From b36c4864d7445effb3507c4815ea060562747edc Mon Sep 17 00:00:00 2001
From: Grant Long <grantmlong@gmail.com>
Date: Tue, 25 Sep 2018 09:47:25 -0400
Subject: [PATCH] master nb for l4

---
 lecture-4/DataDive-Lecture4ii-Master.ipynb | 762 +++++++++++++++++++++
 1 file changed, 762 insertions(+)
 create mode 100644 lecture-4/DataDive-Lecture4ii-Master.ipynb

diff --git a/lecture-4/DataDive-Lecture4ii-Master.ipynb b/lecture-4/DataDive-Lecture4ii-Master.ipynb
new file mode 100644
index 0000000..2af999c
--- /dev/null
+++ b/lecture-4/DataDive-Lecture4ii-Master.ipynb
@@ -0,0 +1,762 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Dive, Part II: Exploring Data\n",
+    "\n",
+    "The second part of today's exercise takes a look at stock data downloaded from [Yahoo Finance](https://finance.yahoo.com/lookup). Stock returns can translate to enormous swings of wealth, and thus have long been the subject of statistical analysis. Today we'll take a look at just a handful of properties of stock returns. The file linked below includes all available data for four stocks: Apple (AAPL), Facebook (FB), General Electric (GE), and IBM (IBM), and one index: the Dow Jones Industrial Average (DJIA).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Download Raw Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Raw rows: 14280\n",
+      "Full rows: 1597\n"
+     ]
+    }
+   ],
+   "source": [
+    "raw_df = pd.read_csv('https://grantmlong.com/data/stocks.csv')\n",
+    "raw_df.set_index('Date', inplace=True)\n",
+    "print('Raw rows: %i' % raw_df.shape[0])\n",
+    "price_df = raw_df.dropna(axis=0)\n",
+    "print('Full rows: %i' % price_df.shape[0])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Transform Prices to Returns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GE</th>\n",
+       "      <th>IBM</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>FB</th>\n",
+       "      <th>DJIA</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9/10/18</th>\n",
+       "      <td>0.008871</td>\n",
+       "      <td>0.001375</td>\n",
+       "      <td>-0.013421</td>\n",
+       "      <td>0.006992</td>\n",
+       "      <td>-0.002295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9/11/18</th>\n",
+       "      <td>-0.014389</td>\n",
+       "      <td>0.005767</td>\n",
+       "      <td>0.025283</td>\n",
+       "      <td>0.010720</td>\n",
+       "      <td>0.004408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9/12/18</th>\n",
+       "      <td>0.022709</td>\n",
+       "      <td>0.000546</td>\n",
+       "      <td>-0.012419</td>\n",
+       "      <td>-0.023744</td>\n",
+       "      <td>0.001073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9/13/18</th>\n",
+       "      <td>0.007930</td>\n",
+       "      <td>0.016238</td>\n",
+       "      <td>0.024155</td>\n",
+       "      <td>-0.003951</td>\n",
+       "      <td>0.005657</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9/14/18</th>\n",
+       "      <td>0.007149</td>\n",
+       "      <td>-0.004162</td>\n",
+       "      <td>-0.011351</td>\n",
+       "      <td>0.005949</td>\n",
+       "      <td>0.000332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9/17/18</th>\n",
+       "      <td>0.001577</td>\n",
+       "      <td>-0.002629</td>\n",
+       "      <td>-0.026626</td>\n",
+       "      <td>-0.010720</td>\n",
+       "      <td>-0.003539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9/18/18</th>\n",
+       "      <td>-0.003150</td>\n",
+       "      <td>0.004191</td>\n",
+       "      <td>0.001652</td>\n",
+       "      <td>-0.001744</td>\n",
+       "      <td>0.007092</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9/19/18</th>\n",
+       "      <td>0.015798</td>\n",
+       "      <td>0.003366</td>\n",
+       "      <td>0.000596</td>\n",
+       "      <td>0.017218</td>\n",
+       "      <td>0.006050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9/20/18</th>\n",
+       "      <td>-0.031104</td>\n",
+       "      <td>0.014021</td>\n",
+       "      <td>0.007602</td>\n",
+       "      <td>0.018153</td>\n",
+       "      <td>0.009514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9/21/18</th>\n",
+       "      <td>-0.023274</td>\n",
+       "      <td>0.001323</td>\n",
+       "      <td>-0.010771</td>\n",
+       "      <td>-0.018612</td>\n",
+       "      <td>0.003246</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               GE       IBM      AAPL        FB      DJIA\n",
+       "Date                                                     \n",
+       "9/10/18  0.008871  0.001375 -0.013421  0.006992 -0.002295\n",
+       "9/11/18 -0.014389  0.005767  0.025283  0.010720  0.004408\n",
+       "9/12/18  0.022709  0.000546 -0.012419 -0.023744  0.001073\n",
+       "9/13/18  0.007930  0.016238  0.024155 -0.003951  0.005657\n",
+       "9/14/18  0.007149 -0.004162 -0.011351  0.005949  0.000332\n",
+       "9/17/18  0.001577 -0.002629 -0.026626 -0.010720 -0.003539\n",
+       "9/18/18 -0.003150  0.004191  0.001652 -0.001744  0.007092\n",
+       "9/19/18  0.015798  0.003366  0.000596  0.017218  0.006050\n",
+       "9/20/18 -0.031104  0.014021  0.007602  0.018153  0.009514\n",
+       "9/21/18 -0.023274  0.001323 -0.010771 -0.018612  0.003246"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "return_df = price_df.pct_change(periods=1).dropna(axis=0)\n",
+    "return_df.tail(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Part I: Visualize Returns\n",
+    "\n",
+    "Look at the Build histograms for the returns for each of the stocks. What do the distributions of these stocks look like?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def describe_returns(stock):\n",
+    "    return_df[stock].hist(density=True, bins=100)\n",
+    "    print(return_df[stock].describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count    1596.000000\n",
+      "mean        0.000039\n",
+      "std         0.011801\n",
+      "min        -0.082790\n",
+      "25%        -0.005667\n",
+      "50%         0.000087\n",
+      "75%         0.006192\n",
+      "max         0.088645\n",
+      "Name: IBM, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/DLy02ta3gVf0so+AV1TzvwA4v1rPql7WOaS8rwVeUj1+FXCwbZlFj48h550A7uuw3m8BvwAE8I/Hj49hZ14wz0ZgX9P7uM6vkR2h07rNwM7q8U7gikXmeROwKzN/kJlHgF3AJe0zRMQFwLm0SqdJteTtst6bgYtrGu30nTczf5SZuwEy83+Bu2l9TqEJz9yCotrW8VtQtOu0jy4HZjLz6cz8HvBQtb5e1rnseTPznsw8/lmP+4HTIuIFNeWqPW+nFUbEOuBFmfmNbDXl51n8+Bp25rcDX6wxV+NGudDHM/MQQPV9sR9/erk9wdtp/e/c/u7wlRHxnYi4OSLOox515P1c9aPeH7QdfM8sk5nHgKPAi1dIXiJiNfAW4I62yXXu317+jjvto07LNnlbi0HytrsSuCczn26bttjxMey850fEPRHxtYj4xbb5D3RZ5zAzH/fr/GShN7GPa7Oi74ceEf8C/NQiL32o11UsMm3hZT1XA+9oe/4PwBcz8+mIeDet/8UvogcN570mMw9GxJnAl6rMn++yzIk31vD+jYhTaP2D+ERWN3FjgP3bT4Yu83SavthAp67LwQbJ23ox4pXA9cAvt73e6fgY1CB5DwE/nZnfj4hNwN9X2fs+ZntUxz7+eeBHmXlf2+tN7eParOhCz8w3dHotIg5HxLrMPFT9CPfYIrMdgOfc9G0DrfNgx9fxauCUzJxr2+b32+b/a1r/cIaeNzMPVt9/GBFfoPVj5ed59hYMB6oCPQv4wbDzVnYAezPz423b7Hv/dtDLLSg67aMTLdv1thZDyEtEbAC+AvxGZj58fIETHB9Dy1v91Pt0lWsuIh4Gfraav/0UXJ37d6DMba9fzYLReYP7uD7DPonf7xfw5zz3TbuPLjLPWuB7tN6oW1M9Xtv2+nbgwwuWWdf2+FeB/xh2Xlr/8Z5dzfN8Wuf83l09/y2e++bOTcPOW732EVqjmOc1uX+rfbOP1puax98Ae+WCeRbdR8Aree6bovtovaHWdZ1Dyru6mv/KRda56PEx5LznAKuqxy8FDrYdH3cCr+PZN0UvrSPvoJmr58+jVfgvXY59XOfX0AMM8Jf2YlrnZfdW348fKJPAp9vm+01ab3Y9BLxzwTr2AT+3YNqf0XrD6dvA7oWvDyMvcAatK3G+U2X7y7Z/KKcBf1fN/632g3CIeTfQ+vH1AeDe6utdTe1f4FLgv2hd2fChatqfAG/tto9onV56mNZdQN98onXWeOz2lRf4feDJtn16L633NjoeH0POe2Xb3/XdwFva1jkJ3Fet85NUH3IcdubqtSkWDDSa3sd1fflJUUkqxChf5SJJamOhS1IhLHRJKoSFLkmFsNAlqRAWuiQVwkKXpEJY6JJUiP8HqObNcU+G2m8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for stock in ['IBM']:\n",
+    "    describe_returns(stock)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which is has the highest average return? Which is the most volatile?\n",
+    "\n",
+    "1. Identify and plot the summary statistics to answer each of these \n",
+    "2. Are there other plots that might also be useful in illustrating these concepts?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x121f4bf60>"
+      ]
+     },
+     "execution_count": 6,
+     "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": [
+    "return_df.mean().plot.bar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1224b8748>"
+      ]
+     },
+     "execution_count": 7,
+     "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": [
+    "return_df.std().plot.bar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x12258d588>"
+      ]
+     },
+     "execution_count": 8,
+     "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": [
+    "return_df.boxplot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Part II: Confidence Intervals\n",
+    "\n",
+    "Based on this data, find the most you would lose on a \\$10,000 investment with 95 percent and 99 percent confidence.\n",
+    "* Is it fair to call this a confidence interval?\n",
+    "* How else might you calculate such as confidence interval?\n",
+    "* [Time permitting] For the stocks with more data available, how does the inclusion of the historical returns change things?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GE</th>\n",
+       "      <th>IBM</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>FB</th>\n",
+       "      <th>DJIA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.01</th>\n",
+       "      <td>-378.804348</td>\n",
+       "      <td>-353.672390</td>\n",
+       "      <td>-388.865738</td>\n",
+       "      <td>-582.390419</td>\n",
+       "      <td>-210.105596</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.05</th>\n",
+       "      <td>-203.188549</td>\n",
+       "      <td>-172.399996</td>\n",
+       "      <td>-239.482145</td>\n",
+       "      <td>-314.956837</td>\n",
+       "      <td>-125.310548</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              GE         IBM        AAPL          FB        DJIA\n",
+       "0.01 -378.804348 -353.672390 -388.865738 -582.390419 -210.105596\n",
+       "0.05 -203.188549 -172.399996 -239.482145 -314.956837 -125.310548"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "return_df.quantile([0.01, 0.05])*10000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Part III: Correlation\n",
+    "\n",
+    "For each of the four stocks, which are most correlated with each other?\n",
+    "* Why might this be the case?\n",
+    "* Can we visualize these correlations?\n",
+    "\n",
+    "For each of the four stocks, which are most correlated with the broader market index?\n",
+    "* How might we use the data?\n",
+    "* If we regress these returns against the market which has the biggest slope and intercept?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FB</th>\n",
+       "      <th>AAPL</th>\n",
+       "      <th>GE</th>\n",
+       "      <th>IBM</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>FB</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.237430</td>\n",
+       "      <td>0.187338</td>\n",
+       "      <td>0.172981</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAPL</th>\n",
+       "      <td>0.237430</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.229567</td>\n",
+       "      <td>0.289330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GE</th>\n",
+       "      <td>0.187338</td>\n",
+       "      <td>0.229567</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.408434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>IBM</th>\n",
+       "      <td>0.172981</td>\n",
+       "      <td>0.289330</td>\n",
+       "      <td>0.408434</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            FB      AAPL        GE       IBM\n",
+       "FB    1.000000  0.237430  0.187338  0.172981\n",
+       "AAPL  0.237430  1.000000  0.229567  0.289330\n",
+       "GE    0.187338  0.229567  1.000000  0.408434\n",
+       "IBM   0.172981  0.289330  0.408434  1.000000"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "return_df[['FB', 'AAPL', 'GE', 'IBM']].corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1226af4e0>"
+      ]
+     },
+     "execution_count": 11,
+     "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": [
+    "return_df.plot.scatter('FB', 'AAPL')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x12276a4a8>"
+      ]
+     },
+     "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": [
+    "return_df.plot.scatter('FB', 'IBM')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'np' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-353bc05b3ba5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0malpha_fb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta_fb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpolyfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDJIA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFB\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0malpha_ibm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta_ibm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpolyfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDJIA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIBM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0malpha_aapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta_aapl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpolyfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDJIA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAAPL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0malpha_ge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta_ge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpolyfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDJIA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGE\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "alpha_fb, beta_fb = np.polyfit(df.DJIA.values, df.FB.values, 1)\n",
+    "alpha_ibm, beta_ibm = np.polyfit(df.DJIA.values, df.IBM.values, 1)\n",
+    "alpha_aapl, beta_aapl = np.polyfit(df.DJIA.values, df.AAPL.values, 1)\n",
+    "alpha_ge, beta_ge = np.polyfit(df.DJIA.values, df.GE.values, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.bar(['FB', 'IBM', 'GE', 'AAPL'], [alpha_fb, alpha_ibm, alpha_ge, alpha_aapl])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.bar(['FB', 'IBM', 'GE', 'AAPL'], [beta_fb, beta_ibm, beta_ge, beta_aapl])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [py36]",
+   "language": "python",
+   "name": "Python [py36]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}