ch14.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Data Analysis Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 1.USA.gov Data from Bitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
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   },
   "outputs": [],
   "source": [
    "from numpy.random import randn\n",
    "import numpy as np\n",
    "np.random.seed(123)\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)\n",
    "pd.options.display.max_rows = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In [5]: path = 'datasets/bitly_usagov/example.txt'\n",
    "\n",
    "In [6]: open(path).readline()\n",
    "Out[6]: '{ \"a\": \"Mozilla\\\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\\\/535.11\n",
    "(KHTML, like Gecko) Chrome\\\\/17.0.963.78 Safari\\\\/535.11\", \"c\": \"US\", \"nk\": 1,\n",
    "\"tz\": \"America\\\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\":\n",
    "\"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\":\n",
    "\"http:\\\\/\\\\/www.facebook.com\\\\/l\\\\/7AQEFzjSi\\\\/1.usa.gov\\\\/wfLQtf\", \"u\":\n",
    "\"http:\\\\/\\\\/www.ncbi.nlm.nih.gov\\\\/pubmed\\\\/22415991\", \"t\": 1331923247, \"hc\":\n",
    "1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\\n'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "path = 'datasets/bitly_usagov/example.txt'\n",
    "records = [json.loads(line) for line in open(path)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In [18]: records[0]\n",
    "Out[18]:\n",
    "{'a': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko)\n",
    "Chrome/17.0.963.78 Safari/535.11',\n",
    " 'al': 'en-US,en;q=0.8',\n",
    " 'c': 'US',\n",
    " 'cy': 'Danvers',\n",
    " 'g': 'A6qOVH',\n",
    " 'gr': 'MA',\n",
    " 'h': 'wfLQtf',\n",
    " 'hc': 1331822918,\n",
    " 'hh': '1.usa.gov',\n",
    " 'l': 'orofrog',\n",
    " 'll': [42.576698, -70.954903],\n",
    " 'nk': 1,\n",
    " 'r': 'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n",
    " 't': 1331923247,\n",
    " 'tz': 'America/New_York',\n",
    " 'u': 'http://www.ncbi.nlm.nih.gov/pubmed/22415991'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Counting Time Zones in Pure Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "time_zones = [rec['tz'] for rec in records]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "time_zones = [rec['tz'] for rec in records if 'tz' in rec]\n",
    "time_zones[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "counts = get_counts(time_zones)\n",
    "counts['America/New_York']\n",
    "len(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "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:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "top_counts(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "counts = Counter(time_zones)\n",
    "counts.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Counting Time Zones with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "frame = pd.DataFrame(records)\n",
    "frame.info()\n",
    "frame['tz'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tz_counts = frame['tz'].value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "clean_tz = frame['tz'].fillna('Missing')\n",
    "clean_tz[clean_tz == ''] = 'Unknown'\n",
    "tz_counts = clean_tz.value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "subset = tz_counts[:10]\n",
    "sns.barplot(y=subset.index, x=subset.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "frame['a'][1]\n",
    "frame['a'][50]\n",
    "frame['a'][51][:50]  # long line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "results = pd.Series([x.split()[0] for x in frame.a.dropna()])\n",
    "results[:5]\n",
    "results.value_counts()[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "cframe = frame[frame.a.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "cframe = cframe.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "cframe['os'] = np.where(cframe['a'].str.contains('Windows'),\n",
    "                        'Windows', 'Not Windows')\n",
    "cframe['os'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "by_tz_os = cframe.groupby(['tz', 'os'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "agg_counts = by_tz_os.size().unstack().fillna(0)\n",
    "agg_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Use to sort in ascending order\n",
    "indexer = agg_counts.sum(1).argsort()\n",
    "indexer[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "count_subset = agg_counts.take(indexer[-10:])\n",
    "count_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "agg_counts.sum(1).nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Rearrange the data for plotting\n",
    "count_subset = count_subset.stack()\n",
    "count_subset.name = 'total'\n",
    "count_subset = count_subset.reset_index()\n",
    "count_subset[:10]\n",
    "sns.barplot(x='total', y='tz', hue='os',  data=count_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def norm_total(group):\n",
    "    group['normed_total'] = group.total / group.total.sum()\n",
    "    return group\n",
    "\n",
    "results = count_subset.groupby('tz').apply(norm_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sns.barplot(x='normed_total', y='tz', hue='os',  data=results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "g = count_subset.groupby('tz')\n",
    "results2 = count_subset.total / g.total.transform('sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## MovieLens 1M Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Make display smaller\n",
    "pd.options.display.max_rows = 10\n",
    "\n",
    "unames = ['user_id', 'gender', 'age', 'occupation', 'zip']\n",
    "users = pd.read_table('datasets/movielens/users.dat', sep='::',\n",
    "                      header=None, names=unames)\n",
    "\n",
    "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
    "ratings = pd.read_table('datasets/movielens/ratings.dat', sep='::',\n",
    "                        header=None, names=rnames)\n",
    "\n",
    "mnames = ['movie_id', 'title', 'genres']\n",
    "movies = pd.read_table('datasets/movielens/movies.dat', sep='::',\n",
    "                       header=None, names=mnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "users[:5]\n",
    "ratings[:5]\n",
    "movies[:5]\n",
    "ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data = pd.merge(pd.merge(ratings, users), movies)\n",
    "data\n",
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "mean_ratings = data.pivot_table('rating', index='title',\n",
    "                                columns='gender', aggfunc='mean')\n",
    "mean_ratings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ratings_by_title = data.groupby('title').size()\n",
    "ratings_by_title[:10]\n",
    "active_titles = ratings_by_title.index[ratings_by_title >= 250]\n",
    "active_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Select rows on the index\n",
    "mean_ratings = mean_ratings.loc[active_titles]\n",
    "mean_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "mean_ratings = mean_ratings.rename(index={'Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954)':\n",
    "                           'Seven Samurai (Shichinin no samurai) (1954)'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "top_female_ratings = mean_ratings.sort_values(by='F', ascending=False)\n",
    "top_female_ratings[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Measuring Rating Disagreement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sorted_by_diff = mean_ratings.sort_values(by='diff')\n",
    "sorted_by_diff[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Reverse order of rows, take first 10 rows\n",
    "sorted_by_diff[::-1][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# 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.loc[active_titles]\n",
    "# Order Series by value in descending order\n",
    "rating_std_by_title.sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## US Baby Names 1880–2010"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In [4]: names.head(10)\n",
    "Out[4]:\n",
    "        name sex  births  year\n",
    "0       Mary   F    7065  1880\n",
    "1       Anna   F    2604  1880\n",
    "2       Emma   F    2003  1880\n",
    "3  Elizabeth   F    1939  1880\n",
    "4     Minnie   F    1746  1880\n",
    "5   Margaret   F    1578  1880\n",
    "6        Ida   F    1472  1880\n",
    "7      Alice   F    1414  1880\n",
    "8     Bertha   F    1320  1880\n",
    "9      Sarah   F    1288  1880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "!head -n 10 datasets/babynames/yob1880.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "names1880 = pd.read_csv('datasets/babynames/yob1880.txt',\n",
    "                        names=['name', 'sex', 'births'])\n",
    "names1880"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "years = range(1880, 2011)\n",
    "\n",
    "pieces = []\n",
    "columns = ['name', 'sex', 'births']\n",
    "\n",
    "for year in years:\n",
    "    path = 'datasets/babynames/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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "total_births = names.pivot_table('births', index='year',\n",
    "                                 columns='sex', aggfunc=sum)\n",
    "total_births.tail()\n",
    "total_births.plot(title='Total births by sex and year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def add_prop(group):\n",
    "    group['prop'] = group.births / group.births.sum()\n",
    "    return group\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "names.groupby(['year', 'sex']).prop.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def get_top1000(group):\n",
    "    return group.sort_values(by='births', ascending=False)[:1000]\n",
    "grouped = names.groupby(['year', 'sex'])\n",
    "top1000 = grouped.apply(get_top1000)\n",
    "# Drop the group index, not needed\n",
    "top1000.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "pieces = []\n",
    "for year, group in names.groupby(['year', 'sex']):\n",
    "    pieces.append(group.sort_values(by='births', ascending=False)[:1000])\n",
    "top1000 = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "top1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Analyzing Naming Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "boys = top1000[top1000.sex == 'M']\n",
    "girls = top1000[top1000.sex == 'F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "total_births = top1000.pivot_table('births', index='year',\n",
    "                                   columns='name',\n",
    "                                   aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "total_births.info()\n",
    "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]\n",
    "subset.plot(subplots=True, figsize=(12, 10), grid=False,\n",
    "            title=\"Number of births per year\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Measuring the increase in naming diversity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "table = top1000.pivot_table('prop', index='year',\n",
    "                            columns='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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df = boys[boys.year == 2010]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "prop_cumsum = df.sort_values(by='prop', ascending=False).prop.cumsum()\n",
    "prop_cumsum[:10]\n",
    "prop_cumsum.values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df = boys[boys.year == 1900]\n",
    "in1900 = df.sort_values(by='prop', ascending=False).prop.cumsum()\n",
    "in1900.values.searchsorted(0.5) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def get_quantile_count(group, q=0.5):\n",
    "    group = group.sort_values(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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "diversity.head()\n",
    "diversity.plot(title=\"Number of popular names in top 50%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### The “last letter” revolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# 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', index=last_letters,\n",
    "                          columns=['sex', 'year'], aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')\n",
    "subtable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "subtable.sum()\n",
    "letter_prop = subtable / subtable.sum()\n",
    "letter_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.subplots_adjust(hspace=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "letter_prop = table / table.sum()\n",
    "dny_ts = letter_prop.loc[['d', 'n', 'y'], 'M'].T\n",
    "dny_ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "dny_ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Boy names that became girl names (and vice versa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "all_names = pd.Series(top1000.name.unique())\n",
    "lesley_like = all_names[all_names.str.lower().str.contains('lesl')]\n",
    "lesley_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "filtered = top1000[top1000.name.isin(lesley_like)]\n",
    "filtered.groupby('name').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "table = filtered.pivot_table('births', index='year',\n",
    "                             columns='sex', aggfunc='sum')\n",
    "table = table.div(table.sum(1), axis=0)\n",
    "table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "table.plot(style={'M': 'k-', 'F': 'k--'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## USDA Food Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "{\n",
    "  \"id\": 21441,\n",
    "  \"description\": \"KENTUCKY FRIED CHICKEN, Fried Chicken, EXTRA CRISPY,\n",
    "Wing, meat and skin with breading\",\n",
    "  \"tags\": [\"KFC\"],\n",
    "  \"manufacturer\": \"Kentucky Fried Chicken\",\n",
    "  \"group\": \"Fast Foods\",\n",
    "  \"portions\": [\n",
    "    {\n",
    "      \"amount\": 1,\n",
    "      \"unit\": \"wing, with skin\",\n",
    "      \"grams\": 68.0\n",
    "    },\n",
    "\n",
    "    ...\n",
    "  ],\n",
    "  \"nutrients\": [\n",
    "    {\n",
    "      \"value\": 20.8,\n",
    "      \"units\": \"g\",\n",
    "      \"description\": \"Protein\",\n",
    "      \"group\": \"Composition\"\n",
    "    },\n",
    "\n",
    "    ...\n",
    "  ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "db = json.load(open('datasets/usda_food/database.json'))\n",
    "len(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "db[0].keys()\n",
    "db[0]['nutrients'][0]\n",
    "nutrients = pd.DataFrame(db[0]['nutrients'])\n",
    "nutrients[:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "info_keys = ['description', 'group', 'id', 'manufacturer']\n",
    "info = pd.DataFrame(db, columns=info_keys)\n",
    "info[:5]\n",
    "info.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pd.value_counts(info.group)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "nutrients = []\n",
    "\n",
    "for rec in db:\n",
    "    fnuts = pd.DataFrame(rec['nutrients'])\n",
    "    fnuts['id'] = rec['id']\n",
    "    nutrients.append(fnuts)\n",
    "\n",
    "nutrients = pd.concat(nutrients, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "nutrients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "nutrients.duplicated().sum()  # number of duplicates\n",
    "nutrients = nutrients.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "col_mapping = {'description' : 'food',\n",
    "               'group'       : 'fgroup'}\n",
    "info = info.rename(columns=col_mapping, copy=False)\n",
    "info.info()\n",
    "col_mapping = {'description' : 'nutrient',\n",
    "               'group' : 'nutgroup'}\n",
    "nutrients = nutrients.rename(columns=col_mapping, copy=False)\n",
    "nutrients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ndata = pd.merge(nutrients, info, on='id', how='outer')\n",
    "ndata.info()\n",
    "ndata.iloc[30000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "result = ndata.groupby(['nutrient', 'fgroup'])['value'].quantile(0.5)\n",
    "result['Zinc, Zn'].sort_values().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "by_nutrient = ndata.groupby(['nutgroup', 'nutrient'])\n",
    "\n",
    "get_maximum = lambda x: x.loc[x.value.idxmax()]\n",
    "get_minimum = lambda x: x.loc[x.value.idxmin()]\n",
    "\n",
    "max_foods = by_nutrient.apply(get_maximum)[['value', 'food']]\n",
    "\n",
    "# make the food a little smaller\n",
    "max_foods.food = max_foods.food.str[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "max_foods.loc['Amino Acids']['food']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 2012 Federal Election Commission Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fec = pd.read_csv('datasets/fec/P00000001-ALL.csv')\n",
    "fec.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fec.iloc[123456]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "unique_cands = fec.cand_nm.unique()\n",
    "unique_cands\n",
    "unique_cands[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "parties = {'Bachmann, Michelle': 'Republican',\n",
    "           'Cain, Herman': 'Republican',\n",
    "           'Gingrich, Newt': 'Republican',\n",
    "           'Huntsman, Jon': 'Republican',\n",
    "           'Johnson, Gary Earl': 'Republican',\n",
    "           'McCotter, Thaddeus G': 'Republican',\n",
    "           'Obama, Barack': 'Democrat',\n",
    "           'Paul, Ron': 'Republican',\n",
    "           'Pawlenty, Timothy': 'Republican',\n",
    "           'Perry, Rick': 'Republican',\n",
    "           \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n",
    "           'Romney, Mitt': 'Republican',\n",
    "           'Santorum, Rick': 'Republican'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fec.cand_nm[123456:123461]\n",
    "fec.cand_nm[123456:123461].map(parties)\n",
    "# Add it as a column\n",
    "fec['party'] = fec.cand_nm.map(parties)\n",
    "fec['party'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "(fec.contb_receipt_amt > 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fec = fec[fec.contb_receipt_amt > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fec_mrbo = fec[fec.cand_nm.isin(['Obama, Barack', 'Romney, Mitt'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Donation Statistics by Occupation and Employer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fec.contbr_occupation.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "occ_mapping = {\n",
    "   'INFORMATION REQUESTED PER BEST EFFORTS' : 'NOT PROVIDED',\n",
    "   'INFORMATION REQUESTED' : 'NOT PROVIDED',\n",
    "   'INFORMATION REQUESTED (BEST EFFORTS)' : 'NOT PROVIDED',\n",
    "   'C.E.O.': 'CEO'\n",
    "}\n",
    "\n",
    "# If no mapping provided, return x\n",
    "f = lambda x: occ_mapping.get(x, x)\n",
    "fec.contbr_occupation = fec.contbr_occupation.map(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "emp_mapping = {\n",
    "   'INFORMATION REQUESTED PER BEST EFFORTS' : 'NOT PROVIDED',\n",
    "   'INFORMATION REQUESTED' : 'NOT PROVIDED',\n",
    "   'SELF' : 'SELF-EMPLOYED',\n",
    "   'SELF EMPLOYED' : 'SELF-EMPLOYED',\n",
    "}\n",
    "\n",
    "# If no mapping provided, return x\n",
    "f = lambda x: emp_mapping.get(x, x)\n",
    "fec.contbr_employer = fec.contbr_employer.map(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "by_occupation = fec.pivot_table('contb_receipt_amt',\n",
    "                                index='contbr_occupation',\n",
    "                                columns='party', aggfunc='sum')\n",
    "over_2mm = by_occupation[by_occupation.sum(1) > 2000000]\n",
    "over_2mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "over_2mm.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def get_top_amounts(group, key, n=5):\n",
    "    totals = group.groupby(key)['contb_receipt_amt'].sum()\n",
    "    return totals.nlargest(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "grouped = fec_mrbo.groupby('cand_nm')\n",
    "grouped.apply(get_top_amounts, 'contbr_occupation', n=7)\n",
    "grouped.apply(get_top_amounts, 'contbr_employer', n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Bucketing Donation Amounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "bins = np.array([0, 1, 10, 100, 1000, 10000,\n",
    "                 100000, 1000000, 10000000])\n",
    "labels = pd.cut(fec_mrbo.contb_receipt_amt, bins)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "grouped = fec_mrbo.groupby(['cand_nm', labels])\n",
    "grouped.size().unstack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "bucket_sums = grouped.contb_receipt_amt.sum().unstack(0)\n",
    "normed_sums = bucket_sums.div(bucket_sums.sum(axis=1), axis=0)\n",
    "normed_sums\n",
    "normed_sums[:-2].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Donation Statistics by State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "grouped = fec_mrbo.groupby(['cand_nm', 'contbr_st'])\n",
    "totals = grouped.contb_receipt_amt.sum().unstack(0).fillna(0)\n",
    "totals = totals[totals.sum(1) > 100000]\n",
    "totals[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "percent = totals.div(totals.sum(1), axis=0)\n",
    "percent[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Conclusion"
   ]
  }
 ],
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