10 min to pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Object Creation\n",
    "Create a Series by passing list of values with default integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a DataFrame by passing NumPy array, with a datetime index and labeled columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20130101', periods=6)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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>2013-01-01</th>\n",
       "      <td>0.016060</td>\n",
       "      <td>1.164073</td>\n",
       "      <td>-0.328314</td>\n",
       "      <td>0.309657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>-1.034145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>-2.392699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>-0.413595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>1.254065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.911894</td>\n",
       "      <td>0.838113</td>\n",
       "      <td>-0.616153</td>\n",
       "      <td>-1.601621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.016060  1.164073 -0.328314  0.309657\n",
       "2013-01-02  1.629874 -1.730005  0.095680 -1.034145\n",
       "2013-01-03 -2.458939  0.665381  0.758674 -2.392699\n",
       "2013-01-04  0.169774  2.042630  0.820160 -0.413595\n",
       "2013-01-05  1.778459 -1.260840  0.257498  1.254065\n",
       "2013-01-06  0.911894  0.838113 -0.616153 -1.601621"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2013-01-02  1.0  3   test  foo\n",
       "1  1.0 2013-01-02  1.0  3  train  foo\n",
       "2  1.0 2013-01-02  1.0  3   test  foo\n",
       "3  1.0 2013-01-02  1.0  3  train  foo"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'A': 1.,\n",
    "                     'B': pd.Timestamp('20130102'),\n",
    "                     'C': pd.Series(1, index= list(range(4)), dtype='float32'),\n",
    "                    'D': np.array([3]*4, dtype='int32'),\n",
    "                    'E': pd.Categorical(['test', 'train', 'test', 'train']),\n",
    "                    'F': 'foo'})\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns of the resulting DataFrame have different dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Viewing Data\n",
    "Here is how to view the top and bottom rows of the frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>2013-01-01</th>\n",
       "      <td>0.016060</td>\n",
       "      <td>1.164073</td>\n",
       "      <td>-0.328314</td>\n",
       "      <td>0.309657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>-1.034145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>-2.392699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>-0.413595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>1.254065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.016060  1.164073 -0.328314  0.309657\n",
       "2013-01-02  1.629874 -1.730005  0.095680 -1.034145\n",
       "2013-01-03 -2.458939  0.665381  0.758674 -2.392699\n",
       "2013-01-04  0.169774  2.042630  0.820160 -0.413595\n",
       "2013-01-05  1.778459 -1.260840  0.257498  1.254065"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>-0.413595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>1.254065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.911894</td>\n",
       "      <td>0.838113</td>\n",
       "      <td>-0.616153</td>\n",
       "      <td>-1.601621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-04  0.169774  2.042630  0.820160 -0.413595\n",
       "2013-01-05  1.778459 -1.260840  0.257498  1.254065\n",
       "2013-01-06  0.911894  0.838113 -0.616153 -1.601621"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the index, columns and underlying NumPy data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.01606031,  1.1640731 , -0.3283139 ,  0.30965739],\n",
       "       [ 1.62987447, -1.73000542,  0.0956796 , -1.0341455 ],\n",
       "       [-2.45893894,  0.665381  ,  0.75867369, -2.39269877],\n",
       "       [ 0.16977432,  2.04263016,  0.82016036, -0.41359503],\n",
       "       [ 1.77845948, -1.26084011,  0.25749779,  1.25406524],\n",
       "       [ 0.91189353,  0.83811337, -0.61615294, -1.6016206 ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "describe() shows a quick statistical summary of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.341187</td>\n",
       "      <td>0.286559</td>\n",
       "      <td>0.164591</td>\n",
       "      <td>-0.646390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.550998</td>\n",
       "      <td>1.467141</td>\n",
       "      <td>0.574213</td>\n",
       "      <td>1.318512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>-0.616153</td>\n",
       "      <td>-2.392699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.054489</td>\n",
       "      <td>-0.779285</td>\n",
       "      <td>-0.222316</td>\n",
       "      <td>-1.459752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.540834</td>\n",
       "      <td>0.751747</td>\n",
       "      <td>0.176589</td>\n",
       "      <td>-0.723870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.450379</td>\n",
       "      <td>1.082583</td>\n",
       "      <td>0.633380</td>\n",
       "      <td>0.128844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>1.254065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean   0.341187  0.286559  0.164591 -0.646390\n",
       "std    1.550998  1.467141  0.574213  1.318512\n",
       "min   -2.458939 -1.730005 -0.616153 -2.392699\n",
       "25%    0.054489 -0.779285 -0.222316 -1.459752\n",
       "50%    0.540834  0.751747  0.176589 -0.723870\n",
       "75%    1.450379  1.082583  0.633380  0.128844\n",
       "max    1.778459  2.042630  0.820160  1.254065"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transposing your data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2013-01-01 00:00:00</th>\n",
       "      <th>2013-01-02 00:00:00</th>\n",
       "      <th>2013-01-03 00:00:00</th>\n",
       "      <th>2013-01-04 00:00:00</th>\n",
       "      <th>2013-01-05 00:00:00</th>\n",
       "      <th>2013-01-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.016060</td>\n",
       "      <td>1.629874</td>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.169774</td>\n",
       "      <td>1.778459</td>\n",
       "      <td>0.911894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.164073</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>0.838113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.328314</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>-0.616153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.309657</td>\n",
       "      <td>-1.034145</td>\n",
       "      <td>-2.392699</td>\n",
       "      <td>-0.413595</td>\n",
       "      <td>1.254065</td>\n",
       "      <td>-1.601621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06\n",
       "A    0.016060    1.629874   -2.458939    0.169774    1.778459    0.911894\n",
       "B    1.164073   -1.730005    0.665381    2.042630   -1.260840    0.838113\n",
       "C   -0.328314    0.095680    0.758674    0.820160    0.257498   -0.616153\n",
       "D    0.309657   -1.034145   -2.392699   -0.413595    1.254065   -1.601621"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by an axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.309657</td>\n",
       "      <td>-0.328314</td>\n",
       "      <td>1.164073</td>\n",
       "      <td>0.016060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.034145</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>1.629874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.392699</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>-2.458939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.413595</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.169774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.254065</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>1.778459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-1.601621</td>\n",
       "      <td>-0.616153</td>\n",
       "      <td>0.838113</td>\n",
       "      <td>0.911894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2013-01-01  0.309657 -0.328314  1.164073  0.016060\n",
       "2013-01-02 -1.034145  0.095680 -1.730005  1.629874\n",
       "2013-01-03 -2.392699  0.758674  0.665381 -2.458939\n",
       "2013-01-04 -0.413595  0.820160  2.042630  0.169774\n",
       "2013-01-05  1.254065  0.257498 -1.260840  1.778459\n",
       "2013-01-06 -1.601621 -0.616153  0.838113  0.911894"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>D</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>-1.034145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>1.254065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>-2.392699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.911894</td>\n",
       "      <td>0.838113</td>\n",
       "      <td>-0.616153</td>\n",
       "      <td>-1.601621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.016060</td>\n",
       "      <td>1.164073</td>\n",
       "      <td>-0.328314</td>\n",
       "      <td>0.309657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>-0.413595</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  1.629874 -1.730005  0.095680 -1.034145\n",
       "2013-01-05  1.778459 -1.260840  0.257498  1.254065\n",
       "2013-01-03 -2.458939  0.665381  0.758674 -2.392699\n",
       "2013-01-06  0.911894  0.838113 -0.616153 -1.601621\n",
       "2013-01-01  0.016060  1.164073 -0.328314  0.309657\n",
       "2013-01-04  0.169774  2.042630  0.820160 -0.413595"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use optimized pandas data access methods: .at, .iat, .loc and .iloc\n",
    "## Getting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting a single column, yeilding a Series equivalent of df.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    0.016060\n",
       "2013-01-02    1.629874\n",
       "2013-01-03   -2.458939\n",
       "2013-01-04    0.169774\n",
       "2013-01-05    1.778459\n",
       "2013-01-06    0.911894\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting via [], which slices the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.016060</td>\n",
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       "      <td>-0.328314</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>-1.034145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>-2.392699</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.016060  1.164073 -0.328314  0.309657\n",
       "2013-01-02  1.629874 -1.730005  0.095680 -1.034145\n",
       "2013-01-03 -2.458939  0.665381  0.758674 -2.392699"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>-2.392699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>-0.413595</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  1.629874 -1.730005  0.095680 -1.034145\n",
       "2013-01-03 -2.458939  0.665381  0.758674 -2.392699\n",
       "2013-01-04  0.169774  2.042630  0.820160 -0.413595"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['20130102':'20130104']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selection by Label\n",
    "For getting a cross section using a label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.016060\n",
       "B    1.164073\n",
       "C   -0.328314\n",
       "D    0.309657\n",
       "Name: 2013-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecing on a multi-axis by label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.016060</td>\n",
       "      <td>1.164073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.911894</td>\n",
       "      <td>0.838113</td>\n",
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      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-01  0.016060  1.164073\n",
       "2013-01-02  1.629874 -1.730005\n",
       "2013-01-03 -2.458939  0.665381\n",
       "2013-01-04  0.169774  2.042630\n",
       "2013-01-05  1.778459 -1.260840\n",
       "2013-01-06  0.911894  0.838113"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['A', 'B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing label slicing, both endpoints are _included_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-02  1.629874 -1.730005\n",
       "2013-01-03 -2.458939  0.665381\n",
       "2013-01-04  0.169774  2.042630\n",
       "2013-01-05  1.778459 -1.260840"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130102': '20130105', ['A', 'B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduction in the dimensions of the returned object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.629874\n",
       "B   -1.730005\n",
       "Name: 2013-01-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130102', ['A', 'B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For getting a scalar value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016060311025490312"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0], 'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For getting fast access to a scalar (eqauivalent to prior method):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016060311025490312"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0], 'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selection by Position\n",
    "\n",
    "Select via the position of the passed integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.169774\n",
       "B    2.042630\n",
       "C    0.820160\n",
       "D   -0.413595\n",
       "Name: 2013-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By integer slices, acting similar to numpy/python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.04263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.26084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A        B\n",
       "2013-01-04  0.169774  2.04263\n",
       "2013-01-05  1.778459 -1.26084"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5, 0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By lists of integer position locations, similar to numpy/python style:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>0.095680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.758674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>0.257498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2013-01-02  1.629874  0.095680\n",
       "2013-01-03 -2.458939  0.758674\n",
       "2013-01-05  1.778459  0.257498"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.iloc[[1,2,4], [0,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For slicing rows explicitly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>-1.034145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>-2.392699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  1.629874 -1.730005  0.095680 -1.034145\n",
       "2013-01-03 -2.458939  0.665381  0.758674 -2.392699"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For slicing columns explicitly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>1.164073</td>\n",
       "      <td>-0.328314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.260840</td>\n",
       "      <td>0.257498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.838113</td>\n",
       "      <td>-0.616153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2013-01-01  1.164073 -0.328314\n",
       "2013-01-02 -1.730005  0.095680\n",
       "2013-01-03  0.665381  0.758674\n",
       "2013-01-04  2.042630  0.820160\n",
       "2013-01-05 -1.260840  0.257498\n",
       "2013-01-06  0.838113 -0.616153"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, 1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For getting a value explicitly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.7300054190346486"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For getting fast access to a scalar (equivalent to the prior method):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.7300054190346486"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boolean Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a single column's values to select data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <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>2013-01-01</th>\n",
       "      <td>0.016060</td>\n",
       "      <td>1.164073</td>\n",
       "      <td>-0.328314</td>\n",
       "      <td>0.309657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>-1.034145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>-0.413595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>1.254065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.911894</td>\n",
       "      <td>0.838113</td>\n",
       "      <td>-0.616153</td>\n",
       "      <td>-1.601621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.016060  1.164073 -0.328314  0.309657\n",
       "2013-01-02  1.629874 -1.730005  0.095680 -1.034145\n",
       "2013-01-04  0.169774  2.042630  0.820160 -0.413595\n",
       "2013-01-05  1.778459 -1.260840  0.257498  1.254065\n",
       "2013-01-06  0.911894  0.838113 -0.616153 -1.601621"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.A > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting values from a DataFrame where a boolean condition is met"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>2013-01-01</th>\n",
       "      <td>0.016060</td>\n",
       "      <td>1.164073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.309657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>1.254065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.911894</td>\n",
       "      <td>0.838113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.016060  1.164073       NaN  0.309657\n",
       "2013-01-02  1.629874       NaN  0.095680       NaN\n",
       "2013-01-03       NaN  0.665381  0.758674       NaN\n",
       "2013-01-04  0.169774  2.042630  0.820160       NaN\n",
       "2013-01-05  1.778459       NaN  0.257498  1.254065\n",
       "2013-01-06  0.911894  0.838113       NaN       NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the isin() method for flitering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.016060</td>\n",
       "      <td>1.164073</td>\n",
       "      <td>-0.328314</td>\n",
       "      <td>0.309657</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>-1.034145</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>-2.392699</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>-0.413595</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>1.254065</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.911894</td>\n",
       "      <td>0.838113</td>\n",
       "      <td>-0.616153</td>\n",
       "      <td>-1.601621</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2013-01-01  0.016060  1.164073 -0.328314  0.309657    one\n",
       "2013-01-02  1.629874 -1.730005  0.095680 -1.034145    one\n",
       "2013-01-03 -2.458939  0.665381  0.758674 -2.392699    two\n",
       "2013-01-04  0.169774  2.042630  0.820160 -0.413595  three\n",
       "2013-01-05  1.778459 -1.260840  0.257498  1.254065   four\n",
       "2013-01-06  0.911894  0.838113 -0.616153 -1.601621  three"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "\n",
    "df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>-2.392699</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>1.254065</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D     E\n",
       "2013-01-03 -2.458939  0.665381  0.758674 -2.392699   two\n",
       "2013-01-05  1.778459 -1.260840  0.257498  1.254065  four"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['E'].isin(['two', 'four'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting\n",
    "Setting a new column automatically aligns the data by the indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-02    1\n",
       "2013-01-03    2\n",
       "2013-01-04    3\n",
       "2013-01-05    4\n",
       "2013-01-06    5\n",
       "2013-01-07    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.016060</td>\n",
       "      <td>1.164073</td>\n",
       "      <td>-0.328314</td>\n",
       "      <td>0.309657</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>-1.034145</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>-2.392699</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>-0.413595</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>1.254065</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.911894</td>\n",
       "      <td>0.838113</td>\n",
       "      <td>-0.616153</td>\n",
       "      <td>-1.601621</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    F\n",
       "2013-01-01  0.016060  1.164073 -0.328314  0.309657  NaN\n",
       "2013-01-02  1.629874 -1.730005  0.095680 -1.034145  1.0\n",
       "2013-01-03 -2.458939  0.665381  0.758674 -2.392699  2.0\n",
       "2013-01-04  0.169774  2.042630  0.820160 -0.413595  3.0\n",
       "2013-01-05  1.778459 -1.260840  0.257498  1.254065  4.0\n",
       "2013-01-06  0.911894  0.838113 -0.616153 -1.601621  5.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['F'] = s1\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting values by label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.164073</td>\n",
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       "      <td>0.309657</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>-1.034145</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>-2.392699</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>-0.413595</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>1.254065</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.911894</td>\n",
       "      <td>0.838113</td>\n",
       "      <td>-0.616153</td>\n",
       "      <td>-1.601621</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D    F\n",
       "2013-01-01  0.000000  1.164073 -0.328314  0.309657  NaN\n",
       "2013-01-02  1.629874 -1.730005  0.095680 -1.034145  1.0\n",
       "2013-01-03 -2.458939  0.665381  0.758674 -2.392699  2.0\n",
       "2013-01-04  0.169774  2.042630  0.820160 -0.413595  3.0\n",
       "2013-01-05  1.778459 -1.260840  0.257498  1.254065  4.0\n",
       "2013-01-06  0.911894  0.838113 -0.616153 -1.601621  5.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0], 'A'] = 0\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting values by position:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iat[0, 1] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting by assigning with a NumPy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'D'] = np.array(([5]) * len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
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       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
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       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.911894</td>\n",
       "      <td>0.838113</td>\n",
       "      <td>-0.616153</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2013-01-01  0.000000  0.000000 -0.328314  5  NaN\n",
       "2013-01-02  1.629874 -1.730005  0.095680  5  1.0\n",
       "2013-01-03 -2.458939  0.665381  0.758674  5  2.0\n",
       "2013-01-04  0.169774  2.042630  0.820160  5  3.0\n",
       "2013-01-05  1.778459 -1.260840  0.257498  5  4.0\n",
       "2013-01-06  0.911894  0.838113 -0.616153  5  5.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A _where_ operation with setting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-1.629874</td>\n",
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       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
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       "      <th>2013-01-04</th>\n",
       "      <td>-0.169774</td>\n",
       "      <td>-2.042630</td>\n",
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       "      <td>-5</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>-0.257498</td>\n",
       "      <td>-5</td>\n",
       "      <td>-4.0</td>\n",
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       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.911894</td>\n",
       "      <td>-0.838113</td>\n",
       "      <td>-0.616153</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2013-01-01  0.000000  0.000000 -0.328314 -5  NaN\n",
       "2013-01-02 -1.629874 -1.730005 -0.095680 -5 -1.0\n",
       "2013-01-03 -2.458939 -0.665381 -0.758674 -5 -2.0\n",
       "2013-01-04 -0.169774 -2.042630 -0.820160 -5 -3.0\n",
       "2013-01-05 -1.778459 -1.260840 -0.257498 -5 -4.0\n",
       "2013-01-06 -0.911894 -0.838113 -0.616153 -5 -5.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "\n",
    "df2[df2 > 0] = -df2\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-indexing allows you to change/add/elete the index on a specified axis. This returns a copy of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.328314</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F   E\n",
       "2013-01-01  0.000000  0.000000 -0.328314  5  NaN NaN\n",
       "2013-01-02  1.629874 -1.730005  0.095680  5  1.0 NaN\n",
       "2013-01-03 -2.458939  0.665381  0.758674  5  2.0 NaN\n",
       "2013-01-04  0.169774  2.042630  0.820160  5  3.0 NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.328314</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2013-01-01  0.000000  0.000000 -0.328314  5  NaN  1.0\n",
       "2013-01-02  1.629874 -1.730005  0.095680  5  1.0  1.0\n",
       "2013-01-03 -2.458939  0.665381  0.758674  5  2.0  NaN\n",
       "2013-01-04  0.169774  2.042630  0.820160  5  3.0  NaN"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[dates[0]:dates[1], 'E'] = 1\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To drop any rows that have missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.09568</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B        C  D    F    E\n",
       "2013-01-02  1.629874 -1.730005  0.09568  5  1.0  1.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill missing data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.328314</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F    E\n",
       "2013-01-01  0.000000  0.000000 -0.328314  5  5.0  1.0\n",
       "2013-01-02  1.629874 -1.730005  0.095680  5  1.0  1.0\n",
       "2013-01-03 -2.458939  0.665381  0.758674  5  2.0  5.0\n",
       "2013-01-04  0.169774  2.042630  0.820160  5  3.0  5.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(value=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the boolean mask where values are _nan_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>E</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A      B      C      D      F      E\n",
       "2013-01-01  False  False  False  False   True  False\n",
       "2013-01-02  False  False  False  False  False  False\n",
       "2013-01-03  False  False  False  False  False   True\n",
       "2013-01-04  False  False  False  False  False   True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Operations\n",
    "## Stats\n",
    "Operations in general _exclude_ missing data.  \n",
    "  \n",
    "Performing a descriptive statistic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.338510\n",
       "B    0.092546\n",
       "C    0.164591\n",
       "D    5.000000\n",
       "F    3.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same operation on another axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    1.167922\n",
       "2013-01-02    1.199110\n",
       "2013-01-03    1.193023\n",
       "2013-01-04    2.206513\n",
       "2013-01-05    1.955023\n",
       "2013-01-06    2.226771\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Operating with objects that have different dimensionality and need alignment. In addition, pandas automatically boradcasts along the specified dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    NaN\n",
       "2013-01-02    NaN\n",
       "2013-01-03    1.0\n",
       "2013-01-04    3.0\n",
       "2013-01-05    5.0\n",
       "2013-01-06    NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan, 6,8], index=dates).shift(2)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-3.458939</td>\n",
       "      <td>-0.334619</td>\n",
       "      <td>-0.241326</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.830226</td>\n",
       "      <td>-0.957370</td>\n",
       "      <td>-2.179840</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-3.221541</td>\n",
       "      <td>-6.260840</td>\n",
       "      <td>-4.742502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C    D    F\n",
       "2013-01-01       NaN       NaN       NaN  NaN  NaN\n",
       "2013-01-02       NaN       NaN       NaN  NaN  NaN\n",
       "2013-01-03 -3.458939 -0.334619 -0.241326  4.0  1.0\n",
       "2013-01-04 -2.830226 -0.957370 -2.179840  2.0  0.0\n",
       "2013-01-05 -3.221541 -6.260840 -4.742502  0.0 -1.0\n",
       "2013-01-06       NaN       NaN       NaN  NaN  NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(s, axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apply\n",
    "Applying functions to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.328314</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>0.095680</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-2.458939</td>\n",
       "      <td>0.665381</td>\n",
       "      <td>0.758674</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.169774</td>\n",
       "      <td>2.042630</td>\n",
       "      <td>0.820160</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.778459</td>\n",
       "      <td>-1.260840</td>\n",
       "      <td>0.257498</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.911894</td>\n",
       "      <td>0.838113</td>\n",
       "      <td>-0.616153</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    F\n",
       "2013-01-01  0.000000  0.000000 -0.328314  5  NaN\n",
       "2013-01-02  1.629874 -1.730005  0.095680  5  1.0\n",
       "2013-01-03 -2.458939  0.665381  0.758674  5  2.0\n",
       "2013-01-04  0.169774  2.042630  0.820160  5  3.0\n",
       "2013-01-05  1.778459 -1.260840  0.257498  5  4.0\n",
       "2013-01-06  0.911894  0.838113 -0.616153  5  5.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <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",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.328314</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.629874</td>\n",
       "      <td>-1.730005</td>\n",
       "      <td>-0.232634</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.829064</td>\n",
       "      <td>-1.064624</td>\n",
       "      <td>0.526039</td>\n",
       "      <td>15</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.659290</td>\n",
       "      <td>0.978006</td>\n",
       "      <td>1.346200</td>\n",
       "      <td>20</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>1.119169</td>\n",
       "      <td>-0.282834</td>\n",
       "      <td>1.603698</td>\n",
       "      <td>25</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>2.031063</td>\n",
       "      <td>0.555279</td>\n",
       "      <td>0.987545</td>\n",
       "      <td>30</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C   D     F\n",
       "2013-01-01  0.000000  0.000000 -0.328314   5   NaN\n",
       "2013-01-02  1.629874 -1.730005 -0.232634  10   1.0\n",
       "2013-01-03 -0.829064 -1.064624  0.526039  15   3.0\n",
       "2013-01-04 -0.659290  0.978006  1.346200  20   6.0\n",
       "2013-01-05  1.119169 -0.282834  1.603698  25  10.0\n",
       "2013-01-06  2.031063  0.555279  0.987545  30  15.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    4.237398\n",
       "B    3.772636\n",
       "C    1.436313\n",
       "D    0.000000\n",
       "F    4.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogramming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "1    3\n",
       "2    5\n",
       "3    4\n",
       "4    3\n",
       "5    4\n",
       "6    2\n",
       "7    4\n",
       "8    1\n",
       "9    2\n",
       "dtype: int32"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.randint(0, 7, size=10))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    3\n",
       "5    2\n",
       "3    2\n",
       "2    2\n",
       "1    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# String Methods\n",
    "Series is equipped with a set of string processing methods in the str attribute that make it easy to operae on each element of the array, as in the code snippet below. Not that the pattern-matching in str generally uses regular expressions by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       a\n",
       "1       b\n",
       "2       c\n",
       "3    aaba\n",
       "4    baca\n",
       "5     NaN\n",
       "6    caba\n",
       "7     dog\n",
       "8     cat\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])\n",
    "s.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge\n",
    "## Concat\n",
    "pandas provides various facilities for easily combining together Series, DataFrame and Panel objects with various set logic for the indexes and relational algebra functionality in the case of join / merge-type operations\n",
    "\n",
    "Concatenating pandas objects together with concat():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-1.095604</td>\n",
       "      <td>-0.142812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.312015</td>\n",
       "      <td>0.045040</td>\n",
       "      <td>0.034939</td>\n",
       "      <td>0.538975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.594270</td>\n",
       "      <td>0.400783</td>\n",
       "      <td>-0.423093</td>\n",
       "      <td>0.488188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.189603</td>\n",
       "      <td>-0.017875</td>\n",
       "      <td>-1.187451</td>\n",
       "      <td>0.827464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.131702</td>\n",
       "      <td>1.036652</td>\n",
       "      <td>1.294945</td>\n",
       "      <td>-0.472687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.317883</td>\n",
       "      <td>0.336852</td>\n",
       "      <td>0.958643</td>\n",
       "      <td>-1.552852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.777202</td>\n",
       "      <td>1.419976</td>\n",
       "      <td>-0.111756</td>\n",
       "      <td>0.760590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.995439</td>\n",
       "      <td>1.014435</td>\n",
       "      <td>-0.413143</td>\n",
       "      <td>-0.345396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.525662</td>\n",
       "      <td>0.858151</td>\n",
       "      <td>-1.314335</td>\n",
       "      <td>-0.152080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.688460</td>\n",
       "      <td>0.437497</td>\n",
       "      <td>0.339439</td>\n",
       "      <td>0.648650</td>\n",
       "    </tr>\n",
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       "</table>\n",
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       "          0         1         2         3\n",
       "0 -0.663246 -0.332036 -1.095604 -0.142812\n",
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       "2 -0.594270  0.400783 -0.423093  0.488188\n",
       "3  0.189603 -0.017875 -1.187451  0.827464\n",
       "4  0.131702  1.036652  1.294945 -0.472687\n",
       "5 -0.317883  0.336852  0.958643 -1.552852\n",
       "6  1.777202  1.419976 -0.111756  0.760590\n",
       "7  0.995439  1.014435 -0.413143 -0.345396\n",
       "8 -0.525662  0.858151 -1.314335 -0.152080\n",
       "9  0.688460  0.437497  0.339439  0.648650"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(10,4))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[          0         1         2         3\n",
       " 0 -0.663246 -0.332036 -1.095604 -0.142812\n",
       " 1  0.312015  0.045040  0.034939  0.538975\n",
       " 2 -0.594270  0.400783 -0.423093  0.488188,\n",
       "           0         1         2         3\n",
       " 3  0.189603 -0.017875 -1.187451  0.827464\n",
       " 4  0.131702  1.036652  1.294945 -0.472687\n",
       " 5 -0.317883  0.336852  0.958643 -1.552852\n",
       " 6  1.777202  1.419976 -0.111756  0.760590,\n",
       "           0         1         2         3\n",
       " 7  0.995439  1.014435 -0.413143 -0.345396\n",
       " 8 -0.525662  0.858151 -1.314335 -0.152080\n",
       " 9  0.688460  0.437497  0.339439  0.648650]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pieces = [df[:3], df[3:7], df[7:]]\n",
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.034939</td>\n",
       "      <td>0.538975</td>\n",
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       "      <td>-0.423093</td>\n",
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       "      <th>3</th>\n",
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       "      <td>-0.017875</td>\n",
       "      <td>-1.187451</td>\n",
       "      <td>0.827464</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.131702</td>\n",
       "      <td>1.036652</td>\n",
       "      <td>1.294945</td>\n",
       "      <td>-0.472687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.317883</td>\n",
       "      <td>0.336852</td>\n",
       "      <td>0.958643</td>\n",
       "      <td>-1.552852</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.777202</td>\n",
       "      <td>1.419976</td>\n",
       "      <td>-0.111756</td>\n",
       "      <td>0.760590</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.995439</td>\n",
       "      <td>1.014435</td>\n",
       "      <td>-0.413143</td>\n",
       "      <td>-0.345396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.525662</td>\n",
       "      <td>0.858151</td>\n",
       "      <td>-1.314335</td>\n",
       "      <td>-0.152080</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.688460</td>\n",
       "      <td>0.437497</td>\n",
       "      <td>0.339439</td>\n",
       "      <td>0.648650</td>\n",
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       "          0         1         2         3\n",
       "0 -0.663246 -0.332036 -1.095604 -0.142812\n",
       "1  0.312015  0.045040  0.034939  0.538975\n",
       "2 -0.594270  0.400783 -0.423093  0.488188\n",
       "3  0.189603 -0.017875 -1.187451  0.827464\n",
       "4  0.131702  1.036652  1.294945 -0.472687\n",
       "5 -0.317883  0.336852  0.958643 -1.552852\n",
       "6  1.777202  1.419976 -0.111756  0.760590\n",
       "7  0.995439  1.014435 -0.413143 -0.345396\n",
       "8 -0.525662  0.858151 -1.314335 -0.152080\n",
       "9  0.688460  0.437497  0.339439  0.648650"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(pieces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join\n",
    "SQL syle merges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2</td>\n",
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      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  foo     2"
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     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>key</th>\n",
       "      <th>rval</th>\n",
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       "  </thead>\n",
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       "      <td>5</td>\n",
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       "</div>"
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      "text/plain": [
       "   key  rval\n",
       "0  foo     4\n",
       "1  foo     5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>lval</th>\n",
       "      <th>rval</th>\n",
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       "      <td>5</td>\n",
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       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  foo     1     5\n",
       "2  foo     2     4\n",
       "3  foo     2     5"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another example can be given as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  bar     2"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   key  rval\n",
       "0  foo     4\n",
       "1  bar     5"
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     "execution_count": 65,
     "metadata": {},
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   "source": [
    "right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th>rval</th>\n",
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      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  bar     2     5"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Append\n",
    "Appends rows to a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>0.194542</td>\n",
       "      <td>-0.182481</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.296792</td>\n",
       "      <td>0.180877</td>\n",
       "      <td>0.210569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.125391</td>\n",
       "      <td>-0.218031</td>\n",
       "      <td>0.131682</td>\n",
       "      <td>0.091264</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.874580</td>\n",
       "      <td>1.465831</td>\n",
       "      <td>1.427410</td>\n",
       "      <td>0.192609</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.747945</td>\n",
       "      <td>-0.317806</td>\n",
       "      <td>-0.620446</td>\n",
       "      <td>-0.355783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.493046 -1.929769 -0.509749 -1.087925\n",
       "1  0.046011  0.548101  0.328145  1.816828\n",
       "2  1.035626  0.387886  0.194542 -0.182481\n",
       "3  2.230360 -0.143009  0.217990  1.206309\n",
       "4 -0.080462  0.296792  0.180877  0.210569\n",
       "5 -0.125391 -0.218031  0.131682  0.091264\n",
       "6  0.874580  1.465831  1.427410  0.192609\n",
       "7 -1.747945 -0.317806 -0.620446 -0.355783"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 4), columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.180877</td>\n",
       "      <td>0.210569</td>\n",
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       "      <th>5</th>\n",
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       "      <td>0.131682</td>\n",
       "      <td>0.091264</td>\n",
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       "      <th>6</th>\n",
       "      <td>0.874580</td>\n",
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       "      <td>1.427410</td>\n",
       "      <td>0.192609</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.747945</td>\n",
       "      <td>-0.317806</td>\n",
       "      <td>-0.620446</td>\n",
       "      <td>-0.355783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.493046 -1.929769 -0.509749 -1.087925\n",
       "1  0.046011  0.548101  0.328145  1.816828\n",
       "2  1.035626  0.387886  0.194542 -0.182481\n",
       "3  2.230360 -0.143009  0.217990  1.206309\n",
       "4 -0.080462  0.296792  0.180877  0.210569\n",
       "5 -0.125391 -0.218031  0.131682  0.091264\n",
       "6  0.874580  1.465831  1.427410  0.192609\n",
       "7 -1.747945 -0.317806 -0.620446 -0.355783"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2.230360\n",
       "B   -0.143009\n",
       "C    0.217990\n",
       "D    1.206309\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df.iloc[3]\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>0</th>\n",
       "      <td>-0.493046</td>\n",
       "      <td>-1.929769</td>\n",
       "      <td>-0.509749</td>\n",
       "      <td>-1.087925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.046011</td>\n",
       "      <td>0.548101</td>\n",
       "      <td>0.328145</td>\n",
       "      <td>1.816828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.035626</td>\n",
       "      <td>0.387886</td>\n",
       "      <td>0.194542</td>\n",
       "      <td>-0.182481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.230360</td>\n",
       "      <td>-0.143009</td>\n",
       "      <td>0.217990</td>\n",
       "      <td>1.206309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.080462</td>\n",
       "      <td>0.296792</td>\n",
       "      <td>0.180877</td>\n",
       "      <td>0.210569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.125391</td>\n",
       "      <td>-0.218031</td>\n",
       "      <td>0.131682</td>\n",
       "      <td>0.091264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.874580</td>\n",
       "      <td>1.465831</td>\n",
       "      <td>1.427410</td>\n",
       "      <td>0.192609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.747945</td>\n",
       "      <td>-0.317806</td>\n",
       "      <td>-0.620446</td>\n",
       "      <td>-0.355783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.230360</td>\n",
       "      <td>-0.143009</td>\n",
       "      <td>0.217990</td>\n",
       "      <td>1.206309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -0.493046 -1.929769 -0.509749 -1.087925\n",
       "1  0.046011  0.548101  0.328145  1.816828\n",
       "2  1.035626  0.387886  0.194542 -0.182481\n",
       "3  2.230360 -0.143009  0.217990  1.206309\n",
       "4 -0.080462  0.296792  0.180877  0.210569\n",
       "5 -0.125391 -0.218031  0.131682  0.091264\n",
       "6  0.874580  1.465831  1.427410  0.192609\n",
       "7 -1.747945 -0.317806 -0.620446 -0.355783\n",
       "8  2.230360 -0.143009  0.217990  1.206309"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(s, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouping\n",
    "By \"group by\" we are refering to a process involving one or more of following steps:\n",
    "* **Splitting** the data into gropus based on some criteria\n",
    "* **Applying** a function to each group independently\n",
    "* **Combining** the results into a data structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>1.650447</td>\n",
       "      <td>-2.207658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.008808</td>\n",
       "      <td>-0.153895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.808817</td>\n",
       "      <td>1.475358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>1.878052</td>\n",
       "      <td>-0.471448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.903591</td>\n",
       "      <td>-0.312657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.003513</td>\n",
       "      <td>-0.060657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-2.095931</td>\n",
       "      <td>0.600607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>0.833427</td>\n",
       "      <td>-0.401106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one  1.650447 -2.207658\n",
       "1  bar    one -0.008808 -0.153895\n",
       "2  foo    two -1.808817  1.475358\n",
       "3  bar  three  1.878052 -0.471448\n",
       "4  foo    two  0.903591 -0.312657\n",
       "5  bar    two -1.003513 -0.060657\n",
       "6  foo    one -2.095931  0.600607\n",
       "7  foo  three  0.833427 -0.401106"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',\n",
    "                    'foo', 'bar', 'foo', 'foo'],\n",
    "              'B': ['one', 'one', 'two', 'three',\n",
    "                    'two', 'two', 'one', 'three'],\n",
    "              'C': np.random.randn(8),\n",
    "              'D': np.random.randn(8)})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping and then applying the sum() function to the resulting groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>0.865731</td>\n",
       "      <td>-0.686000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>-0.517283</td>\n",
       "      <td>-0.845456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar  0.865731 -0.686000\n",
       "foo -0.517283 -0.845456"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping by multiple columns forms a heirarchical index, and again we can apply the sum function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.008808</td>\n",
       "      <td>-0.153895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>1.878052</td>\n",
       "      <td>-0.471448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-1.003513</td>\n",
       "      <td>-0.060657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.445483</td>\n",
       "      <td>-1.607051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0.833427</td>\n",
       "      <td>-0.401106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.905226</td>\n",
       "      <td>1.162701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one   -0.008808 -0.153895\n",
       "    three  1.878052 -0.471448\n",
       "    two   -1.003513 -0.060657\n",
       "foo one   -0.445483 -1.607051\n",
       "    three  0.833427 -0.401106\n",
       "    two   -0.905226  1.162701"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A', 'B']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return a copy of DataFrame excluding elements from groups that do not staisfy boolean criteria specified by func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A  B    C\n",
       "0  foo  1  2.0\n",
       "1  bar  2  5.0\n",
       "2  foo  3  8.0\n",
       "3  bar  4  1.0\n",
       "4  foo  5  2.0\n",
       "5  bar  6  9.0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar'],\n",
    "              'B': [1, 2, 3, 4, 5, 6],\n",
    "              'C': [2.0, 5., 8., 1., 2., 9.]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>A</th>\n",
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       "      <th>C</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>6</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A  B    C\n",
       "1  bar  2  5.0\n",
       "3  bar  4  1.0\n",
       "5  bar  6  9.0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby('A')\n",
    "grouped.filter(lambda x: x['B'].mean() > 3.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reshaping\n",
    "## Stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('bar', 'one'),\n",
       " ('bar', 'two'),\n",
       " ('baz', 'one'),\n",
       " ('baz', 'two'),\n",
       " ('foo', 'one'),\n",
       " ('foo', 'two'),\n",
       " ('qux', 'one'),\n",
       " ('qux', 'two')]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuples = list(zip(*[['bar', 'bar', 'baz', 'baz', \n",
    "            'foo', 'foo', 'qux', 'qux'],\n",
    "           ['one','two', 'one', 'two', 'one', 'two', 'one', 'two']]))\n",
    "tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['bar', 'baz', 'foo', 'qux'], ['one', 'two']],\n",
       "           labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]],\n",
       "           names=['first', 'second'])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>one</th>\n",
       "      <td>-0.235272</td>\n",
       "      <td>2.355797</td>\n",
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       "      <th>two</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.976782</td>\n",
       "      <td>-0.357650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1.290798</td>\n",
       "      <td>-0.762492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>0.273945</td>\n",
       "      <td>1.549777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.279846</td>\n",
       "      <td>0.739005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">qux</th>\n",
       "      <th>one</th>\n",
       "      <td>-1.362241</td>\n",
       "      <td>-0.431109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.064434</td>\n",
       "      <td>-0.820117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.235272  2.355797\n",
       "      two     0.338042 -0.132886\n",
       "baz   one    -0.976782 -0.357650\n",
       "      two     1.290798 -0.762492\n",
       "foo   one     0.273945  1.549777\n",
       "      two    -0.279846  0.739005\n",
       "qux   one    -1.362241 -0.431109\n",
       "      two    -0.064434 -0.820117"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(8,2), index=index, columns=['A', 'B'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.235272</td>\n",
       "      <td>2.355797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.338042</td>\n",
       "      <td>-0.132886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.976782</td>\n",
       "      <td>-0.357650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1.290798</td>\n",
       "      <td>-0.762492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.235272  2.355797\n",
       "      two     0.338042 -0.132886\n",
       "baz   one    -0.976782 -0.357650\n",
       "      two     1.290798 -0.762492"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[:4]\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stack() method 'compresses' a level in the DataFrames' columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first  second   \n",
       "bar    one     A   -0.235272\n",
       "               B    2.355797\n",
       "       two     A    0.338042\n",
       "               B   -0.132886\n",
       "baz    one     A   -0.976782\n",
       "               B   -0.357650\n",
       "       two     A    1.290798\n",
       "               B   -0.762492\n",
       "dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked = df2.stack()\n",
    "stacked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a 'stacked' DataFrame or Series (having a MultiIndex as the index), the inverse operation of stack() is unstack(), which by default unstacks the last level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.235272</td>\n",
       "      <td>2.355797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.338042</td>\n",
       "      <td>-0.132886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.976782</td>\n",
       "      <td>-0.357650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1.290798</td>\n",
       "      <td>-0.762492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.235272  2.355797\n",
       "      two     0.338042 -0.132886\n",
       "baz   one    -0.976782 -0.357650\n",
       "      two     1.290798 -0.762492"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>second</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.235272</td>\n",
       "      <td>0.338042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2.355797</td>\n",
       "      <td>-0.132886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.976782</td>\n",
       "      <td>1.290798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.357650</td>\n",
       "      <td>-0.762492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "second        one       two\n",
       "first                      \n",
       "bar   A -0.235272  0.338042\n",
       "      B  2.355797 -0.132886\n",
       "baz   A -0.976782  1.290798\n",
       "      B -0.357650 -0.762492"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.235272</td>\n",
       "      <td>-0.976782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2.355797</td>\n",
       "      <td>-0.357650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>0.338042</td>\n",
       "      <td>1.290798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.132886</td>\n",
       "      <td>-0.762492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "first          bar       baz\n",
       "second                      \n",
       "one    A -0.235272 -0.976782\n",
       "       B  2.355797 -0.357650\n",
       "two    A  0.338042  1.290798\n",
       "       B -0.132886 -0.762492"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.379091</td>\n",
       "      <td>0.083504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.990927</td>\n",
       "      <td>0.312636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.546904</td>\n",
       "      <td>0.447138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.578812</td>\n",
       "      <td>-0.991291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>-1.340753</td>\n",
       "      <td>0.001060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.785893</td>\n",
       "      <td>-0.773077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>-2.508299</td>\n",
       "      <td>1.170237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>three</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.259810</td>\n",
       "      <td>-0.950836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.309932</td>\n",
       "      <td>-1.061019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.055315</td>\n",
       "      <td>0.241464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.311139</td>\n",
       "      <td>1.089708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>three</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.293254</td>\n",
       "      <td>-1.976396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A  B    C         D         E\n",
       "0     one  A  foo  0.379091  0.083504\n",
       "1     one  B  foo  0.990927  0.312636\n",
       "2     two  C  foo -0.546904  0.447138\n",
       "3   three  A  bar  0.578812 -0.991291\n",
       "4     one  B  bar -1.340753  0.001060\n",
       "5     one  C  bar  0.785893 -0.773077\n",
       "6     two  A  foo -2.508299  1.170237\n",
       "7   three  B  foo  0.259810 -0.950836\n",
       "8     one  C  foo -0.309932 -1.061019\n",
       "9     one  A  bar  0.055315  0.241464\n",
       "10    two  B  bar  0.311139  1.089708\n",
       "11  three  C  bar  1.293254 -1.976396"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 3,\n",
    "              'B': ['A', 'B', 'C'] * 4,\n",
    "              'C': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n",
    "              'D': np.random.randn(12),\n",
    "              'E': np.random.randn(12)})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can product pivot tables from this data very easily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>bar</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>0.055315</td>\n",
       "      <td>0.379091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.340753</td>\n",
       "      <td>0.990927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.785893</td>\n",
       "      <td>-0.309932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">three</th>\n",
       "      <th>A</th>\n",
       "      <td>0.578812</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.259810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1.293254</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.508299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.311139</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.546904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C             bar       foo\n",
       "A     B                    \n",
       "one   A  0.055315  0.379091\n",
       "      B -1.340753  0.990927\n",
       "      C  0.785893 -0.309932\n",
       "three A  0.578812       NaN\n",
       "      B       NaN  0.259810\n",
       "      C  1.293254       NaN\n",
       "two   A       NaN -2.508299\n",
       "      B  0.311139       NaN\n",
       "      C       NaN -0.546904"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series\n",
    "pandas has simple, powerful and efficient functionality for performing resampling operations and during frequency conversion (e.g. converting second data into 5-minute data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range('1/1/2012', periods=100, freq='S')\n",
    "ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01    23507\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5Min').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01    235.07\n",
       "Freq: 5T, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>217</td>\n",
       "      <td>498</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            open  high  low  close\n",
       "2012-01-01   217   498    1     85"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min').ohlc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01    498\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5min').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "498"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For downsampling _closed_ can be set to 'left' or 'right' to specify which end of the interval is closed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2011-12-31 23:55:00    217.000000\n",
       "2012-01-01 00:00:00    235.252525\n",
       "Freq: 5T, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample('5Min', closed='right').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "212.45762711864407"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts[:59].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note</b> - don't forget to check out https://elitedatascience.com/imbalanced-classes - for use cases of upsampling / downsampling / random forest algo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time zone representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-03-06', '2012-03-07', '2012-03-08', '2012-03-09',\n",
       "               '2012-03-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06    1.326767\n",
       "2012-03-07   -0.622441\n",
       "2012-03-08   -1.332608\n",
       "2012-03-09    0.804298\n",
       "2012-03-10    0.058507\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06 00:00:00+00:00    1.326767\n",
       "2012-03-07 00:00:00+00:00   -0.622441\n",
       "2012-03-08 00:00:00+00:00   -1.332608\n",
       "2012-03-09 00:00:00+00:00    0.804298\n",
       "2012-03-10 00:00:00+00:00    0.058507\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc = ts.tz_localize('UTC')\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting to another timezone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06 05:30:00+05:30    1.326767\n",
       "2012-03-07 05:30:00+05:30   -0.622441\n",
       "2012-03-08 05:30:00+05:30   -1.332608\n",
       "2012-03-09 05:30:00+05:30    0.804298\n",
       "2012-03-10 05:30:00+05:30    0.058507\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('Asia/Calcutta')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting between timespan representations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-01-31', '2012-02-29', '2012-03-31', '2012-04-30',\n",
       "               '2012-05-31'],\n",
       "              dtype='datetime64[ns]', freq='M')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2012', periods=5, freq='M')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-31    0.274067\n",
       "2012-02-29   -0.811241\n",
       "2012-03-31   -0.033143\n",
       "2012-04-30    1.194062\n",
       "2012-05-31   -0.633113\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01    0.274067\n",
       "2012-02   -0.811241\n",
       "2012-03   -0.033143\n",
       "2012-04    1.194062\n",
       "2012-05   -0.633113\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = ts.to_period()\n",
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01    0.274067\n",
       "2012-02-01   -0.811241\n",
       "2012-03-01   -0.033143\n",
       "2012-04-01    1.194062\n",
       "2012-05-01   -0.633113\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps.to_timestamp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting between period and timestamp enables some convenient arithmetic functions to be used. In the following example, we convert a quarterly frequency with year ending in November to 9am of the end of the month following the quarter end:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['1990Q1', '1990Q2', '1990Q3', '1990Q4', '1991Q1', '1991Q2',\n",
       "             '1991Q3', '1991Q4', '1992Q1', '1992Q2', '1992Q3', '1992Q4',\n",
       "             '1993Q1', '1993Q2', '1993Q3', '1993Q4', '1994Q1', '1994Q2',\n",
       "             '1994Q3', '1994Q4', '1995Q1', '1995Q2', '1995Q3', '1995Q4',\n",
       "             '1996Q1', '1996Q2', '1996Q3', '1996Q4', '1997Q1', '1997Q2',\n",
       "             '1997Q3', '1997Q4', '1998Q1', '1998Q2', '1998Q3', '1998Q4',\n",
       "             '1999Q1', '1999Q2', '1999Q3', '1999Q4', '2000Q1', '2000Q2',\n",
       "             '2000Q3', '2000Q4'],\n",
       "            dtype='period[Q-NOV]', freq='Q-NOV')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')\n",
    "prng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990Q1    0.401787\n",
       "1990Q2    0.398497\n",
       "1990Q3    0.329889\n",
       "1990Q4   -1.025884\n",
       "1991Q1    0.351712\n",
       "Freq: Q-NOV, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(len(prng)), prng)\n",
    "ts[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-03-01 09:00    0.401787\n",
       "1990-06-01 09:00    0.398497\n",
       "1990-09-01 09:00    0.329889\n",
       "1990-12-01 09:00   -1.025884\n",
       "1991-03-01 09:00    0.351712\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.index = (prng.asfreq('M', 'e')+1).asfreq('H', 's') + 9\n",
    "ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categoricals\n",
    "pandas can include categorical data in a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>id</th>\n",
       "      <th>raw_grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade\n",
       "0   1         a\n",
       "1   2         b\n",
       "2   3         b\n",
       "3   4         a\n",
       "4   5         a\n",
       "5   6         e"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'id': [1,2,3,4,5,6], 'raw_grade' : list('abbaae')})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the raw grades to a categorical data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    a\n",
       "4    a\n",
       "5    e\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): [a, b, e]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['grade'] = df.raw_grade.astype('category')\n",
    "df.grade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the categories to meaningful names (assigning to Series.cat.categories is inplace!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Very Good\n",
       "1         Good\n",
       "2         Good\n",
       "3    Very Good\n",
       "4    Very Good\n",
       "5     Very Bad\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): [Very Good, Good, Very Bad]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.grade.cat.categories = ['Very Good', 'Good', 'Very Bad']\n",
    "df.grade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reorder the categories and simultaneously add the missing categories (methods under Series.cat return a new Series by default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Very Good\n",
       "1         Good\n",
       "2         Good\n",
       "3    Very Good\n",
       "4    Very Good\n",
       "5     Very Bad\n",
       "Name: grade, dtype: category\n",
       "Categories (5, object): [Very Bad, Bad, Medium, Good, Very Good]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.grade = df.grade.cat.set_categories(['Very Bad', 'Bad', 'Medium', 'Good', 'Very Good' ])\n",
    "df.grade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting is per order in the categories, not lexical order"
   ]
  },
  {
   "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>id</th>\n",
       "      <th>raw_grade</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>e</td>\n",
       "      <td>Very Bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>Very Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade      grade\n",
       "5   6         e   Very Bad\n",
       "1   2         b       Good\n",
       "2   3         b       Good\n",
       "0   1         a  Very Good\n",
       "3   4         a  Very Good\n",
       "4   5         a  Very Good"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='grade')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping by categorical column also shows empty categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "Very Bad     1\n",
       "Bad          0\n",
       "Medium       0\n",
       "Good         2\n",
       "Very Good    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('grade').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2734c0679b0>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JTtlmPQ7WdeK0mEEjLaoyGMq7g6ON3X5ZNn1ON4zi+MFN544CAJxoscHh9mDh+CKU5Vn8gr2pn8qdSt1ihtGofAu0GoaGTlpnl5BkNOBfNGNsDWNsb4Cfq/t7W4BtAVNIOOfLOOdVnPOqwsLCUM87LViNOmw/1Y6bX9wMACjOGnzRsrI8i+q57+LmfU6PXKr5F1dOwu3zKuTXxokVPiVrfrQAI/Ms6HN6BkzPbO62o0lcdzfLrEee1eC3Di8hJDkM2KXDOV80iOPWAChTPC8F0H+hlyFIWmikTSyvEEkLX5n6CQC1HX0oUhyv1+mWW+w6rQblBd6uHeXkLQAYU5SJ62eV4bcrD8Hu8vhlFilVPbpGfpxnMSDbrEdHb+jVOwkh8ROrLp33AFzPGDMyxioAjAWwOUaflbJ8q0oqa9yHq7JIneHTZvOmRjrdHrg9XG7hA1AF8UAF26R9V+2rD/qZvoE926yngE9IEos0LfMrjLEaAHMALGeMrQIAzvk+AG8B2A9gJYC7OOeDK8GYxpQB36TX9NuSHohRp8Vvr52GR66cBABoVwT8XrFKpvL4XznLO6QSaKBWqv1zzxs7sfdMR8CB2KON3arnGg2DTsPw5bGWsGfqEkJiL6KAzzl/h3Neyjk3cs6LOeeXKF57jHNeyTkfzzn/MPJTTT/KlaUyjOFPuPL19ZlluHqGEMjberytbKkssknRktdqmGr9XV/K1bmu+N/PseSPn/ntc7TRP/3zlPi+gwFSQwkhiUUzbRNI2cLPCLNoWjBZZuGLo13RrVLXLrTOs0zqIZuP7l2ALQ8FHqK5bd5o1fNAs2erm3sAAN9bWImDv74UAHDv4nEAorfOLiEkeijgJ5CyqFq4ZZGD0WoYTHqNarGTz44I6a7SAuuSTJM+6LjByHwLfnzxuH4/60xbL0blW/CTSyfI3UVSLr4tjFo8hJD4oICfQD+9dIKcPx/NcgRmvRa9ioB7pLEbJTlm5FoNYR2nqJ+sIbeHY8epdr/ZwdIA8KbqlrA+ixASe7SmbQKZ9Fr867bZWPj0uojq6Pgy67XyQC0gfJmUDKJsw0if3H63h0MrppL+9dNjONPei4oC9XlLAf+5dcdwuq0X44oy4PRw/Ghx/3cLhJDYo4CfYOUFVmx44EK5mFo0mAxaVZdOn8uDbHP4g8JVo3Jx63nlaOjsw4o99ei2u+TjbBRb8L6TrMyKgWHlGrsU8AlJPOrSSQLDs83Izxh8Dr4vs14d8O1ON0y68C+1TqvBL66cjIXjigAAnYqB4AniYis/u3yS6j2WMBdwIYTEDwX8NGTy6dIZaLbsQKR1c5UTqrQaDQw6DeaNLVDtaw7yOcovIEJIYlDAT0O+g7Z9TjeMg2jhS8YPywIAfHmsWd7W63AFnKGr1TD8+popfts7afYtIQlHAT8NFWQYcLLFBpfb47eq1WBUFFhRlGnEkQbvzFqbwx20NT+uKMNvW2M/i6gHsu1kqzxOQAiJDgr4aWjumAK09DhQ09aLR5fvBwBsPB5Z8CzJNaO2w7sgis3pVg3QKilr7ktCXUwFADjnuPYvG3D9so3hnyghJCgK+GlImkylnB2r00R2qYdlmdAo1rl3uj1YvrsOx8WZtr6UZZ4fXDIBgDBJK1RNiswf6vsnJHoopSIN5YsTrDYfb4VYAw32CAOn2aCVZ89K9XLOqywIuC9jDB/eMx8GnQajC6z43erDYbXwaxRfDjVtNowpyozgzAkhEmrhpyEpxfPJlQflmvu/vNp/IDUcylTPE2LL/kf9lF6YODwLlYUZYIyhNNeCY03d+OX7+/otnbztZBsW/+5T1Zq8i363Hre/vCWicyeECKiFn4ZGiFUwrQYtjjf3YOH4QiyeVBzRMS0Gb6qntGBLgTW0uQPDs01Yd6gJ6w41gYHh51eqc/dPNPdg4dPrxBpAHqzYq67Bv+ZAY0TnTggRUAs/DTHGMG9MAXocbhys78L4YZF3iUjlGjjn6OoTAn6mKbT2QoFiUlmTz8zcg/WdWPj0OgDCMowAsP7w0FrbmJB4oYCfpqSgDHhnxUbCZNCCc2ESV5e4aHlGiAE/X1G0rdehLpt8z+s7g77PEMHcgWh76J09VBCOpLzk+YsiUfXM12fIj0fmRV6YTcq5X3OgAb9bfRiA/xKNwRQoSjDrtRpwztHY1YeXvjgOdz8rYykniwVaQavP6VYt1BIrTrcHr246hW9QmihJcdSHn6aUZYuzzZFfZmlW7d2v7Qj7vXNG58uPPZzjuXXH8NSqQwO+T7qTAIQFVTJ9Flu/79+7sHx3HQ4/ellM7waotj9JF9TCT1PK1rFvoByMWRX5qufhVL9UjiF09Drxx7VHVK/nWgY+P+WSjZJ1B4XB3Jq22Lbyg80FON7cg/L7l2P7qbaYfn60nWnvxf/tOEPrDg9BFPDTlHJh8lAHV/tTUWDFDxeNBQCMK87ADy4aG/J7lYXbTrXY4HB5cNGEInnblJJs/PWbZwMAvnZOKe65aCz+9P/OUh3jmdX+dwQ5FmFs4HBDt99r0dQbpIX/6SHhC+ed7Wdi+vnR9qePj+KHb+7EBhqTGHIo4A8BwWrehKtqVB4AINcS3spZAPD1qlIAQLM4+/eqGSPwgwvHABC6iy6dMhwnnrgcT183HfcuHocrpo1Qvf/dnbWqgWjOuTyZ67XNp8L/ZcLQG6SFL32pcqRWS7lW/Hc72RL78Q+SXCjgp7Fb5pZjwrBMVWs/EueMysWiicX4zVenhv3e335tOq6eMQIOl5B6aTHoMGG4UIWzudt/gXQA+NnlE3GP4k7i8RUH5cf7ajvlxztj3KUSPOAL/021nhGpu08qlUGGDgr4aeyRqyZj5Q8XRO14ZoMWL9xchdGF/tUwQ1GW610y0azXYlyxcJweuyvg/rfPH417FWMFDZ3eqp9vbjkNQBic7uxzqVr/0dYXpEvnhc+OAwBe3XQKq/c3xOzzI7H2QAMufGYdbGI67N4zHfhIPNfGruito0xSAwV8EjezKvLkx2aDBqMLMnDL3HI88/XpIb3fo2hKH6jrxNkjc/CNqjIAwNRHPoruySq8suFkwO2nFCmhd7yyFS63J2bnMFh3vLIV1U09cn2ie9/0zntot9EaBUMNBXwSN1ajdyzBpNdCo2F45KrJmDwiO6T3KwNUt92Fggxj0BLN0bRyn7fUg1usRvfhnjq//Q41dPltSzSpeF63eBdl1Hv/5Ft7AnelkfRFAZ/EjXK9W6Mu9ED98X3noyDDoFowvatPyMuPxRq6nx1pwlOrhPEC31a7FDi/++p2v/cdqlcHfKfbk/DyzmLtPHnFMZebY/GkYiyaWIw2GwX8oYYCPokbqyI4K1v7AxldmIFrZpSgpq0Xj7y3Dz96ayfOtPci06QLuMxipG76+2b8+ZNj8Hi4XN1zSokwwNzfWMGP3toFj8fb7fTs2iOY8PBK1HWEXho62nTibGhpEltXnwtZJj1yLXrq0hmCaKYtiRuLIsgPzzb3s6c/qTzDS1+ekLdlmnToUdTmcXs4tJroZCQBQJvNIVcGLcu1YO+ZTtXs30BsTjc2Vbdg8/FWfLBb6Pb54mgLvnZOadTOKxx6DYMD3oDf2etEllkHvVZDLfwhKKIWPmPsKcbYQcbYbsbYO4yxHMVrDzDGjjLGDjHGLon8VEmqs0bQ/aJcRUuSZdLj0snD5OfB0idD4VJ0v0jplrXtfXImS3mBUI/IN+DfMrccf7+5CjfPGQUAsNlduO3lrfjb+mq5G+U/204P+rxCdby5By98Vo1XN53EfkXKqreF74Tbw9FldyHbrEeORQ+7yxN0UhlJT5F26awGMIVzPg3AYQAPAABjbBKA6wFMBnApgOcYY7EfXSNJzSQOGP7s8olhvzfQOrkZJh3yM4x49BphcZdtJ9vwzw0nwj52t92Fy5/9HBMeXgkAGC0G99UHGnC0UZjFe1aZ0Jbx7dKpKs/FRROLMVGcU9Cm6CbpEvv7I11EPhQP/Hc3Hl1+AA+9sxdX/ulzebt0w9PV58JvVhwAABRlmuTJc9TKH1oi6tLhnCtz4TYC+Jr4+GoAb3DO7QCOM8aOApgFYEMkn0dSG2MMJ564fFDvHVfsX+I526xX/ffmFzcDAC6aWIwROaF3Gd36j81yhs2J5h4UZZpwrKkHbT0O9DndMOo0GFMkzBk42tiNZz46LL9X6pqSsl8CrejVY499K1q5ZrGUSdTndMtfQF19TrwsppcOzzbBLk6Aa+1xhPVvRVJbNAdtvw3gQ/FxCQDlfWyNuM0PY2wpY2wrY2xrUxMtfEECyzDqcOzxJfLzKSVZuFhcxaskVx2wlLNwQ7HlhHem7sKn16GmXciv73W6xT5vPfLEmv5PrjyI/XXe40uF4Qxa4QY2UMC3Ofrv94+G1h4HLpxQhEsmF6NEDOBPrvTOTN5V0yE/nj06D8PFVdHCWWuYpL4BAz5jbA1jbG+An6sV+zwEwAXgVWlTgEMFnIDOOV/GOa/inFcVFhYO5ncgQ4RyQPbJa6fJ/dPKGbwAVOmbg3G6VQiCvQ63mNWiQ47FgOml2VAk4eDHF49DhlG4SZbKFXT6BHyDVgObw63K3ok2zjlOtvRgZJ4F+RlGufW+Vfwiqyy0YufpdgDAZVOGwWLQYVS+8G8mdVmRoWHAgM85X8Q5nxLg510AYIzdDOAKADdyb73VGgBlisOUAqiN9smToUdqmUpdLABQmGmUB02B6NWI6XW60dnnlMtL52eoB46lriTAuzqX7xKO+RnCncGvPtgfs6Ub221O9DjcKMuzwKDVwOn2gHOOU6023DBrpOrfSiOOSOdYDBhfnIkVASaQkfQVaZbOpQB+CuAqzrmy9N57AK5njBkZYxUAxgLYHMlnEQIAby6dg+e/VeU3ceuuC8dArxWCWWtPlAK+w43OPpdcXjrLp8y08gtAauE/8eFBn32EgP/SlyfwrRdj8ycgTQbLNOmg1zI43R68v7sOHb1OlOdbMK1UTp5T3SXNqsijLp0hJtI+/D8ByASwmjG2kzH2VwDgnO8D8BaA/QBWAriLc075XyRiI/MtWCz23SsVZZpw5LElGJZliig9U6nL7kRrj13OaPFdSEZZdlofZMWtCcOy1MeMQZE3u0v4fU16LfRiF9IPXhdWJqsqz1N9UV0+bbj8uDjLiHabU05HrWmz4Q9rDtPCKGksooDPOR/DOS/jnM8Qf+5UvPYY57yScz6ec/5hf8chJFpMeg36nKEXMet1uKFhwIhsE9bed77qtb1nOnG6tVdeLtJ3IRmpHxwAuoNMyLpk8jDcMrccM8tzAQB7znQE3C8S0u9r1Gn81hk+Z1SuXH4i06TDJYp5C4XiZLa/rDuG/bWduO2lrfjDmiPU6k9jVFqBpBWTXhtWC/9Uqw0eDjywZCIKFF001ylmxkpZQKWKweFdv7hYVSZ6Som6ANyzN5yFiyYU4dzReXjkqslYdlOV8L7THdh5uh2/WXEgai1pqYVu0msDru0rlZ8o8Um/zDYLdy5/XHsES579DEcau8TjJV/VTxIdFPBJWjEbtGEVLJMqRuZnGFSDsD+5dIL8ON8qfBFcNUNYhWvyiCzVvgCQZzXgK2cJmcezK/Jw1fQR+PstM+VuoFyrASPzLNhd046v/20D/ra+GvtqO6NSsVIK0CadRh7HALwDyRYxk8i3sqjv7+BbWZOkH6qlQ9KKWa8Nq1yANNNUyrN/6ztzcKrVJnd3AECOuMh6hlGHtfedj6wgi8JnBAmskullOdh+sk1e9euK/xVmxG544MKwawsp+fbhSz68Z75qP99Cc74BXxKse4qkPmrhk7RiDrNLR2phSwOzsyry/AqdSQEfACoLM1RfBkpSn36wgDm9NBtn2nvhu+JkU1dkWUVyC98n4EtfTFLXUXGWSfW+bEuQgG+nKprpilr4JK2YDKEHfJvDhRPNPQDUQV0ipDjyoC16X2eNFAZmjzYFnswk5cP7dt0Hmp0bDm8fvgYGRcCXBpnnjy3E/1wyHt88d5TqfYUZgb+4OqmFn7aohU/SSmmOGSeae1AfQsGyuU98jBc+P44Moy7ggiz/um02Fk0sQlGQFr0vKaAHqzNflOltYc8o8+bGP/HhwYgGcKUvjAyjDnqd9/aKDZ/TAAAdeUlEQVTBJKaNajUMd10wxq8LRznA+/lPL8DuRy4GALQEWVSepD4K+CStLJ5UDA8Htp5s7Xe/XodbDsy51sAt+Nmj8/HCzTPlEg4DyTbrMasiD89cF3iN3iJFiWdlwN9X24mGCGYHn2q1wWrQIs9qQI6YeROqd+86D/9v9kiU5JiRZdIjw6ijxc3TGHXpkLQipUre/doOXDFtRND9qpu93S7hBsn+vPWdOUFfy7N4P2ficHX1z/ZeB4w6DVbtq8c3ZpaB+Xb09+N0qw1leRYwxjAs2zTwGxSml+VguuLLpzjLiE3V/X9ZktRFAZ+kFSnbZqCVr5RdPhOG+ZdejgWN4pwqFTn8AHDpHz7DhGGZOFjfhXd31uK1O2aHHPRPttpQWSjU8B8hZvsMduWv88YU4JUNJ9HndMtdQiR9UJcOSTvXnl2K4gH63ZW55rfNr4j1KfnJzzD6zew9KC6CvqG6BTVtoc125ZwLLXxxUli2RY+nvjYNy246Z1DnJa074Fv1k6QHauGTtJNh1A44eUjKRPn0fxYGXE0rVpYuGI1V++oxPNvUbwv6YH0XyvIsQV+XdNtdsLs8qpTL66rK+nlH/6RspfZeJ4qywuseIsmPWvgk7ViNOtgc7n4zX6RceWXmTDw8uGQiPv2fC+Rgrxy8VarvDG3gVBp4DpZTHy4pkyfSVFGSnCjgk7RjNerg8vB+g9bLX54A4F1nN1He+d5c1fPb51WAMaC2vRfbT7UNmK4pZxpZojPwLA1gB0stJamNAj5JO9LqUm9tPR3w9d017XILOpxsmFjw/fzyAivyLAa8/OUJfPW5L/HmlsC/g0QqDRFo4thgSC38O17ZSjV10hAFfJJ2bpg9EgDQ6whc9VFa7PzCCUVxO6dQ5VkNyDAJXVKAdyA3mEaxLEOwWbPhUnYNvbeTFqlLNxTwSdopyDAi06STW7++pJZrqDNo42V8cSbmVubDpJj1O9ANyBkxm2d4TnTGIjKN3jwOq5HSMtMNBXySlvKtBrQEKT08eYRQuz7QylmJ9OE985FjMajGFXwXP3/pi+O46e+b5KJv9Z29KMgwBiwNMRjKuQIWgw5fHmuOau1+kliUlknSUp7VgJbuwOUKSnLNaOmx46KJyRHwCzONaOqyy8FWma5pU5R63l3Tjkfe3w8A2HW6HRdMKEJnnwvZ5uj+Geda9GizOfHp4Ub8a+MpAMDf1lfjvsXj8P2Lxkb1s0h8UQufpKU8qzHo4iI2uyuq5RQitfwH8/DG0nPl58EC/skWm/y4QRx07u5zyXX4o+Xt7wqZQ1Kwl7y84URUP4fEH7XwSVrKtxqwq6Y94Gs9DrffYiCJVJRpUs0HUHbp9Di8mTIuj3cQes2BBmw/1YbOPicyTNH9MzYGmRCWFWTBFJI6qIVP0lJ+hgFNXXY0KiYwnW614c5/bkN1U3fUW8XRxODtR1e28A/WeTN21hxoxFtba3CgrhNWQ5QDvqJs8h3zK/DVs4WlG6ubetBtd6GzzwmXm9a9TUUU8ElaGimWJZj1+FoAwLpDjZj/20+wcl89mrsd8jqvyWjlvnr5sU3Rwv/yWAvK8sy4arq3Cmif04ON1S1R/XxlwK8oyFCVez5U34Vpj3yEZ1YfjupnkviggE/S0sThWarnd/5rm+q5NYm6dHzdoxgYtdm9LfxuuwvTS3Nw0xz1ylWXThkW1c9XZvzMrcxXTQ7bdFz4cnlj8ym/95HkRwGfpKVppdlynRqn24NynwJp+hAXNUmE7y6slB8r+/BtDhesBh2qRuXiksneDKMnr50W1c/Xa70B3reAW4NYVrrN5kRLtx19Tjeqm7qxv7YzqudAYiN572sJiQBjDFdNH4Gdp9tx9q9Wo8unTEBnX/LWijHptfifS8bj44ONOKyYaWtzuGE2aMEYw3cXjsGqfQ0Aol8eQnk837r6ypW5znl0DcYXZ6K6uRtON8d3FoxGVXle0s1vIF7J28whJELSwKxvsAeEFmoyu+uCMZgzOh89DhdW72+Ay+2BTZFdJHVJFWfFZrbw1p8twmc/uUB+vvwH8wAA6480qfY71NAFp1uYlPW39dX4xbt7Y3I+JDqohU/SljXAwOyiicVYc6AharVnYsls0MLDhUJmSxeMhtvD5d+posCK2+ZV4Ja55TH57AKffx/p30uZNRSItM4ASU4U8EnayvTJTx+Vb8ELN1dh1b56zK3MT9BZha4kxyw/3nGqDQBgFnPkdVoNHr5iUtzOpSjLhIIMI5qDzF6W6LSJrT5K+hdRlw5j7NeMsd2MsZ2MsY8YYyPE7Ywx9ixj7Kj4+tnROV1CQjfCp6CYS+x6uGTyMGSakn8S0ZQSb6bRlhNCwB+h+BKIt6+cpV4U/oppw/32SebBcBJ5H/5TnPNpnPMZAD4A8HNx+2UAxoo/SwH8JcLPISRsvsHRkWKThSoKMvy2zR2TuDuTQrG66K3nlWPdjxfid1+focrZD0Y5l4AkVkQBn3OuzMWyApBK6l0N4BUu2AgghzHm3xwgJIYsBp08AQtAys0O1WqYKif/qa9NQ1YC70y+MXMkFk0swu3zR6O8wAqDTgO7S/1v2uWT/dTZ58Skn6/CfW/tQm17aAuzk9iJ+P6LMfYYY+w0gBvhbeGXAFAu1VMjbiMkrj6+73y8KRYm800xTAX3Lh4nP050dc9ssx4v3DxTNbbw3YWVqud9Tg/sLu/ArhTk395eg7lPfJxyX7rpZsCAzxhbwxjbG+DnagDgnD/EOS8D8CqAu6W3BThUwILajLGljLGtjLGtTU1NgXYhZNB0Wo1c9OuC8cm3wlU4cpKweNlPL52AL+6/ULWtS5Gp47tq1sbq1ricFwlswIDPOV/EOZ8S4Oddn11fA3Ct+LgGQJnitVIAAddL45wv45xXcc6rCgsLB/M7ENKvicOz8Py3qvDoV6Yk+lQiokniO5R/3zkH3z6vAoA34J9uteG5dcdU+316uDHu50a8Is3SUa6GcBWAg+Lj9wB8S8zWORdAB+e8LpLPIiQSiycVR21VqHh7/+55eO7G5E50m1meh/PEAWWpVf/29hq//Rwu6tJJpEj78J8Qu3d2A7gYwD3i9hUAqgEcBfA8gO9F+DmEDFlTS7OxZGry5zwUZwlpsL9fcxiH6ruw7pDQRXvH/Ap5H4eblkpMpIgmXnHOrw2ynQO4K5JjE0JSy6h8b0aU3eXG7pp23DG/ArfPH43mbgfe2XEGThq0TSiaJUEIiYpMk14uyXDVn76AhwNjijJQnGXC778xA+X5Fgr4CUYBnxASNX+8fobqubK8sl6roYCfYBTwCSFR4zvzdkpJtvxYr9XQoG2CUcAnhESNMhNq6YLRqpnBep2GBm0TjAI+ISRqjHpvSHlwyUTVawYtg5Na+AlFAZ8QEjX9FVMz6KgPP9Eo4BNCoqa/yW00aJt4FPAJIVHTXwtfr02fPvwXPqvGz/5vD4QpR6mDAj4hJGoyTTpkmnQBF0cxaDVwuPpfIjFVPLr8AP618RS6A6yXnMxoiUNCSNTotBrs/PnFAV8z6jXoc6Z2l87pVhvqOvrk5z12d0qsniahgE8Iiapg6w5YDbqUX/3q+mUbcUaxkEuqtfCpS4cQEhcWgxY2R2p36TR09qme91DAJ4QQfxaDDnaXB25Pag10KlkM6iykZ1YfRp8zdb7EKOATQuJCCpap1K3T1GVXZeL4LkKz/nATNh9PnVW8KOATQuLCYpQCfmq0iPfVdmDmY2vw723ehVw0zH98orHLHs/TiggFfEJIXEgt/FTp995f2wkA+Ml/dmPvmQ4AQKDx6CYK+IQQomYxCEmBqdLCdyomiX15rBkAwAK08Fu6KeATQoiKNQEBv66jF0cbu8J+X7fdhX21HfJzs3jugRJOe1No0Jby8AkhcWGO0aBtc7ddXmlLqc/pxpzffAwA2PHwYuRaDSEd75ODjbj1pS2qbUatBqdbbXJ//Wu3z8a5o/Mx78mPU2oyGbXwCSFxYRUHbbefao/aMd/YfApVj67BwfpOv9ceeW+f/Lg5jG6X36465LdtX20Hzn/qEwDAyh/Ox9wxBdBoGEwGLfpSqFwEBXxCSFxY9EKHwrNrj+D+t3dH5Zgf7q0HAJxp6/V77Y0tp+XH7b3OkI5nd7lxoM775XHDrDIAwMsbTkKaPlBZmCG/btJpYQ+zhd/rcONX7+9PyCxdCviEkLiQ0jIBIRhHo9JkZ58QyM16/7LMo/ItsIrdSB02J5bvrsOpFlu/xztYp+7v9+0q+sctM6HXesOmSa+BPcwW/isbTuDFL45j2frqsN4XDRTwCSFxkWFUDxlGo++7u09oJdsDrKTV1GXHeWMKAAD7ajtx12vb8YM3dvR7vF016u6mTJMOc0bny88vmFCket2o04Y901Y6V7cn/n3/NGhLCIkLk16LkhyzXHysy+6UB3IHSyrT4Jsp4/Zw2BxulOZaAHjTKgfqyz/RrL4DyDLp8frSc7F8dx1cAQK0Sa9Bc3d4XTMe8c4m0CSuWKOATwiJm/ICixzwe+xuIDOy40mdQr0+qZ5S//iIHBMYA0609ADAgHV82m0O1XOp9PHlAer7A8KX2J4zHbA5XPI8g4FIp5CIgE9dOoSQuHn2+rNw+VQheErdMeFo6rKj3eaAR4yaUmvZt4UvZehkmnTINOrQ0Cm07H1DrMfDVWMJbTYHppZko2pULgAgy9x/EJe6qf6y7ljQfThXf4b0OFgZ6ViigE8IiZv8DCO+ee4oAEKXTrhmPrYGM361Gr98XwjoUhz17Ud/Z8cZAMIkrxyLN//ed6bs7N+sxXV/3SA/b7M5kWPRy3cOpgCDwUo/vmQ8AKgWRfH1o7d2oeKBFXKgd/t8WcUTBXxCSFxlmoRW8ckWG/7xxfFBZev8c+NJAAAXQ3OwFMccix5WxWCxchF1zjmauuzYerJN3tbZ50SWWa/oZ+//PIqzTBhTlBH0bqWl2y5/+fSI3U5tNuGLLhEzdCngE0LiSuoGeeC/e/DL9/ejJkAO/UA8HDjR3AOnSwjM9T4tbKtBi0nDs3DNjBJVXr1DEfCbu7399SdbevC9V7ehtccBi16LBy6biIoCKyYMyxrwXIoyjWgKMhjcocj/7xQfN4qLqCSiiFxUAj5j7MeMMc4YKxCfM8bYs4yxo4yx3Yyxs6PxOYSQ1JdhUveLh9rS9S1SdsEz6+SgqVx2kHMOm9ONiyYW+XXhOBXpm8qB3j+uPYIVe+rRbnPCYtBiVkUePvnxQtXdQTBWoy5o8Fb+bl3iXUC9GPClln48RRzwGWNlABYDOKXYfBmAseLPUgB/ifRzCCHpwTcfP9Riauc8ukb1nHOgSwy0tYqA3+f0gHNvdU6DYqKUsoWvLImgnGBlDjHbRmLtZ+nGPlXAFwJ8a49wZ+GbERQP0Wjh/x7AT+DNkAKAqwG8wgUbAeQwxgLnNRFChhSjTh127ntrZ0THM+g0qG3vk8cCpP58qXaPNMN30cRiON3ejBllC7+txxt8fZcxHIjZoJMDfkevU5Xrr5xcJnXvSP9t60mxFj5j7CoAZzjnu3xeKgFwWvG8RtwW6BhLGWNbGWNbm5qaIjkdQkgK8O1mOdbUM+B7Ovrp/phako1epxstPQ6s3Fsnd69ILfw3lp6L+xaPw1kjcwB4W/nK1rdyVatwB1MtBi16xQqgsx9fgyrFnchrm70dH83ddthdbvnLIREt/AHvXRhjawAMC/DSQwAeBHBxoLcF2BZwKJ5zvgzAMgCoqqpK3dWNCSGDtvl4K2ZV5AV87YujzVgpFkkLpGpULradbMOjH+zH/+2slbtnss3CpKkJw7IwYVgWnhdr1/zozV24bOowuT6/r3AHka0GLWxONzjnqhZ9bXsvlu+uk5/Xd9jl1r1Jr0lIH/6AAZ9zvijQdsbYVAAVAHaJ39ilALYzxmZBaNGXKXYvBVAb8dkSQtLS50eaggb8G1/YJD8enm3CO987D27OseV4K17bfAqXTR2Ov62vxmdH1OUT8qx61XGkFv3yPXVYvqcOU0rUGTjv3z0PV/7pc1x3TmlY52426MC5/1iEtCyipKGrT75TKc+34mB9F/qc7gFz/aNp0KUVOOd7AMiVhBhjJwBUcc6bGWPvAbibMfYGgNkAOjjndYGPRAgZagw6DRyKjJkdp0Orkf/RvQvkcgclZ5XgmrNK5ADf0qPuIlFOuALglzq594y6hv6UkiyceOLy0H4BBWk2rlS+QdKm6LLJFDN5pDLNZXkWHKzvwrpDjbh0SvyGN2NVS2cFgCUAjgKwAbg1Rp9DCElB6368EI1ddkwanoVffbAP72w/E7AejXJS1vTSbDnYK+VbDX5fIACQ6xPwW7oD95m/87254Ai8Xm0oJg4X7hQ+F+8wAKFkQ2evN1WzJNeMIw3d2FTdAgAYlScUdfvb+uq4BvyoTbzinJdzzpvFx5xzfhfnvJJzPpVzvjVan0MISX0jcsyYUZYDg06DCycUocfhxu6aDr/9ehTdJG/dOSfgsRhjKMkx+23Ptai/HH5y6XiU5fnvN7UkG2ePzA33V5BJwfs3Hx6Ut938j814bMUBAMAL36qCxaDF/rpOPP3RYQDAjWJ5iUnDB57YFU0005YQklBS10uguvJTfrEKAPDLqybDqAve1z0sy+S3zbfFPirfinfvmic/f+nWmfjPnXOg00YWBgPddXymaO0vmlTsVwY6z2pARYFVNRM3Hqg8MiEkoaSJUb6LmGw41iI/HmjG65YTrepj6gIHcWWrf2Z5XkgzaQcS7LOU9vjcvWSZdMgy6+Me8KmFTwhJKJM+cMC/8YWN8mPrAJOhvuaTWVOW6991A6hb/YGWRYyGfyu6nr5/4RgAQKdPcTXGGLLNenx2pNlv7CGWKOATQhJK6qqxK7p0HC4PlGuVDNQS//U1UwAAV04fgQcum4CXbp0VdN+Hr5iEqlG50MSoHv24Yu+qLsUBupqkL7husdTCL8Ta/fFAXTqEkISSSi0oW/htPrNQS4O02CV6rQbVjy8BYwNn29w2rwK3zasY5NkG9vK3Z+HmFzejIMOAbLMe00uzsaumQy7T8PZ35+L1zafwn2010GuE31f6QpOWX4wHCviEkISSWvjKrg3fSUyj8q0DHidWLfZQnD+uEKvvXYA8qzAAXZJrxi5Fv/05o3KRZdLhP9tqoNMK5ynV3I9nmWQK+ISQhDIG6MOXCpv94RszMKcyPyHLAYZrrKIrp7IwA4C3JDLgXT1LLw5Su9xCwLc7qQ+fEDJEeLN0vK36XqcQKHOthoD94Mnuuwsrcef5lbiuyjuYLKVmTi3JBgA8dPlEAMCw7Pj9ftTCJ4QklEbDoNcyVQv/1U1ClclYZdLEmsWgw/2XTVBtK8gw4tXbZ2NaqRDwzxtTgCVTh+FQfVfczosCPiEk4Ux6rao+/X+3C+vApmrAD+a8MQWq51ZFLf14oC4dQkjCZRp1qv5uSSr03UfCatQFXYA9FijgE0ISLtOkl5cAVMrPMATYO31YjVqhimacFkOhgE8ISbhMk7qFn2HU4etVpSk5YBuOysIMeDgw41ersf5w7Ff8o4BPCEk4k16LDdUt+PJoMw7Vd6Hb7kJpriXRpxVziyYVy4/f3HK6nz2jgwZtCSEJ5xannT7+4QF5YRLf8sbpKEtRadO3HlAsUAufEJJwz3x9OgD1KlTxXPovGcwfWzDwThGigE8ISbgRARYwcSmrp6WxCcOEGbqR1uUPBXXpEEKSQr7VoFqXtmrU4FehSiX/vnMO2m3xqYtPAZ8QkhSkomIA8O3zKlS1adJZpkkfcNWsWKAuHUJIUtAoyhrrtek94SpRKOATQpKCMsRHY+lB4o8CPiEkKUgLl1w/swxLF4xO8NmkJwr4hJCkIPXofOf8yiGXkhkvFPAJIUlBM8DShCRyFPAJIUnh9vnCOrMFaV4wLZFoZIQQkhS+Nacc35pTnujTSGvUwieEkCGCAj4hhAwREQV8xtgjjLEzjLGd4s8SxWsPMMaOMsYOMcYuifxUCSGERCIaffi/55w/rdzAGJsE4HoAkwGMALCGMTaOcx6/xRsJIYSoxKpL52oAb3DO7Zzz4wCOApgVo88ihBASgmgE/LsZY7sZYy8yxqTydiUAlMu31Ijb/DDGljLGtjLGtjY1xX6JL0IIGaoGDPiMsTWMsb0Bfq4G8BcAlQBmAKgD8Iz0tgCHCljcmnO+jHNexTmvKiwsHOSvQQghZCAD9uFzzheFciDG2PMAPhCf1gAoU7xcCqA27LMjhBASNYzzwa8qwxgbzjmvEx/fC2A25/x6xthkAK9B6LcfAWAtgLEDDdoyxroAHArwUjaAjgFOpwBAcwinHcqx4rlPun9eqOcUyvVLxvNOxmPF+/Oi9beXjP9OyXhOgfYbzzkfeAEBzvmgfwD8E8AeALsBvAdguOK1hwAcgxDALwvxeFuDbF822PcO8lhx2yfdPy+Mcxrw+iXpeSfdsRLweVH520vGf6dkPKdA+4V6DSJKy+Sc39TPa48BeCyS4yu8H6XjhHqseO6T7p9H1y45r0u0jxWNz0vGf6dkPKdw9lOJqEsn2hhjWznnVfF+L0k8un6pi65d4oV6DZKttMKyBL2XJB5dv9RF1y7xQroGSdXCJ4QQEjvJ1sInhBASIxTwkxBjrHuA19cxxqjPNEnR9Utd6X7tUi7gD3RBSHKj65e66NqlvpQL+EMFY2whY+wDxfM/McZuSeApkTDQ9Utd6XztUjLgM8YyGGNrGWPbGWN7xLo+YIyVM8YOMMaeZ4ztY4x9xBgzJ/p8iRpdv9RF1y61pWTAB9AH4Cuc87MBXADgGcbkJe/HAvgz53wygHYA1yboHElwdP1SF127FJaqi5gzAI8zxhYA8EAovVwsvnacc75TfLwNQHn8Ty8qXFB/IZsSdSIxQNcvddG1S2Gp2sK/EUAhgHM45zMANMB7UeyK/dxI3S+1kwAmMcaMjLFsABcl+oSiiK5f6qJrl8JS9YJkA2jknDsZYxcAGJXoE4oWxpgOgJ1zfpox9haEwnRHAOxI7JlFFV2/1EXXLoWlVMCXLgiAVwG8zxjbCmAngIMJPbHomgyhyig45z8B8BPfHTjnC+N8TlFB10+QitePrp0gFa+dUkqVVmCMTQfwPOc8LdfHZYzdCeAHAH7IOf8o0ecTbXT9Uhddu/SQMgF/qFyQdEXXL3XRtUsfKRPwCSGERCZVs3QIIYSEKakDPmOsjDH2iTiDbx9j7B5xex5jbDVj7Ij431xxO2OMPcsYO8oY280YO1txrJvF/Y8wxm5O1O80VET52q1kjLUrp7uT2InWtWOMzWCMbRCPsZsx9o1E/l4Eka1pG+sfAMMBnC0+zgRwGMAkAL8FcL+4/X4AT4qPlwD4EMLkkHMBbBK35wGoFv+bKz7OTfTvl84/0bp24msXAbgSwAeJ/r2Gwk8U/+7GARgrPh4BoA5ATqJ/v6H8k9QtfM55Hed8u/i4C8ABCDP7rgbwsrjbywCuER9fDeAVLtgIIIcxNhzAJQBWc85bOedtAFYDuDSOv8qQE8VrB875WgBd8Tz/oSxa145zfphzfkQ8Ti2ARgiTtkiCJHXAV2KMlQM4C8AmAMWc8zpA+J8TQJG4WwmA04q31Yjbgm0ncRDhtSMJFK1rxxibBcAAMc+dJEZKBHzGWAaAtyGkhXX2t2uAbbyf7STGonDtSIJE69qJd2r/BHAr59wT3bMk4Uj6gM8Y00P4n+5Vzvl/xc0N0u2++N9GcXsNgDLF20sB1PazncRQlK4dSYBoXTvGWBaA5QB+Jnb3kARK6oAvll39O4ADnPPfKV56D4CUaXMzgHcV278lZg2cC6BDvPVcBeBixliumFlwsbiNxEgUrx2Js2hdO8aYAcA7EPr3/x2n0yf9SfSocX8/AOZBuDXcDaFux04IGQH5ANZCKGy0FkCeuD8D8GcI/YR7AFQpjvVtAEfFn1sT/bul+0+Ur91nAJoA9EJoTV6S6N8vnX+ide0AfBOAU3GMnQBmJPr3G8o/NNOWEEKGiKTu0iGEEBI9FPAJIWSIoIBPCCFDBAV8QggZIijgE0LIEEEBnxBChggK+IQQMkT8fxXFiYyf/2zqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n",
    "ts = ts.cumsum()  # Generates random plot!!\n",
    "\n",
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a DataFrame, the plot() method is a convenience to plot all the column with labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2734bf5f048>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,\n",
    "             columns=list('ABCD'))\n",
    "df = df.cumsum()\n",
    "plt.figure(); df.plot(); plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}