsample a DataFrame for a single company which only has a DatetimeIndex. If we use forward-fill again, then it gives the same \"staircase\" plot as we saw for `asfreq` above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f792792ce48>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QrTfgyPc6GAWT5G389NMtnK2+2uH1vDzUGBMbhs4+jo0Tg2DiCP0XGOhECrDvTAley7LFlU5vSFrLaDJh4qD7bdB++zWO0Bnod2OgEylAnUEAAOS8Eo9O3h6StvHjjxcRHd27Q+vU1At4ev1RVOs7duifLRgEEZ5qTrncjYFOpADGO6cv9gj2g6+XtECv6uyJ8CC/Dq1juDPFU9cgSGrTGkaTiSP0X2CgEymA6U6gezh4TtnLQw0vDxVKqurwQ2m1Q9uurDU4vL/OjoFOpABNI3QPGc5eDPLzxkenivDRqSKHt/1wROv3anBHDHQiBTCZRKhUgFqGEWvGzCdw5UaNw9sFgJjwQFnadVYMdCIFMJrkO8kmJjyQweokuIuYSAEEk8j5ZGKgEymBYBJlmT8n58JAJ1IAI0foBAY6kSJwyoUA7hQlsqkdXxXixCXH3wn+u5Kb8OBZk26PgU5kQxtzL6P8dgNCA6XfdEEKD7UKI/tpHNomOR8GOpENGQURo/prsP75OLlLITfE72hENmQSebQJyYeBTmRDJlGEmoFOMmGgE9mQYJLn9HsigIFOZFMmUQTvikZy4VuPyIZ4xibJiYFOZEMmUYSKgU4yYaAT2ZCJZ2ySjNoV6Lm5uRg9ejRGjhyJjRs33vP6tWvXMHXqVKSkpCApKQlHjx61eaFErkAQGegkH4snFgmCgDfeeAObN2+GRqPBhAkTkJCQgN69f76Z7HvvvYexY8fihRdewMWLFzF79mx89tlndi2cyBmZTOBhiyQbiyP0vLw8REVFITIyEt7e3khMTEROTk6zZVQqFW7fvg0AqK6uRmhoqH2qJXJyjcehy10FuSuLI3SdToewsDDzY41Gg7y8vGbLzJkzBzNnzsT27dtRV1eHzZs3W2zYZDJBq9VKKFleer3eJeu2BvvcfoJJRGVFuUv+vPh7dn0WA10UxXue++Ve/OzsbIwfPx4zZszAmTNnsGjRIhw4cADqNq7+plarERMTI6FkeWm1Wpes2xrsc/uIoggRlxHavTtiYvrYqTL74e/ZNbT1AWQx0MPCwlBaWmp+rNPp7plSyczMRHp6OgDgkUceQX19PSorKxESEiK1ZiKrGAQTBNO9g5H2ahBM0BuEDq1jujP44U5RkovFQB8wYAAKCgpQVFQEjUaD7OxsvPXWW82WCQ8Px4kTJ5CamopLly6hvr4eXbt2tVvRRG35obQaSe98gQajycotFUhay4unipJMLAa6p6cnli9fjlmzZkEQBKSlpSE6OhobNmxAbGwsRowYgcWLF2PZsmXYsmULVCoV/vznP/PkCpJNcWUtGowmTBschbAgX0nbKLtehu6h3Tu8nodKhfGP9pDUJpG12nU99Pj4eMTHxzd7bv78+eZ/9+7dGx999JFtKyOSyCA0jswnP3E/YsIDJW1DqzUgJqa35QWJnAi/G5LiNAiNc9mc+iB3w3c8KY7hzty5NwOd3Azf8aQ4TVMuXp7cj0PuhYFOimMOdI7Qyc3wJtFkNx9+cQXbvyx0eLs36wwAGOjkfhjoZDdHL5ShqrYBT0V3/PA/a0V08UOgL9/e5F74jie7MRhNiA4NwNuTH5G7FCK3wO+kZDcNgok7JokciIFOdmMQTDx0kMiB+L+N7KbBaOKOSSIH4hy6womiiDNFVajWGyVvo6ikFtc9yjq83s06A7w9GehEjsJAV7iL128j9d3jNthSqeVFWtClk7cN2iai9mCgK1x1fePIfFliDB65v4ukbRQWFCCqZ09J6/aTeHEsIuo4BrrCNd3koY8mAI9FSQv0TrWliJG4LhE5Dic4Fa4p0D15Fx0ixWOgK5zpTqCrGehEisdAVzhB5AidyF0w0BXOyBE6kdtgoCtc05SLB+/xSqR4DHSFa9op6sEROpHiMdAVziQy0IncBQNd4YwcoRO5DQa6wnHKhch98ExRB6msacDXhZUObzev+CYA7hQlcgcMdAdZ9+kP2PnVVVnaVquAAN6OjUjx+L/cQar1RtwX5IuN0x53eNvBnbwQ4u/j8HaJyLHaFei5ublYtWoVTCYTnnvuOcyePfueZQ4ePIh33nkHKpUKffv2xVtvvWXzYl2ZwWiCv68nYnsEyV0KESmUxUAXBAFvvPEGNm/eDI1GgwkTJiAhIQG9e/c2L1NQUICNGzdi165dCAoKQnl5ucWGS2414G+bTlpXvQR+Xh74Y0osugc4dsRqEEy82QMR2ZXFQM/Ly0NUVBQiIyMBAImJicjJyWkW6Lt378aUKVMQFNQ4+gwJCbHYsEkEquoMUuuWRN8g4AddNVIf7YFR/cMc2naDwNuxEZF9WQx0nU6HsLCfw0+j0SAvL6/ZMgUFBQCASZMmwWQyYc6cORg2bFib2+0R4Ik/Jzj25gcltwyY9XE1fiy4iki1tCNO9Ho9tFpth9e7WX0bJhGS1pWb1D67MvbZPSitzxYDXbxzpuHdVL84BE4QBBQWFiIjIwOlpaWYMmUKDhw4gMDA1gNbrVYjJiZGQsnShVTrgY+LUG7yR7lnd0nbuFpRiPvv7/i6evE6QgN8Hd5nW9BqtS5ZtzXYZ/fgin1u6wPIYqCHhYWhtPTn+0nqdDqEhoY2W0aj0SAuLg5eXl6IjIzEAw88gIKCAjz88MNWlG17gb5e8PZUY9OxK9h07IoVW5J2f80+mgAr2iQiapvFQB8wYAAKCgpQVFQEjUaD7Ozse45gefrpp5GdnY3U1FRUVFSgoKDAPOfuTHy9PPDpgmEou10veRsFBYXo2TNK0roPhTHQich+LAa6p6cnli9fjlmzZkEQBKSlpSE6OhobNmxAbGwsRowYgaeeegrHjh3DuHHj4OHhgUWLFqFLF+e8B2XPbp3Rs1tnyev71+kQ07OrDSsiIrKNdh2HHh8fj/j4+GbPzZ8/3/xvlUqFJUuWYMmSJbatjoiI2o3H0RERKQQDnYhIIRjoREQKwUAnIlIIBjoRkUIw0ImIFIKBTkSkEAx0IiKFYKATESkEA52ISCEY6ERECsFAJyJSCAY6EZFCMNCJiBSCgU5EpBAMdCIihWCgExEpBAOdiEghGOhERArBQCciUggGOhGRQjDQiYgUgoFORKQQDHQiIoVgoBMRKUS7Aj03NxejR4/GyJEjsXHjxlaXO3z4MB566CF89913NiuQiIjax2KgC4KAN954A+np6cjOzsaBAwdw8eLFe5a7ffs2MjIyMHDgQLsUSkREbbMY6Hl5eYiKikJkZCS8vb2RmJiInJyce5bbsGEDZs2aBR8fH7sUSkREbbMY6DqdDmFhYebHGo0GOp2u2TLnzp1DaWkphg8fbvsKiYioXTwtLSCK4j3PqVQq879NJhPWrFmDNWvWdKhhk8kErVbboXWcgV6vd8m6rcE+uwf22fmFaLcBMdNafd1ioIeFhaG0tNT8WKfTITQ01Py4pqYGFy5cwLRpjY2UlZXh5ZdfxnvvvYcBAwa0ul21Wo2YmJh2dcKZaLVal6zbGuyze2CfXcCXZ1HexssWA33AgAEoKChAUVERNBoNsrOz8dZbb5lfDwgIwFdffWV+PHXqVCxatKjNMCciIglamDG5m8VA9/T0xPLlyzFr1iwIgoC0tDRER0djw4YNiI2NxYgRI2xWKxERSWcx0AEgPj4e8fHxzZ6bP39+i8tmZGRYXxUREbWg7RE6zxQlIlIIBjoRkauwMIfOQCciUggGOhGRy+AInYjILTDQiYhcBefQiYiUgoFOROQWGOhERK6CUy5ERO6BgU5E5DI4QicicgsMdCIiV8E5dCIi98BAJyJyGRyhExG5BQY6EZGr4Bw6EZFSMNCJiNxCu+4pSkTkdPL3APvnASZB8iYeEk2ASsK41scfmJUDdImS3LYkFqZcGOhE5JpKvwMMtcDgOZI3UVlRjpCuIR1b6VZJ44dJVaHjA90CBjoRuSbBAHj6AqP+KHkT17VahMTEdGylwuONgW7FNwPpOIdOREpkEgC1l+PbVXk0/i2aHN+2BRyhK13NDWBLIqC/JXkTvY0G4KDE/zjDXgUGzZTcNlGrTAbAQ4YIa5pzlyPQ2x6gM9AVr+IKUHYeeHAEEHifpE3UVFUhODi44yt+vw+4+qV7BbooAiajPG2rPQGVSp625SAY5Bmhq+8EuixTLm1joCud0ND495B5QK//J2kTP2m1CO7oPCPQGOZyhFtdJfDpa407zCS679Yt4PvAjq944VOgoVpyu1aJGgpMz5anbTmYjICHu0252OAol9zcXKxatQomkwnPPfccZs+e3ez1zZs347//+7/h4eGBrl27YvXq1ejRo4f0msl2mgLdw9vxbas95Qn04q+BMxlAUCTg6SNpE74NDUCNhJ9ZQBhw3xig+0OS2pXs/EHgxgXHtik3wdD4HnM0dVOgu+AIXRAEvPHGG9i8eTM0Gg0mTJiAhIQE9O7d27xMTEwM9uzZAz8/P+zcuRPr1q3D3/72N7sWTu3UFKiyBboMb/qmD7GJ24H74iRt4rJWixgp30rkcusnoOqqPG1f+xa48aPj2628Ik+gq2SccrH2OPS8vDxERUUhMjISAJCYmIicnJxmgf7kk0+a/x0XF4f9+/dLLZdsrSnc5BrJyDFCFwyNf8vxdVwuKrV8I8adzwO3dfK0HTXU8W268lEuOp0OYWFh5scajQZ5eXmtLp+ZmYlhw4ZZbNhkMkGr1bazTOeh1+tdqu6AoiuIAHC5sBj1N6VNP0jtc896AwTxJooc/PMKLCpEDwCXCq6ioULakbmu9nvWVN1EkNGIC1bULLXPffTVqO45DuX9XpTctlQGv1CIDu6z961CPAigpLgItzwc+x55oF7f5usWA11sYYivamVPelZWFvLz87F9+3aLhanVatf6SnuHVupX8fpq4NoZ2xdkiUclAKBXdF+gW28LC7dMcp+PBwCePo7/Pdd/CwB4MPohoGsvSZuQ3Ge5FHYDCmFVzZL7rAKCw3sh+InRktuWi6Q+lzdOX/YID0MPR79HPmt76tRioIeFhaG0tNT8WKfTITQ09J7ljh8/jn/84x/Yvn07vL1lmK91dv9cCZxKl6lxFeAr4YgNa8k1h266M+UixyFtclGp5ZsCMAk/7yh0B668U3TAgAEoKChAUVERNBoNsrOz8dZbbzVb5ty5c1i+fDnS09MREtK+6yJ41FcBZyyP5G3Oyw+Iedbx86t1lUBgDyB1o2PbBYBOIYD/vR/Cdqf2AIz1jm+Xc+iOJQo/zyu7AzlPLLL2sEVPT08sX74cs2bNgiAISEtLQ3R0NDZs2IDY2FiMGDECa9euRW1tLebPnw8ACA8Pxz/+8Y82t+tV8xNw+Lcd6IgNTf0YeDDBsW0KBsAnEOgpw04cuag9gYZyoMzBh9PdunanfTcKdLUHR+iO0vTh5aonFsXHxyM+Pr7Zc03hDQBbtmzpcMP64N7Agu86vJ5VblwAtqc1zmc7mmBwrxEjAHh3BnT5wN8HOb5tlRrw8nV8u3JRqWU8jE50rxF604fXxX8C9dIvqSFJbXmbL8t3pqjaCwi+37FtGu8cwifLNECDPMeCy2nMGqBfsjxtB/Zo/EBxFyqZRuhNHyLuNEL3CQT8ugDa/Y1/nIh7nfrfdNYgA90xgiKAARPkrsI9qNQAxMYRsyOv59I0by/lJhGuyrsT8MoFQJAhRwDgcnGrL7lpoLd9LKddCAbJp6ETWaS+62QXR05/uOMIHQA8vRv/OBk3C/Q7c6qHFwOfLpO0iYekjoCM9UD0SEltElnU9J509A5K8wjdzQLdSblXoPsGAonrG28dJVFFeTm6tfPQzHs8lCi5XaI2yXU6uruO0J2UewU6YPW1ucu0WnRzpTMIyT2Yj4128JEuTR8gHKE7BTfak0GkYGqO0MkdR+hEStQ0Qt84XHK49qqvBz7r4I77pqtputNRLk6MgU6kBNGjgZLTP1/HRoL6W9XwCQzo+Ir3PeL4M6+pRQx0IiXo1huY8KFVmyjRahHI/UMujd+TiIgUgoFORKQQDHQiIoVgoBMRKQQDnYhIIRjoREQKwUAnIlIIBjoRkUKoRFFs+66jdvLtt9/Cx4fXByci6oj6+nrExcW1+JpsgU5ERLbFKRciIoVgoBMRKQQDnYhIIRjoREQKwUAnIlIIBgD7Dv8AAAj9SURBVDoRkUK4faD/9NNPmDp1KsaOHYvExERs3boVAFBVVYXp06dj1KhRmD59Om7evAkAuHTpEiZOnIjY2Fh8+GHzGwrk5uZi9OjRGDlyJDZu3OjwvrSXrfrc2nackS1/zwAgCAJSUlLw0ksvObQfHWHLPt+6dQvz5s3DmDFjMHbsWJw5c8bh/WkPW/Z5y5YtSExMxDPPPIOFCxeivr7e4f3pMNHN6XQ6MT8/XxRFUayurhZHjRol/vjjj+Kbb74pvv/++6IoiuL7778vrl27VhRFUbxx44Z49uxZcf369WJ6erp5O0ajURwxYoR49epVsb6+XkxKShJ//PFHx3eoHWzV59a244xs1ecmmzZtEhcuXCjOnj3bcZ3oIFv2edGiReLu3btFURTF+vp68ebNmw7sSfvZqs+lpaXi8OHDxbq6OlEURXHevHninj17HNybjnP7EXpoaCj69+8PAPD390evXr2g0+mQk5ODlJQUAEBKSgr++c9/AgBCQkLw8MMPw9Oz+d378vLyEBUVhcjISHh7eyMxMRE5OTmO7Uw72arPrW3HGdmqzwBQWlqKzz//HBMmTHBcBySwVZ9v376NU6dOmfvr7e2NwMBAB/ak/Wz5exYEAXq9HkajEXq9HqGhoY7riERuH+h3Ky4uhlarxcCBA1FeXm7+BYaGhqKioqLNdXU6HcLCwsyPNRqN04bb3azpc2vbcXbW9nn16tX4/e9/D7Xadf77WNPnoqIidO3aFUuWLEFKSgqWLl2K2tpaR5RtFWv6rNFoMGPGDAwfPhxDhw6Fv78/hg4d6oiyreI670g7q6mpwbx58/CHP/wB/v7+HV5fbOEKCiqVyhal2Y21fbb1dhzB2lr/93//F127dkVsbKwdqrMPa/tsNBpx7tw5TJ48Gfv27YOfn59T7yMCrO/zzZs3kZOTg5ycHPzf//0f6urqkJWVZYdKbYuBDsBgMGDevHlISkrCqFGjADR+Fbt+/ToA4Pr16+jatWub2wgLC0Npaan5sU6nc+qvaLboc2vbcVa26PPp06fx2WefISEhAQsXLsSXX36JV1991e61S2Wr93ZYWJj529eYMWNw7tw5+xZuBVv0+fjx44iIiEDXrl3h5eWFUaNGOe2O4Lu5faCLooilS5eiV69emD59uvn5hIQE7Nu3DwCwb98+jBgxos3tDBgwAAUFBSgqKkJDQwOys7ORkJBg19qlslWfW9uOM7JVn1955RXk5ubis88+w/r16/Hkk0/iL3/5i11rl8pWfe7evTvCwsJw+fJlAMCJEyfw4IMP2q9wK9iqz/fddx/Onj2Luro6iKLo1H2+m9tfbfHrr7/GlClT0KdPH/Oc6MKFC/Hwww9jwYIF+OmnnxAeHo4NGzYgODgYZWVlSEtLw+3bt6FWq9GpUyccPHgQ/v7+OHr0KFavXg1BEJCWloaXX35Z5t61zFZ9Pn/+fIvbiY+Pl7N7LbLl77nJV199hU2bNuH999+Xq1ttsmWftVotli5dCoPBgMjISKxZswZBQUEy9/Betuzz22+/jYMHD8LT0xMxMTFYtWoVvL29Ze5h29w+0ImIlMLtp1yIiJSCgU5EpBAMdCIihWCgExEpBAOdiEgh7r2AAZETiYmJQZ8+fSAIAiIiIrB27Vq7X0dk7969GDJkCDQazT2vLV68GCdPnoS/vz/q6+sxcOBAvPLKKy0ue7ctW7Zg4sSJ8PPzs1fZRByhk3Pz9fVFVlYWDhw4gKCgIOzYscOu7QmCgI8//th8VmFLFi1ahP379+Pw4cPo168fpk2bhoaGhja3u23bNtTV1dm6XKJmGOjkMuLi4ppd8Cw9PR1paWlISkrC22+/DaDxgkxjxozBf/zHfyApKQnz5s0zB+mJEyeQkpKCpKQkLFmyxBzCCQkJeOeddzB58mQcOHAA+fn5ePXVV5GcnAy9Xt9qPSqVCi+++CK6deuG3NxcAMCKFSuQmpqKxMREc03btm3D9evX8Zvf/AZTp04FAHzxxReYOHEixo8fj3nz5qGmpsb2PzByOwx0cgmCIODEiRPmyyl88cUXKCwsRGZmJrKysvD999/j1KlTAIArV67g+eefxyeffILOnTtj586dqK+vx+LFi/HXv/4Vn3zyCQRBwM6dO83b9/Hxwa5du5CcnIzY2Fj85S9/QVZWFnx9fS3W1q9fP/Np8b/73e+wd+9e7N+/H6dOncL58+cxbdo0hIaGYuvWrcjIyEBFRQXee+89bN68GR9//DFiY2OxefNmO/zUyN1wDp2cml6vR3JyMkpKStC/f38MGTIEAHDs2DEcO3bMfI3r2tpaFBQUIDw8HOHh4XjssccAAM8++ywyMjIwZMgQRERE4IEHHgAAjB8/Hjt27MCLL74IABg3bpzkGu8+2frQoUPYvXs3jEYjysrKcOnSJfTt27fZ8mfPnsXFixcxefJkAI0Xk4qLi5PcPlETBjo5taY59Orqarz00kvYsWMHpk2bBlEUMXv2bEyaNKnZ8sXFxfdctlilUrV4eeO7WbOzUqvVYvDgwSgqKsKmTZuQmZmJoKAgLF68uMXblomiiCFDhmD9+vWS2yRqCadcyCUEBARg2bJl2LRpEwwGA4YOHYo9e/aY5551Oh3Ky8sBANeuXTNf6jQ7OxuPPfYYevXqhZKSEhQWFgIAsrKyMGjQoBbb6ty5c7vmtEVRxLZt21BWVoannnoKNTU18PPzQ0BAAG7cuGGeV//lNuPi4nD69GlzLXV1dbhy5YrEnwzRzzhCJ5fRr18/9O3bF9nZ2UhJScGlS5fMI/ROnTph3bp1UKvVePDBB/Hxxx9j+fLl6NmzJyZPngwfHx+sWbMG8+fPhyAIiI2NNU95/NL48eOxYsUK+Pr64r/+67/umUdfu3Yt3n33Xej1egwcOBDbtm2Dt7c3+vbti379+iExMRGRkZF49NFHzes8//zz+Nd//Vd0794dGRkZWLNmDRYuXGjeMbtgwQLzdBCRVLzaIilKcXEx/u3f/g0HDhyQuxQih+OUCxGRQnCETkSkEByhExEpBAOdiEghGOhERArBQCciUggGOhGRQvx/e/Nq/a7lHAMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_income.loc['MSFT'].resample('D').ffill().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `resample` function can also use more advanced methods for filling in the empty data-points. For example, linear interpolation fills in the empty data-points with straight lines between the known points.\n",
    "\n",
    "This is a form of cheating called \"look-ahead bias\" because we are using future data when filling in the empty data-points. This should be avoided when developing and testing trading-strategies, but it is still useful in some forms of data-analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f792748a358>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_income.loc['MSFT'].resample('D').interpolate(method='linear').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `resample` function can also be used to downsample data. For example, we can downsample by taking the average of every month's share-price data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Close</th>\n",
       "      <th>Adj. Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2007-01-31</td>\n",
       "      <td>30.530500</td>\n",
       "      <td>22.822000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-02-28</td>\n",
       "      <td>29.190000</td>\n",
       "      <td>21.864211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-03-31</td>\n",
       "      <td>27.695455</td>\n",
       "      <td>20.774545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-04-30</td>\n",
       "      <td>28.725000</td>\n",
       "      <td>21.548000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-05-31</td>\n",
       "      <td>30.752727</td>\n",
       "      <td>23.108636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Close  Adj. Close\n",
       "Date                             \n",
       "2007-01-31  30.530500   22.822000\n",
       "2007-02-28  29.190000   21.864211\n",
       "2007-03-31  27.695455   20.774545\n",
       "2007-04-30  28.725000   21.548000\n",
       "2007-05-31  30.752727   23.108636"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prices.loc['MSFT'].resample('M').mean().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the downsampled monthly share-price data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7927804240>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_prices.loc['MSFT'].resample('M').mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resample on MultiIndex\n",
    "\n",
    "If we try to resample a DataFrame with multiple tickers, then it raises an exception because Pandas' `resample` function does not support DataFrames with MultiIndex:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'MultiIndex'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    df_income.resample('D').ffill()\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas' `resample` function provides an argument `level=` which can be used to select a specific column in a MultiIndex, but its semantics are unclear from the documentation, and it does not work when upsampling anyway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upsampling from level= or on= selection is not supported, use .set_index(...) to explicitly set index to datetime-like\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    df_income.resample('D', level=REPORT_DATE).ffill()\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we did for the `asfreq` function above, we need to split the DataFrame into sub-groups for each of the Tickers, and perform the `resample` operation on each of those sub-groups, and then glue the results back together into a single DataFrame.\n",
    "\n",
    "The following could be made more compact (but maybe also harder to understand) by instead using a lambda-function with chained calls:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Report Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">AAPL</td>\n",
       "      <td>2007-09-30</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-01</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-02</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-03</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-04</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3495000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">MSFT</td>\n",
       "      <td>2019-06-26</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-27</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-28</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-29</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>39240000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12421 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Revenue   Net Income\n",
       "Ticker Report Date                           \n",
       "AAPL   2007-09-30   2.457800e+10   3495000000\n",
       "       2007-10-01   2.457800e+10   3495000000\n",
       "       2007-10-02   2.457800e+10   3495000000\n",
       "       2007-10-03   2.457800e+10   3495000000\n",
       "       2007-10-04   2.457800e+10   3495000000\n",
       "...                          ...          ...\n",
       "MSFT   2019-06-26   1.103600e+11  16571000000\n",
       "       2019-06-27   1.103600e+11  16571000000\n",
       "       2019-06-28   1.103600e+11  16571000000\n",
       "       2019-06-29   1.103600e+11  16571000000\n",
       "       2019-06-30   1.258430e+11  39240000000\n",
       "\n",
       "[12421 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _resample(df_grp):\n",
    "    # Remove TICKER from the MultiIndex.\n",
    "    df_grp = df_grp.reset_index(TICKER, drop=True)\n",
    "    \n",
    "    # Resample and forward-fill NA values.\n",
    "    df_result = df_grp.resample('D').ffill()\n",
    "\n",
    "    return df_result\n",
    "\n",
    "# Split the DataFrame into sub-groups and apply the _resample()\n",
    "# function on each of those sub-groups, and then glue the\n",
    "# results back together into a single DataFrame.\n",
    "df_income.groupby(TICKER).apply(_resample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resample Helper-Function\n",
    "\n",
    "Because Pandas' syntax is complicated for resampling a DataFrame with a MultiIndex, the simfin package also provides a helper-function for `resample`, that can take both Pandas DataFrames and Series, either having a single DatatimeIndex, or a MultiIndex with two indices, one of which is a DatetimeIndex and the other is e.g. the Ticker or SimFin_Id.\n",
    "\n",
    "Note that the helper-function uses the keyword argument `rule` instead of `freq` that was used in the `asfreq()` functions above. This inconsistency is simply inherited from Pandas, which uses the keyword `rule` in the `resample` function and the keyword `freq` in the `asfreq` function.\n",
    "\n",
    "For example, this is how you would upsample a DataFrame with annual data for a single stock into daily data-points using the forward-fill method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Report Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-01</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-02</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-03</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-04</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Revenue   Net Income\n",
       "Report Date                           \n",
       "2008-06-30   6.042000e+10  17681000000\n",
       "2008-07-01   6.042000e+10  17681000000\n",
       "2008-07-02   6.042000e+10  17681000000\n",
       "2008-07-03   6.042000e+10  17681000000\n",
       "2008-07-04   6.042000e+10  17681000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.resample(df=df_income.loc['MSFT'], rule='D', method='ffill').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the upsampled data. Because we are using the forward-fill method, the last-known values are merely copied forward to the unknown data-points, which shows up as a \"staircase\" pattern in the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7927858940>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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QrTfgyPc6GAWT5G389NMtnK2+2uH1vDzUGBMbhs4+jo0Tg2DiCP0XGOhECrDvTAley7LFlU5vSFrLaDJh4qD7bdB++zWO0Bnod2OgEylAnUEAAOS8Eo9O3h6StvHjjxcRHd27Q+vU1At4ev1RVOs7duifLRgEEZ5qTrncjYFOpADGO6cv9gj2g6+XtECv6uyJ8CC/Dq1juDPFU9cgSGrTGkaTiSP0X2CgEymA6U6gezh4TtnLQw0vDxVKqurwQ2m1Q9uurDU4vL/OjoFOpABNI3QPGc5eDPLzxkenivDRqSKHt/1wROv3anBHDHQiBTCZRKhUgFqGEWvGzCdw5UaNw9sFgJjwQFnadVYMdCIFMJrkO8kmJjyQweokuIuYSAEEk8j5ZGKgEymBYBJlmT8n58JAJ1IAI0foBAY6kSJwyoUA7hQlsqkdXxXixCXH3wn+u5Kb8OBZk26PgU5kQxtzL6P8dgNCA6XfdEEKD7UKI/tpHNomOR8GOpENGQURo/prsP75OLlLITfE72hENmQSebQJyYeBTmRDJlGEmoFOMmGgE9mQYJLn9HsigIFOZFMmUQTvikZy4VuPyIZ4xibJiYFOZEMmUYSKgU4yYaAT2ZCJZ2ySjNoV6Lm5uRg9ejRGjhyJjRs33vP6tWvXMHXqVKSkpCApKQlHjx61eaFErkAQGegkH4snFgmCgDfeeAObN2+GRqPBhAkTkJCQgN69f76Z7HvvvYexY8fihRdewMWLFzF79mx89tlndi2cyBmZTOBhiyQbiyP0vLw8REVFITIyEt7e3khMTEROTk6zZVQqFW7fvg0AqK6uRmhoqH2qJXJyjcehy10FuSuLI3SdToewsDDzY41Gg7y8vGbLzJkzBzNnzsT27dtRV1eHzZs3W2zYZDJBq9VKKFleer3eJeu2BvvcfoJJRGVFuUv+vPh7dn0WA10UxXue++Ve/OzsbIwfPx4zZszAmTNnsGjRIhw4cADqNq7+plarERMTI6FkeWm1Wpes2xrsc/uIoggRlxHavTtiYvrYqTL74e/ZNbT1AWQx0MPCwlBaWmp+rNPp7plSyczMRHp6OgDgkUceQX19PSorKxESEiK1ZiKrGAQTBNO9g5H2ahBM0BuEDq1jujP44U5RkovFQB8wYAAKCgpQVFQEjUaD7OxsvPXWW82WCQ8Px4kTJ5CamopLly6hvr4eXbt2tVvRRG35obQaSe98gQajycotFUhay4unipJMLAa6p6cnli9fjlmzZkEQBKSlpSE6OhobNmxAbGwsRowYgcWLF2PZsmXYsmULVCoV/vznP/PkCpJNcWUtGowmTBschbAgX0nbKLtehu6h3Tu8nodKhfGP9pDUJpG12nU99Pj4eMTHxzd7bv78+eZ/9+7dGx999JFtKyOSyCA0jswnP3E/YsIDJW1DqzUgJqa35QWJnAi/G5LiNAiNc9mc+iB3w3c8KY7hzty5NwOd3Azf8aQ4TVMuXp7cj0PuhYFOimMOdI7Qyc3wJtFkNx9+cQXbvyx0eLs36wwAGOjkfhjoZDdHL5ShqrYBT0V3/PA/a0V08UOgL9/e5F74jie7MRhNiA4NwNuTH5G7FCK3wO+kZDcNgok7JokciIFOdmMQTDx0kMiB+L+N7KbBaOKOSSIH4hy6womiiDNFVajWGyVvo6ikFtc9yjq83s06A7w9GehEjsJAV7iL128j9d3jNthSqeVFWtClk7cN2iai9mCgK1x1fePIfFliDB65v4ukbRQWFCCqZ09J6/aTeHEsIuo4BrrCNd3koY8mAI9FSQv0TrWliJG4LhE5Dic4Fa4p0D15Fx0ixWOgK5zpTqCrGehEisdAVzhB5AidyF0w0BXOyBE6kdtgoCtc05SLB+/xSqR4DHSFa9op6sEROpHiMdAVziQy0IncBQNd4YwcoRO5DQa6wnHKhch98ExRB6msacDXhZUObzev+CYA7hQlcgcMdAdZ9+kP2PnVVVnaVquAAN6OjUjx+L/cQar1RtwX5IuN0x53eNvBnbwQ4u/j8HaJyLHaFei5ublYtWoVTCYTnnvuOcyePfueZQ4ePIh33nkHKpUKffv2xVtvvWXzYl2ZwWiCv68nYnsEyV0KESmUxUAXBAFvvPEGNm/eDI1GgwkTJiAhIQG9e/c2L1NQUICNGzdi165dCAoKQnl5ucWGS2414G+bTlpXvQR+Xh74Y0osugc4dsRqEEy82QMR2ZXFQM/Ly0NUVBQiIyMBAImJicjJyWkW6Lt378aUKVMQFNQ4+gwJCbHYsEkEquoMUuuWRN8g4AddNVIf7YFR/cMc2naDwNuxEZF9WQx0nU6HsLCfw0+j0SAvL6/ZMgUFBQCASZMmwWQyYc6cORg2bFib2+0R4Ik/Jzj25gcltwyY9XE1fiy4iki1tCNO9Ho9tFpth9e7WX0bJhGS1pWb1D67MvbZPSitzxYDXbxzpuHdVL84BE4QBBQWFiIjIwOlpaWYMmUKDhw4gMDA1gNbrVYjJiZGQsnShVTrgY+LUG7yR7lnd0nbuFpRiPvv7/i6evE6QgN8Hd5nW9BqtS5ZtzXYZ/fgin1u6wPIYqCHhYWhtPTn+0nqdDqEhoY2W0aj0SAuLg5eXl6IjIzEAw88gIKCAjz88MNWlG17gb5e8PZUY9OxK9h07IoVW5J2f80+mgAr2iQiapvFQB8wYAAKCgpQVFQEjUaD7Ozse45gefrpp5GdnY3U1FRUVFSgoKDAPOfuTHy9PPDpgmEou10veRsFBYXo2TNK0roPhTHQich+LAa6p6cnli9fjlmzZkEQBKSlpSE6OhobNmxAbGwsRowYgaeeegrHjh3DuHHj4OHhgUWLFqFLF+e8B2XPbp3Rs1tnyev71+kQ07OrDSsiIrKNdh2HHh8fj/j4+GbPzZ8/3/xvlUqFJUuWYMmSJbatjoiI2o3H0RERKQQDnYhIIRjoREQKwUAnIlIIBjoRkUIw0ImIFIKBTkSkEAx0IiKFYKATESkEA52ISCEY6ERECsFAJyJSCAY6EZFCMNCJiBSCgU5EpBAMdCIihWCgExEpBAOdiEghGOhERArBQCciUggGOhGRQjDQiYgUgoFORKQQDHQiIoVgoBMRKUS7Aj03NxejR4/GyJEjsXHjxlaXO3z4MB566CF89913NiuQiIjax2KgC4KAN954A+np6cjOzsaBAwdw8eLFe5a7ffs2MjIyMHDgQLsUSkREbbMY6Hl5eYiKikJkZCS8vb2RmJiInJyce5bbsGEDZs2aBR8fH7sUSkREbbMY6DqdDmFhYebHGo0GOp2u2TLnzp1DaWkphg8fbvsKiYioXTwtLSCK4j3PqVQq879NJhPWrFmDNWvWdKhhk8kErVbboXWcgV6vd8m6rcE+uwf22fmFaLcBMdNafd1ioIeFhaG0tNT8WKfTITQ01Py4pqYGFy5cwLRpjY2UlZXh5ZdfxnvvvYcBAwa0ul21Wo2YmJh2dcKZaLVal6zbGuyze2CfXcCXZ1HexssWA33AgAEoKChAUVERNBoNsrOz8dZbb5lfDwgIwFdffWV+PHXqVCxatKjNMCciIglamDG5m8VA9/T0xPLlyzFr1iwIgoC0tDRER0djw4YNiI2NxYgRI2xWKxERSWcx0AEgPj4e8fHxzZ6bP39+i8tmZGRYXxUREbWg7RE6zxQlIlIIBjoRkauwMIfOQCciUggGOhGRy+AInYjILTDQiYhcBefQiYiUgoFOROQWGOhERK6CUy5ERO6BgU5E5DI4QicicgsMdCIiV8E5dCIi98BAJyJyGRyhExG5BQY6EZGr4Bw6EZFSMNCJiNxCu+4pSkTkdPL3APvnASZB8iYeEk2ASsK41scfmJUDdImS3LYkFqZcGOhE5JpKvwMMtcDgOZI3UVlRjpCuIR1b6VZJ44dJVaHjA90CBjoRuSbBAHj6AqP+KHkT17VahMTEdGylwuONgW7FNwPpOIdOREpkEgC1l+PbVXk0/i2aHN+2BRyhK13NDWBLIqC/JXkTvY0G4KDE/zjDXgUGzZTcNlGrTAbAQ4YIa5pzlyPQ2x6gM9AVr+IKUHYeeHAEEHifpE3UVFUhODi44yt+vw+4+qV7BbooAiajPG2rPQGVSp625SAY5Bmhq+8EuixTLm1joCud0ND495B5QK//J2kTP2m1CO7oPCPQGOZyhFtdJfDpa407zCS679Yt4PvAjq944VOgoVpyu1aJGgpMz5anbTmYjICHu0252OAol9zcXKxatQomkwnPPfccZs+e3ez1zZs347//+7/h4eGBrl27YvXq1ejRo4f0msl2mgLdw9vxbas95Qn04q+BMxlAUCTg6SNpE74NDUCNhJ9ZQBhw3xig+0OS2pXs/EHgxgXHtik3wdD4HnM0dVOgu+AIXRAEvPHGG9i8eTM0Gg0mTJiAhIQE9O7d27xMTEwM9uzZAz8/P+zcuRPr1q3D3/72N7sWTu3UFKiyBboMb/qmD7GJ24H74iRt4rJWixgp30rkcusnoOqqPG1f+xa48aPj2628Ik+gq2SccrH2OPS8vDxERUUhMjISAJCYmIicnJxmgf7kk0+a/x0XF4f9+/dLLZdsrSnc5BrJyDFCFwyNf8vxdVwuKrV8I8adzwO3dfK0HTXU8W268lEuOp0OYWFh5scajQZ5eXmtLp+ZmYlhw4ZZbNhkMkGr1bazTOeh1+tdqu6AoiuIAHC5sBj1N6VNP0jtc896AwTxJooc/PMKLCpEDwCXCq6ioULakbmu9nvWVN1EkNGIC1bULLXPffTVqO45DuX9XpTctlQGv1CIDu6z961CPAigpLgItzwc+x55oF7f5usWA11sYYivamVPelZWFvLz87F9+3aLhanVatf6SnuHVupX8fpq4NoZ2xdkiUclAKBXdF+gW28LC7dMcp+PBwCePo7/Pdd/CwB4MPohoGsvSZuQ3Ge5FHYDCmFVzZL7rAKCw3sh+InRktuWi6Q+lzdOX/YID0MPR79HPmt76tRioIeFhaG0tNT8WKfTITQ09J7ljh8/jn/84x/Yvn07vL1lmK91dv9cCZxKl6lxFeAr4YgNa8k1h266M+UixyFtclGp5ZsCMAk/7yh0B668U3TAgAEoKChAUVERNBoNsrOz8dZbbzVb5ty5c1i+fDnS09MREtK+6yJ41FcBZyyP5G3Oyw+Iedbx86t1lUBgDyB1o2PbBYBOIYD/vR/Cdqf2AIz1jm+Xc+iOJQo/zyu7AzlPLLL2sEVPT08sX74cs2bNgiAISEtLQ3R0NDZs2IDY2FiMGDECa9euRW1tLebPnw8ACA8Pxz/+8Y82t+tV8xNw+Lcd6IgNTf0YeDDBsW0KBsAnEOgpw04cuag9gYZyoMzBh9PdunanfTcKdLUHR+iO0vTh5aonFsXHxyM+Pr7Zc03hDQBbtmzpcMP64N7Agu86vJ5VblwAtqc1zmc7mmBwrxEjAHh3BnT5wN8HOb5tlRrw8nV8u3JRqWU8jE50rxF604fXxX8C9dIvqSFJbXmbL8t3pqjaCwi+37FtGu8cwifLNECDPMeCy2nMGqBfsjxtB/Zo/EBxFyqZRuhNHyLuNEL3CQT8ugDa/Y1/nIh7nfrfdNYgA90xgiKAARPkrsI9qNQAxMYRsyOv59I0by/lJhGuyrsT8MoFQJAhRwDgcnGrL7lpoLd9LKddCAbJp6ETWaS+62QXR05/uOMIHQA8vRv/OBk3C/Q7c6qHFwOfLpO0iYekjoCM9UD0SEltElnU9J509A5K8wjdzQLdSblXoPsGAonrG28dJVFFeTm6tfPQzHs8lCi5XaI2yXU6uruO0J2UewU6YPW1ucu0WnRzpTMIyT2Yj4128JEuTR8gHKE7BTfak0GkYGqO0MkdR+hEStQ0Qt84XHK49qqvBz7r4I77pqtputNRLk6MgU6kBNGjgZLTP1/HRoL6W9XwCQzo+Ir3PeL4M6+pRQx0IiXo1huY8KFVmyjRahHI/UMujd+TiIgUgoFORKQQDHQiIoVgoBMRKQQDnYhIIRjoREQKwUAnIlIIBjoRkUKoRFFs+66jdvLtt9/Cx4fXByci6oj6+nrExcW1+JpsgU5ERLbFKRciIoVgoBMRKQQDnYhIIRjoREQKwUAnIlIIBgD7Dv8AAAj9SURBVDoRkUK4faD/9NNPmDp1KsaOHYvExERs3boVAFBVVYXp06dj1KhRmD59Om7evAkAuHTpEiZOnIjY2Fh8+GHzGwrk5uZi9OjRGDlyJDZu3OjwvrSXrfrc2nackS1/zwAgCAJSUlLw0ksvObQfHWHLPt+6dQvz5s3DmDFjMHbsWJw5c8bh/WkPW/Z5y5YtSExMxDPPPIOFCxeivr7e4f3pMNHN6XQ6MT8/XxRFUayurhZHjRol/vjjj+Kbb74pvv/++6IoiuL7778vrl27VhRFUbxx44Z49uxZcf369WJ6erp5O0ajURwxYoR49epVsb6+XkxKShJ//PFHx3eoHWzV59a244xs1ecmmzZtEhcuXCjOnj3bcZ3oIFv2edGiReLu3btFURTF+vp68ebNmw7sSfvZqs+lpaXi8OHDxbq6OlEURXHevHninj17HNybjnP7EXpoaCj69+8PAPD390evXr2g0+mQk5ODlJQUAEBKSgr++c9/AgBCQkLw8MMPw9Oz+d378vLyEBUVhcjISHh7eyMxMRE5OTmO7Uw72arPrW3HGdmqzwBQWlqKzz//HBMmTHBcBySwVZ9v376NU6dOmfvr7e2NwMBAB/ak/Wz5exYEAXq9HkajEXq9HqGhoY7riERuH+h3Ky4uhlarxcCBA1FeXm7+BYaGhqKioqLNdXU6HcLCwsyPNRqN04bb3azpc2vbcXbW9nn16tX4/e9/D7Xadf77WNPnoqIidO3aFUuWLEFKSgqWLl2K2tpaR5RtFWv6rNFoMGPGDAwfPhxDhw6Fv78/hg4d6oiyreI670g7q6mpwbx58/CHP/wB/v7+HV5fbOEKCiqVyhal2Y21fbb1dhzB2lr/93//F127dkVsbKwdqrMPa/tsNBpx7tw5TJ48Gfv27YOfn59T7yMCrO/zzZs3kZOTg5ycHPzf//0f6urqkJWVZYdKbYuBDsBgMGDevHlISkrCqFGjADR+Fbt+/ToA4Pr16+jatWub2wgLC0Npaan5sU6nc+qvaLboc2vbcVa26PPp06fx2WefISEhAQsXLsSXX36JV1991e61S2Wr93ZYWJj529eYMWNw7tw5+xZuBVv0+fjx44iIiEDXrl3h5eWFUaNGOe2O4Lu5faCLooilS5eiV69emD59uvn5hIQE7Nu3DwCwb98+jBgxos3tDBgwAAUFBSgqKkJDQwOys7ORkJBg19qlslWfW9uOM7JVn1955RXk5ubis88+w/r16/Hkk0/iL3/5i11rl8pWfe7evTvCwsJw+fJlAMCJEyfw4IMP2q9wK9iqz/fddx/Onj2Luro6iKLo1H2+m9tfbfHrr7/GlClT0KdPH/Oc6MKFC/Hwww9jwYIF+OmnnxAeHo4NGzYgODgYZWVlSEtLw+3bt6FWq9GpUyccPHgQ/v7+OHr0KFavXg1BEJCWloaXX35Z5t61zFZ9Pn/+fIvbiY+Pl7N7LbLl77nJV199hU2bNuH999+Xq1ttsmWftVotli5dCoPBgMjISKxZswZBQUEy9/Betuzz22+/jYMHD8LT0xMxMTFYtWoVvL29Ze5h29w+0ImIlMLtp1yIiJSCgU5EpBAMdCIihWCgExEpBAOdiEgh7r2AAZETiYmJQZ8+fSAIAiIiIrB27Vq7X0dk7969GDJkCDQazT2vLV68GCdPnoS/vz/q6+sxcOBAvPLKKy0ue7ctW7Zg4sSJ8PPzs1fZRByhk3Pz9fVFVlYWDhw4gKCgIOzYscOu7QmCgI8//th8VmFLFi1ahP379+Pw4cPo168fpk2bhoaGhja3u23bNtTV1dm6XKJmGOjkMuLi4ppd8Cw9PR1paWlISkrC22+/DaDxgkxjxozBf/zHfyApKQnz5s0zB+mJEyeQkpKCpKQkLFmyxBzCCQkJeOeddzB58mQcOHAA+fn5ePXVV5GcnAy9Xt9qPSqVCi+++CK6deuG3NxcAMCKFSuQmpqKxMREc03btm3D9evX8Zvf/AZTp04FAHzxxReYOHEixo8fj3nz5qGmpsb2PzByOwx0cgmCIODEiRPmyyl88cUXKCwsRGZmJrKysvD999/j1KlTAIArV67g+eefxyeffILOnTtj586dqK+vx+LFi/HXv/4Vn3zyCQRBwM6dO83b9/Hxwa5du5CcnIzY2Fj85S9/QVZWFnx9fS3W1q9fP/Np8b/73e+wd+9e7N+/H6dOncL58+cxbdo0hIaGYuvWrcjIyEBFRQXee+89bN68GR9//DFiY2OxefNmO/zUyN1wDp2cml6vR3JyMkpKStC/f38MGTIEAHDs2DEcO3bMfI3r2tpaFBQUIDw8HOHh4XjssccAAM8++ywyMjIwZMgQRERE4IEHHgAAjB8/Hjt27MCLL74IABg3bpzkGu8+2frQoUPYvXs3jEYjysrKcOnSJfTt27fZ8mfPnsXFixcxefJkAI0Xk4qLi5PcPlETBjo5taY59Orqarz00kvYsWMHpk2bBlEUMXv2bEyaNKnZ8sXFxfdctlilUrV4eeO7WbOzUqvVYvDgwSgqKsKmTZuQmZmJoKAgLF68uMXblomiiCFDhmD9+vWS2yRqCadcyCUEBARg2bJl2LRpEwwGA4YOHYo9e/aY5551Oh3Ky8sBANeuXTNf6jQ7OxuPPfYYevXqhZKSEhQWFgIAsrKyMGjQoBbb6ty5c7vmtEVRxLZt21BWVoannnoKNTU18PPzQ0BAAG7cuGGeV//lNuPi4nD69GlzLXV1dbhy5YrEnwzRzzhCJ5fRr18/9O3bF9nZ2UhJScGlS5fMI/ROnTph3bp1UKvVePDBB/Hxxx9j+fLl6NmzJyZPngwfHx+sWbMG8+fPhyAIiI2NNU95/NL48eOxYsUK+Pr64r/+67/umUdfu3Yt3n33Xej1egwcOBDbtm2Dt7c3+vbti379+iExMRGRkZF49NFHzes8//zz+Nd//Vd0794dGRkZWLNmDRYuXGjeMbtgwQLzdBCRVLzaIilKcXEx/u3f/g0HDhyQuxQih+OUCxGRQnCETkSkEByhExEpBAOdiEghGOhERArBQCciUggGOhGRQvx/e/Nq/a7lHAMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sf.resample(df=df_income.loc['MSFT'], rule='D', method='ffill').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also interpolate between known values when doing the upsampling by setting the argument `method='linear'` instead of `'ffill'`. As noted above, this is a form of cheating, because we are using data before it was actually known. This should not be done in trading strategies, although it is still useful in some data-analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7927140da0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sf.resample(df=df_income.loc['MSFT'], rule='D', method='linear').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because resampling to daily data is a very common operation, a convenience function is provided for doing that, so we don't have to write the argument `rule='D'`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f792a51f518>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sf.resample_daily(df=df_income.loc['MSFT'], method='linear').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the helper-function for resampling a DataFrame with multiple tickers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f792a75f390>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resample annual Income Statements for multiple tickers.\n",
    "df_daily = sf.resample(df=df_income, rule='D', method='linear')\n",
    "\n",
    "# Plot.\n",
    "sns.lineplot(x=REPORT_DATE, y=REVENUE, hue=TICKER,\n",
    "             data=df_daily.reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also provide a custom method for the resampling function. For example, this method fills in the unknown values with quadratic interpolation between the known values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f792a7a6e48>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Custom method for the resampling.\n",
    "method = lambda x: x.interpolate(method='quadratic')\n",
    "\n",
    "# Do the resampling using the custom method.\n",
    "df_daily = sf.resample(df=df_income, rule='D', method=method)\n",
    "\n",
    "# Plot.\n",
    "sns.lineplot(x=REPORT_DATE, y=REVENUE, hue=TICKER,\n",
    "             data=df_daily.reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use the helper-function to downsample data. For example, we can downsample the share-prices from daily to 3-month data using averaging. We resample all columns in the DataFrame but only plot the ADJ_CLOSE which is the close-price adjusted for both stock-splits and dividends (aka. Total Return). We also normalize the time-series so they all start at 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f792a513f98>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Downsample to 3-month data-points using averaging.\n",
    "df_monthly = sf.resample(df=df_prices, rule='3M', method='mean')\n",
    "\n",
    "# Drop rows with any empty data.\n",
    "df_monthly = df_monthly.dropna(how='any')\n",
    "\n",
    "# Normalize so the time-series for each stock begins at 1.0\n",
    "_normalize = lambda df_grp: df_grp.div(df_grp.iloc[0])\n",
    "df_norm = df_monthly.groupby(TICKER).apply(_normalize)\n",
    "\n",
    "# Plot.\n",
    "sns.lineplot(x=DATE, y=ADJ_CLOSE, hue=TICKER,\n",
    "             data=df_norm.reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reindex\n",
    "\n",
    "Both of the above methods `resample` and `asfreq` only change the data *between* the start- and end-dates of the original data. For example, the annual Income Statements for ticker MSFT range between 2008-06-30 and 2019-06-30, so if we upsample to daily data, we still only get data-points between these two dates. At the time of this writing, we are already in 2019-11-xx (November), so there's more than 4 months of data-points we do not get when upsampling the Income Statements using `resample` and `asfreq`.\n",
    "\n",
    "We can instead use the `reindex` function to resample a DataFrame onto another index. This allows us to get new data-points beyond the original date-range of the data. For example, we can use this to calculate valuation ratios such as P/E or P/Sales for all dates where we have share-prices, using the last-known values from the Income Statements.\n",
    "\n",
    "However, there are several difficulties when using `reindex` for resampling, which we will demonstrate below.\n",
    "\n",
    "First let us show the annual Income Statements for ticker MSFT that we want to upsample to daily data-points until the most recent share-price date 2019-11-05. Note that we have all annual reports between 2008-06-30 and 2019-06-30, none of them are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Report Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>17681000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>5.843700e+10</td>\n",
       "      <td>14569000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>6.248400e+10</td>\n",
       "      <td>18760000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-06-30</td>\n",
       "      <td>6.994300e+10</td>\n",
       "      <td>23150000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-06-30</td>\n",
       "      <td>7.372300e+10</td>\n",
       "      <td>16978000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>7.784900e+10</td>\n",
       "      <td>21863000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>8.683300e+10</td>\n",
       "      <td>22074000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>9.358000e+10</td>\n",
       "      <td>12193000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>9.115400e+10</td>\n",
       "      <td>20539000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>9.657100e+10</td>\n",
       "      <td>25489000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>39240000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Revenue   Net Income\n",
       "Report Date                           \n",
       "2008-06-30   6.042000e+10  17681000000\n",
       "2009-06-30   5.843700e+10  14569000000\n",
       "2010-06-30   6.248400e+10  18760000000\n",
       "2011-06-30   6.994300e+10  23150000000\n",
       "2012-06-30   7.372300e+10  16978000000\n",
       "2013-06-30   7.784900e+10  21863000000\n",
       "2014-06-30   8.683300e+10  22074000000\n",
       "2015-06-30   9.358000e+10  12193000000\n",
       "2016-06-30   9.115400e+10  20539000000\n",
       "2017-06-30   9.657100e+10  25489000000\n",
       "2018-06-30   1.103600e+11  16571000000\n",
       "2019-06-30   1.258430e+11  39240000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income.loc['MSFT']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the index from the daily share-prices for ticker MSFT, which contains data-points for all trading-days (excluding weekends and holidays) between 2007-01-03 and 2019-11-05."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2007-01-03', '2007-01-04', '2007-01-05', '2007-01-08',\n",
       "               '2007-01-09', '2007-01-10', '2007-01-11', '2007-01-12',\n",
       "               '2007-01-16', '2007-01-17',\n",
       "               ...\n",
       "               '2019-10-23', '2019-10-24', '2019-10-25', '2019-10-28',\n",
       "               '2019-10-29', '2019-10-30', '2019-10-31', '2019-11-01',\n",
       "               '2019-11-04', '2019-11-05'],\n",
       "              dtype='datetime64[ns]', name='Date', length=3234, freq=None)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_index = df_prices.loc['MSFT'].index\n",
    "new_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then reindex the data for the annual Income Statements using the index of the daily share-prices. Because we haven't specified any filling-logic, the missing values are set to NaN for unknown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2007-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-01-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-01-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Revenue  Net Income\n",
       "Date                           \n",
       "2007-01-03      NaN         NaN\n",
       "2007-01-04      NaN         NaN\n",
       "2007-01-05      NaN         NaN\n",
       "2007-01-08      NaN         NaN\n",
       "2007-01-09      NaN         NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income2 = df_income.loc['MSFT'].reindex(index=new_index)\n",
    "df_income2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us try and drop all rows with NaN from the reindexed DataFrame. This shows us the dates that actually contain valid data. We might expect this to equal the original data from `df_income.loc['MSFT']` above, but if you look closely you can see that several rows are missing for the dates: 2012-06-30, 2013-06-30, 2018-06-30, and 2019-06-30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>1.768100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>5.843700e+10</td>\n",
       "      <td>1.456900e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>6.248400e+10</td>\n",
       "      <td>1.876000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-06-30</td>\n",
       "      <td>6.994300e+10</td>\n",
       "      <td>2.315000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>8.683300e+10</td>\n",
       "      <td>2.207400e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>9.358000e+10</td>\n",
       "      <td>1.219300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>9.115400e+10</td>\n",
       "      <td>2.053900e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>9.657100e+10</td>\n",
       "      <td>2.548900e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Revenue    Net Income\n",
       "Date                                  \n",
       "2008-06-30  6.042000e+10  1.768100e+10\n",
       "2009-06-30  5.843700e+10  1.456900e+10\n",
       "2010-06-30  6.248400e+10  1.876000e+10\n",
       "2011-06-30  6.994300e+10  2.315000e+10\n",
       "2014-06-30  8.683300e+10  2.207400e+10\n",
       "2015-06-30  9.358000e+10  1.219300e+10\n",
       "2016-06-30  9.115400e+10  2.053900e+10\n",
       "2017-06-30  9.657100e+10  2.548900e+10"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income2.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing dates are all in the weekends:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Saturday', 'Sunday', 'Saturday', 'Sunday'], dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of missing dates.\n",
    "dates_missing = ['2012-06-30', '2013-06-30', '2018-06-30']\n",
    "\n",
    "# Convert list of dates to Pandas index.\n",
    "index_missing = pd.DatetimeIndex(dates_missing)\n",
    "\n",
    "# Show the names of the days.\n",
    "index_missing.day_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The financial reports can easily have dates that fall in weekends, because their dates are usually just chosen as the last day in a given month, such as these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2012-06-30</td>\n",
       "      <td>7.372300e+10</td>\n",
       "      <td>16978000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>7.784900e+10</td>\n",
       "      <td>21863000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>16571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>39240000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Revenue   Net Income\n",
       "2012-06-30  7.372300e+10  16978000000\n",
       "2013-06-30  7.784900e+10  21863000000\n",
       "2018-06-30  1.103600e+11  16571000000\n",
       "2019-06-30  1.258430e+11  39240000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income.loc['MSFT'].loc[index_missing]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But the share-prices only have dates for valid trading-days, not weekends and holidays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"None of [DatetimeIndex(['2012-06-30', '2013-06-30', '2018-06-30', '2019-06-30'], dtype='datetime64[ns]', freq=None)] are in the [index]\"\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    df_prices.loc['MSFT'].loc[index_missing]\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The solution is to reindex with the union of the indices from the annual Income Statements and the Share-Prices, which retains all the original data-points from the Income Statements, even though their dates may not be present in the Share-Prices data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>1.768100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>5.843700e+10</td>\n",
       "      <td>1.456900e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>6.248400e+10</td>\n",
       "      <td>1.876000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-06-30</td>\n",
       "      <td>6.994300e+10</td>\n",
       "      <td>2.315000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-06-30</td>\n",
       "      <td>7.372300e+10</td>\n",
       "      <td>1.697800e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>7.784900e+10</td>\n",
       "      <td>2.186300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>8.683300e+10</td>\n",
       "      <td>2.207400e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>9.358000e+10</td>\n",
       "      <td>1.219300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>9.115400e+10</td>\n",
       "      <td>2.053900e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>9.657100e+10</td>\n",
       "      <td>2.548900e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>1.657100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Revenue    Net Income\n",
       "2008-06-30  6.042000e+10  1.768100e+10\n",
       "2009-06-30  5.843700e+10  1.456900e+10\n",
       "2010-06-30  6.248400e+10  1.876000e+10\n",
       "2011-06-30  6.994300e+10  2.315000e+10\n",
       "2012-06-30  7.372300e+10  1.697800e+10\n",
       "2013-06-30  7.784900e+10  2.186300e+10\n",
       "2014-06-30  8.683300e+10  2.207400e+10\n",
       "2015-06-30  9.358000e+10  1.219300e+10\n",
       "2016-06-30  9.115400e+10  2.053900e+10\n",
       "2017-06-30  9.657100e+10  2.548900e+10\n",
       "2018-06-30  1.103600e+11  1.657100e+10\n",
       "2019-06-30  1.258430e+11  3.924000e+10"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index for the annual Income Statements.\n",
    "index_src = df_income.loc['MSFT'].index\n",
    "\n",
    "# Index for the valid trading-days of the Share-Prices.\n",
    "index_target = df_prices.loc['MSFT'].index\n",
    "\n",
    "# Union of the two indices.\n",
    "new_union_index = index_src.union(index_target)\n",
    "\n",
    "# Reindex the annual Income Statements using the union-index.\n",
    "# The new data-points are not filled so they are NaN.\n",
    "df_income2 = df_income.loc['MSFT'].reindex(index=new_union_index)\n",
    "\n",
    "# Remove NaN to show that the original data is preserved.\n",
    "df_income2.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reindexing function also allows us to specify the filling-logic for the missing data-points. For example, we can use 'ffill' (forward-fill) to use the last-known values. Note that the annual data-points have been upsampled beyond their original end-point on 2019-06-30, all the way up to the end of the share-price data on 2019-11-05, exactly as we wanted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>1.768100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-01</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>1.768100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-02</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>1.768100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-03</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>1.768100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-07-07</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>1.768100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-04</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2863 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Revenue    Net Income\n",
       "2008-06-30  6.042000e+10  1.768100e+10\n",
       "2008-07-01  6.042000e+10  1.768100e+10\n",
       "2008-07-02  6.042000e+10  1.768100e+10\n",
       "2008-07-03  6.042000e+10  1.768100e+10\n",
       "2008-07-07  6.042000e+10  1.768100e+10\n",
       "...                  ...           ...\n",
       "2019-10-30  1.258430e+11  3.924000e+10\n",
       "2019-10-31  1.258430e+11  3.924000e+10\n",
       "2019-11-01  1.258430e+11  3.924000e+10\n",
       "2019-11-04  1.258430e+11  3.924000e+10\n",
       "2019-11-05  1.258430e+11  3.924000e+10\n",
       "\n",
       "[2863 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income.loc['MSFT'].reindex(index=new_union_index,\n",
    "                              method='ffill').dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reindex on MultiIndex\n",
    "\n",
    "Unlike the `asfreq` and `resample` functions, we can actually perform a `reindex` operation directly on a DataFrame with MultiIndex. Here we use the union of the indices from the DataFrames with Income Statements and Share-Prices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"13\" valign=\"top\">AAPL</td>\n",
       "      <td>2007-09-30</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3.495000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-09-30</td>\n",
       "      <td>3.749100e+10</td>\n",
       "      <td>6.119000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-09-30</td>\n",
       "      <td>4.290500e+10</td>\n",
       "      <td>8.235000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-09-30</td>\n",
       "      <td>6.522500e+10</td>\n",
       "      <td>1.401300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-09-30</td>\n",
       "      <td>1.082490e+11</td>\n",
       "      <td>2.592200e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-09-30</td>\n",
       "      <td>1.565080e+11</td>\n",
       "      <td>4.173300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-09-30</td>\n",
       "      <td>1.709100e+11</td>\n",
       "      <td>3.703700e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>1.827950e+11</td>\n",
       "      <td>3.951000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>2.337150e+11</td>\n",
       "      <td>5.339400e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-09-30</td>\n",
       "      <td>2.156390e+11</td>\n",
       "      <td>4.568700e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-09-30</td>\n",
       "      <td>2.292340e+11</td>\n",
       "      <td>4.835100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>2.655950e+11</td>\n",
       "      <td>5.953100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2.601740e+11</td>\n",
       "      <td>5.525600e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"12\" valign=\"top\">AMZN</td>\n",
       "      <td>2007-12-31</td>\n",
       "      <td>1.483500e+10</td>\n",
       "      <td>4.760000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>1.916600e+10</td>\n",
       "      <td>6.450000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-12-31</td>\n",
       "      <td>2.450900e+10</td>\n",
       "      <td>9.020000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-12-31</td>\n",
       "      <td>3.420400e+10</td>\n",
       "      <td>1.152000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-12-31</td>\n",
       "      <td>4.807700e+10</td>\n",
       "      <td>6.310000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>6.109300e+10</td>\n",
       "      <td>-3.900000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>7.445200e+10</td>\n",
       "      <td>2.740000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-12-31</td>\n",
       "      <td>8.898800e+10</td>\n",
       "      <td>-2.410000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>1.070060e+11</td>\n",
       "      <td>5.960000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>1.359870e+11</td>\n",
       "      <td>2.371000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>1.778660e+11</td>\n",
       "      <td>3.033000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2.328870e+11</td>\n",
       "      <td>1.007300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"12\" valign=\"top\">MSFT</td>\n",
       "      <td>2008-06-30</td>\n",
       "      <td>6.042000e+10</td>\n",
       "      <td>1.768100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2009-06-30</td>\n",
       "      <td>5.843700e+10</td>\n",
       "      <td>1.456900e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-06-30</td>\n",
       "      <td>6.248400e+10</td>\n",
       "      <td>1.876000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-06-30</td>\n",
       "      <td>6.994300e+10</td>\n",
       "      <td>2.315000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2012-06-30</td>\n",
       "      <td>7.372300e+10</td>\n",
       "      <td>1.697800e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>7.784900e+10</td>\n",
       "      <td>2.186300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>8.683300e+10</td>\n",
       "      <td>2.207400e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>9.358000e+10</td>\n",
       "      <td>1.219300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>9.115400e+10</td>\n",
       "      <td>2.053900e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>9.657100e+10</td>\n",
       "      <td>2.548900e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>1.657100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Revenue    Net Income\n",
       "Ticker Date                                  \n",
       "AAPL   2007-09-30  2.457800e+10  3.495000e+09\n",
       "       2008-09-30  3.749100e+10  6.119000e+09\n",
       "       2009-09-30  4.290500e+10  8.235000e+09\n",
       "       2010-09-30  6.522500e+10  1.401300e+10\n",
       "       2011-09-30  1.082490e+11  2.592200e+10\n",
       "       2012-09-30  1.565080e+11  4.173300e+10\n",
       "       2013-09-30  1.709100e+11  3.703700e+10\n",
       "       2014-09-30  1.827950e+11  3.951000e+10\n",
       "       2015-09-30  2.337150e+11  5.339400e+10\n",
       "       2016-09-30  2.156390e+11  4.568700e+10\n",
       "       2017-09-30  2.292340e+11  4.835100e+10\n",
       "       2018-09-30  2.655950e+11  5.953100e+10\n",
       "       2019-09-30  2.601740e+11  5.525600e+10\n",
       "AMZN   2007-12-31  1.483500e+10  4.760000e+08\n",
       "       2008-12-31  1.916600e+10  6.450000e+08\n",
       "       2009-12-31  2.450900e+10  9.020000e+08\n",
       "       2010-12-31  3.420400e+10  1.152000e+09\n",
       "       2011-12-31  4.807700e+10  6.310000e+08\n",
       "       2012-12-31  6.109300e+10 -3.900000e+07\n",
       "       2013-12-31  7.445200e+10  2.740000e+08\n",
       "       2014-12-31  8.898800e+10 -2.410000e+08\n",
       "       2015-12-31  1.070060e+11  5.960000e+08\n",
       "       2016-12-31  1.359870e+11  2.371000e+09\n",
       "       2017-12-31  1.778660e+11  3.033000e+09\n",
       "       2018-12-31  2.328870e+11  1.007300e+10\n",
       "MSFT   2008-06-30  6.042000e+10  1.768100e+10\n",
       "       2009-06-30  5.843700e+10  1.456900e+10\n",
       "       2010-06-30  6.248400e+10  1.876000e+10\n",
       "       2011-06-30  6.994300e+10  2.315000e+10\n",
       "       2012-06-30  7.372300e+10  1.697800e+10\n",
       "       2013-06-30  7.784900e+10  2.186300e+10\n",
       "       2014-06-30  8.683300e+10  2.207400e+10\n",
       "       2015-06-30  9.358000e+10  1.219300e+10\n",
       "       2016-06-30  9.115400e+10  2.053900e+10\n",
       "       2017-06-30  9.657100e+10  2.548900e+10\n",
       "       2018-06-30  1.103600e+11  1.657100e+10\n",
       "       2019-06-30  1.258430e+11  3.924000e+10"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index for the annual Income Statements.\n",
    "index_src = df_income.index\n",
    "\n",
    "# Index for the valid trading-days of the share-prices.\n",
    "index_target = df_prices.index\n",
    "\n",
    "# Union of the two indices.\n",
    "new_union_index = index_src.union(index_target)\n",
    "\n",
    "# Set the names of the new index, this is not set by union().\n",
    "new_union_index.names = index_target.names\n",
    "\n",
    "# Reindex the annual Income Statements using the union-index.\n",
    "# The new data-points are not filled so they are NaN.\n",
    "df_income2 = df_income.reindex(index=new_union_index)\n",
    "\n",
    "# Remove NaN to show that the original data is preserved.\n",
    "df_income2.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But if we use forward-fill on the missing data-points and plot the result, something has gone very wrong, because the operation does not respect the grouping between tickers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7929f21748>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reindex and forward-fill the annual Income Statements.\n",
    "df_income2 = df_income.reindex(index=new_union_index,\n",
    "                               method='ffill').dropna()\n",
    "\n",
    "# Plot.\n",
    "sns.lineplot(x=DATE, y=REVENUE, hue=TICKER,\n",
    "             data=df_income2.reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas' `reindex` function provides an argument `level` which supports DataFrames with a MultiIndex, but it is unclear from the brief documentation how it is supposed to work, and regardless of whether we use `level=TICKER` or `level=DATE` it raises the same exception:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fill method not supported if level passed\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    df_income2 = df_income.reindex(index=new_union_index,\n",
    "                                   method='ffill', level=TICKER)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to manually use the `groupby` function on the reindexed DataFrame to group them by Tickers, and then fill the missing NaN values on each of those sub-groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7929d6fba8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fEMnzaFXzmAG7iYhIlgy9G9LexG7uEYCbewRImodDybSbSF7tGCKSjGFtIgeXQ/7kOYDMYEBEAOzXTeTyuFAdEbkCmfVuOCF5dhNxzMBGBg4ciClTpuAvf/kLAECr1WLs2LEYOnQo1q1bhytXrmDZsmUoLi6GVqtFjx498Pbbb6OoqAiTJ09G7969DWnNmjULmzZtAgDk5eWhd+/eUCqViIuLw+LFix1SP3J98uzccEJ8Arlz8/HxwdmzZ6FWq+Hl5YX9+/cjPPyXjS/+/ve/4/bbb8eDDz4IADh9+rTh2E033YTMzEyj9O69914AQEJCAt577z0EBwfboRbUmRmeM5DZN1qnwwFkGjdunGHvghsXrgOAsrIyREREGP49aNAgexePqE32es7A9bFl4BS+zPsS/zn7H4uv0+v1UJpY5nBa/2mY0ndKu2lMnjwZb7zxBsaPH48zZ84gLS0NR48eBQDMnj0b//d//4cPP/wQt99+O1JTUw0th4KCAsNyFMOHD8fzzz9vcfmJrGWvPZBdnkxbBpIFg+LiYixZsgRXrlyBUqnEjBkzDF0kzYQQWLVqFfbu3QsvLy+88soriImJkapIkhs0aBCKioqQlZWF+Ph4o2NxcXHYtWsXvvnmG+zbtw/Tpk0zLHPdWjcRkb0JmQ58Oh0uVGdMpVLhmWeeQUxMDGpqapCWloYxY8YYLdO8b98+5OfnY8eOHTh+/DheeOGFFpu7WGpK3ylmfYv/tXorlrC+UUJCAlavXo33338fV69eNToWGBiI5ORkJCcnY/78+Th8+LCsgx+5GLYMbESe3USSjRmEhYUZbnR+fn7o06eP0ZLOAJCdnY2UlBQoFArExsaiurq6xQ5gcjN9+nQ8/vjjGDhwoNHr3333Herr6wEANTU1KCgoQLdu3RxRRKI2yewLrfNhN5FpRUVFyM3NxdChQ41eLy0tNRpUjYiIQGlpKcLCwkym1dDQgNzcXKPXNBqN4UbbUUIIq9Jovj4gIAAzZsxAfX09GhoaoNPpUF9fj5ycHKxcuRIqlQp6vR4pKSno378/Ll68CL1ebzJvvV4PtVptdtmEEC1+P3KkVqtdoh6AfOpSXte0z3ZJcQlyc1uuKCqXerRH6noEXLqE7gB+zsuDxte6+1J7bFkXyYNBbW0t0tPT8eyzz8LPz8/oWGvbOLY3rc3T0xPR0dFGr+Xm5lrdxWNtN1FOTk6L18aNG4dx48YBABYsWIAFCxa0OKdfv37YunWryXSbZyeZS6FQtPj9yFFubq5L1AOQT11KqtQAmlqs0dE3tTgul3q0R/J61B8BAPTrNwAIjJIuH1hWl/aChqRTSzUaDdLT05GcnIwJEya0OB4REYGSkl+WsS0pKWmzVUBE0jGsTSSv3g3nI9MBZMmCgRACy5YtQ58+fTB37txWz0lISMAXX3wBIQRycnLg7+/PYEDkIJxaaivyHECWrJvo6NGjyMzMxIABAwxz6DMyMnDp0iUATUsuxMfHY+/evUhMTIS3tzdeeumlDucnhOj0T0621u1GZC4+dGYjHEA2NmLECJw5c6bNcxQKhU0esPLy8kJ5eTlCQkI6bUAQQqC8vLzT1p+sZ69tL12fPFctdYknkCMjI1FUVITLly93OA2NRgN3dwftmWojXl5eUKlUji4GyZRM11dzPtzpzHHc3d2NVv3sCFeaKUFkDXndwpyQTLuJ5NWOISLJsavRVuT1e2QwICIA3OnMZtgyICI54x7ItiLPAWR5lZaIJCPTL7TOxzCALC8uMYBM1Bn8XHYNlXUaydIvrlIDYDCwmkyjKoMBkQyUVKlx55p9dsnL14O3BevIc44u/+pEMnBN3dQiWDi+H27rEyJZPp7uSgy/KUiy9DsFIc8xAwYDIhnQ6ptuMDHdu2Bs/1AHl4baxm4iIpKI7nowUCnldYNxOpp6+BfuBjQnpcvjUvNy9vL6WzEYEMlAczBwU8nrBuN0Tn2JyG+flT4fD3/AzVP6fGyIwYBIBrSGloG8+qGdjub6Dm5ztwG+Ei6X7xMMqOS11hmDAZEMGLqJZNYP7Xyu9+cH9Qa6cA/yG/FrBpEMaPVNDzJxzMBKMp3pYw/8jRDJwPVYwDEDq8lzpo89sJuIyEqFFXV45L0jqNNoWz2uadTA3aPYqjzqG9kysAlu2mASgwGRlU6XXMOZ0mtIGBSGQO+Wg4ZVVVUICAiwOh8/LzcM7tbF6nQIbBm0gsGAyEoaXdO39qfvGoSBEf4tjrvKxkkugS0DkzhmQGSl5mDgzv5859e8oihbBi0wGBBZqVHbHAz4cXJ+3MHHFL57iayk0TXdYDzc+HFyejJdXtoezH73Xrx4Ed9++y0AQK1Wo6amRrJCEclJo1YHAPBgy0AGOGZgilnv3k8++QTp6elYvnw5AKCkpAS///3vJS0YkVw0twzc2TJwfmwZmGTWu/df//oXNm3aBD8/PwBAr169UFFRIWnBiOSikQPIMsKWgSlmBQMPDw94eHgY/q3Vtv5wDVFnZJhNxEXknB9bBiaZ9ZzByJEj8c9//hNqtRr79+/Hv//9byQkJEhdNiJZ0Oj0cFMqoOTTwTLAloEpZgWDxYsX47PPPsOAAQPw8ccfIz4+Hvfcc4/UZSOy2uVrDfjXwQvQ6qSbUvhtXjlnEskFWwYmmRUMlEolZsyYgRkzZkhdHiKb2vpDMV7bdRZKBaCQ8AYw/KZAydImW2LLwBSzgkFCQkKrH6Ts7GybF4jIlpo3hTn23AQE+MhrsxGSAFsGJpkVDDZv3mz4ubGxEdu2bUNVVVWb1yxduhR79uxBSEgIsrKyWhw/ePAgHn/8cURGRgIAEhMTsXDhQkvKTtQuYfjwO7Yc5CzYMjDFrI7OoKAgw3/h4eF46KGHcODAgTavSU1NxTvvvNPmOSNGjEBmZiYyMzMZCEhSHNslAGwZtMGslsGPP/5o+Fmv1+PkyZOora1t85qRI0eiqKjIutIRWUl//cMv5XgByQlbBqaYFQxeeeWVXy5wc0OPHj3w2muvWZ15Tk4OpkyZgrCwMDz99NPo379/u9c0NDQgNzfX6rx/Ta1WS5KuvbEexkpLrwIAfjpzBt7ujpnxw7+J8wgpK0MYgNwzZwCl/Ffwt+XfxKzfxgcffGCTzG4UExOD3bt3w9fXF3v37sXvf/977Nixo93rPD09JVkb3lXWnGc9jIWW5gGoQPSgQfD2UFlfsA7g38SJlHUFAERHDwaUjnk/2JIlf5P2goZZwaCxsRHbt2/HxYsXjZ4+tqafv3lpCwCIj4/HihUrUFFRgeDg4A6nSfRrAs3dRA4uCDkHbm5jklnBYMGCBfD390dMTIzRshTWuHz5MkJDQ6FQKHDixAno9XoEBQXZJG2iZhwvJGN8Q5hiVjAoLS3F+vXrLUo4IyMDhw4dQmVlJcaNG4dFixYZWhWzZs3C9u3bsWnTJqhUKnh5eWHNmjUc5COba55aquA3QQL47aANZgWDYcOG4cyZMxg4cKDZCa9Zs6bN43PmzMGcOXPMTo+oI/jZJyPN215SC2YFg6NHj+I///kPevToYdRN9N///leyghHZwvUHkKFkNCAATaNIbCe2xqxg8Pbbb0tdDiJJGAaQHVwOchJCsJloglkTr3v06IHi4mIcOHAAPXr0gLe3N/R6NrfI+bGbiIwJ8KtB68wKBmvXrsU777yDt956CwCg0Wjw1FNPSVowIlsQfAKZbiSkW8pc7szqJtq5cye++OILTJs2DQAQHh7e7nIURM5AgK0C2ai/CpzbI+0g75Wf+IYwwaxg4O7uDoVCYfh2VVdXJ2mhiGxFCA4ey8aBN4G9r7R/npW0XqHgYuYtmRUMJk2ahOXLl6O6uhqffPIJNm/ezI1uSBb0QrCHWC40dYDKA5j/jaTZnLt0FeZPku88zAoG8+bNw/79++Hr64vz588jPT0dY8aMkbpsRFYTYMtAVhRKIGyQpFnoy+W92J5UzAoG7777Lu666y4GAJIdvRCcPCIbnOnjSGYFg5qaGsybNw8BAQFISkrCxIkTERoaKnXZiKzHWCAffAbAocyaWrpw4UJs2bIFy5cvR1lZGebMmYOHHnpI4qIRWY/dRETmsWi3j5CQEISGhiIwMBDl5eVSlYnIZvR6wS+bciHYTeRIZnUT/fvf/8a2bdtQUVGBiRMn4sUXX0S/fv2kLhuR1dgykJlO+rcqrinG8cvH2zxHqVBibI+x8HH3kaQMZgWDS5cu4dlnn5X/LkfU6XBqqZx03pbBy4dexteFX7d73gujX0DagDRJymBWMFi8eDGOHDmCzZs3Iy0tDRUVFaitrUVUVJQkhSKyFfY8yEgnHkCu09YhOjgar8SZfuhOqVCiZ5eekpXBrGCwdu1anDx5EufPn0daWpphbaKPPvpIsoIR2Qq7ieSi864bpNVr4evuiz6BfRxWBrMGkHfu3Ik333wT3t7eALg2EcmHXnAAWV465x9Lp9fBTWnWd3PJmBUMuDYRyRXXJpKRTvyAoFavhUqpcmgZOrw20T333CN12YisxgFkOem8Azw6oYObwrEtA65NRC6NS1jLSCfea0ArtA7vJjI79zFjxhgCgE6nw5dffokpU6ZIVjAiW2iaoMJoIBud9G+l1WuhUji2m6jNMYOamhqsW7cOK1euxP/+9z8IIfDhhx/izjvvxLZt2+xVRqIOE+wmkpFO3E2k1zn3mMFTTz2FgIAAxMbG4tNPP8X69euh0Wjwxhtv8AE0kgUOIMtIJ37OQCd0cFc6dsudNoNBUVER3nzzTQDAPffcg9tuuw1ff/01/Pz87FI4ImtxaqmcdN4xA41e4/BuojaDgZvbL4dVKhUiIyMZCEhWOm/Hgww56ePidZo6lNdLuzCnRqdx7m6i06dPY/jw4QCa+l4bGhowfPjwpn5YhQLff/+9XQpJ1FEcQJYZJ/xbzcyaifzqfMnz8XbzljyPtrQZDHJzuT0cSefHS1XYc+Zyq8cuX65E15Kfrc7jdEm1M95fqFXO2TIoV5djdLfRSO6bLGk+t3e/XdL02+PYia3Uqf1t51nsyi1t44xKm+QT15+78lmt6AhCf/wIKO0qXR6XcpyyZSCEQN/AvpIHA0djMCCH0en1GNIjAJ8tGN3i2OnTZzBo0ECb5OOutGgPJ2rN7j+h67k90ufTK076PCwkIDpFVyODATlM08YzgKdby4EzD5Wi1dfJQfQ61HWNhc+C9tfct4oT3nT1Qg+FE3Zf2RqDATmWE374qRVCDwEF0ElbWUqF69dbshouXboUo0ePxt13393qcSEEXnzxRSQmJiI5ORk//vijVEUhJ9WJl6KRn078QFhnaRlIFgxSU1PxzjvvmDy+b98+5OfnY8eOHfjTn/6EF154QaqikBNz/Y+YqxCQ8Hbh1PRC3ynGDCT7644cORIBAQEmj2dnZyMlJQUKhQKxsbGorq5GWVmZVMUhJ8SGgYx04r0GBESn6CZy2JhBaWkpIiIiDP+OiIhAaWkpwsLC2ryuoaFBkucf1Gq1SzxXIad61NbUQK3Rt1peOdWjPa5Ql551tdApPWRfD8Dyv4der0f5lXKnrLst31sOCwailQ5jc5pinp6ekiySl5ub6xKL78mpHj7fVkPfoG21vHKqR3tcoi77vVCjUThdPX6u/BmX61t/cNGUgqoC3BR+k9nnCwh07drV6eoOWPbeai9oOCwYREREoKSkxPDvkpKSdlsF5Ho6ac+D/AgBOHghtV+r09Thnv/eA63QWn7xGctO93f3tzwPmXFYMEhISMCHH36IpKQkHD9+HP7+/gwGRM5K6B1dghbUOjW0Qov7B9+PO2+60+zrLly4gJ49e5p9vlKhRExITEeKKCuSBYOMjAwcOnQIlZWVGDduHBYtWgSttimCz5o1C/Hx8di7dy8SExPh7e2Nl156SaqikJPiInKOcbriNJ7Y/QQadA3mX+RRAQEFFB//1uxLfN19sWHiBoT7hlteSDPo9DoAQK8uvTA8fLjZ13lXeCM63Pm6fBxNsmCwZs2aNo8rFAo8//zzUmVPRCacrTyLS7WXMKn3JPi5m7kk/akvoYEb3G9KMOv0CnUFsguykXc1T7pgIJqCgaP3DnYV/C2SwwhwS0pHaNQ1AgAybs1AhG9EO/wfr6wAABK6SURBVGdfd2IXrin84D96uVmn51flI7sgG/N3ze9oMc3m6B3CXAWDAZHcVZwDrpi/3Lem5AQAwD1/P2DuwKi6CvA2fxC1Z5eeWPqbpahssM3Ks6Z4KD0wLnKcpHl0FgwG5DCdeIUD2/r3TODKT2af3tjFHwgJgvvmeYDe/Ef/dF3M72dXKBS4L/o+s88nx2MwIHIiF6ov4MNTH0Jvyewd9zqg71CgW6xZp5++VgBU58HjwW2AysPsbEoqFQg0v1QkMwwG5DBNKxywaXCjree24qMzHyHYK9j8izwUgKgByo+bfcmwsGHwuGkUYMEyC+Ka8z2BS7bDYEDkROq19fBUeWLvzL3mX/TqQGBAAjDl79IVjFye66++RE5LoPMufmZKvbYeXm5ell0k9BZ9wydqDd9BRE5ErVPDS2VhMABH4sl6DAbkMJ14VWST1Fo1vN28LbuILQOyAb6DiJyIWqvuQDeRAMMqWYsDyOQwAoBSRvews5VnsT1/u8XXXblyBaHqUPPyuHoWXb27WpYBWwZkAwwGRGbacHIDss5ldWw67CXzT43rEWdh4hwzIOsxGJDjyGzV0qqGKgwOGYyP7/7Yousk39xGCLYMyGoMBuQSyurKkF+VL2kexbXFCPEOMf8CIYCP56D3pVPAbk/pCtZwDRwzIGsxGJDDCAibtQye2P0ETpaftElabRkcMtj8k3WNwOksoEsfILS/dIXqOhCImSZd+tQpMBiQS6hqrMKobqMw/xZpl0weFDzI/JOv7/Nd3esueKW+LFGJiGyDwYAkd77qPIprilu8XqM4BXelAt9ebLkoW0FVASovmr/8ca2mFhE+ERgZMdKqstqUE24VSWQKgwFJbvbW2bjWeK3lgevT6efvMnGhhZuWB3o62Zqa14OB4OAuyQCDAUlKCIFrjdeQ0i8Faf3TjI49+58f4OmmxPPJLTcbz8/PR69evSzKy6IuHLto3iuAg7vk/BgMSFJavRYAEOUfhdgw4/X2vfV18BaqFq8DgGe5J6LDZL5peXM3kYymz1LnxWBArdr702Xs++my1elohLopvTMVKC04ZXSssKIOAyPM30pRdq4PIAuu+kIywGBArXp910/IKbwKHw8r3yLKOih6AccLanC8urDF4SE9AqxL35mxZUAywmDQyX1d8DXK1eUtXq9U/YzoASrMHdPbqvRrNbV49QjwXNIQzBw00aq0ZEdwzIDkg8GgEyurK0P61+mtH7w+02fFd7bJK8I3wjYJ2VLBQaDe/OmrFmuobvo/ZxORDDAYdMCZijP48+E/Q6PTSJpPqHcoVo9bDXeVuyTpN0/3fO625xAfGW907MGNhxDm74m/TB9qdT7uKnfL9vS1h8oLwIYJdslK5+5nl3yIrMFg0AEqhQpeKi+oFCrJ8qhQV2BXwS7sKdqD7r7dzb7ufM156K+Y97BTXlUeACDcJxzhvuFGx5S6QHgqvFu87jKav7UnrgR6WbpKqAVUHqiuUKCHdDkQ2QSDQQf0C+qHN+58Q9I8jpUdwwPbHkDGngzLLz7V/ik3CvRq+bCWXghZ7TVgMV1j0/9DBwA9hkubV2WutOkT2QCDgZOK7RqLDRM3oE5TZ9F1hYWFiIqKMvt8H3cf3BJ6S4vXhQCUrjwLRtf0/AOU0nTBEckNg4GTUigUHVpnJ7cmF9FR1j+s1dQycOFgoL8+3qPiR4AI4B7IZIJeCNeeHt88+K/ycGw5iJyEpMFg3759mDhxIhITE/HWW2+1OP7555/jtttuw9SpUzF16lR8+umnUhaHLODy3UR6dhMR3UiyNrJOp8PKlSuxceNGhIeHY/r06UhISEC/fv2Mzps8eTKWL18uVTGog1x/AJndREQ3kqxlcOLECfTs2RNRUVHw8PBAUlISsrOzpcqObEzv8i2D68GALQMiABK2DEpLSxER8ctTp+Hh4Thx4kSL83bs2IHDhw+jd+/eWLp0Kbp169Zmug0NDcjNtf1UPbVaLUm69marejQ0NqK6usphv5OG2mqc/uGYZOl3KTyL7gDyLhSisVK650UAvrecjavUA7BtXSQLBsKwLssvfr3f7fjx43H33XfDw8MDmzZtwtNPP43333+/zXQ9PT0RHW37pY1zc3MlSdfebFUPN7diBAUGOuZ3cv4biE+mQiF0kmfVd2AMEHiTpHnwveVcXKUegGV1aS9oSBYMIiIiUFJSYvh3aWkpwsLCjM4JCgoy/Dxjxgy8+uqrUhWHLOTQqaVXC5oCwZg/AN5B7Z/fUb6hQID5z2QQuTLJgsGQIUOQn5+PwsJChIeHY8uWLfjrX/9qdE5ZWZkhQOzevRt9+/aVqjhkIb0QUDpq4nHz0s8j50n+rZ2ImkgWDNzc3LB8+XI88sgj0Ol0SEtLQ//+/fH666/j5ptvxh133IEPPvgAu3fvhkqlQkBAAF5++WWpikMW0ouW3Xr2c72Lkat9EtmNpPPq4uPjER9vvBrmE088Yfj5ySefxJNPPillEVzSAxsO4dD5lnsQAIDQCyiU+VbnodbooXJUMGhuGXAfACK74SRrGTp2oRL9w/xxe9+QFsfKy8sREtLydYspgLThkdan0xGCLQMie2MwkKEGrR5j+oXimUmDWhxziZkShu0iGQyI7IWfNpnR6wUadXp4urnyn665ZcBuIiJ7YcvAhoQQWPHfUyiqrJc0DwDwcpf2QSmHYjcRkd0xGNhQtVqLd7/NR3gXT4T4ekqWzy2RARjVx8m2kbQldhMR2R2DgQ3p9E3faBfE98VDY3o7uDQSEQKovQzoJXw6WF0lXdpE1CoGAxtqDgYqV17u88h6YIv004EFFFBwrwEiu2EwsKFfgoELd29UX2rqvklaI2k2hdV63OTpJ2keRPQLBgMb0onmYODggkhJ6AGlGzBirqTZ1LrIqpJEcuHKty270+k6QctA6DmwS+SC+Km2oU7RMtDrGAyIXBA/1Tak0zdNiXTtloFgMCByQfxU25Du+vR4hy3wZg9CDyhc+IE3ok6q0wwg1zRo8f53+ahraH1+/JUrFQgtOGNVHldqGgA4cGrp5Z8QnPsBUB7W/rkddel7LhNB5II6TTAoqVLjzT15qGtsPRgIIaBQWP+wk5+nG24K9rE6nQ755lWEn/hY+ny6D5M+DyKyq04TDPqF+eGHFyaaPO4Sq31q1Wjw7wnPRQekzcdNuqU2iMgxOk0w6BT0OgilO+DhoJYJEckWg4G91FwGrlg3JtF+HmUQSg7uEpHlGAzsZfPDwPl9kmejDxsheR5E5HoYDOylvhKI/A1wx3OSZnPxqgoDJM2BiFwRg4G96LRAUATQe5y02XBNHyLqAAYDAPjPAvQ5/x2wW8JZMuV5QPhg6dInIrICgwEAhPRFQ3kJPLv4S5dH14HAsDnSpU9EZAUGAwAYtxgXuyahi9yfMyAi6iCuTURERAwGRETEYEBERGAwICIiMBgQEREYDIiICAwGREQEBgMiIgKgEEIIRxfCEjk5OfD05OYqRESWaGhoQGxsrMnjsgsGRERke+wmIiIiBgMiImIwICIiMBgQEREYDIiICAwGREQEFw4GxcXFuP/++zFp0iQkJSXhvffeAwBcvXoVc+fOxYQJEzB37lxUVVUBAIQQePHFF5GYmIjk5GT8+OOPhrRWr16NpKQkTJo0CS+++CLsORvX0nrk5eVh5syZuPnmm7F+/XqjtPbt24eJEyciMTERb731lt3qYOu6mEpHbvVoptPpkJKSgvnz58u2HtXV1UhPT8ddd92FSZMm4dixY7Kty7vvvoukpCTcfffdyMjIQENDg9PW48svv0RycjKSk5Nx77334vTp04a0LP68CxdVWloqTp48KYQQ4tq1a2LChAni7Nmz4s9//rNYt26dEEKIdevWidWrVwshhNizZ4+YN2+e0Ov14tixY2L69OlCCCGOHj0qZs6cKbRardBqtWLGjBniwIEDTluPK1euiOPHj4s1a9aId955x5COVqsVd9xxhygoKBANDQ0iOTlZnD171m71sGVdTKUjt3o027Bhg8jIyBCPPvqo3eoghG3rsWTJEvHJJ58IIYRoaGgQVVVVdqyJ7epSUlIixo8fL+rr64UQQqSnp4vNmzc7bT2OHj0qrl69KoRouoc137c68nl32ZZBWFgYYmJiAAB+fn7o06cPSktLkZ2djZSUFABASkoKdu3aBQCG1xUKBWJjY1FdXY2ysjIoFAo0NjZCo9EY/h8aGuq09QgJCcEtt9wCNzfjHU1PnDiBnj17IioqCh4eHkhKSkJ2drbd6mHLuphKR271AICSkhLs2bMH06dPt1v5m9mqHjU1NTh8+LChDh4eHujSpYsda2Lbv4lOp4NarYZWq4VarUZYWJjT1mP48OEICAgAAMTGxqKkpARAxz7vLhsMblRUVITc3FwMHToU5eXlhj9uWFgYKioqAAClpaWIiIgwXBMREYHS0lIMGzYMo0aNwtixYzF27FjExcWhb9++TlsPU35dv/DwcLveQH/NmrqYSscRrK3HSy+9hKeeegpKpWM/itbUo7CwEMHBwVi6dClSUlKwbNky1NXV2aPYrbKmLuHh4Xj44Ycxfvx4jB07Fn5+fhg7dqw9it2CpfX47LPPMG7cOAAd+7y7fDCora1Feno6nn32Wfj5+Zk8T7QyDqBQKHDhwgXk5eVh79692LdvHw4cOIDDhw9LWeRWmVsPU0zVzxGsrYut03FU/l9//TWCg4Nx8803S1A681lbD61Wi1OnTmHWrFn44osv4O3t7ZAxKcD6ulRVVSE7OxvZ2dn45ptvUF9fj8zMTAlK2jZL63HgwAF89tlnWLx4MYCOfd5dOhhoNBqkp6cjOTkZEyZMANDUPCwrKwMAlJWVITg4GEBTS6C5iQU0Nd/DwsKwc+dODB06FL6+vvD19UVcXBxycnKcth6m/Lp+paWldm3+NrNFXUylY0+2qMf333+P3bt3IyEhARkZGThw4IDhw2wvtnpvRUREGFpnd911F06dOiVtwVthi7p8++23iIyMRHBwMNzd3TFhwgS7D4ZbWo/Tp0/jj3/8I9544w0EBQUB6Njn3WWDgRACy5YtQ58+fTB37lzD6wkJCfjiiy8AAF988QXuuOMOo9eFEMjJyYG/vz/CwsLQvXt3HD58GFqtFhqNBocPH7ZrN5Gl9TBlyJAhyM/PR2FhIRobG7FlyxYkJCRIWvZfs1VdTKVjL7aqx5NPPol9+/Zh9+7dWLNmDW677Ta8+uqrkpb9RraqR9euXREREYFz584BAL777ju7d6Xaqi7du3fH8ePHUV9fDyGE3etiaT0uXbqERYsWYfXq1ejdu7fh/I583l121dIjR45g9uzZGDBggKE/NiMjA7fccgv+8Ic/oLi4GN26dcPrr7+OwMBACCGwcuVKfPPNN/D29sZLL72EIUOGQKfTYcWKFTh8+DAUCgXi4uKwdOlSp63H5cuXkZaWhpqaGiiVSvj4+GDr1q3w8/PD3r178dJLL0Gn0yEtLQ0LFiywWz1sWZfTp0+3mk58fLys6nFj8//gwYPYsGED1q1bZ5c62Loeubm5WLZsGTQaDaKiovDyyy8bBjblVpe///3v2Lp1K9zc3BAdHY1Vq1bBw8PDKeuxbNky7NixA927dwcAqFQqfP755wBg8efdZYMBERGZz2W7iYiIyHwMBkRExGBAREQMBkREBAYDIiIC0HJhDiIyEh0djQEDBkCr1UKlUmHatGl48MEH21xCoqioCMeOHUNycrIdS0rUcQwGRO3w8vIyLElQXl6OJ598EteuXUN6errJay5evIisrCwGA5INPmdA1I5hw4YZLUlQWFiI6dOn48CBA7h48SKWLFmC+vp6AMBzzz2H4cOHY8aMGcjLy0NkZCSmTZuG+++/H6+++ioOHTqExsZGzJ49G/fee6+jqkTUAlsGRBaKioqCXq9HeXk5QkJCsHHjRnh6eiI/Px8ZGRn4/PPP8eSTTxo9Ufzxxx/D398fmzdvRmNjI+69916MGTMGUVFRDq4NURMGA6IOaG5Qa7VarFy5EqdPn4ZSqUR+fn6r5+/fvx9nzpzB9u3bAQDXrl3DhQsXGAzIaTAYEFmosLAQKpUKISEhWLt2LUJDQ5GZmQm9Xo9bbrml1WuEEPjjH/+IuLg4O5eWyDycWkpkgYqKCjz//POYPXs2FAoFrl27hq5du0KpVCIzMxM6nQ4A4Ovri9raWsN1Y8eOxaZNm6DRaAAA58+fd+gGMES/xpYBUTvUajWmTp1qmFo6depUw/LC9913HxYtWoSvvvoKo0aNgo+PDwBg4MCBUKlUmDJlClJTU/HAAw/g4sWLSE1NhRACQUFBeOONNxxZLSIjnE1ERETsJiIiIgYDIiICgwEREYHBgIiIwGBARERgMCAiIjAYEBERgP8HjVLKEUp/ScEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reindex the annual Income Statements using the union-index.\n",
    "# The new data-points are not filled so they are NaN.\n",
    "df_income2 = df_income.reindex(index=new_union_index)\n",
    "\n",
    "# Split the DataFrame into sub-groups and apply the ffill()\n",
    "# function on each of those sub-groups, and then glue the\n",
    "# results back together into a single DataFrame.\n",
    "df_income3 = df_income2.groupby(TICKER).ffill()\n",
    "\n",
    "# Remove empty rows.\n",
    "df_income3.dropna(inplace=True)\n",
    "\n",
    "# Plot.\n",
    "sns.lineplot(x=DATE, y=REVENUE, hue=TICKER,\n",
    "             data=df_income3.reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reindex Helper-Function\n",
    "\n",
    "The above procedure for reindexing DataFrames using Pandas' native functions, is quite complicated and confusing. We have therefore provided a helper-function in the simfin package.\n",
    "\n",
    "For example, we can reindex the annual Income Statements to match the index of the daily share-prices using forward-fill of missing values. Note how the resulting DataFrame has values for the last dates of the target DataFrame with share-prices, exactly as we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">AAPL</td>\n",
       "      <td>2007-10-01</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3.495000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-02</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3.495000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-03</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3.495000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-04</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3.495000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2007-10-05</td>\n",
       "      <td>2.457800e+10</td>\n",
       "      <td>3.495000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">MSFT</td>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-04</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8890 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Revenue    Net Income\n",
       "Ticker Date                                  \n",
       "AAPL   2007-10-01  2.457800e+10  3.495000e+09\n",
       "       2007-10-02  2.457800e+10  3.495000e+09\n",
       "       2007-10-03  2.457800e+10  3.495000e+09\n",
       "       2007-10-04  2.457800e+10  3.495000e+09\n",
       "       2007-10-05  2.457800e+10  3.495000e+09\n",
       "...                         ...           ...\n",
       "MSFT   2019-10-30  1.258430e+11  3.924000e+10\n",
       "       2019-10-31  1.258430e+11  3.924000e+10\n",
       "       2019-11-01  1.258430e+11  3.924000e+10\n",
       "       2019-11-04  1.258430e+11  3.924000e+10\n",
       "       2019-11-05  1.258430e+11  3.924000e+10\n",
       "\n",
       "[8890 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_daily = sf.reindex(df_src=df_income, df_target=df_prices,\n",
    "                      group_index=TICKER, method='ffill').dropna()\n",
    "df_daily"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the reindexed DataFrame. Because we have used 'forward-fill' of the missing values, it looks like \"staircase\" patterns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7929dd7d30>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=DATE, y=REVENUE, hue=TICKER,\n",
    "             data=df_daily.reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example of reindexing with linear interpolation. As noted further above, this is a form of cheating called \"look-ahead bias\" because we are interpolating between last-known and future values to fill in the missing values. This should be avoided when making trading-strategies, but may still be useful for some forms of analysis.\n",
    "\n",
    "Note that the final segment after the last-known values just uses forward-fill."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7929b34da0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reindex using linear interpolation.\n",
    "df_daily = sf.reindex(df_src=df_income, df_target=df_prices,\n",
    "                      group_index=TICKER, method='linear').dropna()\n",
    "\n",
    "# Plot.\n",
    "sns.lineplot(x=DATE, y=REVENUE, hue=TICKER,\n",
    "             data=df_daily.reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use a custom filling-method with the reindexing, e.g. quadratic interpolation.\n",
    "\n",
    "Note that the final segment beyond the last-known values is missing when using this filling-method, unlike the linear interpolation above, which just used forward-fill for the final segment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7928a8ab38>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LdRjp3cuvkDPp+xOkF2va1j/Es+lOfvwrqC6Ch94GI5xNLRKDltyq7LaNjQ1BQUGcP38egB07djB06NCbHmPx4sVERUXRt2/f+seefPJJVq5ciVKp1G0DhGZHkiRe33yeU9eK+WRiJ/qFeNz1MWytzPn+iUiCPB14evVZErPLdBDp3UsvquLR706QXVrDimk96Bt89227Z5X5cOwraD8G/Ls13Xm1yPT6GOLWQ+yau97NSq0Cs1tcPneJgc6Tbrv/zcpuOztrZlMOGzaM3377DU9PT8zMzPDy8iI/P7/B/nv27CExMZGNGzc2eNzX15euXbuydetWBg4ceNftEoRb2Xg2i82x2bz0UAijOvnd83GcbS1ZMa0H474+ytTlp9n8bB9aujdB5+4txGWWMmPFaVSSxOoZPegW2MTVUg9+BEo5PLioac+rReKKQUu2b9/O8OHDgT/Lbv+hf//+HD16lG3btjFs2LAb9s3Ly+O9997j008/xcrqxjrwzzzzDEuXLkWSJN01QGhW0gqreGtrEr3buDMn+vbL6TaGj7MNq2b0QKlWM2XZSfLL5VqI8u7tSsrlse+PY29tweZn+zR9Uii6oul07jYV3IOa9txaZHpXDJ0n3fHT/c3U6aDs9uTJkwGwsrIiPDyc5cuXs23bNn7//ff6fSVJYv78+cyaNatB38NfBQYGEhYWxo4dO+4pPkH4K0mSeH3LeSzMZfzr0c6Ya2mlsmAvR5Y+2Z0pS08y8bvjrH2qF/4u97dcbmMpVWo+23OJrw9coVMLF5Y+GVk/zLZJ7XsHzK0gan7Tn1uLxBWDFvxRdvv3339n//79HDx4kICAAPLy/lxEfPr06cybN++GFeqWLl2KtbV1/RoNt/LMM8+wbNkyncQvNC+bzmVz7EoRC4a2w8fZRqvH7hboyuoZPSiqqmPit8e5WlCp1ePfTGFlLU8sO8XXB64wqUdLfprVSz9JIeMEXNgKfV8ER++mP78Wmd4Vgx5s376dp556qsFjgwcP5ttvv63/OSQkhJCQGy/ZP//8c3x8fOpXuQNwcnK6oWJrSEgI7du358KFC1qOXmhOyqoVvLf9ApGBrkzSUfG4boFurH+qF08sO8WY/xzly8ldiWqrmxFBOxOv88aWRCprlXz8SEcmRLbQyXnuSK2GXa+Doy/0maOfGLRIJAYtuJuy2wDjxo1j3LhxACQmJt7yuB9++GGDn7/66qv7iFIQ4OsDqZTVKHh3dAedLnbfwd+Zrc/35alVZ5i2/BQvPtiW5wYGYWmunZsUhZW1/HPbBbbG5RDh78ynEzvRtqkmrt1M0mbIPgujvwYre/3FoSUiMQhCM5FdWsPyY2mM7eJPez8nnZ+vhZsdm5/rw+ubz/OvvZfYk5zLh+M63tfaB3VKNSuPpfHFvsvUKFS8/JB2E849UdTA3rfBpyN0uvv+TUMkEoMgNBP/2nMJgFcGhzbZOe2sLPj8sS4MCffhH78kMuLLI4zp7Mfs6GCCvRr/Cb9OqWZLbBZfH7hCelE1A0M9+ceI9gR5GkDZ7BPfQFkmjPkazEyj21YkBkFoBq4WVLL5XBYz+rVuspFCfzU0wpc+wR58d/AKy45e45e4HHq2dmNER1/6h3gS6G53w3rLlbVK4jNL2XMhj20JORRW1tHB34nlU7szsJ1Xk7fhpioL4PBnEDpMU/7CRIjEIAjNwHcHr2JpbsasAfobW+9sa8lrD7djer/WbDyTxU+nM3hzaxIA9lbmtHCzw87KHLWk6UPILq1BksDKwoyHwryYGNmCqLaeNyQQvTrwPihrYNC7+o5Eq0RiEAQTd72shs2xWTzWvSWejnoYxvk3Hg7WPPtAEM9EtSG9qJqjVwq5nFdJVkk1tUo1kgSt3O0Y3zWALi1d6BboaphrJ+RfhLMroPtM8Lj/SYKGRCQGQTBxPxy+hlqCWQPa6DuUBmQyGa087GnlYaSjeHb/A6wcIWqBviPROpEYtCQ0NJRRo0bx8ccfA6BUKunXrx+dOnXiu+++o7CwkDfeeIPr16+jVCrx9/dnyZIlZGVlMWzYMFq3bl1/rEmTJrF+/XoArly5QuvWrTEzM6N///7MmzdPL+0TjFNpdR3rT2UwqpMfLdz0V7/I5FzaBal7YPB7YO+u72i0TiQGLbGzs+Py5cvI5XJsbGw4evQo3t5/zn784osv6NOnD08++SQAFy9erH+uZcuWbN26tcHxHnvsMQCio6NZuXIlbm5NXPNFMAmrj6dTXafi6SjDulowaspa2LkAPNpCj6f1HY1OmMbYKgMxYMCA+rUX/lpUDyA/Px8fH5/6n9u1a9fU4QnNjFyhYsWxNAaGetLOR/fzFpqN4/+B4qvw8IdgcWPRS1NgclcMv175lS2Xt9z1fmq1GrNbjEEeGzKWUUGj7niMYcOG8fXXXzNw4EBSUlIYP348Z8+eBeDxxx/n5ZdfZs2aNfTp04dx48bVX1FkZGTUl8To2rUrb7311l3HLwh/t+lcFkVVdXodiWRyynPg0CcQOhyCjW9ltsYyucSgT+3atSMrK4tt27YRFRXV4Ln+/fuzd+9eDh8+zKFDhxg7dmx9ae6b3UoShPuhUkssOXSVTgHO9GojbkNqzZ63QK2EIYv1HYlOmVxiGBU0qlGf7v+u5j7Kbv9VdHQ0H330EatWraK0tLTBcy4uLowcOZKRI0fy9NNPc/r0acLDw+/7nILwdzsTc0krqubrx7sa1rh/Y5Z+HM5vgAGvglvrO29vxHTWx3D9+nWmTJnC0KFDGT58OCtXrrxhG0mSeO+99xg0aBAjR44kKSlJV+E0mUceeYTnnnuO0NCGZQeOHz9OTU0NAJWVlWRkZODr2/j1dQWhsdRqiS/2XaaNpz1Dwn3uvINwZ2oV7HgVnAKg38v6jkbndHbFYG5uzoIFCwgPD6eyspLx48fTt2/fBovRHDp0iLS0NHbv3k18fDxvv/32DUtbGhsfH5/6kUd/lZSUxD//+U/Mzc2RJIkJEybQsWNHsrKy9BClYMp+S7xOSl4FX0zqorVFeJq9sysg9zw8stwkqqfeic4Sg5eXF15emnomDg4OtGnThry8vAaJYd++fYwZMwaZTEbnzp0pLy8nPz+/fj9jEhsbe8NjPXv2pGfPngDMnDmTmTNn3rBNQEBAg2VA/27//v3aC1IweSq1xOd7L9PW24HhEeKKVCuqimD/P6FVfwgfq+9omkST9DFkZWWRnJxMp06dGjyel5fXYAinj48PeXl5t00MtbW1JCcnN3hMoVDU36a5V5Ik3fcxDIFCoUClUt3wOzJGcrncJNoBTdeW369WkppfyetRXlxKuXjnHe5Bc3tdfE+9h7O8nGuhz1B7UTe/0/ul7ddE54mhqqqKF154gddffx0Hh4Ylcm+2uP2dOsqsra0JCwtr8FhycvJ9dxxrq/NZ3ywtLbG0tLzhd2SMkpOTTaId0DRtkStUzPrvQcJ8nZg5JFJnC/E0q9cl7Shc2wZ9X6JN7xFNF9hdasxrcjeJQ6cT3BQKBS+88AIjR45k8ODBNzzv4+NDbm5u/c+5ubn3fBvpZkmmuRG/g+ZtxbE0MotreGNYmE5XZ2s2lHWwfS44t4So1/QdTZPSWWKQJIk33niDNm3aMG3atJtuEx0dzS+//IIkScTFxeHo6HhPicHGxoaioqJm/cYoSRJFRUXY2Gh3cXfBOBRW1vKf/ak82M6LfiEe+g7HNBz/CgouwrCPm0WH81/p7FbS2bNn2bp1K23btq2f1Tt37lxycnIATaG4qKgoDh48yKBBg7C1teX999+/p3MFBASQlZVFQUHBPcerUCiwtDTA0r53wcbGhoCAAFJTU/UditDE/rXnEtUKFQuHmcYtHr0rSYODH0G7ERD6sL6jaXI6SwyRkZGkpKTcdhuZTKaV8g+WlpYNqpPeC1O6byo0L/GZpaw7lcGTvVsR7GUAS10aO0mC314FmRkM/T99R6MXooieIBgxpUrNws3n8XSwZu7gtvoOxzQk/xcu74aBr4NzgL6j0QuTK4khCM3JsqPXuHC9nG9juuJkiKucGZvaCtgxH7wjoOcz+o5Gb0RiEAQjlZpfyWd7LvFQmLcofaEte9+BiuswcRWYN9+3R3ErSRCMUK1SxYs/xmJrac7isR1EoTxtSD8Gp5dorhRadNd3NHrVfFOiIBixT3dfIimnnCVPROLtJIYo3zdFDfw6B1xaQvQ/9B2N3onEIAhGZs+FPL4/dJXHe7ZkUHvvO+8g3NnB/4OiVJjyC1iLkV3iVpIgGJFLeRW89GMsEf7OvDmivb7DMQ05cXD0C+gSA0ED9R2NQRCJQRCMREFFLTNXnsHWyoLvn+iGjaW5vkMyfmol/Dob7D1g8Hv6jsZgiFtJgmAESqvrmLL0JAUVtax9qie+zsZf8NEQuF9cq1ln4dE1YOuq73AMhrhiEAQDV1pdx5PLTnG1oIolT0TStaV4A9OK/It4JC2F9qMhbKS+ozEo4opBEAxYVkk1U5efJqNYs36zKJCnJSoFbJmF2tIOs2Gf6DsagyMSgyAYqN8v5jN3QxxKtcTq6T3o2cZd3yGZjkOfwPV4cvu8T4CD8a0YqWsiMQiCgSmpquPj3SmsO5lBmK8T/5nchTaeYgil1mSfhUMfQ8dHqWgRre9oDJJIDIJgIPLK5aw+ns7qE+lU1iqZ3rc1rz0cKkYfaZOiBrY8Aw7eMPQjSLuu74gMkkgMgtBEVGqJosparpfJyS2Xk1cuJ/d/318tqCI+qxSAQWHezB3clnY+TnqO2ATt+ycUXoKYzWDrAojEcDMiMQiCFlXIFVwtqOJKQSVXCipJL6rmepmczMIKimuuoVQ3XGXQwkyGl6M1/q62vPxQW0Z18qOVR/NaLazJpB2BE19D5AwIflDf0Rg0kRgE4R7lV8g5n1VGQlYZ57PLuJBTTm65vP55czMZAa62+DnbEuFtQ7tAb3ycbfF1ssHH2QZvJxvc7a3E+sxNQV4GvzwLrq1g8D/1HY3BE4lBEBpBkiSuFFRx8loRJ64Wc/pacX0SMJNBsJcDvYPcCfZyINjLgSBPB1q62WFloZkqpFkhsJ0+m9B8SRJsmwtl2TBtR7Nbv/leiMQgCLdQUFHLgZR8Dl4q4MTVYgorawHwdrKmR2t3OrdwoWOAM+19nbC3Fv+VDFb8j5D4Mwx8A1r21Hc0RkH8NQvCXxRV1rL5XDbbEnKIzyoDwNPRmr7B7vRqo/lq5W4n1j8wFkVX4Ld5ENgX+r+i72iMRqMTQ3Z2Nunp6fTp0we5XI5SqcTBQYytFkxDRlE1X+y/zNa4bBQqiU4Bzswb3JYHQr0I93MSicAYKetg0wwws4Bx34OZGPbbWI1KDBs2bOCnn36irKyMvXv3kpuby1tvvcXKlSt1HZ8g6FRZtYKPd1/kx1OZmJvJmNyjJY/3CqStt6O+QxPu1+/vQU4sTFwNzgH6jsaoNCoxrF27lo0bNzJx4kQAWrVqRXFxsU4DEwRd255wnbd+TaKkuo7JPVoyOzpYrIZmKq78Dkf/Dd2mQvtR+o7G6DQqMVhZWWFlZVX/s1Kp1FlAgqBrcoWKt39N4sfTmXQMcGbFtO508HfWd1iCtlTkwZanwSMUhnyg72iMUqMSQ/fu3fn222+Ry+UcPXqUdevWER0taowIxiezuJqnVp3hYm4Fzz0QxNxBbbEwF9XnTYZKqelXkJfDlC1gZafviIxSoxLDvHnz+Pnnn2nbti0//fQTUVFRTJgwQdexCYJWJWSVMn3FaRQqiRXTuvNAqKiqaXIOvA9ph2HMt+Adru9ojFajEoOZmRkTJ06s72MQBGPze0o+z605h7uDFT/O6k6wl+hcNjmXdsHhT6HrE9B5kr6jMWqNSgzR0dE3Ha63b98+rQckCNr2+8V8nl59lhBvB5ZP646Xo+hgNjkl6bB5FvhEaKqmCvelUYlh06ZN9d/X1dWxY8cOysrKdBaUIGjLwUsFPL3mLG19HFg7oxfOdpb6DknQNmUtbHxSU/pi4iqwFOth369G9bq5urrWf3l7ezN16lROnDih69gE4b6cSStm1qozBHs6sGZGT5EUTNXOBZr5CmP+A25t9B2NSWjUFUNSUlL992q1msTERKqqqm67z8KFC0cYLxMAACAASURBVDlw4ADu7u5s27bthudPnjzJc889R0CAZuLJoEGDmD179t3ELgi3dK2wiqdWncHPxZY1M3viYmd1550E43NmOZxZBn1egLCR+o7GZDQqMXz44Yd/7mBhgb+/P59//vlt9xk3bhwxMTHMnz//lttERkby3XffNTJUQWicospapi4/hUwmY8W07rjZi6RgktKPw2+vQvBD8NDb+o7GpDQqMaxevfquD9y9e3eysrLuej9BuB8KlZpn154jt0zO+lm9CHQXJZZNUlkWbJgCLi1h/A+iDpKWNSox1NXVsWvXLrKzsxvMer7fWz9xcXGMGjUKLy8v5s+fT0hIyB33qa2tJTk5+b7OezNyuVwnx9UHU2nLvbRjyekiTl0r49X+nthW5ZKcnKuj6O6OqbwmoP+2yJRyAvc/g1VtFWkDvqAuLRe4t9dZ323RFm23o1GJ4dlnn8XR0ZHw8PAGpTHuR3h4OPv378fe3p6DBw/y/PPPs3v37jvuZ21tTVhYmFZi+CvNQiraP64+mEpb7rYdv52/zuYLV3midyDPD++gw8junqm8JqDntkiSZlhqSQpMWk9Q6ND7OpypvC6NacfdJI5GJYa8vDyWLl3a6IM2xl9LdkdFRfHOO+9QXFyMm5ubVs8jNA/XCqt4dWM8XVq68I/h7fUdjqArRz+H8xsg+h9wn0lBuLVGDVft0qULKSkpWj1xQUEBkqRZGD0hIQG1Wo2rq6tWzyE0D0qVmpd/isPC3Iz/TO5av5ymYGISN8PetyF8HPSfp+9oTFqjrhjOnj3Lli1b8Pf3b3Ar6b///e8t95k7dy6nTp2ipKSEAQMGMGfOnPr+iUmTJrFr1y7Wr1+Pubk5NjY2fPbZZ2IxFOGe/Of3K8RllvLV5C74uYjJTSYp4yRseQZa9IIx34B4r9CpRiWGJUuW3PWBP/vss9s+HxMTQ0xMzF0fVxD+Kj6zlC/2X2ZMZz9GdPTTdziCLhRfhR8ngbM/PLYOLEVJE11r1DW3v78/169f58SJE/j7+2Nra4tardZ1bIJwW3KFipc3xOHtaM07ow2rs1nQkupiWDsBJDU8/jPYu+s7omahUYnhq6++4ocffuD7778HQKFQ8Oqrr+o0MEG4k+8OXuVqQRUfju+Is60od2FyFDXw4+NQmgGPrQf3IH1H1Gw0KjHs2bOHb775Bltbzf1bb2/vO5bEEARdulZYxX8OpDKykx8D2nrqOxxB21RK+Hk6ZBzX9CkE9tZ3RM1Ko/oYLC0tkclk9Z3D1dXVOg1KEG5HkiQWbU3E2tyMN4cb/xh04W8kCf77IqT8BkM/hohH9B1Rs9OoxDB06FAWLVpEeXk5GzZsYNOmTWLRHkFvtiVc5/DlQt4ZFY6Xk+iINDl7FkHcGohaAD1n6TuaZqlRiWHGjBkcPXoUe3t7rl27xgsvvEDfvn11HZsg3KBcruDdbReI8HcmplegvsMRtO3ov+HYF9D9KXhggb6jabYalRhWrFjBww8/LJKBoHef7kqhqLKWZU92x9xMjGU3KWdXaK4WwsdpVmETcxX0plGJobKykhkzZuDs7Mzw4cMZMmQIHh4euo5NEBpIyCpl9Yl0pvQKJCLAWd/hCNoUu0bTrxA8CMZ+B2Zi9ro+Neq3P3v2bLZv386iRYvIz88nJiaGqVOn6jg0QfiTSi3xxpZE3B2seWVIqL7DEbQp/kfYOhuCouHRNWAh1s/Qt7tKy+7u7nh4eODi4kJRUZGuYhKEG6w9mc757DLeHNEeJxsxZ8FkJGyEX56F1gPErGYD0qhbSevWrWPHjh0UFxczZMgQ3nvvPYKDg3UdmyAAkF8h5+OdKfQL9mBkR199hyNoS+Jm2DILWvaBST+CpahzZSgalRhycnJ4/fXXTaJuuWB83tuWTK1Szbujw0WhRVMRtw62Pg8tesLkn8DKTt8RCX/RqFtJ8+bNo6qqik2bNgFQXFxMZmamTgMTBIAjlwv5NT6HZx4Ioo2nw513EAzfqSWa20et+kPMJrAWrytAZkUm7x5/l9cOvqbvUEStJMFw1ak0M5wD3e147gFRJ8ckHPkX/DYPQofB5A1gJdbkvlJ6hYWHFzJyy0i2pm6llXMrfYfUuFtJe/bs4ZdffmHs2LGAqJUkNI2fE0u5WljFquk9sLEUi70bNUmC/e/B4U+gwyMw9lswb96DCJKKkvgh4Qf2ZuzF1sKWmLAYngx/Ek87/df+ErWSBIOUXlTFjwmlDO/oK4rkGTuVArbPhXOroOuTMOJfYNY8E70kSZzLP8eS80s4mn0UR0tHZnWcRUxYDK42hrOC5T3XSpowYYKuYxOaKU2RvCQszGHRCLF+s1GrrYSNT0LqXhjwKgx8o1nOaJYkiUNZh/jh/A/EFcThZuPGi11f5NHQR3G0ctR3eDcQtZIEg7MjMZeDlwp4urs73qJInvGqyIV1EyE3EUb+G7pN1XdETU6pVrIzbSdLzy8ltTQVX3tfFvZYyNiQsdhaGO7w3EYlBoC+ffvWJwOVSsWvv/7KqFGjdBaY0DyVVSt469ckwv2cGNnOSd/hCPeqIAXWPALVRZo5Cm0H6zuiJiVXyvkl9RdWJK0guzKbIOcg3u/3Pg+3fhhLM8PvW7ltYqisrGTt2rXk5eURHR1N3759Wbt2LUuXLqVdu3YiMQha98GOZIqr6lg+tTvm5Tn6Dke4F5d2w6YZYGED07aDXxd9R9RkKuoq+CnlJ9ZcWEORvIiOnh2Z330+US2iMJMZT/2n2yaGV199FWdnZzp37szGjRtZunQpCoWCr7/+Wkx2E7Tu2JVCfjydydNRbejg70yySAzGRZLg6Oew9x3w6aApceHSUt9RNYnCmkLWXFjDTyk/UamopK9fX2ZEzCDSO9IoJ2XeNjFkZWXxzTffADBhwgR69erF77//joODmJAiaJdcoWLh5vMEutvx0oNt9R2OcLfqquHX2ZC4SVM2e/R/msVs5qyKLFYkrWDL5S0o1AoGBQ5iRsQM2rsb96CJ2yYGC4s/nzY3NycgIEAkBUEn/rX3EulF1ax7qie2Vs1zKKPRKkmDn6ZA7nl48C3o97LJjzy6VHKJZYnL2HltJzKZjNFBo5nWYRqBTqaxeNRtE8PFixfp2rUroBluVVtbS9euXZEkCZlMxrlz55okSMG0JWaX8cPhazwa2YI+QWKdD6OS/F/45XnN95N+hNCH9RuPjsXlx/HD+R84mHWwflLalPZT8Lb31ndoWnXbxJCcnNxUcQjNlFKlZv6mBNzsrXh9mOi3MhrKOs1qaye/0XQuP7Ic3FrrOyqdUEtqDmYeZHnScmLzY3GxduH5zs8zqd0knK1Nc8GoRg9XFQRd+OHINZJyyvnm8a442xn+MD4BLCuzYdlzkHMOej4Lg94BC2t9h6V1dao6tl3dxoqkFVwru4afvR8LeixgbPBY7CxNu/9EJAZBb64VVvGvPZcYEu7N0AixzoLBkyQ4t4rWuxZo6hw9ugbCRuo7Kq0rrytnQ8oG1iavpbCmkDC3MP6v//8xuNVgLMyax1tm82ilYHBUaol5G+OxtjDj3dEd9B2OcCcVefDrHLi8C7lXJPaTV4BLC31HpVW5VbmsvrCany/9TLWymj5+fXi/3/v08u1llENO74dIDIJeLDtyjbPpJfzr0U6i7IUhkyRI2gLbXwFFNTz8IRlOAwgzoaRwqeQSKxJXsOPaDiQkHm79MFPDp9LOrZ2+Q9MbkRiEJpeaX8HHu1MY1N6bMZ399R2OcCulGbB9HlzeBb6dYdz34BkKJjAoRZIkTuee5quUr4g9FYuthS2PtXuMKe2n4Ofgp+/w9E5niWHhwoUcOHAAd3d3tm3bdsPzkiSxePFiDh48iI2NDR9++CHh4eG6CkcwEEqVmlc2JmBvZc77YyOa3SW6UVAp4eS38Ptizc+DF0PPZ8Dc+D9H1qpq2XltJ+suruNC0QWcLZyZ02UOj4Y+arIjjO6Fzl7pcePGERMTw/z582/6/KFDh0hLS2P37t3Ex8fz9ttvs3HjRl2FIxiI7w5dJT6zlC8ndcHT0fRGshi9tKOwc75mslrIEBj+iUmUtbheeZ2fUn5i0+VNlNaWEuQcxKLei2iraEun8E76Ds/g6CwxdO/enaysrFs+v2/fPsaMGYNMJqNz586Ul5eTn5+Pl5eXrkIS9Oxibjmf773E8AhfRnYSl+sGpfiqZl5C8n/BKQAmrID2Y4x6BnOtqpYDmQf49cqvHMk+AsDAFgOZ1G4SPXx6IJPJxFytW9DbtWFeXh4+Pj71P/v4+JCXl3fHxFBbW6uTF1Mul5vMH4khtkWplnh5ezZ2ljJi2ls1Kj5DbMe9MtS2mNWW4XFxFa6XNoCZBYUdZlEcOhnJzAYuXrzpPobaFtDcor5cdZmDhQc5VnSMKlUVbpZujPIZxSCvQXhae0IpXCzVtM2Q23I3tN0OvSUGSZJueKwx95utra11Utk1OTnZZCrGGmJb/r33MqnFdXwb05XeHRo3Z8EQ23GvDK4tNaVw4ms4/jXUVULnyRD9Jl5Ovtzpmt3Q2qJSq4jNj2Vvxl72ZewjtyoXG3MboltGMzp4ND19emJ+i6VEDa0t96ox7bibxKG3xODj40Nubm79z7m5ueI2komKyyzli/2XGd3Zj4cbmRQEHZGXw8nv4PiXIC+DsFHwwELwNq5qoMXyYk7knOBYzjEOZx+mWF6MlZkVffz7MLvzbB5s+SAOVqLg573SW2KIjo5mzZo1DB8+nPj4eBwdHUViMEFVtUpe+jEWHycbMZFNn2or4dT3cOwLqCmB0OHwwALw7ajvyBqlqKaI+IJ44vLjOH79OBeLNbeCnKyc6OPXh4cCH6K/f3+TL1XRVHSWGObOncupU6coKSlhwIABzJkzB6VSCcCkSZOIiori4MGDDBo0CFtbW95//31dhSLo0T+3XSC9uJr1T/XC2VbUQmpKKrWK9OIUMs8tIyvlv2RJcnJ9/Shz6U4ZtZQdmYtcJUetVqOSVEhI2FrYYmdhh72lPY5WjnjZeeFj74O3nTd+Dn74O/gjV8l1FrMkSRTWFHK59DKpJakkFSURXxBPdmU2ABZmFnT27MwLXV6gt19vwtzCbnmbSLh3OksMn3322W2fl8lkvPXWW7o6vWAAdibm8uPpTJ59IIhebdz1HY7Ju155nVO5pzhfeJ7kwkQuF6dQI2k+jOFkhZ25Iz4OXrhY2uFv7Ux79/bYmNtgbmaOmcwMGTLkSjlVyiqqFFWU15YTXxDP7vTdKNXKBudyS3LD38H/zy9Hf/zt/XGydsLOwg47SztsLWwxk5mhltRIkoQaNVWKKirrKqmoq6CiroK86jxyq3PJrcwlpyqHtPI0ymrL6s/jZetFJ69OTGo3iY6eHQlzC8PGQsyU1zXjn7EiGKS8cjkLNyfQwd+Jlx8SK7LpQmFNIaeun+JUruYrsyITAAeZJaHyGsbLq2nnEkLrLtMJCHkYV2vXe5pQqJbUFMuLuV55nazKLGKvxlJnW0d2ZTZJRUnsTd+LUlLe+UC3YGlmia+9Lz72PgwKHESISwghriEEuQThZuN2z8cV7p1IDILWqf9XIK9GoeLfj3XBysJ4FkE3ZKXyUk7nna5PBlfLrgLgaOlIpEcEk3Gmx+UjBNeUYxY2Eka8olkr4T6ZyczwsPXAw9aDCM8IWslbNRgBo1KryKvOI6cyhypFFdXKaqoV1VQrq5EkSXM1IpMhQ4a9pT0OVg44WTnhYOmAp50nbjZumMnE34ghEYlB0LoVx9I4fLmQxWM7EOQpRobcK7WkJrk4mcNZhzmcdZjzhefr+wG6endlTPAYeji0pF3idsxPrgG1Ajo8Av3nglfTDcE0NzPHz8FP1BgyISIxCFqVfL2cD3de5KEwbyb3MP5SCk2tvK6c4znHOZx1mCPZRyiSFyFDRoRHBM92epbefr0J9wjHsuASHPsSzs8DZJp5CP1eArc2+m6CYAJEYhC0pqpWyfPrzuFia8n/jRcF8hpDkiQul17WXBVkHyYuPw6VpMLJyom+/n3p79+fvv59NffaJQmuHoBdj8KVfWBpB91nQp854Byg76YIJkQkBkErJEniH78kklZYxbqneuHuIArk3Uq1opoT109wOFtziyivOg+AMLcwpneYzoCAAXTw6PDnamEqBSRs0MxByD0P9l4Q/SZETgc70TkraJ9IDIJWbDybxZbYbF5+qK0Ymvo3kiSRU5PD6aTTHM4+zNm8syjUCuwt7ent25vnA56nr39fvOz+NsFTXg7nVsKJb6A8GzxCYdRX0HGiSa6xLBgOkRiE+3Y5r4JFWxPpE+TO7OhgfYdjEKoV1ZzJO8OR7CMczjpMVqWm0nCQcxCPhz1Of//+dPHqgqX5TSb9laTBqSVwbhXUlkOr/jDicwh+CMzE6B1B90RiEO7LH/0KDtYWfP5oZ8zNmme/wh8jiI7nHOdYzjFi82NRqpXYmNvQ07cnD7s/zCORj+DvcIsV6yQJ0g7DiW8h5TeQmUH70Zr+A/+uTdsYodkTiUG4Z5Ik8erP8aTmV7Jqek+8mtHazdWKahILE4nNjyW2IJaE/AQqFBUAtHNrx5T2U+jj14cuXl2wNrcmOTn55kmhrhrOb9QUtstPAjt36P+Kpv/AWSx7KuiHSAzCPfvm4BV+O5/LwqHt6Bfioe9wdKZKUcWlkkskFyWTUpJCclEyl0su18/2DXYJZkjrIXTz7kYv31542Dbid1GW9b/bRSs1Re28I2D0f6DDeLC01XGLBOH2RGIQ7smBlHw+3pXCyE5+zBpg/GPnFWoFeVV5ZJRnkF6Rrvm3PJ208rT6UhMALtYuhLqFMrXDVLp4daGTZ6fGrxUsSZBxXLOecvI2QIJ2IzTrKQf2MerV0gTTIhKDcNfSCqt4YX0sod6OBj9fQaFWUFRTREF1AQU1BRRUF5Bfk09hTSH51fn1j5fIS5D4c/EoWwtbWjq2JMwtjNFBo2nn1o5Qt1C87bzvvr0KOc7XtsHBWZCbADYu0Ge2Zg6CCaynLJgekRiEu1JaXcf0FacxM5Ox5IlI7Kz08yf0xxv+39/gC2oKyK/+843/72/4oKn942bjhqetJz72PnTw6ICXnRdedl4EOgUS6BSIp63n/Se80kw4uxzOrsSvuhA8wzSjizo+ClZi3QDBcInEIDRarVLFrFVnySqpYe1TPWnhpv03t2pFdf0be3xRPGcvnKWgpoDCmkLNJ///fX+zN3wZMtxt3fG09cTLzotw93A87Tzrf/a09awv2lY/eUzb1Gq4sh/OLIVLOzWPtX2YdN9hBEbFiNtFglEQiUFoFEmSeO3nBE6lFfPvxzrTvdW9zbiVJIkieRFXS69yrewa2VXZ5FTmkF2RTU5VDsXy4oY7XNEszuJh64GHjQd+9n5EeETgYetR/2bvZeeFh60H7rbuunvDv5PqYohdA2eWQck1sPeEfnOh21RwaUF1crJICoLREIlBaJRPd19ia1wOrw4JZXTnxg2jlCvlXCy+SHJxMqklqaSWpnK17CqltaX121iaWdavDNbOvR3+Dv5423njaedJeU45PcJ74GztbJj9GJIE2efg9A+QuAlUtdCyD0T/Q7OWsoWVviMUhHsiEoNwR8uOXOOr31N5NLIFzz0QdNNtVGoVqaWpJBQmkFSYRFJREqklqfVDOh2tHAl2CeahwIcIcg4iyCWI1s6t8bLzumUt/uTSZFxsXHTWrntWVw2JP2sSwvV4sHKArlMgcgZ4t9d3dIJw30RiEG5rw5lM3t12gSHh3iwe26H+k7tSreRi8UXO5J7hTN4ZzuWfo6JOM8HLycqJcPdwpnWYRrhHOOHu4fc2msfQFF7W3CqKWwvyMvBqD8M/1XQmWzvqOzpB0BqRGIRb2nH+Ogs2JdA/xIPPHo0guTiJ03mnOZ17mtj8WKoUVQAEOgUyOHAw3by70dmzMwGOAcafBP6grIOU7XBmOVw7CGaW0H6UZqhpy96i30AwSSIxCDe1+0IWL/6yncA2Odi2yOPBn+PqE0Eb5zYMbz2cSJ9Iunl3u7EqqCkouqKZlRy7FqoLwbmFptR11yfAwQTbKwh/IRKDoCkLXZXD+YLzJBQmcCj9LGkVl7BuqaQQcKpuw4g2I4j0iSTSO7JxJR+MkbIWLm6Dsyvg2iGQmUPoUOg2DYIGgpm5viMUhCYhEkMzo1QrySjP4FLJJS6VXCKlJIWkwiSK5EUAWMisqK3yw93yAV7pN4h+AT1MNxH8oTAVzq2AuHVQXaSZjRz9JnSJAUcffUcnCE1OJIZ7lF2ZjYu1C/aW9voO5abkSjkZFRkNav5cKrnEldIr1KpqAbCQWdDKuRV9/fsS4RFBfqE3n2+vIDLQk2VTuuNgbcJ/Hgo5JP9Xc3WQfgTMLCB0mGbeQZuBYt0DoVkz4f/5unMm9wzTdk0DwNXalRaOLQhwDCDAMQBfe1/NZKz/fbnbuN98MZb7oFQrqairoKCmgLyqPPKr88mrziOvOo/simxSi1MpOlXUYB8PWw9CXEJ4LPQx2rq1JdQ1lNbOrbEyt0KSJL4+cIXPdqXQN9hLr6UudK4gBc6uhPh1mqqmrq3gwbeg8+Pg6K3v6ATBIJjo/37d6urdlS8GfsHVsqtkVmSSVZFFfEE8O9N2opbUN2zvaOWIg6UD9pb2mn+t7LG3sMfS3BJzmTnmMnPMZGaYy8yRkKhT1VGnrkOhUlCnrqNWWUt5XTnldeWU1ZZRqai84Rx/lIPws/cj3DGciBYRBDoF0tKpJYGOgThYOdy0LUqVmje3JrH+VAZjOvvx0SOdsLIwsU/Lihq48Kvm6iDjmGZkUbvhmquD1lHi6kAQ/kYkhntgJjNjYMuBDGRgg8cVKgVFck1ht79+FcuLqVJUUaWoolJRSXltOTmVOShUCtSSGpWkQiWp6pOKlbkVVmZWWJlbYWlmiY2FDV52XgS7BONk7YSTlebLw84DHzsfvO288bDzwNJMc2WSnJxMWFjYHdtRLlfw0o9x7L+Yz3MPBPHqkFDTGWYKkJuoWR4z4SeQl4JbGxj0LnSaDA6e+o5OEAyWSAxaZGluiY+9Dz72ht9hmZpfyazVZ0gvqua9MR2I6RWo75C0o7ZCU57i3CrIPgvmVhA2UnN1ENhPXB0IQiOIxNAM7UrK5ZUN8VhbmLF2Zk96tXHXd0j3R5Ig67Rm3kHiFlBUaUpcP/yhZlay3b0V/BOE5kqnieHQoUMsXrwYtVrNhAkTmDVrVoPnN2/ezEcffYS3t6bTLyYmhgkTJugypGZNrlDx4Y6LrDiWRqcAZ76J6YafixEvI1lVBAk/aq4OCi6CpT1EjIeuT4J/NzErWRDukc4Sg0ql4t1332X58uV4e3vzyCOPEB0dTXBwcIPthg0bxqJFi3QVhvA/F3LKeemnWC7lVTKtbyvmP9wOG0sjnLAlSZrSFGdXaiajqerAPxJGfgEdxomaRYKgBTpLDAkJCQQGBtKiRQsAhg8fzr59+25IDIJu1SnVfH/oCl/sS8XZzpKV03sQ1dYIO15rSjUT0M4shaJUzfKYkTM0VU29w/UdnSCYFJ0lhry8PHx8/uyE9fb2JiEh4Ybtdu/ezenTp2ndujULFy7E19dXVyE1O6fTinl983ku51cyPMKXd0eH4+5gre+w7s71eE1564SNoKyBgO4w9jtoPwYsbfQdnSCYJJ0lBkmSbnjs70MhBw4cyIgRI7CysmL9+vXMnz+fVatW3fa4tbW1JCcnazVWALlcrpPj6kNmUSWfLDnIviuVeNlb8M6DPvQIsCU/8yr5+g6uEWSqWhwz99Py0kYouYDa3JqywCGUBo1D7tZOs1HqNf0GeZdM6e9LtMXwaLsdOksMPj4+5Obm1v+cl5eHl1fDqpSurq7130+cOJFPPvnkjse1trZu1Bj9u9XYsf+GrKxGwdcHUll2uACZmYxnooJ44cFg45nFXJqhuTo4txpqiql1bAkPf4hZp8dwtXXF9c5HMFim8Pf1B9EWw9OYdtxN4tDZO0ZERARpaWlkZmbi7e3N9u3b+fTTTxtsk5+fX58s9u/fT1DQzVcHE24vv1zO0qPXWHsig6o6JdFtHHh3Qg/8jWHEkSRB5kk48bWmdhFoahZ1n8nVWm/C2osV0QShqeksMVhYWLBo0SJmzpyJSqVi/PjxhISE8O9//5sOHTrw4IMPsnr1avbv34+5uTnOzs588MEHugrH5EiSRFxmKetPZfBLbA5KtZphEb4890AwsrJsw08KKgVc2ArH/wM558DGGfrMgR6zwDlAs40JXOILgjHS6T2GqKgooqKiGjz24osv1n//yiuv8Morr+gyBJOTWVzNrqRcNp7JIiWvAltLcx6JDGBW/za08tBUek0uy9ZzlLdRXaypWXRqCVTkgHswDPsEOk8GK8OsVCsIzY2R3HxuvqrrlJxLL+XUtSL2Judz4Xo5AJ0CnPlgXAQjOvriaKPd6q06UXhZc7sobr1mdFHrKBj5OQQPEmUqBMHAiMSgZwqVmpLqOkqrFRRX1VFcVUdaURWpeZWk5FVwMbcClVrCTAZdW7ryxrAwBrX3rr86MGhqNVzZDye/gdS9YG4NHSdAr+fE3ANBMGAiMeiQJElkldRwrbCK9OJqMoqqyCiuprBSkwCKKmsplytvuq+vsw3BXg48E9WG7q3c6BboahxXBgC1lRC/Hk5+B0WXwcEbHngdIqeLqqaCYAREYtCizOJqzqQXcz6rnKScMi7klFNR++cbv7WFGS3c7PBytCbczwl3eytc7a3q/3Wzs8LFzooAN1ucjCUJ/FXRFTizTDPctLYM/LrCuCWayWgWVvqOThCERhKJ4T4UVNRy8FIBJ64WceJqEVklNQDYWJoR5uvE6C5+hPs508bDnkB3e7wcrTEzM7HCbgq5pmbR2RWQdlizRGb70dDzWWjRXd/RCYJwD0RiuAuSJHGloJLdF/LYcyGPpQQTIQAADONJREFUuMxSJAlc7Szp2dqdmf1a07ONOyFeDliYm3iHav5FTZnr+PWaJTJdAiH6H9A5BpxEWRNBMGYiMTRCbpmcX+Ky2XIum5S8CgAi/J15+aG2PBjmRZiPk+ldCdxMZQEkbYaEDZB95i9LZD4JrR8Qo4sEwUSIxHALdUo1OxKvs/FMFkevFCJJ0LWlC++ODmdQe298nQ18Apm21FZCym+a5TGv/A6SCrwjYPB70GkS2HvoO0JBELRMJIa/ySuXs/ZkButOZlBYWUsLN1vmRIcwtos/rY1hiKg21FZC6h5NiYqUHaCoBucW0PdF6DgRvIy/towgCLcmEsP/XCuu5Zv1sfx2/joqSWJgqBdP9A5kQIhn87hNVFUEl3ZA8jbN3ANVLdh5aJbG7DgRWvQSt4oEoZlo9okhLrOUr/ansjc5D3src57s04opvQKNYwLZ/ZAkyEvSTDxL3QvpR0FSa64MIqdD2Eho2QvMjHCVN0EQ7kuzTQzxmaV8sjuFw5cLcba1JKaTK/NGR+JiZ8Lj7auL4eoBSN2nSQaV/yuL7hUO/eZqkoFvJ7FWsiA0c80uMaQVVvHx7hS2J1zH3d6KhUPb8XivQDKvXja9pFBVpLkSSD8G6UcgNxGQNMtiBg2E4Icg6EExvFQQhAaaTWJQqNT8c9sF1p3MwOr/27v7mKrr/o/jT27E5MbioMcTl8zCZZeXiMh+u2y/oH7iJBmdRGVKMWvUfrX+8KxBuRm2lj+jxZxbrbnRyuqvVpc6aOW6wxRXUaRgVwleDsMQ5WAYiMrdOXx+f5w4+i1JgePhptdjY+fs8D2ffV6D7+e9z/fm8w0PxbXsDv43/faJs8zEtQwM+JafaDmM49+fwr6jcLbB97vwaZDwT/ifTTA3A/6WqkNEIjKkv0xhaDx7gX99d4q8fybgWnYH9pgJ/LzgAS/82gSt//Y9y6DlMJyugz7fPRbTwyPhtv/2nTieczfEL9aSFCJy3f4yheHvjunU/9+Kse7G8Ax44XwLnP0PtB2Ftnrf69ljvqWrwXeTmSMJFq3zrU30t1T+c9bL/AVJY9t3EZmw/jKFYdwa8EJnM5w74ftpP3H5/a9NvstGB0U7fPcQ/FeB73XWApiVBOFTrW2268lnIjJyKgzB4PX8Nvg3wrmffisAjZcH/4H+y9uG3wS2RJhxB8y77/J7+z8g0jZmEUTkr0OFIVD6LkHHz9Bx0jfY+2cAjb7PBq547sKUSN+Ab/+7b60hW6LvJ26ub1agG8lEZAypMFyvAS+cP+0b9AcH/19PXn5/wW3dfkoUxCX6jv//4wGwzb1cAGIculdARMYtFYZBxvhuAPu16eqDf0ez9ZBPSChMnw2xc3zPLY69zfc+9jbfEtTRdg3+IjIhqTAA/KuAecc+Bs8l6+fTbL6B/tZFvofP3DLn8uA/fbYuARWRSUmFAcCRRGdvKLa5qb8N/rf5CsDUmLHumYhI0KkwAKQX4Z5Rj22+lpMWEdHlLyIiYqHCICIiFioMIiJiocIgIiIWKgwiImKhwiAiIhYqDCIiYqHCICIiFiHGGDPWnRiOuro6pk6deu0NRUTEr7e3l5SUlOvadsIVBhERubF0KElERCxUGERExEKFQURELFQYRETEQoVBREQsVBhERMRi0haGM2fOsH79erKyssjOzuadd94BoKOjg4KCAjIzMykoKKCzsxMAYwxbt25l+fLlOJ1OfvzxR39bpaWlZGdnk5WVxdatWwn2Fb7DzdLY2Mi6detISkrizTfftLRVVVXFfffdx/Lly3n99dcnZI6h2pmIWQZ5vV5ycnJ44okngpoDApvl/PnzuFwuVqxYQVZWFrW1tRM2y9tvv012djb3338/hYWF9Pb2jtscH3zwAU6nE6fTSV5eHg0NDf62RrTPm0nK7XabH374wRhjTFdXl8nMzDTHjx83L7/8sikrKzPGGFNWVmZKS0uNMcbs37/fPPbYY2ZgYMDU1taa3NxcY4wxhw4dMuvWrTMej8d4PB6zdu1aU11dPa6z/PLLL+bIkSNm+/bt5o033vC34/F4zLJly8zPP/9sent7jdPpNMePH59wOYZqJ5gClWXQzp07TWFhoXn88ceDF+I3gcyyceNG8/777xtjjOnt7TWdnZ1BTBK4LK2trWbp0qWmu7vbGGOMy+Uyu3fvHrc5Dh06ZDo6OowxvrFscPwa6T4/aWcMdrudBQsWABAdHU1iYiJut5vKykpycnIAyMnJ4fPPPwfwfx4SEkJKSgrnz5+nra2NkJAQ+vr66O/v97/OmDFjXGeJi4sjOTmZ8HDrk1u///575syZQ0JCAhEREWRnZ1NZWTnhcgzVTjAFKgtAa2sr+/fvJzc3N3gBrhCoLBcuXKCmpsafIyIigunTpwcxSWD/Ll6vl56eHjweDz09Pdjt9nGbIzU1lZtvvhmAlJQUWltbgZHv85O2MFzp1KlT1NfXs2jRItrb2/1/YLvdzrlz5wBwu904HA7/dxwOB263m8WLF7NkyRLS0tJIS0sjPT2duXPnjkkOuL4sQ/l9xlmzZgV9QB00mhxDtTNWRpulpKSEZ555htDQsd8dR5OlubkZm83Gpk2byMnJobi4mEuXLgWj21c1miyzZs3i0UcfZenSpaSlpREdHU1aWlowuv0Hw82xa9cu7rnnHmDk+/zY/yfeYBcvXsTlcvHss88SHR095HbmKucNQkJCOHnyJI2NjRw4cICqqiqqq6upqam5kV0e0vVmGcpQGYNttDkC3c5ojLYPX3zxBTabjaSkpBvQu+EZbRaPx8PRo0d58MEHKS8vZ9q0aUE/jzVotFk6OzuprKyksrKSgwcP0t3dTUVFxQ3o6Z8bbo7q6mp27drF008/DYx8n5/UhaG/vx+Xy4XT6SQzMxPwTR3b2toAaGtrw2azAb4ZwuD0C3zTe7vdzmeffcaiRYuIiooiKiqK9PR06urqxnWWofw+o9vtDur0GAKTY6h2gi0QWQ4fPsy+ffvIyMigsLCQ6upq/04dTIH6/3I4HP7Z24oVKzh69OiN7fhVBCLLV199xezZs7HZbEyZMoXMzMygn0gfbo6GhgY2b97Mjh07iI2NBUa+z0/awmCMobi4mMTERAoKCvyfZ2RkUF5eDkB5eTnLli2zfG6Moa6ujpiYGOx2O/Hx8dTU1ODxeOjv76empiboh5KGm2UoCxcupKmpiebmZvr6+vjoo4/IyMi4oX2/UqByDNVOMAUqS1FREVVVVezbt4/t27dz1113sW3bthva998LVJaZM2ficDg4ceIEAF9//fWE3Vfi4+M5cuQI3d3dGGOCnmW4OU6fPs2GDRsoLS3l9ttv928/0n1+0q6u+t1335Gfn8+8efP8x24LCwtJTk7mqaee4syZM9x666288sor3HLLLRhj2LJlCwcPHmTatGmUlJSwcOFCvF4vL7zwAjU1NYSEhJCens6mTZvGdZazZ8+yZs0aLly4QGhoKJGRkezdu5fo6GgOHDhASUkJXq+XNWvW8OSTT064HA0NDVdt5957751wWa48PPDNN9+wc+dOysrKgpYj0Fnq6+spLi6mv7+fhIQEXnrpJf9J0YmW5dVXX2Xv3r2Eh4czf/58XnzxRSIiIsZljuLiYj799FPi4+MBCAsLY8+ePQAj2ucnbWEQEZGRmbSHkkREZGRUGERExEKFQURELFQYRETEQoVBREQs/rhAiIhYzJ8/n3nz5uHxeAgLC2PVqlU88sgjf7qExalTp6itrcXpdAaxpyKBocIgcg033XSTfzmE9vZ2ioqK6OrqwuVyDfmdlpYWPvzwQxUGmZB0H4PINSxevNiyHEJzczO5ublUV1fT0tLCxo0b6e7uBuC5554jNTWVtWvX0tjYyOzZs1m1ahXr169n27ZtfPvtt/T19ZGfn09eXt5YRRL5U5oxiAxTQkICAwMDtLe3ExcXx1tvvcXUqVNpamqisLCQPXv2UFRUZLmL+b333iMmJobdu3fT19dHXl4ed999NwkJCWOcRuSPVBhERmBwou3xeNiyZQsNDQ2EhobS1NR01e2//PJLjh07xieffAJAV1cXJ0+eVGGQcUmFQWSYmpubCQsLIy4ujtdee40ZM2ZQUVHBwMAAycnJV/2OMYbNmzeTnp4e5N6KDJ8uVxUZhnPnzvH888+Tn59PSEgIXV1dzJw5k9DQUCoqKvB6vQBERUVx8eJF//fS0tJ499136e/vB+Cnn34a04fYiPwZzRhErqGnp4eVK1f6L1dduXKlfynkhx56iA0bNvDxxx+zZMkSIiMjAbjzzjsJCwvjgQceYPXq1Tz88MO0tLSwevVqjDHExsayY8eOsYwlMiRdlSQiIhY6lCQiIhYqDCIiYqHCICIiFioMIiJiocIgIiIWKgwiImKhwiAiIhb/D+/ugB+qrUg1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Custom method for the reindexing.\n",
    "method = lambda x: x.interpolate(method='quadratic')\n",
    "\n",
    "# Do the resampling using the custom method.\n",
    "df_daily = sf.reindex(df_src=df_income, df_target=df_prices,\n",
    "                      group_index=TICKER, method=method).dropna()\n",
    "\n",
    "# Plot.\n",
    "sns.lineplot(x=DATE, y=REVENUE, hue=TICKER,\n",
    "             data=df_daily.reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reindex Helper-Function Arguments\n",
    "\n",
    "By default the helper-function `sf.resample` has two arguments that are set to `union=True` and `only_target_index=True`. For upsampling quarterly or annual financial data so it can be used with daily share-prices, you would normally just leave them to their default `True` values. But sometimes you will need to change them, so they require some explanation.\n",
    "\n",
    "The first argument `union` is a boolean that determines whether the reindexing should use the union of the indices from `df_src` and `df_target`, or only use the index for `df_target`. As discussed further above, reindexing drops the data-points from `df_src` when their dates do not exist in `df_target`, which can create \"holes\" in the data, and that is why it is sometimes necessary to reindex with the union of the two indices.\n",
    "\n",
    "The second argument `only_target_index` is a boolean that determines whether to perform a second reindexing operation. This is only relevant if we have used the union of the indices from `df_src` and `df_target`, because the resulting DataFrame might have rows that do not exist in `df_target`. By setting `only_target_index=True` we ensure the resulting DataFrame matches the index of `df_target`.\n",
    "\n",
    "Normally you would leave these arguments to their default values, so you can ignore them altogether. But sometimes you need different behaviour.\n",
    "\n",
    "For example, let us say that we want to reindex the annual Income Statements, so that we have both the original data and a new data-point for the same date as the latest share-prices. This can be achieved by performing the reindexing with `union=True` so we use the indices of both the DataFrames, and `only_target_index=False` so we keep the rows from `df_income` whose dates were not in `df_prices_latest`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Net Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">AAPL</td>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>2.655950e+11</td>\n",
       "      <td>5.953100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2.601740e+11</td>\n",
       "      <td>5.525600e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>2.601740e+11</td>\n",
       "      <td>5.525600e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">AMZN</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>1.778660e+11</td>\n",
       "      <td>3.033000e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2.328870e+11</td>\n",
       "      <td>1.007300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>2.328870e+11</td>\n",
       "      <td>1.007300e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">MSFT</td>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>1.103600e+11</td>\n",
       "      <td>1.657100e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-05</td>\n",
       "      <td>1.258430e+11</td>\n",
       "      <td>3.924000e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Revenue    Net Income\n",
       "Ticker Date                                  \n",
       "AAPL   2018-09-30  2.655950e+11  5.953100e+10\n",
       "       2019-09-30  2.601740e+11  5.525600e+10\n",
       "       2019-11-05  2.601740e+11  5.525600e+10\n",
       "AMZN   2017-12-31  1.778660e+11  3.033000e+09\n",
       "       2018-12-31  2.328870e+11  1.007300e+10\n",
       "       2019-11-05  2.328870e+11  1.007300e+10\n",
       "MSFT   2018-06-30  1.103600e+11  1.657100e+10\n",
       "       2019-06-30  1.258430e+11  3.924000e+10\n",
       "       2019-11-05  1.258430e+11  3.924000e+10"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reindex the annual Income Statements with latest Share-Prices.\n",
    "# Keep all the rows from the original DataFrame as well.\n",
    "df_latest = sf.reindex(df_src=df_income,\n",
    "                       df_target=df_prices_latest,\n",
    "                       union=True, only_target_index=False,\n",
    "                       method='ffill')\n",
    "\n",
    "# Show the last 3 rows for each Ticker.\n",
    "df_latest.groupby(TICKER).tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License (MIT)\n",
    "\n",
    "This is published under the\n",
    "[MIT License](https://github.com/simfin/simfin-tutorials/blob/master/LICENSE.txt)\n",
    "which allows very broad use for both academic and commercial purposes.\n",
    "\n",
    "You are very welcome to modify and use this source-code in your own project. Please keep a link to the [original repository](https://github.com/simfin/simfin-tutorials).\n"
   ]
  }
 ],
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   "display_name": "Python 3",
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   "nbconvert_exporter": "python",
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