ch02.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Introductory examples"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "1.usa.gov data from bit.ly"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pwd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%cd ../book_scripts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "open(path).readline()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'\n",
      "records = [json.loads(line) for line in open(path)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "records[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "records[0]['tz']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(records[0]['tz'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Counting time zones in pure Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "time_zones = [rec['tz'] for rec in records]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "time_zones = [rec['tz'] for rec in records if 'tz' in rec]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "time_zones[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_counts(sequence):\n",
      "    counts = {}\n",
      "    for x in sequence:\n",
      "        if x in counts:\n",
      "            counts[x] += 1\n",
      "        else:\n",
      "            counts[x] = 1\n",
      "    return counts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import defaultdict\n",
      "\n",
      "def get_counts2(sequence):\n",
      "    counts = defaultdict(int) # values will initialize to 0\n",
      "    for x in sequence:\n",
      "        counts[x] += 1\n",
      "    return counts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counts = get_counts(time_zones)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counts['America/New_York']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(time_zones)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def top_counts(count_dict, n=10):\n",
      "    value_key_pairs = [(count, tz) for tz, count in count_dict.items()]\n",
      "    value_key_pairs.sort()\n",
      "    return value_key_pairs[-n:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top_counts(counts)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from collections import Counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counts = Counter(time_zones)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counts.most_common(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Counting time zones with pandas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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",
      "import pandas as pd\n",
      "plt.rc('figure', figsize=(10, 6))\n",
      "np.set_printoptions(precision=4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'\n",
      "lines = open(path).readlines()\n",
      "records = [json.loads(line) for line in lines]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import DataFrame, Series\n",
      "import pandas as pd\n",
      "\n",
      "frame = DataFrame(records)\n",
      "frame"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame['tz'][:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tz_counts = frame['tz'].value_counts()\n",
      "tz_counts[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clean_tz = frame['tz'].fillna('Missing')\n",
      "clean_tz[clean_tz == ''] = 'Unknown'\n",
      "tz_counts = clean_tz.value_counts()\n",
      "tz_counts[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(10, 4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tz_counts[:10].plot(kind='barh', rot=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame['a'][1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame['a'][50]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "frame['a'][51]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = Series([x.split()[0] for x in frame.a.dropna()])\n",
      "results[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.value_counts()[:8]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cframe = frame[frame.a.notnull()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "operating_system = np.where(cframe['a'].str.contains('Windows'),\n",
      "                            'Windows', 'Not Windows')\n",
      "operating_system[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "by_tz_os = cframe.groupby(['tz', operating_system])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "agg_counts = by_tz_os.size().unstack().fillna(0)\n",
      "agg_counts[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Use to sort in ascending order\n",
      "indexer = agg_counts.sum(1).argsort()\n",
      "indexer[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "count_subset = agg_counts.take(indexer)[-10:]\n",
      "count_subset"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "count_subset.plot(kind='barh', stacked=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "normed_subset = count_subset.div(count_subset.sum(1), axis=0)\n",
      "normed_subset.plot(kind='barh', stacked=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "MovieLens 1M data set"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "encoding = 'latin1'\n",
      "\n",
      "upath = os.path.expanduser('movielens/ml-1m/users.dat')\n",
      "rpath = os.path.expanduser('movielens/ml-1m/ratings.dat')\n",
      "mpath = os.path.expanduser('movielens/ml-1m/movies.dat')\n",
      "\n",
      "unames = ['user_id', 'gender', 'age', 'occupation', 'zip']\n",
      "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
      "mnames = ['movie_id', 'title', 'genres']\n",
      "\n",
      "users = pd.read_csv(upath, sep='::', header=None, names=unames, encoding=encoding)\n",
      "ratings = pd.read_csv(rpath, sep='::', header=None, names=rnames, encoding=encoding)\n",
      "movies = pd.read_csv(mpath, sep='::', header=None, names=mnames, encoding=encoding)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "users[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratings[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "movies[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratings"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.merge(pd.merge(ratings, users), movies)\n",
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.ix[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_ratings = data.pivot_table('rating', rows='title',\n",
      "                                cols='gender', aggfunc='mean')\n",
      "mean_ratings[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratings_by_title = data.groupby('title').size()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratings_by_title[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "active_titles = ratings_by_title.index[ratings_by_title >= 250]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "active_titles[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_ratings = mean_ratings.ix[active_titles]\n",
      "mean_ratings"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_ratings = mean_ratings.rename(index={'Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954)':\n",
      "                           'Seven Samurai (Shichinin no samurai) (1954)'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top_female_ratings = mean_ratings.sort_index(by='F', ascending=False)\n",
      "top_female_ratings[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Measuring rating disagreement"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sorted_by_diff = mean_ratings.sort_index(by='diff')\n",
      "sorted_by_diff[:15]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Reverse order of rows, take first 15 rows\n",
      "sorted_by_diff[::-1][:15]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Standard deviation of rating grouped by title\n",
      "rating_std_by_title = data.groupby('title')['rating'].std()\n",
      "# Filter down to active_titles\n",
      "rating_std_by_title = rating_std_by_title.ix[active_titles]\n",
      "# Order Series by value in descending order\n",
      "rating_std_by_title.order(ascending=False)[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### US Baby Names 1880-2010"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "from numpy.random import randn\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "plt.rc('figure', figsize=(12, 5))\n",
      "np.set_printoptions(precision=4)\n",
      "%pwd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://www.ssa.gov/oact/babynames/limits.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -n 10 baby_names/names/yob1880.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "names1880 = pd.read_csv('baby_names/names/yob1880.txt', names=['name', 'sex', 'births'])\n",
      "names1880"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names1880.groupby('sex').births.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 2010 is the last available year right now\n",
      "years = range(1880, 2011)\n",
      "\n",
      "pieces = []\n",
      "columns = ['name', 'sex', 'births']\n",
      "\n",
      "for year in years:\n",
      "    path = 'baby_names/names/yob%d.txt' % year\n",
      "    frame = pd.read_csv(path, names=columns)\n",
      "\n",
      "    frame['year'] = year\n",
      "    pieces.append(frame)\n",
      "\n",
      "# Concatenate everything into a single DataFrame\n",
      "names = pd.concat(pieces, ignore_index=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names = pd.read_pickle('baby_names/all_names')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births = names.pivot_table('births', rows='year',\n",
      "                                 cols='sex', aggfunc=sum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births.plot(title='Total births by sex and year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def add_prop(group):\n",
      "    # Integer division floors\n",
      "    births = group.births.astype(float)\n",
      "\n",
      "    group['prop'] = births / births.sum()\n",
      "    return group\n",
      "names = names.groupby(['year', 'sex']).apply(add_prop)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.allclose(names.groupby(['year', 'sex']).prop.sum(), 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_top1000(group):\n",
      "    return group.sort_index(by='births', ascending=False)[:1000]\n",
      "grouped = names.groupby(['year', 'sex'])\n",
      "top1000 = grouped.apply(get_top1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pieces = []\n",
      "for year, group in names.groupby(['year', 'sex']):\n",
      "    pieces.append(group.sort_index(by='births', ascending=False)[:1000])\n",
      "top1000 = pd.concat(pieces, ignore_index=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top1000.index = np.arange(len(top1000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top1000"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Analyzing naming trends"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boys = top1000[top1000.sex == 'M']\n",
      "girls = top1000[top1000.sex == 'F']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_births = top1000.pivot_table('births', rows='year', cols='name',\n",
      "                                   aggfunc=sum)\n",
      "total_births"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]\n",
      "subset.plot(subplots=True, figsize=(12, 10), grid=False,\n",
      "            title=\"Number of births per year\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Measuring the increase in naming diversity"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table = top1000.pivot_table('prop', rows='year',\n",
      "                            cols='sex', aggfunc=sum)\n",
      "table.plot(title='Sum of table1000.prop by year and sex',\n",
      "           yticks=np.linspace(0, 1.2, 13), xticks=range(1880, 2020, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = boys[boys.year == 2010]\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prop_cumsum = df.sort_index(by='prop', ascending=False).prop.cumsum()\n",
      "prop_cumsum[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prop_cumsum.values.searchsorted(0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = boys[boys.year == 1900]\n",
      "in1900 = df.sort_index(by='prop', ascending=False).prop.cumsum()\n",
      "in1900.values.searchsorted(0.5) + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_quantile_count(group, q=0.5):\n",
      "    group = group.sort_index(by='prop', ascending=False)\n",
      "    return group.prop.cumsum().values.searchsorted(q) + 1\n",
      "\n",
      "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)\n",
      "diversity = diversity.unstack('sex')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_quantile_count(group, q=0.5):\n",
      "    group = group.sort_index(by='prop', ascending=False)\n",
      "    return group.prop.cumsum().values.searchsorted(q) + 1\n",
      "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)\n",
      "diversity = diversity.unstack('sex')\n",
      "diversity.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diversity.plot(title=\"Number of popular names in top 50%\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "The \"Last letter\" Revolution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# extract last letter from name column\n",
      "get_last_letter = lambda x: x[-1]\n",
      "last_letters = names.name.map(get_last_letter)\n",
      "last_letters.name = 'last_letter'\n",
      "\n",
      "table = names.pivot_table('births', rows=last_letters,\n",
      "                          cols=['sex', 'year'], aggfunc=sum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')\n",
      "subtable.head()\n",
      "subtable.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "letter_prop = subtable / subtable.sum().astype(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n",
      "letter_prop['M'].plot(kind='bar', rot=0, ax=axes[0], title='Male')\n",
      "letter_prop['F'].plot(kind='bar', rot=0, ax=axes[1], title='Female',\n",
      "                      legend=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.subplots_adjust(hspace=0.25)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "letter_prop = table / table.sum().astype(float)\n",
      "\n",
      "dny_ts = letter_prop.ix[['d', 'n', 'y'], 'M'].T\n",
      "dny_ts.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.close('all')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dny_ts.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Boy names that became girl names (and vice versa)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_names = top1000.name.unique()\n",
      "mask = np.array(['lesl' in x.lower() for x in all_names])\n",
      "lesley_like = all_names[mask]\n",
      "lesley_like"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filtered = top1000[top1000.name.isin(lesley_like)]\n",
      "filtered.groupby('name').births.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table = filtered.pivot_table('births', rows='year',\n",
      "                             cols='sex', aggfunc='sum')\n",
      "table = table.div(table.sum(1), axis=0)\n",
      "table.tail()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.close('all')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table.plot(style={'M': 'k-', 'F': 'k--'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}