lectures/lecture13/lecture13.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "seaborn.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "population_dict = {'California': 38332521,'Texas': 26448193,\n",
    " 'New York': 19651127, 'Florida': 19552860,\n",
    " 'Illinois': 12882135} \n",
    "area_dict = {'California': 423967, 'Texas': 695662, \n",
    "'New York': 141297, 'Florida': 170312, 'Illinois': 149995}\n",
    "# let's first create a series from these dictionaries\n",
    "population = pd.Series(population_dict)\n",
    "area = pd.Series(area_dict)\n",
    "\n",
    "# create a DataFrame from these two Series\n",
    "states = pd.DataFrame({'population': population, 'area': area})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area            423967\n",
       "population    38332521\n",
       "Name: California, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.loc['California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'area', u'population'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'California', u'Florida', u'Illinois', u'New York', u'Texas'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area            423967\n",
       "population    38332521\n",
       "Name: California, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              area  population\n",
      "California  423967    38332521\n",
      "Florida     170312     1212121\n",
      "Illinois    149995    12882135\n",
      "New York    141297    19651127\n",
      "Texas       695662    26448193\n"
     ]
    }
   ],
   "source": [
    "states['population'].loc['Florida'] = 1212121\n",
    "print(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    423967\n",
       "Florida       170312\n",
       "Illinois      149995\n",
       "New York      141297\n",
       "Texas         695662\n",
       "Name: area, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states['area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>1212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area  population\n",
       "California  423967    38332521\n",
       "Florida     170312     1212121\n",
       "Illinois    149995    12882135\n",
       "New York    141297    19651127\n",
       "Texas       695662    26448193"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              area  population     density\n",
      "California  423967    38332521   90.413926\n",
      "Florida     170312     1212121    7.117062\n",
      "Illinois    149995    12882135   85.883763\n",
      "New York    141297    19651127  139.076746\n",
      "Texas       695662    26448193   38.018740\n"
     ]
    }
   ],
   "source": [
    "states['density'] = states['population']/ states['area']\n",
    "print(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>California</th>\n",
       "      <th>Florida</th>\n",
       "      <th>Illinois</th>\n",
       "      <th>New York</th>\n",
       "      <th>Texas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>4.239670e+05</td>\n",
       "      <td>1.703120e+05</td>\n",
       "      <td>1.499950e+05</td>\n",
       "      <td>1.412970e+05</td>\n",
       "      <td>6.956620e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>3.833252e+07</td>\n",
       "      <td>1.212121e+06</td>\n",
       "      <td>1.288214e+07</td>\n",
       "      <td>1.965113e+07</td>\n",
       "      <td>2.644819e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>density</th>\n",
       "      <td>9.041393e+01</td>\n",
       "      <td>7.117062e+00</td>\n",
       "      <td>8.588376e+01</td>\n",
       "      <td>1.390767e+02</td>\n",
       "      <td>3.801874e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              California       Florida      Illinois      New York  \\\n",
       "area        4.239670e+05  1.703120e+05  1.499950e+05  1.412970e+05   \n",
       "population  3.833252e+07  1.212121e+06  1.288214e+07  1.965113e+07   \n",
       "density     9.041393e+01  7.117062e+00  8.588376e+01  1.390767e+02   \n",
       "\n",
       "                   Texas  \n",
       "area        6.956620e+05  \n",
       "population  2.644819e+07  \n",
       "density     3.801874e+01  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.23967000e+05,   3.83325210e+07,   9.04139261e+01],\n",
       "       [  1.70312000e+05,   1.21212100e+06,   7.11706163e+00],\n",
       "       [  1.49995000e+05,   1.28821350e+07,   8.58837628e+01],\n",
       "       [  1.41297000e+05,   1.96511270e+07,   1.39076746e+02],\n",
       "       [  6.95662000e+05,   2.64481930e+07,   3.80187404e+01]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "      <td>90.413926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>1212121</td>\n",
       "      <td>7.117062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area  population    density\n",
       "California  423967    38332521  90.413926\n",
       "Florida     170312     1212121   7.117062"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>area</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            area  population     density\n",
       "New York  141297    19651127  139.076746\n",
       "Texas     695662    26448193   38.018740"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>area</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>316246.600000</td>\n",
       "      <td>1.970522e+07</td>\n",
       "      <td>72.102047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>242437.411951</td>\n",
       "      <td>1.397315e+07</td>\n",
       "      <td>50.979386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>141297.000000</td>\n",
       "      <td>1.212121e+06</td>\n",
       "      <td>7.117062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>149995.000000</td>\n",
       "      <td>1.288214e+07</td>\n",
       "      <td>38.018740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>170312.000000</td>\n",
       "      <td>1.965113e+07</td>\n",
       "      <td>85.883763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>423967.000000</td>\n",
       "      <td>2.644819e+07</td>\n",
       "      <td>90.413926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>695662.000000</td>\n",
       "      <td>3.833252e+07</td>\n",
       "      <td>139.076746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                area    population     density\n",
       "count       5.000000  5.000000e+00    5.000000\n",
       "mean   316246.600000  1.970522e+07   72.102047\n",
       "std    242437.411951  1.397315e+07   50.979386\n",
       "min    141297.000000  1.212121e+06    7.117062\n",
       "25%    149995.000000  1.288214e+07   38.018740\n",
       "50%    170312.000000  1.965113e+07   85.883763\n",
       "75%    423967.000000  2.644819e+07   90.413926\n",
       "max    695662.000000  3.833252e+07  139.076746"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr 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>area</th>\n",
       "      <th>population</th>\n",
       "      <th>density</th>\n",
       "      <th>electoral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>423967</td>\n",
       "      <td>38332521</td>\n",
       "      <td>90.413926</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>170312</td>\n",
       "      <td>1212121</td>\n",
       "      <td>7.117062</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>149995</td>\n",
       "      <td>12882135</td>\n",
       "      <td>85.883763</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>141297</td>\n",
       "      <td>19651127</td>\n",
       "      <td>139.076746</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>695662</td>\n",
       "      <td>26448193</td>\n",
       "      <td>38.018740</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area  population     density  electoral\n",
       "California  423967    38332521   90.413926         55\n",
       "Florida     170312     1212121    7.117062         29\n",
       "Illinois    149995    12882135   85.883763         20\n",
       "New York    141297    19651127  139.076746         29\n",
       "Texas       695662    26448193   38.018740         38"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's add the electoral votes to the DataFrame\n",
    "# First let's create a dictionary of what we want to add\n",
    "votes = {'California': 55, 'Florida': 29,'Illinois': 20, 'New York': 29, 'Texas': 38}\n",
    "\n",
    "# now let's turn the dictionary into a pd series\n",
    "votes = pd.Series(votes)\n",
    "\n",
    "# Now we'll use the assign function to add a column named\n",
    "#electoral to the DataFrame\n",
    "states = states.assign(electoral=votes)\n",
    "states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.00</td>\n",
       "      <td>17.540001</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>18766300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>25.790001</td>\n",
       "      <td>30.42</td>\n",
       "      <td>23.299999</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>17187100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.92</td>\n",
       "      <td>20.270000</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>8218800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.10</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>5139800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.00</td>\n",
       "      <td>15.830000</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>6866900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open   High        Low      Close  Adj Close    Volume\n",
       "Date                                                                   \n",
       "2010-06-29  19.000000  25.00  17.540001  23.889999  23.889999  18766300\n",
       "2010-06-30  25.790001  30.42  23.299999  23.830000  23.830000  17187100\n",
       "2010-07-01  25.000000  25.92  20.270000  21.959999  21.959999   8218800\n",
       "2010-07-02  23.000000  23.10  18.709999  19.200001  19.200001   5139800\n",
       "2010-07-06  20.000000  20.00  15.830000  16.110001  16.110001   6866900"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsla = pd.read_csv('code/TSLA.csv', index_col='Date')\n",
    "tsla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.00</td>\n",
       "      <td>17.540001</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>23.889999</td>\n",
       "      <td>18766300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30</th>\n",
       "      <td>25.790001</td>\n",
       "      <td>30.42</td>\n",
       "      <td>23.299999</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>23.830000</td>\n",
       "      <td>17187100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.92</td>\n",
       "      <td>20.270000</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>21.959999</td>\n",
       "      <td>8218800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-02</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.10</td>\n",
       "      <td>18.709999</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>19.200001</td>\n",
       "      <td>5139800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-06</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.00</td>\n",
       "      <td>15.830000</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>16.110001</td>\n",
       "      <td>6866900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open   High        Low      Close  Adj Close    Volume\n",
       "Date                                                                   \n",
       "2010-06-29  19.000000  25.00  17.540001  23.889999  23.889999  18766300\n",
       "2010-06-30  25.790001  30.42  23.299999  23.830000  23.830000  17187100\n",
       "2010-07-01  25.000000  25.92  20.270000  21.959999  21.959999   8218800\n",
       "2010-07-02  23.000000  23.10  18.709999  19.200001  19.200001   5139800\n",
       "2010-07-06  20.000000  20.00  15.830000  16.110001  16.110001   6866900"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsla.to_csv('code/tsla_with_range.csv')\n",
    "tslarange = pd.read_csv('code/tsla_with_range.csv', index_col='Date')\n",
    "tslarange.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b39527e90>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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jysRSpgVONVZtPPiPLazdcoSfPbohrv17gpUm2c6qdw92a+E3tvj40R/fZrf5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\nIMjgLKw8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2b1233a210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "tsla['Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b5e965bd0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TshJPqjyJXZZ8IY53hyjxuamrWt5VPz63aXnM57bq6BvDMMw41Mt3txCJJrh6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\n5nHnr6vmT6/bWPDruAVBEOaiaDLMYcp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uXfJRCcIKQSwPoeixMrQ/CvRYZSreDWwDGgAN\n3IJZGwyl1ONa68usrO//jZlcdhy4dYFabIJQUIjlIQiANiuyHgFeA8S0WdJ9I6ZL6+Va69ut/S5T\nZl+IT2GWht+Fmd38L7kZuSDkBrE8BGEKA7NfxjFlNuA6D7MEfPmM/S4DWoAHrPIOLuZuHSAIBYmI\nhyAAVs0hhVkX658x6/38F2Z57JnNt1zAI1rrV1nH+ple2VUQCh5xWwlFj1XG+p8wCy5uAL6vtf4v\noBuzqqnd1yChlHIDjwOXp5W4/zBm6XRBKBrE8hCKlSal1DPWaxemu+qNwGrgu0qp12GWrX6MqZ4Z\nPwWexewK95fA963KtB2YDaoEoWiQqrqCIAhC1ojbShAEQcgaEQ9BEAQha0Q8BEEQhKwR8RAEQRCy\nRsRDEARByBoRD0EQBCFrRDwEQRCErPn/AeOKkVogCa/iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2b395aed10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsla['Close'].loc['2016':'2017'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
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