PAndas_indesing_statstics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'randrange' from 'numpy.random' (C:\\Users\\nekka\\Anaconda3\\lib\\site-packages\\numpy\\random\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-98-2b73227e3fd4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrandint\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrandrange\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'randrange' from 'numpy.random' (C:\\Users\\nekka\\Anaconda3\\lib\\site-packages\\numpy\\random\\__init__.py)"
     ]
    }
   ],
   "source": [
    "#DATA ALIGNMENT\n",
    "import numpy as np\n",
    "from pandas import Series, DataFrame\n",
    "import pandas as pd\n",
    "from numpy.random import randint,random,randrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0\n",
       "b    1\n",
       "c    2\n",
       "d    3\n",
       "e    4\n",
       "f    5\n",
       "dtype: int32"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = Series(np.arange(6),index = ['a','b','c','d','e','f'])\n",
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     6\n",
       "b     7\n",
       "c     8\n",
       "d     9\n",
       "e    10\n",
       "f    11\n",
       "g    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series1 = Series([6,7,8,9,10,11,12],index = ['a','b','c','d','e','f','g'])\n",
    "series1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     6.0\n",
       "b     8.0\n",
       "c    10.0\n",
       "d    12.0\n",
       "e    14.0\n",
       "f    16.0\n",
       "g    18.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding two series\n",
    "series.add(series1, fill_value = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     6.0\n",
       "b     8.0\n",
       "c    10.0\n",
       "d    12.0\n",
       "e    14.0\n",
       "f    16.0\n",
       "g     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series + series1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NYC</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      A   B   C   D\n",
       "NYC   0   1   2   3\n",
       "LA    4   5   6   7\n",
       "SF    8   9  10  11\n",
       "NJ   12  13  14  15"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = DataFrame(np.arange(16).reshape((4,4)),columns=list('ABCD'), index=['NYC','LA','SF','NJ'])\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>B</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NYC</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
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      "text/plain": [
       "     A  B  D\n",
       "NYC  0  1  2\n",
       "LA   3  4  5\n",
       "SF   6  7  8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe =DataFrame(np.arange(9).reshape((3,3)),columns=list('ABD'), index=['NYC','LA','SF'])\n",
    "dframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NYC</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
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      ],
      "text/plain": [
       "        A     B     C     D\n",
       "LA    7.0   9.0   6.0  12.0\n",
       "NJ   12.0  13.0  14.0  15.0\n",
       "NYC   0.0   2.0   2.0   5.0\n",
       "SF   14.0  16.0  10.0  19.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.add(dframe,fill_value = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    0\n",
       "C    1\n",
       "A    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series2 = Series(range(0,3),index = ['B','C','A'])\n",
    "series2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2\n",
       "B    0\n",
       "C    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sorting\n",
    "series2.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    0\n",
       "C    1\n",
       "A    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series2.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NYC</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "      A   B   C   D\n",
       "NYC   0   1   2   3\n",
       "LA    4   5   6   7\n",
       "SF    8   9  10  11\n",
       "NJ   12  13  14  15"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NYC     6\n",
       "LA     22\n",
       "SF     38\n",
       "NJ     54\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0\n",
       "B    1\n",
       "C    2\n",
       "D    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the min method is going to call minimum value in each coloumn\n",
    "dataframe.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NYC     0\n",
       "LA      4\n",
       "SF      8\n",
       "NJ     12\n",
       "dtype: int32"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.min(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    NYC\n",
       "B    NYC\n",
       "C    NYC\n",
       "D    NYC\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#min value and its index\n",
    "dataframe.idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>D</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NYC</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LA</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SF</th>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ</th>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>36</td>\n",
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      ],
      "text/plain": [
       "      A   B   C   D\n",
       "NYC   0   1   2   3\n",
       "LA    4   6   8  10\n",
       "SF   12  15  18  21\n",
       "NJ   24  28  32  36"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.163978</td>\n",
       "      <td>5.163978</td>\n",
       "      <td>5.163978</td>\n",
       "      <td>5.163978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A          B          C          D\n",
       "count   4.000000   4.000000   4.000000   4.000000\n",
       "mean    6.000000   7.000000   8.000000   9.000000\n",
       "std     5.163978   5.163978   5.163978   5.163978\n",
       "min     0.000000   1.000000   2.000000   3.000000\n",
       "25%     3.000000   4.000000   5.000000   6.000000\n",
       "50%     6.000000   7.000000   8.000000   9.000000\n",
       "75%     9.000000  10.000000  11.000000  12.000000\n",
       "max    12.000000  13.000000  14.000000  15.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "DataFrame.count Counts number of non-NA/null observations.\n",
    "DataFrame.max Maximum of the values in the object.\n",
    "DataFrame.min Minimum of the values in the object.\n",
    "DataFrame.mean Mean of the values.\n",
    "DataFrame.std Standard deviation of the obersvations.\n",
    "DataFrame.select_dtypes Subset of a DataFrame including/excluding columns based on their dtype.\n",
    "'''\n",
    "dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "from pandas_datareader import data, wb\n",
    "import pandas_datareader as pdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Symbols</th>\n",
       "      <th>BP</th>\n",
       "      <th>CVX</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>36.332241</td>\n",
       "      <td>55.557301</td>\n",
       "      <td>51.284389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>36.590214</td>\n",
       "      <td>55.950813</td>\n",
       "      <td>51.484634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>36.780628</td>\n",
       "      <td>55.957851</td>\n",
       "      <td>51.929600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>36.768337</td>\n",
       "      <td>55.747028</td>\n",
       "      <td>51.766449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>36.854328</td>\n",
       "      <td>55.845425</td>\n",
       "      <td>51.558784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>38.015251</td>\n",
       "      <td>56.836235</td>\n",
       "      <td>52.137276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-12</th>\n",
       "      <td>37.775700</td>\n",
       "      <td>56.505962</td>\n",
       "      <td>51.877697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-13</th>\n",
       "      <td>37.959972</td>\n",
       "      <td>56.077297</td>\n",
       "      <td>51.670040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-14</th>\n",
       "      <td>37.916981</td>\n",
       "      <td>55.901638</td>\n",
       "      <td>51.677464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-15</th>\n",
       "      <td>37.861687</td>\n",
       "      <td>55.676762</td>\n",
       "      <td>51.254730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-19</th>\n",
       "      <td>38.279369</td>\n",
       "      <td>55.992981</td>\n",
       "      <td>51.373379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-20</th>\n",
       "      <td>37.505428</td>\n",
       "      <td>54.917831</td>\n",
       "      <td>50.453739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-21</th>\n",
       "      <td>36.590214</td>\n",
       "      <td>53.575615</td>\n",
       "      <td>49.467373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-22</th>\n",
       "      <td>35.545994</td>\n",
       "      <td>52.416126</td>\n",
       "      <td>49.022381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-25</th>\n",
       "      <td>35.963692</td>\n",
       "      <td>52.388027</td>\n",
       "      <td>48.836967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-26</th>\n",
       "      <td>35.926838</td>\n",
       "      <td>52.106915</td>\n",
       "      <td>48.888878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-27</th>\n",
       "      <td>35.662712</td>\n",
       "      <td>51.790703</td>\n",
       "      <td>48.607067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-28</th>\n",
       "      <td>35.214317</td>\n",
       "      <td>51.467449</td>\n",
       "      <td>48.176918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-29</th>\n",
       "      <td>34.471088</td>\n",
       "      <td>50.680416</td>\n",
       "      <td>47.783844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>35.152905</td>\n",
       "      <td>51.706379</td>\n",
       "      <td>49.081718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-02</th>\n",
       "      <td>34.065689</td>\n",
       "      <td>52.317745</td>\n",
       "      <td>49.660202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-03</th>\n",
       "      <td>33.887566</td>\n",
       "      <td>51.446346</td>\n",
       "      <td>49.393200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-04</th>\n",
       "      <td>32.849495</td>\n",
       "      <td>50.153370</td>\n",
       "      <td>47.998924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-05</th>\n",
       "      <td>32.665218</td>\n",
       "      <td>50.019833</td>\n",
       "      <td>48.058250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-08</th>\n",
       "      <td>32.204552</td>\n",
       "      <td>49.281982</td>\n",
       "      <td>48.035862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-09</th>\n",
       "      <td>32.929348</td>\n",
       "      <td>50.111187</td>\n",
       "      <td>48.670353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-10</th>\n",
       "      <td>32.953915</td>\n",
       "      <td>49.717663</td>\n",
       "      <td>48.409107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-11</th>\n",
       "      <td>33.660297</td>\n",
       "      <td>50.406349</td>\n",
       "      <td>48.700218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-12</th>\n",
       "      <td>33.580444</td>\n",
       "      <td>50.377968</td>\n",
       "      <td>48.371769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-16</th>\n",
       "      <td>34.366676</td>\n",
       "      <td>51.782669</td>\n",
       "      <td>49.476570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-19</th>\n",
       "      <td>27.971159</td>\n",
       "      <td>81.110397</td>\n",
       "      <td>70.094421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-20</th>\n",
       "      <td>27.964369</td>\n",
       "      <td>80.496338</td>\n",
       "      <td>69.958519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-21</th>\n",
       "      <td>28.222166</td>\n",
       "      <td>80.916077</td>\n",
       "      <td>70.366272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-23</th>\n",
       "      <td>28.507111</td>\n",
       "      <td>81.980965</td>\n",
       "      <td>71.229759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-26</th>\n",
       "      <td>28.222166</td>\n",
       "      <td>81.607857</td>\n",
       "      <td>70.853996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-27</th>\n",
       "      <td>28.052565</td>\n",
       "      <td>80.356430</td>\n",
       "      <td>69.838577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-28</th>\n",
       "      <td>28.140759</td>\n",
       "      <td>82.066460</td>\n",
       "      <td>70.438225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-29</th>\n",
       "      <td>28.018644</td>\n",
       "      <td>82.229698</td>\n",
       "      <td>70.454224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-30</th>\n",
       "      <td>28.330715</td>\n",
       "      <td>82.151962</td>\n",
       "      <td>70.470215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-03</th>\n",
       "      <td>27.944014</td>\n",
       "      <td>81.351372</td>\n",
       "      <td>70.046463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-04</th>\n",
       "      <td>27.815111</td>\n",
       "      <td>80.807266</td>\n",
       "      <td>69.710678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-05</th>\n",
       "      <td>28.025429</td>\n",
       "      <td>81.747749</td>\n",
       "      <td>70.142410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-06</th>\n",
       "      <td>28.025429</td>\n",
       "      <td>82.742706</td>\n",
       "      <td>70.358307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-07</th>\n",
       "      <td>27.862606</td>\n",
       "      <td>83.162445</td>\n",
       "      <td>70.837997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-10</th>\n",
       "      <td>27.808340</td>\n",
       "      <td>83.139122</td>\n",
       "      <td>70.686081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-11</th>\n",
       "      <td>27.930437</td>\n",
       "      <td>83.745430</td>\n",
       "      <td>71.149818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-12</th>\n",
       "      <td>28.235739</td>\n",
       "      <td>84.009689</td>\n",
       "      <td>71.517578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-13</th>\n",
       "      <td>28.133976</td>\n",
       "      <td>83.877548</td>\n",
       "      <td>70.822006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-14</th>\n",
       "      <td>28.079702</td>\n",
       "      <td>83.807602</td>\n",
       "      <td>70.422226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-17</th>\n",
       "      <td>28.296787</td>\n",
       "      <td>84.476059</td>\n",
       "      <td>71.053871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-18</th>\n",
       "      <td>28.507111</td>\n",
       "      <td>85.579819</td>\n",
       "      <td>71.605522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-19</th>\n",
       "      <td>28.568165</td>\n",
       "      <td>85.432137</td>\n",
       "      <td>70.710083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-20</th>\n",
       "      <td>28.649574</td>\n",
       "      <td>85.797455</td>\n",
       "      <td>71.069870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-21</th>\n",
       "      <td>28.574947</td>\n",
       "      <td>85.276695</td>\n",
       "      <td>69.742653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-24</th>\n",
       "      <td>28.303577</td>\n",
       "      <td>84.437202</td>\n",
       "      <td>69.494789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-26</th>\n",
       "      <td>28.276445</td>\n",
       "      <td>84.305046</td>\n",
       "      <td>69.614738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-27</th>\n",
       "      <td>28.235739</td>\n",
       "      <td>84.351707</td>\n",
       "      <td>69.446808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-28</th>\n",
       "      <td>27.964369</td>\n",
       "      <td>82.742706</td>\n",
       "      <td>68.039665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>28.249310</td>\n",
       "      <td>84.056351</td>\n",
       "      <td>69.198967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>28.778463</td>\n",
       "      <td>85.805222</td>\n",
       "      <td>70.925941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>755 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols            BP        CVX        XOM\n",
       "Date                                       \n",
       "2010-01-04  36.332241  55.557301  51.284389\n",
       "2010-01-05  36.590214  55.950813  51.484634\n",
       "2010-01-06  36.780628  55.957851  51.929600\n",
       "2010-01-07  36.768337  55.747028  51.766449\n",
       "2010-01-08  36.854328  55.845425  51.558784\n",
       "2010-01-11  38.015251  56.836235  52.137276\n",
       "2010-01-12  37.775700  56.505962  51.877697\n",
       "2010-01-13  37.959972  56.077297  51.670040\n",
       "2010-01-14  37.916981  55.901638  51.677464\n",
       "2010-01-15  37.861687  55.676762  51.254730\n",
       "2010-01-19  38.279369  55.992981  51.373379\n",
       "2010-01-20  37.505428  54.917831  50.453739\n",
       "2010-01-21  36.590214  53.575615  49.467373\n",
       "2010-01-22  35.545994  52.416126  49.022381\n",
       "2010-01-25  35.963692  52.388027  48.836967\n",
       "2010-01-26  35.926838  52.106915  48.888878\n",
       "2010-01-27  35.662712  51.790703  48.607067\n",
       "2010-01-28  35.214317  51.467449  48.176918\n",
       "2010-01-29  34.471088  50.680416  47.783844\n",
       "2010-02-01  35.152905  51.706379  49.081718\n",
       "2010-02-02  34.065689  52.317745  49.660202\n",
       "2010-02-03  33.887566  51.446346  49.393200\n",
       "2010-02-04  32.849495  50.153370  47.998924\n",
       "2010-02-05  32.665218  50.019833  48.058250\n",
       "2010-02-08  32.204552  49.281982  48.035862\n",
       "2010-02-09  32.929348  50.111187  48.670353\n",
       "2010-02-10  32.953915  49.717663  48.409107\n",
       "2010-02-11  33.660297  50.406349  48.700218\n",
       "2010-02-12  33.580444  50.377968  48.371769\n",
       "2010-02-16  34.366676  51.782669  49.476570\n",
       "...               ...        ...        ...\n",
       "2012-11-19  27.971159  81.110397  70.094421\n",
       "2012-11-20  27.964369  80.496338  69.958519\n",
       "2012-11-21  28.222166  80.916077  70.366272\n",
       "2012-11-23  28.507111  81.980965  71.229759\n",
       "2012-11-26  28.222166  81.607857  70.853996\n",
       "2012-11-27  28.052565  80.356430  69.838577\n",
       "2012-11-28  28.140759  82.066460  70.438225\n",
       "2012-11-29  28.018644  82.229698  70.454224\n",
       "2012-11-30  28.330715  82.151962  70.470215\n",
       "2012-12-03  27.944014  81.351372  70.046463\n",
       "2012-12-04  27.815111  80.807266  69.710678\n",
       "2012-12-05  28.025429  81.747749  70.142410\n",
       "2012-12-06  28.025429  82.742706  70.358307\n",
       "2012-12-07  27.862606  83.162445  70.837997\n",
       "2012-12-10  27.808340  83.139122  70.686081\n",
       "2012-12-11  27.930437  83.745430  71.149818\n",
       "2012-12-12  28.235739  84.009689  71.517578\n",
       "2012-12-13  28.133976  83.877548  70.822006\n",
       "2012-12-14  28.079702  83.807602  70.422226\n",
       "2012-12-17  28.296787  84.476059  71.053871\n",
       "2012-12-18  28.507111  85.579819  71.605522\n",
       "2012-12-19  28.568165  85.432137  70.710083\n",
       "2012-12-20  28.649574  85.797455  71.069870\n",
       "2012-12-21  28.574947  85.276695  69.742653\n",
       "2012-12-24  28.303577  84.437202  69.494789\n",
       "2012-12-26  28.276445  84.305046  69.614738\n",
       "2012-12-27  28.235739  84.351707  69.446808\n",
       "2012-12-28  27.964369  82.742706  68.039665\n",
       "2012-12-31  28.249310  84.056351  69.198967\n",
       "2013-01-02  28.778463  85.805222  70.925941\n",
       "\n",
       "[755 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices = pdr.get_data_yahoo(['CVX','XOM','BP'],start = datetime.datetime(2010,1,1),end = datetime.datetime(2013,1,1))['Adj Close']\n",
    "prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>2010-01-04</th>\n",
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       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>36.590214</td>\n",
       "      <td>55.950813</td>\n",
       "      <td>51.484634</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>36.780628</td>\n",
       "      <td>55.957851</td>\n",
       "      <td>51.929600</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>36.768337</td>\n",
       "      <td>55.747028</td>\n",
       "      <td>51.766449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>36.854328</td>\n",
       "      <td>55.845425</td>\n",
       "      <td>51.558784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols            BP        CVX        XOM\n",
       "Date                                       \n",
       "2010-01-04  36.332241  55.557301  51.284389\n",
       "2010-01-05  36.590214  55.950813  51.484634\n",
       "2010-01-06  36.780628  55.957851  51.929600\n",
       "2010-01-07  36.768337  55.747028  51.766449\n",
       "2010-01-08  36.854328  55.845425  51.558784"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Preview of the data frame it's gone show couple of values of the data frame\n",
    "prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <th>XOM</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-26</th>\n",
       "      <td>28.276445</td>\n",
       "      <td>84.305046</td>\n",
       "      <td>69.614738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-27</th>\n",
       "      <td>28.235739</td>\n",
       "      <td>84.351707</td>\n",
       "      <td>69.446808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-28</th>\n",
       "      <td>27.964369</td>\n",
       "      <td>82.742706</td>\n",
       "      <td>68.039665</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>28.249310</td>\n",
       "      <td>84.056351</td>\n",
       "      <td>69.198967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>28.778463</td>\n",
       "      <td>85.805222</td>\n",
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      "text/plain": [
       "Symbols            BP        CVX        XOM\n",
       "Date                                       \n",
       "2012-12-26  28.276445  84.305046  69.614738\n",
       "2012-12-27  28.235739  84.351707  69.446808\n",
       "2012-12-28  27.964369  82.742706  68.039665\n",
       "2012-12-31  28.249310  84.056351  69.198967\n",
       "2013-01-02  28.778463  85.805222  70.925941"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>3956100.0</td>\n",
       "      <td>10173800.0</td>\n",
       "      <td>27809100.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>4109600.0</td>\n",
       "      <td>10593700.0</td>\n",
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       "      <th>2010-01-06</th>\n",
       "      <td>6227900.0</td>\n",
       "      <td>11014600.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>4431300.0</td>\n",
       "      <td>9626900.0</td>\n",
       "      <td>27192100.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>3786100.0</td>\n",
       "      <td>5624300.0</td>\n",
       "      <td>24891800.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>8227200.0</td>\n",
       "      <td>11916800.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-12</th>\n",
       "      <td>6126600.0</td>\n",
       "      <td>12175200.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-13</th>\n",
       "      <td>5686600.0</td>\n",
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       "      <td>24884400.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-14</th>\n",
       "      <td>3330700.0</td>\n",
       "      <td>6721000.0</td>\n",
       "      <td>18630800.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-15</th>\n",
       "      <td>3596700.0</td>\n",
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       "      <th>2010-01-19</th>\n",
       "      <td>4914400.0</td>\n",
       "      <td>8722200.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-20</th>\n",
       "      <td>5410900.0</td>\n",
       "      <td>8703800.0</td>\n",
       "      <td>34629500.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-21</th>\n",
       "      <td>7184400.0</td>\n",
       "      <td>12573200.0</td>\n",
       "      <td>39114000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-22</th>\n",
       "      <td>7297200.0</td>\n",
       "      <td>13825000.0</td>\n",
       "      <td>39085500.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-25</th>\n",
       "      <td>4039100.0</td>\n",
       "      <td>9129700.0</td>\n",
       "      <td>29305100.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-26</th>\n",
       "      <td>5684700.0</td>\n",
       "      <td>10558200.0</td>\n",
       "      <td>34083300.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-27</th>\n",
       "      <td>6025600.0</td>\n",
       "      <td>13373200.0</td>\n",
       "      <td>35723500.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-28</th>\n",
       "      <td>5003700.0</td>\n",
       "      <td>12828800.0</td>\n",
       "      <td>37349800.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-29</th>\n",
       "      <td>7643200.0</td>\n",
       "      <td>15265600.0</td>\n",
       "      <td>40880500.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>6031200.0</td>\n",
       "      <td>11828500.0</td>\n",
       "      <td>37703000.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-02</th>\n",
       "      <td>18574000.0</td>\n",
       "      <td>12947600.0</td>\n",
       "      <td>34057900.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-03</th>\n",
       "      <td>8410700.0</td>\n",
       "      <td>14540300.0</td>\n",
       "      <td>24024700.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-04</th>\n",
       "      <td>8364700.0</td>\n",
       "      <td>15248400.0</td>\n",
       "      <td>33858200.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-05</th>\n",
       "      <td>11065800.0</td>\n",
       "      <td>17307000.0</td>\n",
       "      <td>42297500.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-08</th>\n",
       "      <td>7808900.0</td>\n",
       "      <td>11595600.0</td>\n",
       "      <td>30519400.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-09</th>\n",
       "      <td>8017100.0</td>\n",
       "      <td>13051900.0</td>\n",
       "      <td>36243300.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-10</th>\n",
       "      <td>5598300.0</td>\n",
       "      <td>11618700.0</td>\n",
       "      <td>21699100.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-11</th>\n",
       "      <td>6878000.0</td>\n",
       "      <td>11273800.0</td>\n",
       "      <td>23555200.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-12</th>\n",
       "      <td>6761900.0</td>\n",
       "      <td>13809100.0</td>\n",
       "      <td>30636200.0</td>\n",
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       "    <tr>\n",
       "      <th>2010-02-16</th>\n",
       "      <td>11509700.0</td>\n",
       "      <td>14230800.0</td>\n",
       "      <td>30514900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2012-11-19</th>\n",
       "      <td>5814900.0</td>\n",
       "      <td>6850900.0</td>\n",
       "      <td>12911000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-20</th>\n",
       "      <td>4698700.0</td>\n",
       "      <td>5283800.0</td>\n",
       "      <td>10288100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-21</th>\n",
       "      <td>4318800.0</td>\n",
       "      <td>3656400.0</td>\n",
       "      <td>13905300.0</td>\n",
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       "    <tr>\n",
       "      <th>2012-11-23</th>\n",
       "      <td>2263300.0</td>\n",
       "      <td>2776400.0</td>\n",
       "      <td>11931000.0</td>\n",
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       "    <tr>\n",
       "      <th>2012-11-26</th>\n",
       "      <td>4333300.0</td>\n",
       "      <td>4354100.0</td>\n",
       "      <td>16423600.0</td>\n",
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       "    <tr>\n",
       "      <th>2012-11-27</th>\n",
       "      <td>3288100.0</td>\n",
       "      <td>6612400.0</td>\n",
       "      <td>11841600.0</td>\n",
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       "    <tr>\n",
       "      <th>2012-11-28</th>\n",
       "      <td>8763400.0</td>\n",
       "      <td>7024200.0</td>\n",
       "      <td>15959900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-29</th>\n",
       "      <td>4693500.0</td>\n",
       "      <td>5756200.0</td>\n",
       "      <td>11579100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-30</th>\n",
       "      <td>4297800.0</td>\n",
       "      <td>6676400.0</td>\n",
       "      <td>15522100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-03</th>\n",
       "      <td>4180300.0</td>\n",
       "      <td>5736400.0</td>\n",
       "      <td>10937300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-04</th>\n",
       "      <td>6042600.0</td>\n",
       "      <td>6665100.0</td>\n",
       "      <td>11980000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-05</th>\n",
       "      <td>5483000.0</td>\n",
       "      <td>6430800.0</td>\n",
       "      <td>12717800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-06</th>\n",
       "      <td>3527300.0</td>\n",
       "      <td>7392300.0</td>\n",
       "      <td>12077000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-07</th>\n",
       "      <td>4275900.0</td>\n",
       "      <td>4947300.0</td>\n",
       "      <td>10603100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-10</th>\n",
       "      <td>3543700.0</td>\n",
       "      <td>5512100.0</td>\n",
       "      <td>11549400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-11</th>\n",
       "      <td>2661100.0</td>\n",
       "      <td>5922700.0</td>\n",
       "      <td>12634600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-12</th>\n",
       "      <td>4231000.0</td>\n",
       "      <td>5968200.0</td>\n",
       "      <td>14729500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-13</th>\n",
       "      <td>5406000.0</td>\n",
       "      <td>4909800.0</td>\n",
       "      <td>12290300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-14</th>\n",
       "      <td>4557900.0</td>\n",
       "      <td>5889300.0</td>\n",
       "      <td>13363600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-17</th>\n",
       "      <td>5333100.0</td>\n",
       "      <td>6097100.0</td>\n",
       "      <td>15672500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-18</th>\n",
       "      <td>6069300.0</td>\n",
       "      <td>7102700.0</td>\n",
       "      <td>15396600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-19</th>\n",
       "      <td>6572700.0</td>\n",
       "      <td>7623200.0</td>\n",
       "      <td>15092500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-20</th>\n",
       "      <td>4834900.0</td>\n",
       "      <td>5873900.0</td>\n",
       "      <td>15789000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-21</th>\n",
       "      <td>5972800.0</td>\n",
       "      <td>15611700.0</td>\n",
       "      <td>33057000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-24</th>\n",
       "      <td>2415200.0</td>\n",
       "      <td>2284400.0</td>\n",
       "      <td>6385200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-26</th>\n",
       "      <td>2819800.0</td>\n",
       "      <td>4094300.0</td>\n",
       "      <td>10737800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-27</th>\n",
       "      <td>4664200.0</td>\n",
       "      <td>5525700.0</td>\n",
       "      <td>14258500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-28</th>\n",
       "      <td>4196000.0</td>\n",
       "      <td>5277100.0</td>\n",
       "      <td>13358700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>6353800.0</td>\n",
       "      <td>6554000.0</td>\n",
       "      <td>19810800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>5969700.0</td>\n",
       "      <td>6144100.0</td>\n",
       "      <td>16143700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>755 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols             BP         CVX         XOM\n",
       "Date                                          \n",
       "2010-01-04   3956100.0  10173800.0  27809100.0\n",
       "2010-01-05   4109600.0  10593700.0  30174700.0\n",
       "2010-01-06   6227900.0  11014600.0  35044700.0\n",
       "2010-01-07   4431300.0   9626900.0  27192100.0\n",
       "2010-01-08   3786100.0   5624300.0  24891800.0\n",
       "2010-01-11   8227200.0  11916800.0  30685000.0\n",
       "2010-01-12   6126600.0  12175200.0  31496700.0\n",
       "2010-01-13   5686600.0  10842200.0  24884400.0\n",
       "2010-01-14   3330700.0   6721000.0  18630800.0\n",
       "2010-01-15   3596700.0  13482300.0  29411900.0\n",
       "2010-01-19   4914400.0   8722200.0  26081900.0\n",
       "2010-01-20   5410900.0   8703800.0  34629500.0\n",
       "2010-01-21   7184400.0  12573200.0  39114000.0\n",
       "2010-01-22   7297200.0  13825000.0  39085500.0\n",
       "2010-01-25   4039100.0   9129700.0  29305100.0\n",
       "2010-01-26   5684700.0  10558200.0  34083300.0\n",
       "2010-01-27   6025600.0  13373200.0  35723500.0\n",
       "2010-01-28   5003700.0  12828800.0  37349800.0\n",
       "2010-01-29   7643200.0  15265600.0  40880500.0\n",
       "2010-02-01   6031200.0  11828500.0  37703000.0\n",
       "2010-02-02  18574000.0  12947600.0  34057900.0\n",
       "2010-02-03   8410700.0  14540300.0  24024700.0\n",
       "2010-02-04   8364700.0  15248400.0  33858200.0\n",
       "2010-02-05  11065800.0  17307000.0  42297500.0\n",
       "2010-02-08   7808900.0  11595600.0  30519400.0\n",
       "2010-02-09   8017100.0  13051900.0  36243300.0\n",
       "2010-02-10   5598300.0  11618700.0  21699100.0\n",
       "2010-02-11   6878000.0  11273800.0  23555200.0\n",
       "2010-02-12   6761900.0  13809100.0  30636200.0\n",
       "2010-02-16  11509700.0  14230800.0  30514900.0\n",
       "...                ...         ...         ...\n",
       "2012-11-19   5814900.0   6850900.0  12911000.0\n",
       "2012-11-20   4698700.0   5283800.0  10288100.0\n",
       "2012-11-21   4318800.0   3656400.0  13905300.0\n",
       "2012-11-23   2263300.0   2776400.0  11931000.0\n",
       "2012-11-26   4333300.0   4354100.0  16423600.0\n",
       "2012-11-27   3288100.0   6612400.0  11841600.0\n",
       "2012-11-28   8763400.0   7024200.0  15959900.0\n",
       "2012-11-29   4693500.0   5756200.0  11579100.0\n",
       "2012-11-30   4297800.0   6676400.0  15522100.0\n",
       "2012-12-03   4180300.0   5736400.0  10937300.0\n",
       "2012-12-04   6042600.0   6665100.0  11980000.0\n",
       "2012-12-05   5483000.0   6430800.0  12717800.0\n",
       "2012-12-06   3527300.0   7392300.0  12077000.0\n",
       "2012-12-07   4275900.0   4947300.0  10603100.0\n",
       "2012-12-10   3543700.0   5512100.0  11549400.0\n",
       "2012-12-11   2661100.0   5922700.0  12634600.0\n",
       "2012-12-12   4231000.0   5968200.0  14729500.0\n",
       "2012-12-13   5406000.0   4909800.0  12290300.0\n",
       "2012-12-14   4557900.0   5889300.0  13363600.0\n",
       "2012-12-17   5333100.0   6097100.0  15672500.0\n",
       "2012-12-18   6069300.0   7102700.0  15396600.0\n",
       "2012-12-19   6572700.0   7623200.0  15092500.0\n",
       "2012-12-20   4834900.0   5873900.0  15789000.0\n",
       "2012-12-21   5972800.0  15611700.0  33057000.0\n",
       "2012-12-24   2415200.0   2284400.0   6385200.0\n",
       "2012-12-26   2819800.0   4094300.0  10737800.0\n",
       "2012-12-27   4664200.0   5525700.0  14258500.0\n",
       "2012-12-28   4196000.0   5277100.0  13358700.0\n",
       "2012-12-31   6353800.0   6554000.0  19810800.0\n",
       "2013-01-02   5969700.0   6144100.0  16143700.0\n",
       "\n",
       "[755 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To know the volume of the data frame\n",
    "Volume = pdr.get_data_yahoo(['CVX','XOM','BP'],start = datetime.datetime(2010,1,1),end = datetime.datetime(2013,1,1))['Volume']\n",
    "Volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Symbols</th>\n",
       "      <th>BP</th>\n",
       "      <th>CVX</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>3956100.0</td>\n",
       "      <td>10173800.0</td>\n",
       "      <td>27809100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>4109600.0</td>\n",
       "      <td>10593700.0</td>\n",
       "      <td>30174700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>6227900.0</td>\n",
       "      <td>11014600.0</td>\n",
       "      <td>35044700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>4431300.0</td>\n",
       "      <td>9626900.0</td>\n",
       "      <td>27192100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>3786100.0</td>\n",
       "      <td>5624300.0</td>\n",
       "      <td>24891800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols            BP         CVX         XOM\n",
       "Date                                         \n",
       "2010-01-04  3956100.0  10173800.0  27809100.0\n",
       "2010-01-05  4109600.0  10593700.0  30174700.0\n",
       "2010-01-06  6227900.0  11014600.0  35044700.0\n",
       "2010-01-07  4431300.0   9626900.0  27192100.0\n",
       "2010-01-08  3786100.0   5624300.0  24891800.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Volume.head()\n",
    "#this gets amount of the stocks they have 3956100.0\t10173800.0\t27809100.0 traded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Symbols</th>\n",
       "      <th>BP</th>\n",
       "      <th>CVX</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.007100</td>\n",
       "      <td>0.007083</td>\n",
       "      <td>0.003905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.005204</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.008643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>-0.000334</td>\n",
       "      <td>-0.003768</td>\n",
       "      <td>-0.003142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>0.002339</td>\n",
       "      <td>0.001765</td>\n",
       "      <td>-0.004012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols           BP       CVX       XOM\n",
       "Date                                    \n",
       "2010-01-04       NaN       NaN       NaN\n",
       "2010-01-05  0.007100  0.007083  0.003905\n",
       "2010-01-06  0.005204  0.000126  0.008643\n",
       "2010-01-07 -0.000334 -0.003768 -0.003142\n",
       "2010-01-08  0.002339  0.001765 -0.004012"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#returns on your stock\n",
    "returns = prices.pct_change()\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.corr of Symbols           BP       CVX       XOM\n",
       "Date                                    \n",
       "2010-01-04       NaN       NaN       NaN\n",
       "2010-01-05  0.007100  0.007083  0.003905\n",
       "2010-01-06  0.005204  0.000126  0.008643\n",
       "2010-01-07 -0.000334 -0.003768 -0.003142\n",
       "2010-01-08  0.002339  0.001765 -0.004012\n",
       "2010-01-11  0.031500  0.017742  0.011220\n",
       "2010-01-12 -0.006301 -0.005811 -0.004979\n",
       "2010-01-13  0.004878 -0.007586 -0.004003\n",
       "2010-01-14 -0.001133 -0.003132  0.000144\n",
       "2010-01-15 -0.001458 -0.004023 -0.008180\n",
       "2010-01-19  0.011032  0.005680  0.002315\n",
       "2010-01-20 -0.020218 -0.019202 -0.017901\n",
       "2010-01-21 -0.024402 -0.024440 -0.019550\n",
       "2010-01-22 -0.028538 -0.021642 -0.008996\n",
       "2010-01-25  0.011751 -0.000536 -0.003782\n",
       "2010-01-26 -0.001025 -0.005366  0.001063\n",
       "2010-01-27 -0.007352 -0.006069 -0.005764\n",
       "2010-01-28 -0.012573 -0.006242 -0.008850\n",
       "2010-01-29 -0.021106 -0.015292 -0.008159\n",
       "2010-02-01  0.019779  0.020244  0.027161\n",
       "2010-02-02 -0.030928  0.011824  0.011786\n",
       "2010-02-03 -0.005229 -0.016656 -0.005377\n",
       "2010-02-04 -0.030633 -0.025133 -0.028228\n",
       "2010-02-05 -0.005610 -0.002663  0.001236\n",
       "2010-02-08 -0.014103 -0.014751 -0.000466\n",
       "2010-02-09  0.022506  0.016826  0.013209\n",
       "2010-02-10  0.000746 -0.007853 -0.005368\n",
       "2010-02-11  0.021435  0.013852  0.006014\n",
       "2010-02-12 -0.002372 -0.000563 -0.006744\n",
       "2010-02-16  0.023413  0.027883  0.022840\n",
       "...              ...       ...       ...\n",
       "2012-11-19  0.029978  0.019043  0.014112\n",
       "2012-11-20 -0.000243 -0.007571 -0.001939\n",
       "2012-11-21  0.009219  0.005214  0.005828\n",
       "2012-11-23  0.010096  0.013160  0.012271\n",
       "2012-11-26 -0.009996 -0.004551 -0.005275\n",
       "2012-11-27 -0.006010 -0.015335 -0.014331\n",
       "2012-11-28  0.003144  0.021281  0.008586\n",
       "2012-11-29 -0.004339  0.001989  0.000227\n",
       "2012-11-30  0.011138 -0.000945  0.000227\n",
       "2012-12-03 -0.013650 -0.009745 -0.006013\n",
       "2012-12-04 -0.004613 -0.006688 -0.004794\n",
       "2012-12-05  0.007561  0.011639  0.006193\n",
       "2012-12-06  0.000000  0.012171  0.003078\n",
       "2012-12-07 -0.005810  0.005073  0.006818\n",
       "2012-12-10 -0.001948 -0.000280 -0.002145\n",
       "2012-12-11  0.004391  0.007293  0.006561\n",
       "2012-12-12  0.010931  0.003156  0.005169\n",
       "2012-12-13 -0.003604 -0.001573 -0.009726\n",
       "2012-12-14 -0.001929 -0.000834 -0.005645\n",
       "2012-12-17  0.007731  0.007976  0.008969\n",
       "2012-12-18  0.007433  0.013066  0.007764\n",
       "2012-12-19  0.002142 -0.001726 -0.012505\n",
       "2012-12-20  0.002850  0.004276  0.005088\n",
       "2012-12-21 -0.002605 -0.006070 -0.018675\n",
       "2012-12-24 -0.009497 -0.009844 -0.003554\n",
       "2012-12-26 -0.000959 -0.001565  0.001726\n",
       "2012-12-27 -0.001440  0.000553 -0.002412\n",
       "2012-12-28 -0.009611 -0.019075 -0.020262\n",
       "2012-12-31  0.010189  0.015876  0.017039\n",
       "2013-01-02  0.018732  0.020806  0.024957\n",
       "\n",
       "[755 rows x 3 columns]>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get correlation of the stocks\n",
    "correlation = returns.corr\n",
    "correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x142ca455eb8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "prices.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To plot the corelation\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x142cca957b8>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEKCAYAAAAPVd6lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAGARJREFUeJzt3XmYXVWd7vHvS5BBEEFQWwEBMQo4AIoRVEBBFGxbHLrbAF6Eq8Srgoq2XlG0EeURx5ZWUIMKojQBbVH6aSaZReGaIDNcIESGiFcUnBAZAr/7x96lh6JS5ySpk6qdfD/PU89z9lp777XqwPPWyjrrrJ2qQpLUXatMdgckScvGIJekjjPIJanjDHJJ6jiDXJI6ziCXpI4zyCWp4wxySeo4g1ySOm7Vye7AOPzKqaRBZVlvcN1O7xw4c7a66Jhlbm8iOSKXpI4zyCWp4wxySeo4g1ySOs4gl6SOM8glqeMMcknqOINckjrOIJekjjPIJanjDHJJ6jiDXJI6ziCXpI4zyCWp4wxySeo4g1ySOs4gl6SOM8glaYIl2T3JDUnmJ/nQGPWbJDk3yVVJLkiyUU/d05KcneT6JNcl2bRfewa5JE2gJNOAo4E9gK2AvZJsNeq0zwEnVNXzgMOBT/XUnQB8tqq2BGYAd/Zr0yCXpIk1A5hfVQuq6gFgDrDnqHO2As5tX58/Ut8G/qpV9SOAqrqnqu7t16BBLkkTa0Pg9p7jhW1ZryuBN7avXw88Lsn6wDOB3yf5fpLLk3y2HeGPyyCXpCWUZFaSeT0/s3qrx7ikRh3/C7BzksuBnYFfAouAVYEd2/oXAk8H9uvXn1WX/FeQpBXPGs/aZOBz66KaDcxeTPVCYOOe442AOx5xfdUdwBsAkqwNvLGq/pBkIXB5VS1o634AbA98Y7z+OCKXpIk1F5ieZLMkqwEzgdN6T0iyQZKR/D0E+GbPtesleWJ7vAtwXb8GDXJJmkBVtQg4EDgLuB44paquTXJ4kte2p70MuCHJjcCTgSPaax+imVY5N8nVNNM0x/ZrM1Wjp26mjCnbMUlTzljz0ktkwQGfHjhznn7s/17m9iaSI3JJ6jiDXJI6ziCXpI4zyCWp4wxySeo4g1ySOs4gl6SOM8glqeOGttdKktcBzwCurqqzhtWOJK3shjIiT3IMcDCwPvCJJB8dRjuSpOFNrewE7FJVh9DsKfC6QS7q3Rpy9uzFbSwmSeo1rKmVB9rNX6iqe5MMtC9B1SO2hnSvFUkawLCCfIskV7WvA2zeHgeo9jl1kqQJMKwg33JI95UkjTKUIK+qW0eXJdkAuKum8L65ktRFw1q1sn2SC9oHiG6b5BrgGuDXSXYfRpuStLIa1tTKl4EPA48HzgP2qKpLk2wBnAScOaR2JWmlM6wgX7WqzgZIcnhVXQpQVf93wAUskrRcrbnF4A9fnmqGtY784Z7XfxlV5xy5JE2gYY3It07yR5rlhmu2r2mP1xhSm5K0UhrWqpVpw7ivJOnR3P1QkjrOIJekjjPIJanjDHJJ6jiDXJI6ziCXpI4zyCWp4wxySeo4g1ySOs4gl6SOM8glqeMMcknqOINckiZYkt2T3JBkfpIPjVH/tCTnJ7k8yVVJXt2WPybJt5JcneT6JIcM0p5BLkkTKMk04GhgD2ArYK8kW4067VDglKraFpgJHNOW/xOwelU9F3gB8PYkm/Zr0yCXpIk1A5hfVQuq6gFgDrDnqHMKWKd9/Xjgjp7ytZKsCqwJPAD8kT4MckmaWBsCt/ccL2zLeh0GvDnJQuB04KC2/HvAn4FfAbcBn6uqu/s1aJBL0hJKMivJvJ6fWb3VY1wy+hGXewHHV9VGwKuBbydZhWY0/xDwVGAz4P1Jnt6vP8N61JskrbCqajYwezHVC4GNe4434m9TJyPeCuze3uuSJGsAGwB7A2dW1YPAnUl+AmwHLBivPwa5JAFrbrXpRN1qLjA9yWbAL2k+zNx71Dm3AbsCxyfZkuZZxr9py3dJ8h3gscD2wBf7NejUiiRNoKpaBBwInAVcT7M65dokhyd5bXva+4EDklwJnATsV1VFs9plbeAamj8Ix1XVVf3aTHPtlDRlOyZpyhlrXnqJ/P6MSwfOnHX32H6Z25tIjsglqeMMcknqOINckjpuyq5aOXHPmya7CyuFfX44fbK7IGkZOSKXpI4zyCWp4wxySeo4g1ySOs4gl6SOM8glqeMMcknqOINckjrOIJekjjPIJanjDHJJ6jiDXJI6ziCXpI4zyCWp4wxySeq4KbsfuSQtT2tu8bTJ7sJSc0QuSR03UJAneUmStdrXb07yhSSbDLdrkqRBDDoi/wpwb5KtgQ8CtwInDK1XkqSBDRrki6qqgD2Bo6rqKOBxw+uWJGlQg37Y+ackhwBvBnZKMg14zPC6JUka1KAj8jcB9wNvrar/B2wIfHZovZIkDWygEXkb3l/oOb4N58glaUoYN8iT/Amo3qL2OEBV1TpD7JskaQDjBnlV+YGmJE1xA3+zs116uGN7eFFVXTWcLkmSlsSgXwh6D3Ai8KT258QkBw2zY5KkwQw6In8r8KKq+jNAkk8DlwBfGlbHJEmDGXT5YYCHeo4fasskSZNs0BH5ccD/SXIqTYDvCXxjaL2SJA1soBF5VX0B2B+4G7gL2L+qvjjMjklSVyXZPckNSeYn+dAY9U9Lcn6Sy5NcleTVPXWHtNfdkORVg7S3pPuRB3gYp1UkaUztFiZHA7sBC4G5SU6rqut6TjsUOKWqvpJkK+B0YNP29Uzg2cBTgXOSPLOqHmIcg65a+RjwLWA9YAPguCSHLtmvJ0krhRnA/KpaUFUPAHNopqN7FTDyhcrHA3e0r/cE5lTV/VX1C2B+e79xDToi3wvYtqruA0hyJPBz4JMDXi9JK4wks4BZPUWzq2p2+3pD4PaeuoXAi0bd4jDg7HYZ91rAK3quvXTUtRv268+gQX4LsAZwX3u8OnDz4k5OskpVPbyYunWr6vcDtitJU04b2rMXUz3W1HONOt4LOL6qPp9kB+DbSZ4z4LWPMu7USpIvJfl3mp0Pr01yfJLjgGuAe8a5dF6S0X+BSPI2mpG8JK2oFgIb9xxvxN+mTka8FTgFoKouoRkobzDgtY/Sb458HnAZcCrwYeB84ALgI8AZ41z3bmB2kmOTPCHJtkkuAV4F7LS4i5LMSjIvybzzbpnTr++SNBXNBaYn2SzJajQfXp426pzbgF0BkmxJE+S/ac+bmWT1JJsB04Gf9Wuw36ZZ31riX6G57uIkzwc+TjMFcw/NXuZn97nur/9cOXHPm/r+c0KSJsq9WX/gc1cfp66qFiU5EDgLmAZ8s6quTXI4MK+qTgPeDxyb5GCaqZP92qewXZvkFOA6YBHwrn4rVgDSXNvnpOQ1wCeATWjCv+82tklmAkcAJ9NM5F8NfKCq7u7bIAb58rLPD6dPdhekibDMS6J/d8v9A2fOepuuPqWWYA/6Ff0vAm8B1q+qdarqcX1C/BxgH+AVVfVhmk9sr6BZTzlrcddJkpbcoEF+O3BNDTJ8bxxdVf/QroOkGl8CXgLsvBT9lCQtxqDLDz8InJ7kQpoVLMBfv7o/ls2TbFRVC3sL20fG7bNUPZUkjWnQEfkRwL00n6w+rudncTYELklyUZJ3JNlg2bopSVqcQUfkT6iqVw5606o6OMn7aJYazgQ+muRK4CTg1Kr605J3VZI0lkFH5OckGTjI4a/z4hdW1TtoFrh/ETgY+PUS9lGSNI5BR+TvAj6Y5H7gQQZYfjgiyXNpRuVvotkC98NL2VdJ0hgGCvKqGm8+/FGSTKcJ771oniY0B3hlVS1Y4h5KksY1UJAn+R7wTeDMxW2GNcoNNF/nf1NVXd1znx2BO6pqsRtuSZKWzKBz5F+lWTZ4U5Ijk2zR5/wzgIN7Q7z1F5q5cknSBBn0UW/nVNU+wPNptrT9UZKfJtk/yWPGuGSTqrpqjPvMAzZdhv5KkkYZdEROkvWB/YC3AZcDR9EE+4/GOH2NcW615hL0T5LUx6CPevs+8GPgscBrquq1VXVyVR0ErD3GJXOTHDDGfd5Ksy2uJGmCjPthZ5IX0mx0/uWqOi/JW4CvJbkVOKyq7q6q7ca49L3AqUn24W/BvR2wGvD6ieu+JKnfiPxrwP1tiO8EfAo4AfgDi3/MEVX166p6Mc1+5Le0Px+vqh3a/VYkSROk3/LDaT37h7+J5gGj/wn8Z5Ir+t28qs6nWYYoSRqSfiPyaUlGwn5X4LyeukG/FSpJGqJ+YXwScGGS39KsAf8xQJJn0EyvSJImWb9ndh6R5FzgKcDZPQ+WWAU4aNidkyT113d6pKouHaPsxuF0R5Imx93z7+9/Umu9Tcd7/PLyN/AXgiRJU5NBLkkdZ5BLUscZ5JLUcQa5JHWcQS5JHWeQS1LHGeSS1HEGuSR1nEEuSR1nkEtSxxnkktRxBrkkdZwPh1iJ/f3/uovfn3HXZHdjhbfuHttPdhe0nCXZHTgKmAZ8vaqOHFX/b8DL28PHAk+qqnWTbAN8BVgHeAg4oqpO7teeQS5JEyjJNOBoYDeah9fPTXJaVV03ck5VHdxz/kHAtu3hvcC+VXVTkqcClyU5q6p+P16bTq1I0sSaAcyvqgVV9QAwB9hznPP3onkaG1V1Y1Xd1L6+A7gTeGK/Bg1ySZpYGwK39xwvbMseJckmwGY88nnII3UzgNWAm/s1aJBL0hJKMivJvJ6fWb3VY1xSY5QBzAS+V1UPjbr/U4BvA/tX1cP9+uMcuSQtoaqaDcxeTPVCYOOe442AOxZz7kzgXb0FSdYB/hs4dKxHbY7FEbkkTay5wPQkmyVZjSasTxt9UpJnAesBl/SUrQacCpxQVd8dtEGDXJImUFUtAg4EzgKuB06pqmuTHJ7ktT2n7gXMqareaZd/BnYC9ktyRfuzTb82nVqRJOCum+4b+NzNX7HOuPVVdTpw+qiyj406PmyM674DfGfgjrQckUtSxxnkktRxBrkkdZxBLkkdZ5BLUscZ5JLUcQa5JHWcQS5JHWeQS1LHGeSS1HEGuSR1nEEuSR1nkEtSxxnkktRxBrkkdZxBLkkdZ5BLUscZ5JLUcQa5JHWcQS5JHWeQS1LHrTrZHZCkqeCu+fdPdheWmiNySeo4g1ySOs4gl6SOM8glqeMm/MPOJG8Yr76qvj/RbUrSymwYq1a+B1zR/gCkp64Ag1ySJtAwplbeCNwIPA/4BXBEVe3f/vzP8S5MMivJvCTzzrtlzhC6JkkrngkP8qo6tapmAjsDNwOfT3Jxkp0HuHZ2VW1XVdvtsunMie6aJK2Qhvlh533AH4A/AmsBawyxLUlaaQ3jw86XA3sBM4BzgKOqat5EtyNJagzjw85zgauAi4HVgX2T7DtSWVXvHkKbkrTSGkaQ7z+Ee0pSZyTZHTgKmAZ8vaqOHOOcfwYOo1nNd2VV7d1Ttw5wPXBqVR3Yr70JD/Kq+lZPZ9ZuiurPE92OJE1FSaYBRwO7AQuBuUlOq6rres6ZDhwCvKSqfpfkSaNu8wngwkHbHMqHnUnekeQ24FbgtiS3JnnnMNqSpClmBjC/qhZU1QPAHGDPUeccABxdVb8DqKo7RyqSvAB4MnD2oA1OeJAnORT4B+BlVbV+Va0PvBzYo62TpBXZhsDtPccL27JezwSemeQnSS5tp2JIsgrweeADS9LgMObI/wewdVXdN1JQVQva+aArgU8OoU1JWm6SzAJm9RTNrqrZI9VjXFKjjlcFpgMvAzYCfpzkOcCbgdOr6vZkrNuMbSgPlugN8Z6yvyR5eBjtSdLy1Ib27MVULwQ27jneCLhjjHMuraoHgV8kuYEm2HcAdmynotcGVktyT1V9aLz+DGOOfGGSXUcXJtkF+NUQ2pOkqWQuMD3JZklWA2YCp4065wc0U84k2YBmqmVBVe1TVU+rqk2BfwFO6BfiMJwR+buBHya5GLiM5p8ULwRewqMn/CVphVJVi5IcCJxFs/zwm1V1bZLDgXlVdVpb98ok1wEPAR+oqruWts1hBPkfgecAewPPppkvugh4O02gS9IKrapOB04fVfaxntcFvK/9Wdw9jgeOH6S9YQT5hcBXgS9U1SKAJE8Gvg48C8NckibUMIL8BcCRwOVJ3gM8l+avzmeAfce7UJImy93zH7VGozOG8c3O3wFvb0P8HJpPa7evqoUT3ZYkaThfCFo3yddo9lzZneaJQWe0q1YkSRNsGFMrPweOAd7VzpGfnWQb4Jgkt1bVXkNoU5JWWsMI8p1GT6NU1RXAi5McMIT2JGmlNoxHvS12Lryqjp3o9iRpZTfMR71JkpYDg1ySOs4gl6SOM8glqeMMcknqOINckjrOIJekjjPIJanjDHJJ6jiDXJI6ziCXpI4zyCWp4wxySeo4g1ySOs4gl6SOS1VNdh9WGElmVdXsye7Hisz3ePh8j7vHEfnEmjXZHVgJ+B4Pn+9xxxjkktRxBrkkdZxBPrGcVxw+3+Ph8z3uGD/slKSOc0QuSR1nkC+FJA8luSLJlUl+nuTFbfmmSf7S1l2X5KtJfI8HlOTvksxJcnP7/p2epJI8a9R5X0zywSRvSHJuT/lL2/d+1eXf+6ktycZJfpHkCe3xeu3xJkmeneS8JDcmuSnJR5OkPW+/9r/Brj33en1b9o+T9fvokQyZpfOXqtqmqrYGDgE+1VN3c1VtAzwP2Ap43WR0sGva4DgVuKCqNq+qrYAPAxcAM3vOWwX4R+Dkqvo+cF+SvdvwPgZ4Z1UtWu6/wBRXVbcDXwGObIuOpJkLvxM4DTiyqp4JbA28GHhnz+VXA3v1HM8Erhx2nzU4g3zZrQP8bnRhGyY/BZ6x3HvUTS8HHqyqr44UVNUVwHvoCXJgJ+CWqrq1PT4I+CTwcWBuVf10OfW3i/4N2D7Je4GXAp8H9gZ+UlVnA1TVvcCBwId6rvsxMCPJY5KsTfP/9BXLtecal/8EXTprJrkCWAN4CrDL6BOSPBbYFfjYcu5bVz0HuGx0YVVdleThJFtX1ZU0oX5ST/2CJCfThM/my623HVRVDyb5AHAm8MqqeiDJsxn1vlfVzUnWTrLOSBFwDvAq4PE0I/jNlmPX1Ycj8qUzMrWyBbA7cMLInCKweRvyPwH+u6rOmLRerjhOAma20yd7At8dqWinWl4B3ANsMjnd65Q9gF/R/OEECE1Qj6W3fA7NH9FH/CHV1OCIfBlV1SVJNgCe2BaNzJFryVxLM/c9lpOAs4ELgauq6s6euncB1wAfBY5OskO5pnZMSbYBdgO2By5OMofmfd9p1HlPB+6pqj+NjE+q6mdJnkMziLnxb+MWTQWOyJdRki2AacBdk92XjjsPWD3JASMFSV6YZOequpnm/T2SntFgkr8D3gd8sKrOBH4JvG35drsb2n8xfgV4b1XdBnwW+BxwIvDSJK9oz1sT+HfgM2Pc5hCaD6A1xRjkS2fNdpnbFcDJwFuq6qHJ7lSXtaPo1wO7tcsPrwUOA+5oTzkJ2IJmZcuILwCfqarftMfvBT4yssROj3AAcFtV/ag9Pobm/ZxBM111aJIbaFaozAW+PPoGVXVGVZ2/nPqrJeA3OyWp4xyRS1LHGeSS1HEGuSR1nEEuSR1nkEtSxxnkGrokH0lybZKr2mWbL1rG++2X5FHL4/pcc8+ytClNZX6zU0OVZAfgNcDzq+r+9luwq01yt6QViiNyDdtTgN9W1f0AVfVbYMskf/1iT5Ldkny/fX1Pkk8nuSzJOUlmJLkgyYIkr+2578ZJzkxyQ5J/7bnX+5Jc0/68d3RnkjwlyUXtvwyuSbLj0H5zaTkxyDVsZ9OE7o1JjkmyM83X8bdMMrI/zf7Ace3rtWj2JH8B8CeaLWp3o/nW5+E9950B7ANsA/xTku2SvKC914to9hM5IMm2o/qzN3BWux/O1rgdq1YATq1oqKrqnjZgd6TZc/xkmr2uvw28OclxwA7Avu0lD9BsswrN18Xvb7dfvRrYtOfWP6qquwDa0fxLaXbrO7Wq/txTviNwec91c4FvJnkM8IN2z3Op0wxyDV27D80FwAVtIL8FeDvwX8B9wHd7nurzYM/uhQ8DI1MyD496hNvovSWKZkvWfn25KMlOwN8D307y2ao6Yel+M2lqcGpFQ5XkWUmm9xRtA9xaVXfQbIh1KHD8Utx6tyRPaHfrex3N/u8XAa9L8tgka9FMx/x4VH82Ae6sqmOBbwDPX4q2pSnFEbmGbW3gS0nWBRYB84FZbd2JwBOr6rqluO/FNNMzzwD+o6rmASQ5HvhZe87Xq+ryUde9DPhAkgdpHkaxL1LHufuhJk27FvzyqvrGZPdF6jKDXJMiyWXAn4HdRpYmSlo6BrkkdZwfdkpSxxnkktRxBrkkdZxBLkkdZ5BLUscZ5JLUcf8fXOvyd+5BGz0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = returns.dropna().corr()\n",
    "mask = np.zeros_like(corr)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.heatmap(corr, cmap = sns.diverging_palette(286,0,sep=10,n=10,as_cmap=False),annot = False, mask = mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    M\n",
       "1    Y\n",
       "2    M\n",
       "3    Y\n",
       "4    D\n",
       "5    H\n",
       "6    I\n",
       "7    L\n",
       "8    Y\n",
       "9    A\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for the Unique values in the account\n",
    "series3 = Series(['M','Y','M','Y','D','H','I','L','Y','A'])\n",
    "series3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'Y', 'D', 'H', 'I', 'L', 'A'], dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you want to see the Unique values\n",
    "series3.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    3\n",
       "M    2\n",
       "D    1\n",
       "L    1\n",
       "I    1\n",
       "A    1\n",
       "H    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you want to know how many unique values\n",
    "series3.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     One\n",
       "1     Two\n",
       "2     NaN\n",
       "3    Four\n",
       "dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing Data\n",
    "data = Series(['One','Two',np.nan,'Four'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How to find these missing values\n",
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     One\n",
       "1     Two\n",
       "3    Four\n",
       "dtype: object"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can drop and remove all the null values\n",
    "data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        text-align: right;\n",
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       "      0     1    2\n",
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       "4   NaN   NaN  NaN"
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     },
     "execution_count": 74,
     "metadata": {},
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   ],
   "source": [
    "dframe11 = DataFrame([[1,2,3],[np.nan,5,6],[7,np.nan,9],[10,11,np.nan],[np.nan,np.nan,np.nan]])\n",
    "dframe11"
   ]
  },
  {
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   "execution_count": 75,
   "metadata": {},
   "outputs": [
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    "clean_dframe11 = dframe11.dropna()\n",
    "clean_dframe11"
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   "execution_count": 76,
   "metadata": {},
   "outputs": [
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       "      0     1    2\n",
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     "execution_count": 76,
     "metadata": {},
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   "source": [
    "dframe11.dropna(how = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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   "source": [
    "#We can also specify to drop columns instead of rows\n",
    "dframe11.dropna(axis=1)"
   ]
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   "execution_count": 90,
   "metadata": {},
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       "    0    1     2    3\n",
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      ]
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     "execution_count": 90,
     "metadata": {},
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   ],
   "source": [
    "npn = np.nan\n",
    "dframe12 = DataFrame([[1,2,3,npn],[5,npn,7,8],[9,npn,11,npn],[13,npn,npn,npn]])\n",
    "dframe12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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       "   0    1     2    3\n",
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   "source": [
    "'''\n",
    " If you want a threshold by number of actual data points,points that don't have a null, you can do that.\n",
    " So you can say dropna, and then pass the thresh argument.It will drop the row which has more than 2 null values.\n",
    "'''\n",
    "dframe12.dropna(thresh=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   0    1    2    3\n",
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   "source": [
    "dframe12.dropna(thresh=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    0    1     2    3\n",
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       "2   9  0.0  11.0  0.0\n",
       "3  13  0.0   0.0  0.0"
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   ],
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    "#If you wanted to fill where you had null values instead of just dropping them.You can do that with the fillna method.\n",
    "#If you want to fill al th null value with 1\n",
    "dframe12.fillna(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
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       "    0    1     2     3\n",
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       "2   9  6.0  11.0  12.0\n",
       "3  13  6.0  10.0  12.0"
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   "source": [
    "# If you want fill in different values for different columns. we can pass a dictionary\n",
    "dframe12.fillna({1:6,2:10,3:12})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
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       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1     2    3\n",
       "0   1  2.0   3.0  0.0\n",
       "1   5  0.0   7.0  8.0\n",
       "2   9  0.0  11.0  0.0\n",
       "3  13  0.0   0.0  0.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you want to fill the null values permentaly\n",
    "dframe12.fillna(0,inplace=True)\n",
    "dframe12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1  a    0.862630\n",
       "   b    0.431727\n",
       "   c    0.776273\n",
       "2  a    0.532788\n",
       "   b    0.223588\n",
       "   c    0.536692\n",
       "dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index Hierarchy\n",
    "ser = Series(random(6),index=[[1,1,1,2,2,2],['a','b','c','a','b','c']])\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.862630\n",
       "b    0.431727\n",
       "c    0.776273\n",
       "dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check multiple indexes\n",
    "# If you want check the first index\n",
    "ser[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.532788\n",
       "b    0.223588\n",
       "c    0.536692\n",
       "dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if you want to check the second index\n",
    "ser[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.862630\n",
       "2    0.532788\n",
       "dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser[:,'a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.862630</td>\n",
       "      <td>0.431727</td>\n",
       "      <td>0.776273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.532788</td>\n",
       "      <td>0.223588</td>\n",
       "      <td>0.536692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c\n",
       "1  0.862630  0.431727  0.776273\n",
       "2  0.532788  0.223588  0.536692"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#constructing a data frame using multi indexing series\n",
    "dframe1 = ser.unstack()\n",
    "dframe1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">NY</th>\n",
       "      <th>LA</th>\n",
       "      <th>SF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Cold</th>\n",
       "      <th>Hot</th>\n",
       "      <th>Hot</th>\n",
       "      <th>Cold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      NY      LA   SF\n",
       "    Cold Hot Hot Cold\n",
       "A 1    0   1   2    3\n",
       "  2    4   5   6    7\n",
       "B 1    8   9  10   11\n",
       "  2   12  13  14   15"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To construct a data frame using multiple indexe levels\n",
    "dframe2 = DataFrame(np.arange(16).reshape(4,4),index=[['A','A','B','B'],[1,2,1,2]],columns=[['NY','NY','LA','SF'],['Cold','Hot','Hot','Cold']])\n",
    "dframe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>CITIES</th>\n",
       "      <th colspan=\"2\" halign=\"left\">NY</th>\n",
       "      <th>LA</th>\n",
       "      <th>SF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>TEMPERATURE</th>\n",
       "      <th>Cold</th>\n",
       "      <th>Hot</th>\n",
       "      <th>Hot</th>\n",
       "      <th>Cold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDEX_1</th>\n",
       "      <th>INDEX_2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CITIES            NY      LA   SF\n",
       "TEMPERATURE     Cold Hot Hot Cold\n",
       "INDEX_1 INDEX_2                  \n",
       "A       1          0   1   2    3\n",
       "        2          4   5   6    7\n",
       "B       1          8   9  10   11\n",
       "        2         12  13  14   15"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe2.index.names = ['INDEX_1','INDEX_2']\n",
    "dframe2.columns.names = ['CITIES','TEMPERATURE']\n",
    "dframe2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>TEMPERATURE</th>\n",
       "      <th>Cold</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Hot</th>\n",
       "      <th>Cold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>CITIES</th>\n",
       "      <th>NY</th>\n",
       "      <th>NY</th>\n",
       "      <th>LA</th>\n",
       "      <th>SF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDEX_1</th>\n",
       "      <th>INDEX_2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "TEMPERATURE     Cold Hot     Cold\n",
       "CITIES            NY  NY  LA   SF\n",
       "INDEX_1 INDEX_2                  \n",
       "A       1          0   1   2    3\n",
       "        2          4   5   6    7\n",
       "B       1          8   9  10   11\n",
       "        2         12  13  14   15"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe2.swaplevel('CITIES','TEMPERATURE',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>TEMPERATURE</th>\n",
       "      <th>Cold</th>\n",
       "      <th>Hot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDEX_1</th>\n",
       "      <th>INDEX_2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "TEMPERATURE      Cold  Hot\n",
       "INDEX_1 INDEX_2           \n",
       "A       1           3    3\n",
       "        2          11   11\n",
       "B       1          19   19\n",
       "        2          27   27"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe2.sum(level='TEMPERATURE',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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