ch09-20170526.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Aggregation and Group Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from numpy.random import randn\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "np.random.seed(12345)\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "from pandas import Series, DataFrame\n",
    "import pandas as pd\n",
    "np.set_printoptions(precision=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Error 2] : u'../book_scripts'\n",
      "C:\\Users\\Administrator\\pydata-book-master\n"
     ]
    }
   ],
   "source": [
    "%cd ../book_scripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.notebook_repr_html = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GroupBy mechanics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      data1     data2 key1 key2\n",
       "0 -0.204708  1.393406    a  one\n",
       "1  0.478943  0.092908    a  two\n",
       "2 -0.519439  0.281746    b  one\n",
       "3 -0.555730  0.769023    b  two\n",
       "4  1.965781  1.246435    a  one"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame({'key1' : ['a', 'a', 'b', 'b', 'a'],\n",
    "                'key2' : ['one', 'two', 'one', 'two', 'one'],\n",
    "                'data1' : np.random.randn(5),\n",
    "                'data2' : np.random.randn(5)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.SeriesGroupBy object at 0x000000000438CCC0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df['data1'].groupby(df['key1'])\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "a    0.746672\n",
       "b   -0.537585\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1  key2\n",
       "a     one     0.880536\n",
       "      two     0.478943\n",
       "b     one    -0.519439\n",
       "      two    -0.555730\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means = df['data1'].groupby([df['key1'], df['key2']]).mean()\n",
    "means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key2       one       two\n",
       "key1                    \n",
       "a     0.880536  0.478943\n",
       "b    -0.519439 -0.555730"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California  2005    0.478943\n",
       "            2006   -0.519439\n",
       "Ohio        2005   -0.380219\n",
       "            2006    1.965781\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = np.array(['Ohio', 'California', 'California', 'Ohio', 'Ohio'])\n",
    "years = np.array([2005, 2005, 2006, 2005, 2006])\n",
    "df['data1'].groupby([states, years]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         data1     data2\n",
       "key1                    \n",
       "a     0.746672  0.910916\n",
       "b    -0.537585  0.525384"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key1').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              data1     data2\n",
       "key1 key2                    \n",
       "a    one   0.880536  1.319920\n",
       "     two   0.478943  0.092908\n",
       "b    one  -0.519439  0.281746\n",
       "     two  -0.555730  0.769023"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1', 'key2']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1  key2\n",
       "a     one     2\n",
       "      two     1\n",
       "b     one     1\n",
       "      two     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1', 'key2']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterating over groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "      data1     data2 key1 key2\n",
      "0 -0.204708  1.393406    a  one\n",
      "1  0.478943  0.092908    a  two\n",
      "4  1.965781  1.246435    a  one\n",
      "b\n",
      "      data1     data2 key1 key2\n",
      "2 -0.519439  0.281746    b  one\n",
      "3 -0.555730  0.769023    b  two\n"
     ]
    }
   ],
   "source": [
    "for name, group in df.groupby('key1'):\n",
    "    print(name)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('a', 'one')\n",
      "      data1     data2 key1 key2\n",
      "0 -0.204708  1.393406    a  one\n",
      "4  1.965781  1.246435    a  one\n",
      "('a', 'two')\n",
      "      data1     data2 key1 key2\n",
      "1  0.478943  0.092908    a  two\n",
      "('b', 'one')\n",
      "      data1     data2 key1 key2\n",
      "2 -0.519439  0.281746    b  one\n",
      "('b', 'two')\n",
      "     data1     data2 key1 key2\n",
      "3 -0.55573  0.769023    b  two\n"
     ]
    }
   ],
   "source": [
    "for (k1, k2), group in df.groupby(['key1', 'key2']):\n",
    "    print((k1, k2))\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      data1     data2 key1 key2\n",
       "2 -0.519439  0.281746    b  one\n",
       "3 -0.555730  0.769023    b  two"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pieces = dict(list(df.groupby('key1')))\n",
    "pieces['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data1    float64\n",
       "data2    float64\n",
       "key1      object\n",
       "key2      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{dtype('float64'):       data1     data2\n",
       " 0 -0.204708  1.393406\n",
       " 1  0.478943  0.092908\n",
       " 2 -0.519439  0.281746\n",
       " 3 -0.555730  0.769023\n",
       " 4  1.965781  1.246435, dtype('O'):   key1 key2\n",
       " 0    a  one\n",
       " 1    a  two\n",
       " 2    b  one\n",
       " 3    b  two\n",
       " 4    a  one}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby(df.dtypes, axis=1)\n",
    "dict(list(grouped))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting a column or subset of columns"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df.groupby('key1')['data1']\n",
    "df.groupby('key1')[['data2']]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df['data1'].groupby(df['key1'])\n",
    "df[['data2']].groupby(df['key1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              data2\n",
       "key1 key2          \n",
       "a    one   1.319920\n",
       "     two   0.092908\n",
       "b    one   0.281746\n",
       "     two   0.769023"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1', 'key2'])[['data2']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.SeriesGroupBy object at 0x00000000086AE240>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_grouped = df.groupby(['key1', 'key2'])['data2']\n",
    "s_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1  key2\n",
       "a     one     1.319920\n",
       "      two     0.092908\n",
       "b     one     0.281746\n",
       "      two     0.769023\n",
       "Name: data2, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_grouped.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping with dicts and Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               a         b         c         d         e\n",
       "Joe     1.007189 -1.296221  0.274992  0.228913  1.352917\n",
       "Steve   0.886429 -2.001637 -0.371843  1.669025 -0.438570\n",
       "Wes    -0.539741       NaN       NaN -1.021228 -0.577087\n",
       "Jim     0.124121  0.302614  0.523772  0.000940  1.343810\n",
       "Travis -0.713544 -0.831154 -2.370232 -1.860761 -0.860757"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people = DataFrame(np.random.randn(5, 5),\n",
    "                   columns=['a', 'b', 'c', 'd', 'e'],\n",
    "                   index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis'])\n",
    "people.ix[2:3, ['b', 'c']] = np.nan # Add a few NA values\n",
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {'a': 'red', 'b': 'red', 'c': 'blue',\n",
    "           'd': 'blue', 'e': 'red', 'f' : 'orange'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            blue       red\n",
       "Joe     0.503905  1.063885\n",
       "Steve   1.297183 -1.553778\n",
       "Wes    -1.021228 -1.116829\n",
       "Jim     0.524712  1.770545\n",
       "Travis -4.230992 -2.405455"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_column = people.groupby(mapping, axis=1)\n",
    "by_column.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a       red\n",
       "b       red\n",
       "c      blue\n",
       "d      blue\n",
       "e       red\n",
       "f    orange\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_series = Series(mapping)\n",
    "map_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        blue  red\n",
       "Joe        2    3\n",
       "Steve      2    3\n",
       "Wes        1    2\n",
       "Jim        2    3\n",
       "Travis     2    3"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.groupby(map_series, axis=1).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping with functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          a         b         c         d         e\n",
       "3  0.591569 -0.993608  0.798764 -0.791374  2.119639\n",
       "5  0.886429 -2.001637 -0.371843  1.669025 -0.438570\n",
       "6 -0.713544 -0.831154 -2.370232 -1.860761 -0.860757"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.groupby(len).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              a         b         c         d         e\n",
       "3 one -0.539741 -1.296221  0.274992 -1.021228 -0.577087\n",
       "  two  0.124121  0.302614  0.523772  0.000940  1.343810\n",
       "5 one  0.886429 -2.001637 -0.371843  1.669025 -0.438570\n",
       "6 two -0.713544 -0.831154 -2.370232 -1.860761 -0.860757"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_list = ['one', 'one', 'one', 'two', 'two']\n",
    "people.groupby([len, key_list]).min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping by index levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cty          US                            JP          \n",
       "tenor         1         3         5         1         3\n",
       "0      0.560145 -1.265934  0.119827 -1.063512  0.332883\n",
       "1     -2.359419 -0.199543 -1.541996 -0.970736 -1.307030\n",
       "2      0.286350  0.377984 -0.753887  0.331286  1.349742\n",
       "3      0.069877  0.246674 -0.011862  1.004812  1.327195"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = pd.MultiIndex.from_arrays([['US', 'US', 'US', 'JP', 'JP'],\n",
    "                                    [1, 3, 5, 1, 3]], names=['cty', 'tenor'])\n",
    "hier_df = DataFrame(np.random.randn(4, 5), columns=columns)\n",
    "hier_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cty  JP  US\n",
       "0     2   3\n",
       "1     2   3\n",
       "2     2   3\n",
       "3     2   3"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hier_df.groupby(level='cty', axis=1).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      data1     data2 key1 key2\n",
       "0 -0.204708  1.393406    a  one\n",
       "1  0.478943  0.092908    a  two\n",
       "2 -0.519439  0.281746    b  one\n",
       "3 -0.555730  0.769023    b  two\n",
       "4  1.965781  1.246435    a  one"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "a    1.668413\n",
       "b   -0.523068\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby('key1')\n",
    "grouped['data1'].quantile(0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         data1     data2\n",
       "key1                    \n",
       "a     2.170488  1.300498\n",
       "b     0.036292  0.487276"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def peak_to_peak(arr):\n",
    "    return arr.max() - arr.min()\n",
    "grouped.agg(peak_to_peak)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               data1     data2\n",
       "key1                          \n",
       "a    count  3.000000  3.000000\n",
       "     mean   0.746672  0.910916\n",
       "     std    1.109736  0.712217\n",
       "     min   -0.204708  0.092908\n",
       "     25%    0.137118  0.669671\n",
       "     50%    0.478943  1.246435\n",
       "     75%    1.222362  1.319920\n",
       "     max    1.965781  1.393406\n",
       "b    count  2.000000  2.000000\n",
       "     mean  -0.537585  0.525384\n",
       "     std    0.025662  0.344556\n",
       "     min   -0.555730  0.281746\n",
       "     25%   -0.546657  0.403565\n",
       "     50%   -0.537585  0.525384\n",
       "     75%   -0.528512  0.647203\n",
       "     max   -0.519439  0.769023"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 以上是10页内容 20170526完成（）======================================================================================================\n",
    "\n",
    "#======================================================================================================================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size   tip_pct\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2  0.059447\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3  0.160542\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3  0.166587\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2  0.139780\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4  0.146808\n",
       "5       25.29  4.71    Male     No  Sun  Dinner     4  0.186240"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = pd.read_csv('ch08/tips.csv')\n",
    "# Add tip percentage of total bill\n",
    "tips['tip_pct'] = tips['tip'] / tips['total_bill']\n",
    "tips[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Column-wise and multiple function application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = tips.groupby(['sex', 'smoker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex     smoker\n",
       "Female  No        0.156921\n",
       "        Yes       0.182150\n",
       "Male    No        0.160669\n",
       "        Yes       0.152771\n",
       "Name: tip_pct, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pct = grouped['tip_pct']\n",
    "grouped_pct.agg('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   mean       std  peak_to_peak\n",
       "sex    smoker                                  \n",
       "Female No      0.156921  0.036421      0.195876\n",
       "       Yes     0.182150  0.071595      0.360233\n",
       "Male   No      0.160669  0.041849      0.220186\n",
       "       Yes     0.152771  0.090588      0.674707"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pct.agg(['mean', 'std', peak_to_peak])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                    foo       bar\n",
       "sex    smoker                    \n",
       "Female No      0.156921  0.036421\n",
       "       Yes     0.182150  0.071595\n",
       "Male   No      0.160669  0.041849\n",
       "       Yes     0.152771  0.090588"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pct.agg([('foo', 'mean'), ('bar', np.std)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              tip_pct                     total_bill                  \n",
       "                count      mean       max      count       mean    max\n",
       "sex    smoker                                                         \n",
       "Female No          54  0.156921  0.252672         54  18.105185  35.83\n",
       "       Yes         33  0.182150  0.416667         33  17.977879  44.30\n",
       "Male   No          97  0.160669  0.291990         97  19.791237  48.33\n",
       "       Yes         60  0.152771  0.710345         60  22.284500  50.81"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "functions = ['count', 'mean', 'max']\n",
    "result = grouped['tip_pct', 'total_bill'].agg(functions)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               count      mean       max\n",
       "sex    smoker                           \n",
       "Female No         54  0.156921  0.252672\n",
       "       Yes        33  0.182150  0.416667\n",
       "Male   No         97  0.160669  0.291990\n",
       "       Yes        60  0.152771  0.710345"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['tip_pct']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   tip_pct              total_bill           \n",
       "              Durchschnitt Abweichung Durchschnitt Abweichung\n",
       "sex    smoker                                                \n",
       "Female No         0.156921   0.001327    18.105185  53.092422\n",
       "       Yes        0.182150   0.005126    17.977879  84.451517\n",
       "Male   No         0.160669   0.001751    19.791237  76.152961\n",
       "       Yes        0.152771   0.008206    22.284500  98.244673"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftuples = [('Durchschnitt', 'mean'), ('Abweichung', np.var)]\n",
    "grouped['tip_pct', 'total_bill'].agg(ftuples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                tip  size\n",
       "sex    smoker            \n",
       "Female No       5.2   140\n",
       "       Yes      6.5    74\n",
       "Male   No       9.0   263\n",
       "       Yes     10.0   150"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.agg({'tip' : np.max, 'size' : 'sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                tip_pct                               size\n",
       "                    min       max      mean       std  sum\n",
       "sex    smoker                                             \n",
       "Female No      0.056797  0.252672  0.156921  0.036421  140\n",
       "       Yes     0.056433  0.416667  0.182150  0.071595   74\n",
       "Male   No      0.071804  0.291990  0.160669  0.041849  263\n",
       "       Yes     0.035638  0.710345  0.152771  0.090588  150"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.agg({'tip_pct' : ['min', 'max', 'mean', 'std'],\n",
    "             'size' : 'sum'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Returning aggregated data in \"unindexed\" form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      sex smoker  total_bill       tip      size   tip_pct\n",
       "0  Female     No   18.105185  2.773519  2.592593  0.156921\n",
       "1  Female    Yes   17.977879  2.931515  2.242424  0.182150\n",
       "2    Male     No   19.791237  3.113402  2.711340  0.160669\n",
       "3    Male    Yes   22.284500  3.051167  2.500000  0.152771"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(['sex', 'smoker'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group-wise operations and transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      data1     data2 key1 key2\n",
       "0 -0.204708  1.393406    a  one\n",
       "1  0.478943  0.092908    a  two\n",
       "2 -0.519439  0.281746    b  one\n",
       "3 -0.555730  0.769023    b  two\n",
       "4  1.965781  1.246435    a  one"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      mean_data1  mean_data2\n",
       "key1                        \n",
       "a       0.746672    0.910916\n",
       "b      -0.537585    0.525384"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k1_means = df.groupby('key1').mean().add_prefix('mean_')\n",
    "k1_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      data1     data2 key1 key2  mean_data1  mean_data2\n",
       "0 -0.204708  1.393406    a  one    0.746672    0.910916\n",
       "1  0.478943  0.092908    a  two    0.746672    0.910916\n",
       "4  1.965781  1.246435    a  one    0.746672    0.910916\n",
       "2 -0.519439  0.281746    b  one   -0.537585    0.525384\n",
       "3 -0.555730  0.769023    b  two   -0.537585    0.525384"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df, k1_means, left_on='key1', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            a         b         c         d         e\n",
       "one -0.082032 -1.063687 -1.047620 -0.884358 -0.028309\n",
       "two  0.505275 -0.849512  0.075965  0.834983  0.452620"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key = ['one', 'two', 'one', 'two', 'one']\n",
    "people.groupby(key).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               a         b         c         d         e\n",
       "Joe    -0.082032 -1.063687 -1.047620 -0.884358 -0.028309\n",
       "Steve   0.505275 -0.849512  0.075965  0.834983  0.452620\n",
       "Wes    -0.082032 -1.063687 -1.047620 -0.884358 -0.028309\n",
       "Jim     0.505275 -0.849512  0.075965  0.834983  0.452620\n",
       "Travis -0.082032 -1.063687 -1.047620 -0.884358 -0.028309"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.groupby(key).transform(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               a         b         c         d         e\n",
       "Joe     1.089221 -0.232534  1.322612  1.113271  1.381226\n",
       "Steve   0.381154 -1.152125 -0.447807  0.834043 -0.891190\n",
       "Wes    -0.457709       NaN       NaN -0.136869 -0.548778\n",
       "Jim    -0.381154  1.152125  0.447807 -0.834043  0.891190\n",
       "Travis -0.631512  0.232534 -1.322612 -0.976402 -0.832448"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def demean(arr):\n",
    "    return arr - arr.mean()\n",
    "demeaned = people.groupby(key).transform(demean)\n",
    "demeaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                a             b    c             d    e\n",
       "one  0.000000e+00 -1.110223e-16  0.0  7.401487e-17  0.0\n",
       "two -2.775558e-17  0.000000e+00  0.0  0.000000e+00  0.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demeaned.groupby(key).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply: General split-apply-combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\envs\\mlnd\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "     total_bill   tip     sex smoker  day    time  size   tip_pct\n",
       "109       14.31  4.00  Female    Yes  Sat  Dinner     2  0.279525\n",
       "183       23.17  6.50    Male    Yes  Sun  Dinner     4  0.280535\n",
       "232       11.61  3.39    Male     No  Sat  Dinner     2  0.291990\n",
       "67         3.07  1.00  Female    Yes  Sat  Dinner     1  0.325733\n",
       "178        9.60  4.00  Female    Yes  Sun  Dinner     2  0.416667\n",
       "172        7.25  5.15    Male    Yes  Sun  Dinner     2  0.710345"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def top(df, n=5, column='tip_pct'):\n",
    "    return df.sort_index(by=column)[-n:]\n",
    "top(tips, n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\envs\\mlnd\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "            total_bill   tip     sex smoker   day    time  size   tip_pct\n",
       "smoker                                                                   \n",
       "No     88        24.71  5.85    Male     No  Thur   Lunch     2  0.236746\n",
       "       185       20.69  5.00    Male     No   Sun  Dinner     5  0.241663\n",
       "       51        10.29  2.60  Female     No   Sun  Dinner     2  0.252672\n",
       "       149        7.51  2.00    Male     No  Thur   Lunch     2  0.266312\n",
       "       232       11.61  3.39    Male     No   Sat  Dinner     2  0.291990\n",
       "Yes    109       14.31  4.00  Female    Yes   Sat  Dinner     2  0.279525\n",
       "       183       23.17  6.50    Male    Yes   Sun  Dinner     4  0.280535\n",
       "       67         3.07  1.00  Female    Yes   Sat  Dinner     1  0.325733\n",
       "       178        9.60  4.00  Female    Yes   Sun  Dinner     2  0.416667\n",
       "       172        7.25  5.15    Male    Yes   Sun  Dinner     2  0.710345"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('smoker').apply(top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\envs\\mlnd\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                 total_bill    tip     sex smoker   day    time  size  \\\n",
       "smoker day                                                              \n",
       "No     Fri  94        22.75   3.25  Female     No   Fri  Dinner     2   \n",
       "       Sat  212       48.33   9.00    Male     No   Sat  Dinner     4   \n",
       "       Sun  156       48.17   5.00    Male     No   Sun  Dinner     6   \n",
       "       Thur 142       41.19   5.00    Male     No  Thur   Lunch     5   \n",
       "Yes    Fri  95        40.17   4.73    Male    Yes   Fri  Dinner     4   \n",
       "       Sat  170       50.81  10.00    Male    Yes   Sat  Dinner     3   \n",
       "       Sun  182       45.35   3.50    Male    Yes   Sun  Dinner     3   \n",
       "       Thur 197       43.11   5.00  Female    Yes  Thur   Lunch     4   \n",
       "\n",
       "                  tip_pct  \n",
       "smoker day                 \n",
       "No     Fri  94   0.142857  \n",
       "       Sat  212  0.186220  \n",
       "       Sun  156  0.103799  \n",
       "       Thur 142  0.121389  \n",
       "Yes    Fri  95   0.117750  \n",
       "       Sat  170  0.196812  \n",
       "       Sun  182  0.077178  \n",
       "       Thur 197  0.115982  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby(['smoker', 'day']).apply(top, n=1, column='total_bill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoker       \n",
       "No      count    151.000000\n",
       "        mean       0.159328\n",
       "        std        0.039910\n",
       "        min        0.056797\n",
       "        25%        0.136906\n",
       "        50%        0.155625\n",
       "        75%        0.185014\n",
       "        max        0.291990\n",
       "Yes     count     93.000000\n",
       "        mean       0.163196\n",
       "        std        0.085119\n",
       "        min        0.035638\n",
       "        25%        0.106771\n",
       "        50%        0.153846\n",
       "        75%        0.195059\n",
       "        max        0.710345\n",
       "Name: tip_pct, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = tips.groupby('smoker')['tip_pct'].describe()\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoker          No        Yes\n",
       "count   151.000000  93.000000\n",
       "mean      0.159328   0.163196\n",
       "std       0.039910   0.085119\n",
       "min       0.056797   0.035638\n",
       "25%       0.136906   0.106771\n",
       "50%       0.155625   0.153846\n",
       "75%       0.185014   0.195059\n",
       "max       0.291990   0.710345"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.unstack('smoker')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "f = lambda x: x.describe()\n",
    "grouped.apply(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Suppressing the group keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\envs\\mlnd\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...)\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size   tip_pct\n",
       "88        24.71  5.85    Male     No  Thur   Lunch     2  0.236746\n",
       "185       20.69  5.00    Male     No   Sun  Dinner     5  0.241663\n",
       "51        10.29  2.60  Female     No   Sun  Dinner     2  0.252672\n",
       "149        7.51  2.00    Male     No  Thur   Lunch     2  0.266312\n",
       "232       11.61  3.39    Male     No   Sat  Dinner     2  0.291990\n",
       "109       14.31  4.00  Female    Yes   Sat  Dinner     2  0.279525\n",
       "183       23.17  6.50    Male    Yes   Sun  Dinner     4  0.280535\n",
       "67         3.07  1.00  Female    Yes   Sat  Dinner     1  0.325733\n",
       "178        9.60  4.00  Female    Yes   Sun  Dinner     2  0.416667\n",
       "172        7.25  5.15    Male    Yes   Sun  Dinner     2  0.710345"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.groupby('smoker', group_keys=False).apply(top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantile and bucket analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     (-1.23, 0.489]\n",
       "1    (-2.956, -1.23]\n",
       "2     (-1.23, 0.489]\n",
       "3     (0.489, 2.208]\n",
       "4     (-1.23, 0.489]\n",
       "5     (0.489, 2.208]\n",
       "6     (-1.23, 0.489]\n",
       "7     (-1.23, 0.489]\n",
       "8     (0.489, 2.208]\n",
       "9     (0.489, 2.208]\n",
       "Name: data1, dtype: category\n",
       "Categories (4, object): [(-2.956, -1.23] < (-1.23, 0.489] < (0.489, 2.208] < (2.208, 3.928]]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = DataFrame({'data1': np.random.randn(1000),\n",
    "                   'data2': np.random.randn(1000)})\n",
    "factor = pd.cut(frame.data1, 4)\n",
    "factor[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 count       max      mean       min\n",
       "data1                                               \n",
       "(-2.956, -1.23]   95.0  1.670835 -0.039521 -3.399312\n",
       "(-1.23, 0.489]   598.0  3.260383 -0.002051 -2.989741\n",
       "(0.489, 2.208]   297.0  2.954439  0.081822 -3.745356\n",
       "(2.208, 3.928]    10.0  1.765640  0.024750 -1.929776"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_stats(group):\n",
    "    return {'min': group.min(), 'max': group.max(),\n",
    "            'count': group.count(), 'mean': group.mean()}\n",
    "\n",
    "grouped = frame.data2.groupby(factor)\n",
    "grouped.apply(get_stats).unstack()\n",
    "\n",
    "#ADAPT the output is not sorted in the book while this is the case now (swap first two lines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       count       max      mean       min\n",
       "data1                                     \n",
       "0      100.0  1.670835 -0.049902 -3.399312\n",
       "1      100.0  2.628441  0.030989 -1.950098\n",
       "2      100.0  2.527939 -0.067179 -2.925113\n",
       "3      100.0  3.260383  0.065713 -2.315555\n",
       "4      100.0  2.074345 -0.111653 -2.047939\n",
       "5      100.0  2.184810  0.052130 -2.989741\n",
       "6      100.0  2.458842 -0.021489 -2.223506\n",
       "7      100.0  2.954439 -0.026459 -3.056990\n",
       "8      100.0  2.735527  0.103406 -3.745356\n",
       "9      100.0  2.377020  0.220122 -2.064111"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Return quantile numbers\n",
    "grouping = pd.qcut(frame.data1, 10, labels=False)\n",
    "\n",
    "grouped = frame.data2.groupby(grouping)\n",
    "grouped.apply(get_stats).unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Filling missing values with group-specific values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1   -0.125921\n",
       "2         NaN\n",
       "3   -0.884475\n",
       "4         NaN\n",
       "5    0.227290\n",
       "dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = Series(np.random.randn(6))\n",
    "s[::2] = np.nan\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.261035\n",
       "1   -0.125921\n",
       "2   -0.261035\n",
       "3   -0.884475\n",
       "4   -0.261035\n",
       "5    0.227290\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.fillna(s.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio          0.922264\n",
       "New York     -2.153545\n",
       "Vermont            NaN\n",
       "Florida      -0.375842\n",
       "Oregon        0.329939\n",
       "Nevada             NaN\n",
       "California    1.105913\n",
       "Idaho              NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "states = ['Ohio', 'New York', 'Vermont', 'Florida',\n",
    "          'Oregon', 'Nevada', 'California', 'Idaho']\n",
    "group_key = ['East'] * 4 + ['West'] * 4\n",
    "data = Series(np.random.randn(8), index=states)\n",
    "data[['Vermont', 'Nevada', 'Idaho']] = np.nan\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "East   -0.535707\n",
       "West    0.717926\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(group_key).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio          0.922264\n",
       "New York     -2.153545\n",
       "Vermont      -0.535707\n",
       "Florida      -0.375842\n",
       "Oregon        0.329939\n",
       "Nevada        0.717926\n",
       "California    1.105913\n",
       "Idaho         0.717926\n",
       "dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill_mean = lambda g: g.fillna(g.mean())\n",
    "data.groupby(group_key).apply(fill_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ohio          0.922264\n",
       "New York     -2.153545\n",
       "Vermont       0.500000\n",
       "Florida      -0.375842\n",
       "Oregon        0.329939\n",
       "Nevada       -1.000000\n",
       "California    1.105913\n",
       "Idaho        -1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill_values = {'East': 0.5, 'West': -1}\n",
    "fill_func = lambda g: g.fillna(fill_values[g.name])\n",
    "\n",
    "data.groupby(group_key).apply(fill_func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Random sampling and permutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hearts, Spades, Clubs, Diamonds\n",
    "suits = ['H', 'S', 'C', 'D']\n",
    "card_val = (range(1, 11) + [10] * 3) * 4\n",
    "base_names = ['A'] + range(2, 11) + ['J', 'K', 'Q']\n",
    "cards = []\n",
    "for suit in ['H', 'S', 'C', 'D']:\n",
    "    cards.extend(str(num) + suit for num in base_names)\n",
    "\n",
    "deck = Series(card_val, index=cards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AH      1\n",
       "2H      2\n",
       "3H      3\n",
       "4H      4\n",
       "5H      5\n",
       "6H      6\n",
       "7H      7\n",
       "8H      8\n",
       "9H      9\n",
       "10H    10\n",
       "JH     10\n",
       "KH     10\n",
       "QH     10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck[:13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AD     1\n",
       "8C     8\n",
       "5H     5\n",
       "KC    10\n",
       "2C     2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def draw(deck, n=5):\n",
    "    return deck.take(np.random.permutation(len(deck))[:n])\n",
    "draw(deck)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C  2C     2\n",
       "   3C     3\n",
       "D  KD    10\n",
       "   8D     8\n",
       "H  KH    10\n",
       "   3H     3\n",
       "S  2S     2\n",
       "   4S     4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_suit = lambda card: card[-1] # last letter is suit\n",
    "deck.groupby(get_suit).apply(draw, n=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KC    10\n",
       "JC    10\n",
       "AD     1\n",
       "5D     5\n",
       "5H     5\n",
       "6H     6\n",
       "7S     7\n",
       "KS    10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternatively\n",
    "deck.groupby(get_suit, group_keys=False).apply(draw, n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Group weighted average and correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  category      data   weights\n",
       "0        a  1.561587  0.957515\n",
       "1        a  1.219984  0.347267\n",
       "2        a -0.482239  0.581362\n",
       "3        a  0.315667  0.217091\n",
       "4        b -0.047852  0.894406\n",
       "5        b -0.454145  0.918564\n",
       "6        b -0.556774  0.277825\n",
       "7        b  0.253321  0.955905"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame({'category': ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'],\n",
    "                'data': np.random.randn(8),\n",
    "                'weights': np.random.rand(8)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "a    0.811643\n",
       "b   -0.122262\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby('category')\n",
    "get_wavg = lambda g: np.average(g['data'], weights=g['weights'])\n",
    "grouped.apply(get_wavg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2214 entries, 2003-01-02 to 2011-10-14\n",
      "Data columns (total 4 columns):\n",
      "AAPL    2214 non-null float64\n",
      "MSFT    2214 non-null float64\n",
      "XOM     2214 non-null float64\n",
      "SPX     2214 non-null float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 86.5 KB\n"
     ]
    }
   ],
   "source": [
    "close_px = pd.read_csv('ch09/stock_px.csv', parse_dates=True, index_col=0)\n",
    "close_px.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              AAPL   MSFT    XOM      SPX\n",
       "2011-10-11  400.29  27.00  76.27  1195.54\n",
       "2011-10-12  402.19  26.96  77.16  1207.25\n",
       "2011-10-13  408.43  27.18  76.37  1203.66\n",
       "2011-10-14  422.00  27.27  78.11  1224.58"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_px[-4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          AAPL      MSFT       XOM  SPX\n",
       "2003  0.541124  0.745174  0.661265  1.0\n",
       "2004  0.374283  0.588531  0.557742  1.0\n",
       "2005  0.467540  0.562374  0.631010  1.0\n",
       "2006  0.428267  0.406126  0.518514  1.0\n",
       "2007  0.508118  0.658770  0.786264  1.0\n",
       "2008  0.681434  0.804626  0.828303  1.0\n",
       "2009  0.707103  0.654902  0.797921  1.0\n",
       "2010  0.710105  0.730118  0.839057  1.0\n",
       "2011  0.691931  0.800996  0.859975  1.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = close_px.pct_change().dropna()\n",
    "spx_corr = lambda x: x.corrwith(x['SPX'])\n",
    "by_year = rets.groupby(lambda x: x.year)\n",
    "by_year.apply(spx_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2003    0.480868\n",
       "2004    0.259024\n",
       "2005    0.300093\n",
       "2006    0.161735\n",
       "2007    0.417738\n",
       "2008    0.611901\n",
       "2009    0.432738\n",
       "2010    0.571946\n",
       "2011    0.581987\n",
       "dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Annual correlation of Apple with Microsoft\n",
    "by_year.apply(lambda g: g['AAPL'].corr(g['MSFT']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example: Group-wise linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm  # 学要先安装statsmodels，用conda install statsmodels即可安装\n",
    "def regress(data, yvar, xvars):\n",
    "    Y = data[yvar]\n",
    "    X = data[xvars]\n",
    "    X['intercept'] = 1.\n",
    "    result = sm.OLS(Y, X).fit()\n",
    "    return result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           SPX  intercept\n",
       "2003  1.195406   0.000710\n",
       "2004  1.363463   0.004201\n",
       "2005  1.766415   0.003246\n",
       "2006  1.645496   0.000080\n",
       "2007  1.198761   0.003438\n",
       "2008  0.968016  -0.001110\n",
       "2009  0.879103   0.002954\n",
       "2010  1.052608   0.001261\n",
       "2011  0.806605   0.001514"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_year.apply(regress, 'AAPL', ['SPX'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot tables and Cross-tabulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   size       tip   tip_pct  total_bill\n",
       "sex    smoker                                          \n",
       "Female No      2.592593  2.773519  0.156921   18.105185\n",
       "       Yes     2.242424  2.931515  0.182150   17.977879\n",
       "Male   No      2.711340  3.113402  0.160669   19.791237\n",
       "       Yes     2.500000  3.051167  0.152771   22.284500"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(index=['sex', 'smoker'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              tip_pct                size          \n",
       "smoker             No       Yes        No       Yes\n",
       "sex    day                                         \n",
       "Female Fri   0.165296  0.209129  2.500000  2.000000\n",
       "       Sat   0.147993  0.163817  2.307692  2.200000\n",
       "       Sun   0.165710  0.237075  3.071429  2.500000\n",
       "       Thur  0.155971  0.163073  2.480000  2.428571\n",
       "Male   Fri   0.138005  0.144730  2.000000  2.125000\n",
       "       Sat   0.162132  0.139067  2.656250  2.629630\n",
       "       Sun   0.158291  0.173964  2.883721  2.600000\n",
       "       Thur  0.165706  0.164417  2.500000  2.300000"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(['tip_pct', 'size'], index=['sex', 'day'],\n",
    "                 columns='smoker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              tip_pct                          size                    \n",
       "smoker             No       Yes       All        No       Yes       All\n",
       "sex    day                                                             \n",
       "Female Fri   0.165296  0.209129  0.199388  2.500000  2.000000  2.111111\n",
       "       Sat   0.147993  0.163817  0.156470  2.307692  2.200000  2.250000\n",
       "       Sun   0.165710  0.237075  0.181569  3.071429  2.500000  2.944444\n",
       "       Thur  0.155971  0.163073  0.157525  2.480000  2.428571  2.468750\n",
       "Male   Fri   0.138005  0.144730  0.143385  2.000000  2.125000  2.100000\n",
       "       Sat   0.162132  0.139067  0.151577  2.656250  2.629630  2.644068\n",
       "       Sun   0.158291  0.173964  0.162344  2.883721  2.600000  2.810345\n",
       "       Thur  0.165706  0.164417  0.165276  2.500000  2.300000  2.433333\n",
       "All          0.159328  0.163196  0.160803  2.668874  2.408602  2.569672"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table(['tip_pct', 'size'], index=['sex', 'day'],\n",
    "                 columns='smoker', margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day             Fri   Sat   Sun  Thur    All\n",
       "sex    smoker                               \n",
       "Female No       2.0  13.0  14.0  25.0   54.0\n",
       "       Yes      7.0  15.0   4.0   7.0   33.0\n",
       "Male   No       2.0  32.0  43.0  20.0   97.0\n",
       "       Yes      8.0  27.0  15.0  10.0   60.0\n",
       "All            19.0  87.0  76.0  62.0  244.0"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table('tip_pct', index=['sex', 'smoker'], columns='day',\n",
    "                 aggfunc=len, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day                   Fri  Sat  Sun  Thur\n",
       "time   sex    smoker                     \n",
       "Dinner Female No        2   30   43     2\n",
       "              Yes       8   33   10     0\n",
       "       Male   No        4   85  124     0\n",
       "              Yes      12   71   39     0\n",
       "Lunch  Female No        3    0    0    60\n",
       "              Yes       6    0    0    17\n",
       "       Male   No        0    0    0    50\n",
       "              Yes       5    0    0    23"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.pivot_table('size', index=['time', 'sex', 'smoker'],\n",
    "                 columns='day', aggfunc='sum', fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-tabulations: crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from StringIO import StringIO\n",
    "data = \"\"\"\\\n",
    "Sample    Gender    Handedness\n",
    "1    Female    Right-handed\n",
    "2    Male    Left-handed\n",
    "3    Female    Right-handed\n",
    "4    Male    Right-handed\n",
    "5    Male    Left-handed\n",
    "6    Male    Right-handed\n",
    "7    Female    Right-handed\n",
    "8    Female    Left-handed\n",
    "9    Male    Right-handed\n",
    "10    Female    Right-handed\"\"\"\n",
    "data = pd.read_table(StringIO(data), sep='\\s+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Sample  Gender    Handedness\n",
       "0       1  Female  Right-handed\n",
       "1       2    Male   Left-handed\n",
       "2       3  Female  Right-handed\n",
       "3       4    Male  Right-handed\n",
       "4       5    Male   Left-handed\n",
       "5       6    Male  Right-handed\n",
       "6       7  Female  Right-handed\n",
       "7       8  Female   Left-handed\n",
       "8       9    Male  Right-handed\n",
       "9      10  Female  Right-handed"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Handedness  Left-handed  Right-handed  All\n",
       "Gender                                    \n",
       "Female                1             4    5\n",
       "Male                  2             3    5\n",
       "All                   3             7   10"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data.Gender, data.Handedness, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoker        No  Yes  All\n",
       "time   day                \n",
       "Dinner Fri     3    9   12\n",
       "       Sat    45   42   87\n",
       "       Sun    57   19   76\n",
       "       Thur    1    0    1\n",
       "Lunch  Fri     1    6    7\n",
       "       Thur   44   17   61\n",
       "All          151   93  244"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab([tips.time, tips.day], tips.smoker, margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: 2012 Federal Election Commission Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\envs\\mlnd\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2717: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "fec = pd.read_csv('ch09/P00000001-ALL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1001731 entries, 0 to 1001730\n",
      "Data columns (total 16 columns):\n",
      "cmte_id              1001731 non-null object\n",
      "cand_id              1001731 non-null object\n",
      "cand_nm              1001731 non-null object\n",
      "contbr_nm            1001731 non-null object\n",
      "contbr_city          1001712 non-null object\n",
      "contbr_st            1001727 non-null object\n",
      "contbr_zip           1001620 non-null object\n",
      "contbr_employer      988002 non-null object\n",
      "contbr_occupation    993301 non-null object\n",
      "contb_receipt_amt    1001731 non-null float64\n",
      "contb_receipt_dt     1001731 non-null object\n",
      "receipt_desc         14166 non-null object\n",
      "memo_cd              92482 non-null object\n",
      "memo_text            97770 non-null object\n",
      "form_tp              1001731 non-null object\n",
      "file_num             1001731 non-null int64\n",
      "dtypes: float64(1), int64(1), object(14)\n",
      "memory usage: 122.3+ MB\n"
     ]
    }
   ],
   "source": [
    "fec.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cmte_id                             C00431445\n",
       "cand_id                             P80003338\n",
       "cand_nm                         Obama, Barack\n",
       "contbr_nm                         ELLMAN, IRA\n",
       "contbr_city                             TEMPE\n",
       "contbr_st                                  AZ\n",
       "contbr_zip                          852816719\n",
       "contbr_employer      ARIZONA STATE UNIVERSITY\n",
       "contbr_occupation                   PROFESSOR\n",
       "contb_receipt_amt                          50\n",
       "contb_receipt_dt                    01-DEC-11\n",
       "receipt_desc                              NaN\n",
       "memo_cd                                   NaN\n",
       "memo_text                                 NaN\n",
       "form_tp                                 SA17A\n",
       "file_num                               772372\n",
       "Name: 123456, dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.ix[123456]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',\n",
       "       \"Roemer, Charles E. 'Buddy' III\", 'Pawlenty, Timothy',\n",
       "       'Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick', 'Cain, Herman',\n",
       "       'Gingrich, Newt', 'McCotter, Thaddeus G', 'Huntsman, Jon',\n",
       "       'Perry, Rick'], dtype=object)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cands = fec.cand_nm.unique()\n",
    "unique_cands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Obama, Barack'"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cands[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "parties = {'Bachmann, Michelle': 'Republican',\n",
    "           'Cain, Herman': 'Republican',\n",
    "           'Gingrich, Newt': 'Republican',\n",
    "           'Huntsman, Jon': 'Republican',\n",
    "           'Johnson, Gary Earl': 'Republican',\n",
    "           'McCotter, Thaddeus G': 'Republican',\n",
    "           'Obama, Barack': 'Democrat',\n",
    "           'Paul, Ron': 'Republican',\n",
    "           'Pawlenty, Timothy': 'Republican',\n",
    "           'Perry, Rick': 'Republican',\n",
    "           \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n",
    "           'Romney, Mitt': 'Republican',\n",
    "           'Santorum, Rick': 'Republican'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123456    Obama, Barack\n",
       "123457    Obama, Barack\n",
       "123458    Obama, Barack\n",
       "123459    Obama, Barack\n",
       "123460    Obama, Barack\n",
       "Name: cand_nm, dtype: object"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.cand_nm[123456:123461]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123456    Democrat\n",
       "123457    Democrat\n",
       "123458    Democrat\n",
       "123459    Democrat\n",
       "123460    Democrat\n",
       "Name: cand_nm, dtype: object"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.cand_nm[123456:123461].map(parties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add it as a column\n",
    "fec['party'] = fec.cand_nm.map(parties)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Democrat      593746\n",
       "Republican    407985\n",
       "Name: party, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec['party'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     991475\n",
       "False     10256\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(fec.contb_receipt_amt > 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "fec = fec[fec.contb_receipt_amt > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "fec_mrbo = fec[fec.cand_nm.isin(['Obama, Barack', 'Romney, Mitt'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Donation statistics by occupation and employer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RETIRED                                   233990\n",
       "INFORMATION REQUESTED                      35107\n",
       "ATTORNEY                                   34286\n",
       "HOMEMAKER                                  29931\n",
       "PHYSICIAN                                  23432\n",
       "INFORMATION REQUESTED PER BEST EFFORTS     21138\n",
       "ENGINEER                                   14334\n",
       "TEACHER                                    13990\n",
       "CONSULTANT                                 13273\n",
       "PROFESSOR                                  12555\n",
       "Name: contbr_occupation, dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.contbr_occupation.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "occ_mapping = {\n",
    "   'INFORMATION REQUESTED PER BEST EFFORTS' : 'NOT PROVIDED',\n",
    "   'INFORMATION REQUESTED' : 'NOT PROVIDED',\n",
    "   'INFORMATION REQUESTED (BEST EFFORTS)' : 'NOT PROVIDED',\n",
    "   'C.E.O.': 'CEO'\n",
    "}\n",
    "\n",
    "# If no mapping provided, return x\n",
    "f = lambda x: occ_mapping.get(x, x)\n",
    "fec.contbr_occupation = fec.contbr_occupation.map(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_mapping = {\n",
    "   'INFORMATION REQUESTED PER BEST EFFORTS' : 'NOT PROVIDED',\n",
    "   'INFORMATION REQUESTED' : 'NOT PROVIDED',\n",
    "   'SELF' : 'SELF-EMPLOYED',\n",
    "   'SELF EMPLOYED' : 'SELF-EMPLOYED',\n",
    "}\n",
    "\n",
    "# If no mapping provided, return x\n",
    "f = lambda x: emp_mapping.get(x, x)\n",
    "fec.contbr_employer = fec.contbr_employer.map(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_occupation = fec.pivot_table('contb_receipt_amt',\n",
    "                                index='contbr_occupation',\n",
    "                                columns='party', aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "party                 Democrat    Republican\n",
       "contbr_occupation                           \n",
       "ATTORNEY           11141982.97  7.477194e+06\n",
       "CEO                 2074974.79  4.211041e+06\n",
       "CONSULTANT          2459912.71  2.544725e+06\n",
       "ENGINEER             951525.55  1.818374e+06\n",
       "EXECUTIVE           1355161.05  4.138850e+06\n",
       "HOMEMAKER           4248875.80  1.363428e+07\n",
       "INVESTOR             884133.00  2.431769e+06\n",
       "LAWYER              3160478.87  3.912243e+05\n",
       "MANAGER              762883.22  1.444532e+06\n",
       "NOT PROVIDED        4866973.96  2.056547e+07\n",
       "OWNER               1001567.36  2.408287e+06\n",
       "PHYSICIAN           3735124.94  3.594320e+06\n",
       "PRESIDENT           1878509.95  4.720924e+06\n",
       "PROFESSOR           2165071.08  2.967027e+05\n",
       "REAL ESTATE          528902.09  1.625902e+06\n",
       "RETIRED            25305116.38  2.356124e+07\n",
       "SELF-EMPLOYED        672393.40  1.640253e+06"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_2mm = by_occupation[by_occupation.sum(1) > 2000000]\n",
    "over_2mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x171dee48>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g54gYlNuuM1nGahfgIWANoPDU6SsRsaek9YH/JZvdZBmy4IA7gUJI+beAN9N2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\nEc8A3wb+GREPp7JPyQLG8+8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x87679b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "over_2mm.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_top_amounts(group, key, n=5):\n",
    "    totals = group.groupby(key)['contb_receipt_amt'].sum()\n",
    "\n",
    "    # Order totals by key in descending order\n",
    "    return totals.order(ascending=False)[-n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\envs\\mlnd\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: order is deprecated, use sort_values(...)\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "cand_nm        contbr_occupation                     \n",
       "Obama, Barack  COMPUTER ASSISTANT                        3.0\n",
       "               SPRINKLER FITTER FIRE PROTECTION SPECI    3.0\n",
       "               ADMINISTRATION/INSTRUCTOR                 3.0\n",
       "               LEAD UI/UX DEVELOPER                      3.0\n",
       "               POLICY/ LAWYER                            3.0\n",
       "               LAN/WAN ANALYST                           3.0\n",
       "               SR MGR                                    3.0\n",
       "Romney, Mitt   MD - UROLOGIST                            5.0\n",
       "               DISTRICT REPRESENTATIVE                   5.0\n",
       "               INDEPENDENT PROFESSIONAL                  3.0\n",
       "               REMODELER & SEMI RETIRED                  3.0\n",
       "               AFFORDABLE REAL ESTATE DEVELOPER          3.0\n",
       "               IFC CONTRACTING SOLUTIONS                 3.0\n",
       "               3RD GENERATION FAMILY BUSINESS OWNER      3.0\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby('cand_nm')\n",
    "grouped.apply(get_top_amounts, 'contbr_occupation', n=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\envs\\mlnd\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: order is deprecated, use sort_values(...)\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "cand_nm        contbr_employer                   \n",
       "Obama, Barack  SOLIYA                                3.0\n",
       "               CARR ENTERPRISES                      3.0\n",
       "               PENN STATE DICKINSON SCHOOL OF LAW    3.0\n",
       "               CADUCEUS OCCUPATIONAL MEDICINE        3.0\n",
       "               N.A.                                  3.0\n",
       "               REAL ENERGY CONSULTING SERVICES       3.0\n",
       "               JPDSYSTEMS, LLC                       3.0\n",
       "               CASS REGIONAL MED. CENTER             2.5\n",
       "               ARCON CORP                            2.0\n",
       "               THE VICTORIA GROUP, INC.              2.0\n",
       "Romney, Mitt   EASTHAM CAPITAL                       5.0\n",
       "               GREGORY GALLIVAN                      5.0\n",
       "               DIRECT LENDERS LLC                    5.0\n",
       "               LOUGH INVESTMENT ADVISORY LLC         4.0\n",
       "               WATERWORKS INDUSRTIES                 3.0\n",
       "               WILL MERRIFIELD                       3.0\n",
       "               HONOLD COMMUNICTAIONS                 3.0\n",
       "               INDEPENDENT PROFESSIONAL              3.0\n",
       "               UPTOWN CHEAPSKATE                     3.0\n",
       "               UN                                    3.0\n",
       "Name: contb_receipt_amt, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.apply(get_top_amounts, 'contbr_employer', n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bucketing donation amounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "411           (10, 100]\n",
       "412         (100, 1000]\n",
       "413         (100, 1000]\n",
       "414           (10, 100]\n",
       "415           (10, 100]\n",
       "416           (10, 100]\n",
       "417         (100, 1000]\n",
       "418           (10, 100]\n",
       "419         (100, 1000]\n",
       "420           (10, 100]\n",
       "421           (10, 100]\n",
       "422         (100, 1000]\n",
       "423         (100, 1000]\n",
       "424         (100, 1000]\n",
       "425         (100, 1000]\n",
       "426         (100, 1000]\n",
       "427       (1000, 10000]\n",
       "428         (100, 1000]\n",
       "429         (100, 1000]\n",
       "430           (10, 100]\n",
       "431       (1000, 10000]\n",
       "432         (100, 1000]\n",
       "433         (100, 1000]\n",
       "434         (100, 1000]\n",
       "435         (100, 1000]\n",
       "436         (100, 1000]\n",
       "437           (10, 100]\n",
       "438         (100, 1000]\n",
       "439         (100, 1000]\n",
       "440           (10, 100]\n",
       "              ...      \n",
       "701356        (10, 100]\n",
       "701357          (1, 10]\n",
       "701358        (10, 100]\n",
       "701359        (10, 100]\n",
       "701360        (10, 100]\n",
       "701361        (10, 100]\n",
       "701362      (100, 1000]\n",
       "701363        (10, 100]\n",
       "701364        (10, 100]\n",
       "701365        (10, 100]\n",
       "701366        (10, 100]\n",
       "701367        (10, 100]\n",
       "701368      (100, 1000]\n",
       "701369        (10, 100]\n",
       "701370        (10, 100]\n",
       "701371        (10, 100]\n",
       "701372        (10, 100]\n",
       "701373        (10, 100]\n",
       "701374        (10, 100]\n",
       "701375        (10, 100]\n",
       "701376    (1000, 10000]\n",
       "701377        (10, 100]\n",
       "701378        (10, 100]\n",
       "701379      (100, 1000]\n",
       "701380    (1000, 10000]\n",
       "701381        (10, 100]\n",
       "701382      (100, 1000]\n",
       "701383          (1, 10]\n",
       "701384        (10, 100]\n",
       "701385      (100, 1000]\n",
       "Name: contb_receipt_amt, dtype: category\n",
       "Categories (8, object): [(0, 1] < (1, 10] < (10, 100] < (100, 1000] < (1000, 10000] < (10000, 100000] < (100000, 1000000] < (1000000, 10000000]]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.array([0, 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000])\n",
    "labels = pd.cut(fec_mrbo.contb_receipt_amt, bins)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                       493.0          77.0\n",
       "(1, 10]                    40070.0        3681.0\n",
       "(10, 100]                 372280.0       31853.0\n",
       "(100, 1000]               153991.0       43357.0\n",
       "(1000, 10000]              22284.0       26186.0\n",
       "(10000, 100000]                2.0           1.0\n",
       "(100000, 1000000]              3.0           NaN\n",
       "(1000000, 10000000]            4.0           NaN"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby(['cand_nm', labels])\n",
    "grouped.size().unstack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                      318.24         77.00\n",
       "(1, 10]                  337267.62      29819.66\n",
       "(10, 100]              20288981.41    1987783.76\n",
       "(100, 1000]            54798531.46   22363381.69\n",
       "(1000, 10000]          51753705.67   63942145.42\n",
       "(10000, 100000]           59100.00      12700.00\n",
       "(100000, 1000000]       1490683.08           NaN\n",
       "(1000000, 10000000]     7148839.76           NaN"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket_sums = grouped.contb_receipt_amt.sum().unstack(0)\n",
    "bucket_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                    0.805182      0.194818\n",
       "(1, 10]                   0.918767      0.081233\n",
       "(10, 100]                 0.910769      0.089231\n",
       "(100, 1000]               0.710176      0.289824\n",
       "(1000, 10000]             0.447326      0.552674\n",
       "(10000, 100000]           0.823120      0.176880\n",
       "(100000, 1000000]         1.000000           NaN\n",
       "(1000000, 10000000]       1.000000           NaN"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normed_sums = bucket_sums.div(bucket_sums.sum(axis=1), axis=0)\n",
    "normed_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf556cf8>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0bduWd955p7Ssa9euFBYWsmrVKrKyspgyZQoTJ05k4cKFpKenly6+PmPGDL73ve/RuXPn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\nQ1nBiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf5865f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normed_sums[:-2].plot(kind='barh', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Donation statistics by state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm    Obama, Barack  Romney, Mitt\n",
       "contbr_st                             \n",
       "AK             281840.15      86204.24\n",
       "AL             543123.48     527303.51\n",
       "AR             359247.28     105556.00\n",
       "AZ            1506476.98    1888436.23\n",
       "CA           23824984.24   11237636.60\n",
       "CO            2132429.49    1506714.12\n",
       "CT            2068291.26    3499475.45\n",
       "DC            4373538.80    1025137.50\n",
       "DE             336669.14      82712.00\n",
       "FL            7318178.58    8338458.81"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby(['cand_nm', 'contbr_st'])\n",
    "totals = grouped.contb_receipt_amt.sum().unstack(0).fillna(0)\n",
    "totals = totals[totals.sum(1) > 100000]\n",
    "totals[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm    Obama, Barack  Romney, Mitt\n",
       "contbr_st                             \n",
       "AK              0.765778      0.234222\n",
       "AL              0.507390      0.492610\n",
       "AR              0.772902      0.227098\n",
       "AZ              0.443745      0.556255\n",
       "CA              0.679498      0.320502\n",
       "CO              0.585970      0.414030\n",
       "CT              0.371476      0.628524\n",
       "DC              0.810113      0.189887\n",
       "DE              0.802776      0.197224\n",
       "FL              0.467417      0.532583"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent = totals.div(totals.sum(1), axis=0)\n",
    "percent[:10]"
   ]
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