Data Analysis with Pandas - Baby Names.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pandas import *\n",
    "set_option('display.notebook_repr_html',True) # HTML output\n",
    "set_option('display.max_rows',20)\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   year     name      prop  sex soundex\n",
       "0  1880     John  0.081541  boy    J500\n",
       "1  1880  William  0.080511  boy    W450\n",
       "2  1880    James  0.050057  boy    J520\n",
       "3  1880  Charles  0.045167  boy    C642\n",
       "4  1880   George  0.043292  boy    G620"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = read_csv('data/baby-names2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1880</td>\n",
       "      <td>    John</td>\n",
       "      <td> 0.081541</td>\n",
       "      <td> boy</td>\n",
       "      <td> J500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1880</td>\n",
       "      <td> William</td>\n",
       "      <td> 0.080511</td>\n",
       "      <td> boy</td>\n",
       "      <td> W450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 1880</td>\n",
       "      <td>   James</td>\n",
       "      <td> 0.050057</td>\n",
       "      <td> boy</td>\n",
       "      <td> J520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 1880</td>\n",
       "      <td> Charles</td>\n",
       "      <td> 0.045167</td>\n",
       "      <td> boy</td>\n",
       "      <td> C642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 1880</td>\n",
       "      <td>  George</td>\n",
       "      <td> 0.043292</td>\n",
       "      <td> boy</td>\n",
       "      <td> G620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     name      prop  sex soundex\n",
       "0  1880     John  0.081541  boy    J500\n",
       "1  1880  William  0.080511  boy    W450\n",
       "2  1880    James  0.050057  boy    J520\n",
       "3  1880  Charles  0.045167  boy    C642\n",
       "4  1880   George  0.043292  boy    G620"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129995</th>\n",
       "      <td> 1880</td>\n",
       "      <td> Emaline</td>\n",
       "      <td> 0.000041</td>\n",
       "      <td> girl</td>\n",
       "      <td> E545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129996</th>\n",
       "      <td> 1880</td>\n",
       "      <td>   Ester</td>\n",
       "      <td> 0.000041</td>\n",
       "      <td> girl</td>\n",
       "      <td> E236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129997</th>\n",
       "      <td> 1880</td>\n",
       "      <td>   Eulah</td>\n",
       "      <td> 0.000041</td>\n",
       "      <td> girl</td>\n",
       "      <td> E400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129998</th>\n",
       "      <td> 1880</td>\n",
       "      <td> Eulalie</td>\n",
       "      <td> 0.000041</td>\n",
       "      <td> girl</td>\n",
       "      <td> E440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129999</th>\n",
       "      <td> 1880</td>\n",
       "      <td>    Euna</td>\n",
       "      <td> 0.000041</td>\n",
       "      <td> girl</td>\n",
       "      <td> E500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year     name      prop   sex soundex\n",
       "129995  1880  Emaline  0.000041  girl    E545\n",
       "129996  1880    Ester  0.000041  girl    E236\n",
       "129997  1880    Eulah  0.000041  girl    E400\n",
       "129998  1880  Eulalie  0.000041  girl    E440\n",
       "129999  1880     Euna  0.000041  girl    E500"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[names.year == 1880].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boys = names[names.sex == 'boy']\n",
    "girls = names[names.sex == 'girl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1880</td>\n",
       "      <td>    John</td>\n",
       "      <td> 0.081541</td>\n",
       "      <td> boy</td>\n",
       "      <td> J500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1880</td>\n",
       "      <td> William</td>\n",
       "      <td> 0.080511</td>\n",
       "      <td> boy</td>\n",
       "      <td> W450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 1880</td>\n",
       "      <td>   James</td>\n",
       "      <td> 0.050057</td>\n",
       "      <td> boy</td>\n",
       "      <td> J520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 1880</td>\n",
       "      <td> Charles</td>\n",
       "      <td> 0.045167</td>\n",
       "      <td> boy</td>\n",
       "      <td> C642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 1880</td>\n",
       "      <td>  George</td>\n",
       "      <td> 0.043292</td>\n",
       "      <td> boy</td>\n",
       "      <td> G620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     name      prop  sex soundex\n",
       "0  1880     John  0.081541  boy    J500\n",
       "1  1880  William  0.080511  boy    W450\n",
       "2  1880    James  0.050057  boy    J520\n",
       "3  1880  Charles  0.045167  boy    C642\n",
       "4  1880   George  0.043292  boy    G620"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boys.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 129000 entries, 0 to 128999\n",
      "Data columns (total 5 columns):\n",
      "year       129000 non-null int64\n",
      "name       129000 non-null object\n",
      "prop       129000 non-null float64\n",
      "sex        129000 non-null object\n",
      "soundex    129000 non-null object\n",
      "dtypes: float64(1), int64(1), object(3)"
     ]
    }
   ],
   "source": [
    "boys.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 129000 entries, 129000 to 257999\n",
      "Data columns (total 5 columns):\n",
      "year       129000 non-null int64\n",
      "name       129000 non-null object\n",
      "prop       129000 non-null float64\n",
      "sex        129000 non-null object\n",
      "soundex    129000 non-null object\n",
      "dtypes: float64(1), int64(1), object(3)"
     ]
    }
   ],
   "source": [
    "girls.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1880    1000\n",
       "1881    1000\n",
       "1882    1000\n",
       "...\n",
       "2006    1000\n",
       "2007    1000\n",
       "2008    1000\n",
       "Length: 129, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boys.groupby('year').size() # Number of records in each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year  sex \n",
      "1880  boy     1000\n",
      "      girl    1000\n",
      "1881  boy     1000\n",
      "...\n",
      "2007  girl    1000\n",
      "2008  boy     1000\n",
      "      girl    1000\n",
      "Length: 258, dtype: int64\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "groups = names.groupby(['year','sex']).size()\n",
    "print(groups)\n",
    "print(type(groups)) # Even the result is a series with multi-indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex\n",
      "boy     1000\n",
      "girl    1000\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Since multi-indexed, we can check the count of specific year/sex\n",
    "print(groups.ix[2000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n"
     ]
    }
   ],
   "source": [
    "print(groups.ix[2000]['boy']) # 1000 boys in year 2000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Let's find the most popular names for boys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120000</th>\n",
       "      <td> 2000</td>\n",
       "      <td>       Jacob</td>\n",
       "      <td> 0.016514</td>\n",
       "      <td> boy</td>\n",
       "      <td> J210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120001</th>\n",
       "      <td> 2000</td>\n",
       "      <td>     Michael</td>\n",
       "      <td> 0.015346</td>\n",
       "      <td> boy</td>\n",
       "      <td> M240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120002</th>\n",
       "      <td> 2000</td>\n",
       "      <td>     Matthew</td>\n",
       "      <td> 0.013695</td>\n",
       "      <td> boy</td>\n",
       "      <td> M300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120003</th>\n",
       "      <td> 2000</td>\n",
       "      <td>      Joshua</td>\n",
       "      <td> 0.013194</td>\n",
       "      <td> boy</td>\n",
       "      <td> J200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120004</th>\n",
       "      <td> 2000</td>\n",
       "      <td> Christopher</td>\n",
       "      <td> 0.011990</td>\n",
       "      <td> boy</td>\n",
       "      <td> C623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120995</th>\n",
       "      <td> 2000</td>\n",
       "      <td>       Dayne</td>\n",
       "      <td> 0.000071</td>\n",
       "      <td> boy</td>\n",
       "      <td> D500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120996</th>\n",
       "      <td> 2000</td>\n",
       "      <td>   Francesco</td>\n",
       "      <td> 0.000071</td>\n",
       "      <td> boy</td>\n",
       "      <td> F652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120997</th>\n",
       "      <td> 2000</td>\n",
       "      <td>       Isaak</td>\n",
       "      <td> 0.000071</td>\n",
       "      <td> boy</td>\n",
       "      <td> I220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120998</th>\n",
       "      <td> 2000</td>\n",
       "      <td>      Lionel</td>\n",
       "      <td> 0.000071</td>\n",
       "      <td> boy</td>\n",
       "      <td> L540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120999</th>\n",
       "      <td> 2000</td>\n",
       "      <td>       Tracy</td>\n",
       "      <td> 0.000071</td>\n",
       "      <td> boy</td>\n",
       "      <td> T620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year         name      prop  sex soundex\n",
       "120000  2000        Jacob  0.016514  boy    J210\n",
       "120001  2000      Michael  0.015346  boy    M240\n",
       "120002  2000      Matthew  0.013695  boy    M300\n",
       "120003  2000       Joshua  0.013194  boy    J200\n",
       "120004  2000  Christopher  0.011990  boy    C623\n",
       "...      ...          ...       ...  ...     ...\n",
       "120995  2000        Dayne  0.000071  boy    D500\n",
       "120996  2000    Francesco  0.000071  boy    F652\n",
       "120997  2000        Isaak  0.000071  boy    I220\n",
       "120998  2000       Lionel  0.000071  boy    L540\n",
       "120999  2000        Tracy  0.000071  boy    T620\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boys[boys.year == 2000]\n",
    "# the data seems to be already sorted by proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year           2000\n",
       "name          Jacob\n",
       "prop       0.016514\n",
       "sex             boy\n",
       "soundex        J210\n",
       "Name: 120000, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ignoring that, let's find the top boy name for 2000\n",
    "max_bname_idx = boys[boys.year == 2000].prop.idxmax()\n",
    "boys.ix[max_bname_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year           1880\n",
       "name           John\n",
       "prop       0.081541\n",
       "sex             boy\n",
       "soundex        J500\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's find this for all years\n",
    "def top_bname(group):\n",
    "    return group.ix[group.prop.idxmax()]\n",
    "top_bname(boys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      year   name      prop  sex soundex  year_rank      normp\n",
      "year                                                          \n",
      "1880  1880   John  0.081541  boy    J500       1000  16.548082\n",
      "1881  1881   John  0.080975  boy    J500       1000  16.632642\n",
      "1882  1882   John  0.078314  boy    J500       1000  16.496476\n",
      "1883  1883   John  0.079066  boy    J500       1000  16.713289\n",
      "1884  1884   John  0.076476  boy    J500       1000  16.632968\n",
      "...    ...    ...       ...  ...     ...        ...        ...\n",
      "2004  2004  Jacob  0.013196  boy    J210       1000   7.533051\n",
      "2005  2005  Jacob  0.012148  boy    J210       1000   7.128861\n",
      "2006  2006  Jacob  0.011331  boy    J210       1000   6.857703\n",
      "2007  2007  Jacob  0.010948  boy    J210       1000   6.772114\n",
      "2008  2008  Jacob  0.010355  boy    J210       1000   6.572084\n",
      "\n",
      "[129 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "result = boys.groupby('year').apply(top_bname)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a9d0c88>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gie1ltleQftvtW9dmH+ACANu3AiMlbZiXn85t1gRGAH8aWgohhNAaNt8lTUHw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ebaa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.prop.plot()  # proportion of max name over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1100d6fd0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ec5160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boys[boys.name=='Travis'].prop.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>John</th>\n",
       "      <th>1880</th>\n",
       "      <td> 0.081541</td>\n",
       "      <td> boy</td>\n",
       "      <td> J500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>William</th>\n",
       "      <th>1880</th>\n",
       "      <td> 0.080511</td>\n",
       "      <td> boy</td>\n",
       "      <td> W450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James</th>\n",
       "      <th>1880</th>\n",
       "      <td> 0.050057</td>\n",
       "      <td> boy</td>\n",
       "      <td> J520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charles</th>\n",
       "      <th>1880</th>\n",
       "      <td> 0.045167</td>\n",
       "      <td> boy</td>\n",
       "      <td> C642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George</th>\n",
       "      <th>1880</th>\n",
       "      <td> 0.043292</td>\n",
       "      <td> boy</td>\n",
       "      <td> G620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolten</th>\n",
       "      <th>2008</th>\n",
       "      <td> 0.000090</td>\n",
       "      <td> boy</td>\n",
       "      <td> K435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Damari</th>\n",
       "      <th>2008</th>\n",
       "      <td> 0.000089</td>\n",
       "      <td> boy</td>\n",
       "      <td> D560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugh</th>\n",
       "      <th>2008</th>\n",
       "      <td> 0.000089</td>\n",
       "      <td> boy</td>\n",
       "      <td> H200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jensen</th>\n",
       "      <th>2008</th>\n",
       "      <td> 0.000089</td>\n",
       "      <td> boy</td>\n",
       "      <td> J525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yurem</th>\n",
       "      <th>2008</th>\n",
       "      <td> 0.000089</td>\n",
       "      <td> boy</td>\n",
       "      <td> Y650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  prop  sex soundex\n",
       "name    year                       \n",
       "John    1880  0.081541  boy    J500\n",
       "William 1880  0.080511  boy    W450\n",
       "James   1880  0.050057  boy    J520\n",
       "Charles 1880  0.045167  boy    C642\n",
       "George  1880  0.043292  boy    G620\n",
       "...                ...  ...     ...\n",
       "Kolten  2008  0.000090  boy    K435\n",
       "Damari  2008  0.000089  boy    D560\n",
       "Hugh    2008  0.000089  boy    H200\n",
       "Jensen  2008  0.000089  boy    J525\n",
       "Yurem   2008  0.000089  boy    Y650\n",
       "\n",
       "[129000 rows x 3 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Searching for each name like this would take a long time. \n",
    "# So create an index based on year and name\n",
    "idf = boys.set_index(['name','year'])\n",
    "idf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td> 0.000101</td>\n",
       "      <td> boy</td>\n",
       "      <td> T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td> 0.000082</td>\n",
       "      <td> boy</td>\n",
       "      <td> T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td> 0.000080</td>\n",
       "      <td> boy</td>\n",
       "      <td> T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td> 0.000081</td>\n",
       "      <td> boy</td>\n",
       "      <td> T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td> 0.000095</td>\n",
       "      <td> boy</td>\n",
       "      <td> T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td> 0.001164</td>\n",
       "      <td> boy</td>\n",
       "      <td> T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td> 0.001114</td>\n",
       "      <td> boy</td>\n",
       "      <td> T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td> 0.001186</td>\n",
       "      <td> boy</td>\n",
       "      <td> T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td> 0.001087</td>\n",
       "      <td> boy</td>\n",
       "      <td> T612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td> 0.001025</td>\n",
       "      <td> boy</td>\n",
       "      <td> T612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          prop  sex soundex\n",
       "year                       \n",
       "1880  0.000101  boy    T612\n",
       "1882  0.000082  boy    T612\n",
       "1883  0.000080  boy    T612\n",
       "1884  0.000081  boy    T612\n",
       "1885  0.000095  boy    T612\n",
       "...        ...  ...     ...\n",
       "2004  0.001164  boy    T612\n",
       "2005  0.001114  boy    T612\n",
       "2006  0.001186  boy    T612\n",
       "2007  0.001087  boy    T612\n",
       "2008  0.001025  boy    T612\n",
       "\n",
       "[125 rows x 3 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idf.ix['Travis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 8 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit boys[boys.name == 'Travis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 571 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit idf.ix['Travis']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, indexing improves the performance tremendously"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prop       0.005808\n",
      "sex             boy\n",
      "soundex        T612\n",
      "Name: (Travis, 1980), dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a9a4470>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fMyWdMfR+UfG6uK6ZfUY30XbISK3+mFo8kF5M7YpHYl9gZ8Jf6blJ7fOB9sVk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\nc84NEf8fZ5ZFed2TVLUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a554860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(idf.ix['Travis',1980])\n",
    "idf.ix['Travis'].prop.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                prop  sex soundex\n",
      "name  year                       \n",
      "Aaden 2008  0.000442  boy    A350\n",
      "Aarav 2008  0.000101  boy    A610\n",
      "Aaron 1880  0.000861  boy    A650\n",
      "      1881  0.000868  boy    A650\n",
      "      1882  0.000697  boy    A650\n",
      "...              ...  ...     ...\n",
      "      1893  0.000669  boy    A650\n",
      "      1894  0.000632  boy    A650\n",
      "      1895  0.000742  boy    A650\n",
      "      1896  0.000535  boy    A650\n",
      "      1897  0.000713  boy    A650\n",
      "\n",
      "[20 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117077860>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sYL7tuXl7KbC77RWS1gF+AVxqe0Yf5y/dqBhJE8p0xRH5FF835ySxAem3/vcD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11709c518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Another method to get the prop distribution\n",
    "name_sorted = boys.sort_index(by=['name','year'],ascending=True)\n",
    "inded = name_sorted.set_index(['name','year']) \n",
    "# If you only set the name as index, the plot will display the name on the x axis\n",
    "# because inded.ix['john'] will return a table with index 'john'\n",
    "# with ['name','year'], inded.ix['john'] will be a df with years as indexes\n",
    "print(inded[:20])\n",
    "inded.ix['John'].prop.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Aaden    0.000442\n",
       "Aarav    0.000101\n",
       "Aaron    0.002266\n",
       "...\n",
       "Zigmund    0.000052\n",
       "Zion       0.000490\n",
       "Zollie     0.000060\n",
       "Name: prop, Length: 3437, dtype: float64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get name-wise prop aggregation\n",
    "namewise_prop = boys.groupby('name')['prop']\n",
    "namewise_prop.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Kennard    0.000027\n",
       "Danniel    0.000027\n",
       "Deryl      0.000028\n",
       "...\n",
       "William    0.034182\n",
       "James      0.035465\n",
       "John       0.041082\n",
       "Name: prop, Length: 3437, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "namewise_prop.mean().order()   # most famous boy names on average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Charles    0.019521\n",
       "Robert     0.029625\n",
       "William    0.034182\n",
       "James      0.035465\n",
       "John       0.041082\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "namewise_prop.mean().order().tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    129000.000000\n",
       "mean          0.000916\n",
       "std           0.003441\n",
       "min           0.000026\n",
       "25%           0.000071\n",
       "50%           0.000142\n",
       "75%           0.000453\n",
       "max           0.081541\n",
       "dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boys['prop'].describe()  # Useful info about the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "summary = boys.groupby('name')['prop'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    1.000000\n",
      "mean     0.000442\n",
      "std           NaN\n",
      "min      0.000442\n",
      "25%      0.000442\n",
      "50%      0.000442\n",
      "75%      0.000442\n",
      "max      0.000442\n",
      "dtype: float64\n",
      "count    129.000000\n",
      "mean       0.041082\n",
      "std        0.020328\n",
      "min        0.006102\n",
      "25%        0.020822\n",
      "50%        0.045037\n",
      "75%        0.054678\n",
      "max        0.081541\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(summary.ix['Aaden'])\n",
    "print(summary.ix['John'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name        \n",
       "Aaden  count    1.000000\n",
       "       mean     0.000442\n",
       "       std           NaN\n",
       "       min      0.000442\n",
       "       25%      0.000442\n",
       "       50%      0.000442\n",
       "       75%      0.000442\n",
       "       max      0.000442\n",
       "Aarav  count    1.000000\n",
       "       mean     0.000101\n",
       "dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128000</th>\n",
       "      <td> 2008</td>\n",
       "      <td>   Jacob</td>\n",
       "      <td> 0.010355</td>\n",
       "      <td> boy</td>\n",
       "      <td> J210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128001</th>\n",
       "      <td> 2008</td>\n",
       "      <td> Michael</td>\n",
       "      <td> 0.009437</td>\n",
       "      <td> boy</td>\n",
       "      <td> M240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128002</th>\n",
       "      <td> 2008</td>\n",
       "      <td>   Ethan</td>\n",
       "      <td> 0.009301</td>\n",
       "      <td> boy</td>\n",
       "      <td> E350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128003</th>\n",
       "      <td> 2008</td>\n",
       "      <td>  Joshua</td>\n",
       "      <td> 0.008799</td>\n",
       "      <td> boy</td>\n",
       "      <td> J200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128004</th>\n",
       "      <td> 2008</td>\n",
       "      <td>  Daniel</td>\n",
       "      <td> 0.008702</td>\n",
       "      <td> boy</td>\n",
       "      <td> D540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128995</th>\n",
       "      <td> 2008</td>\n",
       "      <td>  Kolten</td>\n",
       "      <td> 0.000090</td>\n",
       "      <td> boy</td>\n",
       "      <td> K435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128996</th>\n",
       "      <td> 2008</td>\n",
       "      <td>  Damari</td>\n",
       "      <td> 0.000089</td>\n",
       "      <td> boy</td>\n",
       "      <td> D560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128997</th>\n",
       "      <td> 2008</td>\n",
       "      <td>    Hugh</td>\n",
       "      <td> 0.000089</td>\n",
       "      <td> boy</td>\n",
       "      <td> H200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128998</th>\n",
       "      <td> 2008</td>\n",
       "      <td>  Jensen</td>\n",
       "      <td> 0.000089</td>\n",
       "      <td> boy</td>\n",
       "      <td> J525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128999</th>\n",
       "      <td> 2008</td>\n",
       "      <td>   Yurem</td>\n",
       "      <td> 0.000089</td>\n",
       "      <td> boy</td>\n",
       "      <td> Y650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year     name      prop  sex soundex\n",
       "128000  2008    Jacob  0.010355  boy    J210\n",
       "128001  2008  Michael  0.009437  boy    M240\n",
       "128002  2008    Ethan  0.009301  boy    E350\n",
       "128003  2008   Joshua  0.008799  boy    J200\n",
       "128004  2008   Daniel  0.008702  boy    D540\n",
       "...      ...      ...       ...  ...     ...\n",
       "128995  2008   Kolten  0.000090  boy    K435\n",
       "128996  2008   Damari  0.000089  boy    D560\n",
       "128997  2008     Hugh  0.000089  boy    H200\n",
       "128998  2008   Jensen  0.000089  boy    J525\n",
       "128999  2008    Yurem  0.000089  boy    Y650\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting based on an index\n",
    "df = boys[boys.year == 2008].sort_index(by='prop',ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128000    0.010355\n",
       "128001    0.009437\n",
       "128002    0.009301\n",
       "...\n",
       "128997    0.000089\n",
       "128998    0.000089\n",
       "128999    0.000089\n",
       "Name: prop, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128999    0.795414\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.prop.cumsum()[-1:]  # Top 1000 names represent ~80% of total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many top names constitute 50% of total?\n",
    "# df.prop.cumsum().searchsorted(0.5) will not work because Series now\n",
    "# subclasses NDFrame. To convert to numpy array, use values\n",
    "df.prop.cumsum().values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128000    0.010355\n",
       "128001    0.019792\n",
       "128002    0.029093\n",
       "...\n",
       "128125    0.497718\n",
       "128126    0.499243\n",
       "128127    0.500748\n",
       "Name: prop, Length: 128, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To verify that the top 127 names make up 50%\n",
    "df.prop.cumsum()[:128]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1880</td>\n",
       "      <td>    John</td>\n",
       "      <td> 0.081541</td>\n",
       "      <td> boy</td>\n",
       "      <td> J500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1880</td>\n",
       "      <td> William</td>\n",
       "      <td> 0.080511</td>\n",
       "      <td> boy</td>\n",
       "      <td> W450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 1880</td>\n",
       "      <td>   James</td>\n",
       "      <td> 0.050057</td>\n",
       "      <td> boy</td>\n",
       "      <td> J520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 1880</td>\n",
       "      <td> Charles</td>\n",
       "      <td> 0.045167</td>\n",
       "      <td> boy</td>\n",
       "      <td> C642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 1880</td>\n",
       "      <td>  George</td>\n",
       "      <td> 0.043292</td>\n",
       "      <td> boy</td>\n",
       "      <td> G620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     name      prop  sex soundex\n",
       "0  1880     John  0.081541  boy    J500\n",
       "1  1880  William  0.080511  boy    W450\n",
       "2  1880    James  0.050057  boy    J520\n",
       "3  1880  Charles  0.045167  boy    C642\n",
       "4  1880   George  0.043292  boy    G620"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bprop1880 = boys[boys.year == 1880].sort_index(by='prop',ascending=False)\n",
    "bprop1880.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.081541\n",
       "1    0.162052\n",
       "2    0.212109\n",
       "...\n",
       "934    0.930662\n",
       "933    0.930704\n",
       "999    0.930746\n",
       "Name: prop, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bprop1880.cumsum() would give string concatenation for string fields, resulting in super long strings.\n",
    "bprop1880.prop.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bprop1880.prop.cumsum().values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 15 names already covered 50% of the entire distribution in 1880\n",
    "Let's see how this changed over the years as people got creative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1880    15\n",
       "1881    15\n",
       "1882    17\n",
       "...\n",
       "2006    115\n",
       "2007    120\n",
       "2008    127\n",
       "Length: 129, dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_quantile_count(group, quantile=0.5):\n",
    "    sorted = group.sort_index(by='prop',ascending=False)\n",
    "    return sorted.prop.cumsum().values.searchsorted(quantile)\n",
    "#get_quantile_count(boys[boys.year==1880])\n",
    "#get_quantile_count(boys[boys.year==2008])\n",
    "boys.groupby('year').apply(get_quantile_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "boys_count = boys.groupby('year').apply(get_quantile_count)\n",
    "girls_count = girls.groupby('year').apply(get_quantile_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x114aec6d8>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xxum0BgN0imfD2I6dgJWrHKI/sAlh1vM5wBKgL3AosKUS/RX4V9z2JTp6WTxW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\n54ncOefa3P8BFGa0l3Kt0t8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114aec5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boys_count.plot(label='boys')\n",
    "girls_count.plot(label='boys')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118e8fd30>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mk4CzgL9N/vs6MzuX8L+4NmyeIvWF5A3ATcAV7t459D4vzThyeQ0qOqxviFPc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\natgiIgXx/wFNfGfmok+0zAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ea5710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Changing the value of the parameter quantile while calling get_quantile_count\n",
    "f = lambda x: get_quantile_count(x,0.1)\n",
    "# apply f\n",
    "boys_count_1 = boys.groupby('year').apply(f)\n",
    "boys_count_1.plot()\n",
    "#OR\n",
    "boys_count_2 = boys.groupby('year').apply(get_quantile_count,quantile=0.1)\n",
    "boys_count_2.plot()\n",
    "\n",
    "# Same plot, hence only one line visible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Finding the rank of a name throughout the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128000    0.010355\n",
      "128001    0.009437\n",
      "128002    0.009301\n",
      "...\n",
      "128997    0.000089\n",
      "128998    0.000089\n",
      "128999    0.000089\n",
      "Name: prop, Length: 1000, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "128000    1000\n",
       "128001     999\n",
       "128002     998\n",
       "...\n",
       "128997    2.5\n",
       "128998    2.5\n",
       "128999    2.5\n",
       "Name: prop, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(boys[boys.year == 2008].prop)\n",
    "boys[boys.year == 2008].prop.rank()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grouped = boys.groupby('year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.081541\n",
       "1    0.080511\n",
       "2    0.050057\n",
       "...\n",
       "128002    0.009301\n",
       "128003    0.008799\n",
       "128004    0.008702\n",
       "Name: prop, Length: 645, dtype: float64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.prop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "995    0.000042\n",
       "996    0.000042\n",
       "997    0.000042\n",
       "...\n",
       "128997    0.000089\n",
       "128998    0.000089\n",
       "128999    0.000089\n",
       "Name: prop, Length: 645, dtype: float64"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = grouped['prop']  # Both methods work for getting a column\n",
    "p.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1000\n",
       "1     999\n",
       "2     998\n",
       "...\n",
       "128997    2.5\n",
       "128998    2.5\n",
       "128999    2.5\n",
       "Name: prop, Length: 129000, dtype: float64"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.transform(Series.rank)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We transformed the Series prop into a rank Series. Why are not using apply because **apply** gives one output **per group** and transform returns a result with as many records as input.  \n",
    "Check comments here.  \n",
    "http://stackoverflow.com/questions/17950835/pandas-dataframe-filtering-using-groupby-and-a-function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_index,col_indexer] = value instead\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "      <th>year_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0     </th>\n",
       "      <td> 1880</td>\n",
       "      <td>    John</td>\n",
       "      <td> 0.081541</td>\n",
       "      <td> boy</td>\n",
       "      <td> J500</td>\n",
       "      <td> 1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1     </th>\n",
       "      <td> 1880</td>\n",
       "      <td> William</td>\n",
       "      <td> 0.080511</td>\n",
       "      <td> boy</td>\n",
       "      <td> W450</td>\n",
       "      <td>  999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2     </th>\n",
       "      <td> 1880</td>\n",
       "      <td>   James</td>\n",
       "      <td> 0.050057</td>\n",
       "      <td> boy</td>\n",
       "      <td> J520</td>\n",
       "      <td>  998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3     </th>\n",
       "      <td> 1880</td>\n",
       "      <td> Charles</td>\n",
       "      <td> 0.045167</td>\n",
       "      <td> boy</td>\n",
       "      <td> C642</td>\n",
       "      <td>  997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4     </th>\n",
       "      <td> 1880</td>\n",
       "      <td>  George</td>\n",
       "      <td> 0.043292</td>\n",
       "      <td> boy</td>\n",
       "      <td> G620</td>\n",
       "      <td>  996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128995</th>\n",
       "      <td> 2008</td>\n",
       "      <td>  Kolten</td>\n",
       "      <td> 0.000090</td>\n",
       "      <td> boy</td>\n",
       "      <td> K435</td>\n",
       "      <td>    7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128996</th>\n",
       "      <td> 2008</td>\n",
       "      <td>  Damari</td>\n",
       "      <td> 0.000089</td>\n",
       "      <td> boy</td>\n",
       "      <td> D560</td>\n",
       "      <td>    2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128997</th>\n",
       "      <td> 2008</td>\n",
       "      <td>    Hugh</td>\n",
       "      <td> 0.000089</td>\n",
       "      <td> boy</td>\n",
       "      <td> H200</td>\n",
       "      <td>    2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128998</th>\n",
       "      <td> 2008</td>\n",
       "      <td>  Jensen</td>\n",
       "      <td> 0.000089</td>\n",
       "      <td> boy</td>\n",
       "      <td> J525</td>\n",
       "      <td>    2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128999</th>\n",
       "      <td> 2008</td>\n",
       "      <td>   Yurem</td>\n",
       "      <td> 0.000089</td>\n",
       "      <td> boy</td>\n",
       "      <td> Y650</td>\n",
       "      <td>    2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        year     name      prop  sex soundex  year_rank\n",
       "0       1880     John  0.081541  boy    J500     1000.0\n",
       "1       1880  William  0.080511  boy    W450      999.0\n",
       "2       1880    James  0.050057  boy    J520      998.0\n",
       "3       1880  Charles  0.045167  boy    C642      997.0\n",
       "4       1880   George  0.043292  boy    G620      996.0\n",
       "...      ...      ...       ...  ...     ...        ...\n",
       "128995  2008   Kolten  0.000090  boy    K435        7.5\n",
       "128996  2008   Damari  0.000089  boy    D560        2.5\n",
       "128997  2008     Hugh  0.000089  boy    H200        2.5\n",
       "128998  2008   Jensen  0.000089  boy    J525        2.5\n",
       "128999  2008    Yurem  0.000089  boy    Y650        2.5\n",
       "\n",
       "[129000 rows x 6 columns]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boys['year_rank'] = p.transform(Series.rank)\n",
    "boys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 129000 entries, 0 to 128999\n",
      "Data columns (total 6 columns):\n",
      "year         129000 non-null int64\n",
      "name         129000 non-null object\n",
      "prop         129000 non-null float64\n",
      "sex          129000 non-null object\n",
      "soundex      129000 non-null object\n",
      "year_rank    129000 non-null float64\n",
      "dtypes: float64(2), int64(1), object(3)"
     ]
    }
   ],
   "source": [
    "boys.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11528a128>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G8snA4XQ4Io9Pmr8DnAW8SQeBXGJ9glZ/f+LwU8DO7fUTtDCJH0gc2a4deaQgGmzLuD+Z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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1152a9a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idf = boys.set_index(['name','year'])\n",
    "idf.ix['Wesley'].year_rank.plot()\n",
    "# You can also use matplotlib to explicitly specify the X and Y axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.081541\n",
      "1    0.080511\n",
      "2    0.050057\n",
      "...\n",
      "128002    0.009301\n",
      "128003    0.008799\n",
      "128004    0.008702\n",
      "Name: prop, Length: 645, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    0.163082\n",
       "1    0.161022\n",
       "2    0.100114\n",
       "...\n",
       "128997    0.000178\n",
       "128998    0.000178\n",
       "128999    0.000178\n",
       "Name: prop, Length: 129000, dtype: float64"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(p.head())\n",
    "p.transform(lambda x:x*2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing groups in SeriesGroupBy object\n",
    "http://stackoverflow.com/questions/14734533/how-to-access-pandas-groupby-dataframe-by-key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1000.000000\n",
       "mean        0.000931\n",
       "std         0.004871\n",
       "min         0.000042\n",
       "25%         0.000059\n",
       "50%         0.000118\n",
       "75%         0.000372\n",
       "max         0.081541\n",
       "dtype: float64"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p.get_group(1880).describe()   # Accessing a single group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-286-6f71bb447269>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-286-6f71bb447269>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    normp\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "normp = p.transform(lambda x: (x - x.mean()) / x.std()) # x.mean() will be applied to the whole group\n",
    "# So this will center the values around the mean and divide by standard deviation.\n",
    "# Good for normalization. But probably meaningless in this context\n",
    "normp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aswinrajkumar/anaconda/lib/python3.4/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_index,col_indexer] = value instead\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118f41588>"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118f3de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boys['normp'] = normp\n",
    "boys[boys.year == 1880].normp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1880    3.936851e-16\n",
       "1881    4.249934e-16\n",
       "1882   -9.131584e-17\n",
       "...\n",
       "2006   -4.678480e-16\n",
       "2007    8.293366e-17\n",
       "2008   -5.273559e-17\n",
       "Name: prop, Length: 129, dtype: float64"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normp.groupby(boys.year).mean() # Normp has no index, so you use\n",
    "# boys.year to group by normp. Cool!\n",
    "# Means should be zero. Why such small values though?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    16.548082\n",
       "1    16.336638\n",
       "2    10.084887\n",
       "...\n",
       "128997   -0.48565\n",
       "128998   -0.48565\n",
       "128999   -0.48565\n",
       "Length: 129000, dtype: float64"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = p.apply(lambda x: (x-x.mean())/x.std())\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1880    0.000000e+00\n",
       "1881   -1.421085e-17\n",
       "1882   -2.842171e-17\n",
       "...\n",
       "2006   -2.842171e-17\n",
       "2007    5.684342e-17\n",
       "2008    5.684342e-17\n",
       "Length: 129, dtype: float64"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pvals = boys.groupby('year').get_group(1880)['prop']\n",
    "pvals - pvals.mean()\n",
    "pvals.std()\n",
    "def normp_mean(group):\n",
    "    pvals = group['prop']\n",
    "    return ((pvals - pvals.mean())/pvals.std()).mean()\n",
    "boys.groupby('year').apply(normp_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Learn difference between apply and transform***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0  </th>\n",
       "      <td> 1880</td>\n",
       "      <td>  boy</td>\n",
       "      <td>  118405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1  </th>\n",
       "      <td> 1881</td>\n",
       "      <td>  boy</td>\n",
       "      <td>  108290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2  </th>\n",
       "      <td> 1882</td>\n",
       "      <td>  boy</td>\n",
       "      <td>  122034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3  </th>\n",
       "      <td> 1883</td>\n",
       "      <td>  boy</td>\n",
       "      <td>  112487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4  </th>\n",
       "      <td> 1884</td>\n",
       "      <td>  boy</td>\n",
       "      <td>  122745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td> 2005</td>\n",
       "      <td> girl</td>\n",
       "      <td> 2024636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td> 2006</td>\n",
       "      <td> girl</td>\n",
       "      <td> 2084511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td> 2007</td>\n",
       "      <td> girl</td>\n",
       "      <td> 2109099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td> 2008</td>\n",
       "      <td> girl</td>\n",
       "      <td> 2072756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td> 2009</td>\n",
       "      <td> girl</td>\n",
       "      <td> 2001968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year   sex   births\n",
       "0    1880   boy   118405\n",
       "1    1881   boy   108290\n",
       "2    1882   boy   122034\n",
       "3    1883   boy   112487\n",
       "4    1884   boy   122745\n",
       "..    ...   ...      ...\n",
       "255  2005  girl  2024636\n",
       "256  2006  girl  2084511\n",
       "257  2007  girl  2109099\n",
       "258  2008  girl  2072756\n",
       "259  2009  girl  2001968\n",
       "\n",
       "[260 rows x 3 columns]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "births = read_csv('data/births.csv')\n",
    "births"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Merge - for joins\n",
    "merged = merge(names, births, on=['year','sex']) # In this case, \n",
    "         # even if you do not specify columns, it will autodetect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>name</th>\n",
       "      <th>prop</th>\n",
       "      <th>sex</th>\n",
       "      <th>soundex</th>\n",
       "      <th>births</th>\n",
       "      <th>persons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1880</td>\n",
       "      <td>    John</td>\n",
       "      <td> 0.081541</td>\n",
       "      <td> boy</td>\n",
       "      <td> J500</td>\n",
       "      <td> 118405</td>\n",
       "      <td> 9654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1880</td>\n",
       "      <td> William</td>\n",
       "      <td> 0.080511</td>\n",
       "      <td> boy</td>\n",
       "      <td> W450</td>\n",
       "      <td> 118405</td>\n",
       "      <td> 9532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 1880</td>\n",
       "      <td>   James</td>\n",
       "      <td> 0.050057</td>\n",
       "      <td> boy</td>\n",
       "      <td> J520</td>\n",
       "      <td> 118405</td>\n",
       "      <td> 5926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 1880</td>\n",
       "      <td> Charles</td>\n",
       "      <td> 0.045167</td>\n",
       "      <td> boy</td>\n",
       "      <td> C642</td>\n",
       "      <td> 118405</td>\n",
       "      <td> 5347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 1880</td>\n",
       "      <td>  George</td>\n",
       "      <td> 0.043292</td>\n",
       "      <td> boy</td>\n",
       "      <td> G620</td>\n",
       "      <td> 118405</td>\n",
       "      <td> 5125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     name      prop  sex soundex  births  persons\n",
       "0  1880     John  0.081541  boy    J500  118405     9654\n",
       "1  1880  William  0.080511  boy    W450  118405     9532\n",
       "2  1880    James  0.050057  boy    J520  118405     5926\n",
       "3  1880  Charles  0.045167  boy    C642  118405     5347\n",
       "4  1880   George  0.043292  boy    G620  118405     5125"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged['persons'] = np.floor(merged.prop * merged.births)\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "girl    3112\n",
       "Name: persons, dtype: float64"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merged.groupby(['name','sex'])['persons'].head()\n",
    "# head command will show the table, but it will not be sorted.\n",
    "# When an aggregate function is applied, each group is processed separately\n",
    "\n",
    "total = merged.groupby(['name','sex'])['persons'].sum()\n",
    "total.ix['Britni']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name      sex \n",
       "Alabama   girl    3\n",
       "Daisye    girl    3\n",
       "Crissie   girl    3\n",
       "Dollye    girl    3\n",
       "Dema      girl    3\n",
       "Chestina  girl    3\n",
       "Ala       girl    3\n",
       "Clem      girl    3\n",
       "...\n",
       "Richard  boy     2535681\n",
       "David    boy     3495940\n",
       "William  boy     3951365\n",
       "Mary     girl    4097626\n",
       "Michael  boy     4207352\n",
       "Robert   boy     4752198\n",
       "John     boy     5016124\n",
       "James    boy     5021269\n",
       "Name: persons, Length: 7455, dtype: float64"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.order()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name    sex \n",
       "Abbott  boy     9\n",
       "Abie    boy     4\n",
       "Acey    boy     4\n",
       "Achsah  girl    3\n",
       "Acy     boy     5\n",
       "Adams   boy     7\n",
       "Adina   girl    4\n",
       "Affie   girl    4\n",
       "...\n",
       "Willy   boy     6\n",
       "Wing    boy     4\n",
       "Wood    boy     9\n",
       "Worley  boy     9\n",
       "York    boy     7\n",
       "Zed     boy     6\n",
       "Zenas   boy     6\n",
       "Zilpah  girl    8\n",
       "Name: persons, Length: 438, dtype: float64"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total[total < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Achsah     3\n",
       "Adina      4\n",
       "Affie      4\n",
       "Ala        3\n",
       "Alabama    3\n",
       "Alexina    7\n",
       "Alfred     3\n",
       "Allena     9\n",
       "...\n",
       "Tella        4\n",
       "Theodocia    7\n",
       "Thursa       4\n",
       "Tiney        5\n",
       "Tom          6\n",
       "Vicy         6\n",
       "Violetta     7\n",
       "Zilpah       8\n",
       "Name: persons, Length: 162, dtype: float64"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now to only select girls out of these, we need to \n",
    "# select the inner index as girl\n",
    "total[total<10].ix[:,'girl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name     sex \n",
      "Aaden    boy        959\n",
      "Aaliyah  girl     39660\n",
      "Aarav    boy        219\n",
      "Aaron    boy     508034\n",
      "         girl      1365\n",
      "Ab       boy         24\n",
      "Abagail  girl      2680\n",
      "Abb      boy         15\n",
      "...\n",
      "Zoe     girl    66271\n",
      "Zoey    girl    19335\n",
      "Zoie    girl     4688\n",
      "Zola    girl     4847\n",
      "Zollie  boy        60\n",
      "Zona    girl     3218\n",
      "Zora    girl     4551\n",
      "Zula    girl     3578\n",
      "Name: persons, Length: 7455, dtype: float64\n",
      "\n",
      "sex          boy   girl\n",
      "name                   \n",
      "Aaden        959    NaN\n",
      "Aaliyah      NaN  39660\n",
      "Aarav        219    NaN\n",
      "Aaron     508034   1365\n",
      "Ab            24    NaN\n",
      "Abagail      NaN   2680\n",
      "Abb           15    NaN\n",
      "Abbey        NaN  14333\n",
      "Abbie         10  16556\n",
      "Abbigail     NaN   6792\n",
      "...          ...    ...\n",
      "Zita         NaN    831\n",
      "Zoa          NaN     73\n",
      "Zoe          NaN  66271\n",
      "Zoey         NaN  19335\n",
      "Zoie         NaN   4688\n",
      "Zola         NaN   4847\n",
      "Zollie        60    NaN\n",
      "Zona         NaN   3218\n",
      "Zora         NaN   4551\n",
      "Zula         NaN   3578\n",
      "\n",
      "[6782 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(total) # multi indexed Series\n",
    "print()\n",
    "print(total.unstack('sex')) # pivot into a dataframe with boy girl columns"
   ]
  }
 ],
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