11. Data Analysis Examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "#### We will look at a collection of miscellaneous example datasets and go into techniques to extract meaning from the raw data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## USA.gov Data from Bitly\n",
    "#### Bitly is a URL shortening service. It partnered with the US government website USA.gov to provide feed of anonymous data gathered from users who shorten lnks ending with '.gov' or '.mil'.\n",
    "#### The live feed and hourly snapshots were available as downloadable text files. The service was shut down in 2017.\n",
    "#### As the dataset, we have an hourly snapshot, where each line is actually a JSON (JavaScript Object Notation) string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{ \"a\": \"Mozilla\\\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\\\/535.11 (KHTML, like Gecko) Chrome\\\\/17.0.963.78 Safari\\\\/535.11\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\": \"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\\\/\\\\/www.facebook.com\\\\/l\\\\/7AQEFzjSi\\\\/1.usa.gov\\\\/wfLQtf\", \"u\": \"http:\\\\/\\\\/www.ncbi.nlm.nih.gov\\\\/pubmed\\\\/22415991\", \"t\": 1331923247, \"hc\": 1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\\n'"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'datasets/bitly_usagov/example.txt'\n",
    "\n",
    "open(path).readline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python has both built-in and third-party libraries for converting a JSON string to Python dictionary object.\n",
    "#### We will use the 'json' module and its 'loads' function on each line of the dataset. The output will be a Python list of dicts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11',\n",
       " 'c': 'US',\n",
       " 'nk': 1,\n",
       " 'tz': 'America/New_York',\n",
       " 'gr': 'MA',\n",
       " 'g': 'A6qOVH',\n",
       " 'h': 'wfLQtf',\n",
       " 'l': 'orofrog',\n",
       " 'al': 'en-US,en;q=0.8',\n",
       " 'hh': '1.usa.gov',\n",
       " 'r': 'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n",
       " 'u': 'http://www.ncbi.nlm.nih.gov/pubmed/22415991',\n",
       " 't': 1331923247,\n",
       " 'hc': 1331822918,\n",
       " 'cy': 'Danvers',\n",
       " 'll': [42.576698, -70.954903]}"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "path = 'datasets/bitly_usagov/example.txt'\n",
    "records = [json.loads(line) for line in open(path)]\n",
    "\n",
    "records[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting Time Zones in pure Python\n",
    "#### If we want to find the most often-occuring time zones in the dataset (present in tz field). There are many ways we could do this.\n",
    "#### For this we can to extract a list of time zones using list comprehensions. But not all records have time zone field. We can check if 'tz' is present in record before adding it to this list.\n",
    "#### We will notice that some of the fields in this list are unknown (i.e. empty string). We will leave this as it is for now.\n",
    "#### To produce counts by time-zone, we have 2 approaches. The first is an iterative approach which adds to the count in a dictionary or a default-dict.\n",
    "#### From the output we can get details like top 10 time zones and their counts with some dictionary acrobatics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# time_zones = [rec['tz'] for rec in records]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['America/New_York',\n",
       " 'America/Denver',\n",
       " 'America/New_York',\n",
       " 'America/Sao_Paulo',\n",
       " 'America/New_York',\n",
       " 'America/New_York',\n",
       " 'Europe/Warsaw',\n",
       " '',\n",
       " '',\n",
       " '']"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_zones = [rec['tz'] for rec in records if 'tz' in rec]\n",
    "\n",
    "time_zones[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ordinary dict with method definition\n",
    "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": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Same method with default dict\n",
    "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\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1251"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = get_counts(time_zones)\n",
    "\n",
    "counts['America/New_York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3440"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(time_zones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "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": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(33, 'America/Sao_Paulo'),\n",
       " (35, 'Europe/Madrid'),\n",
       " (36, 'Pacific/Honolulu'),\n",
       " (37, 'Asia/Tokyo'),\n",
       " (74, 'Europe/London'),\n",
       " (191, 'America/Denver'),\n",
       " (382, 'America/Los_Angeles'),\n",
       " (400, 'America/Chicago'),\n",
       " (521, ''),\n",
       " (1251, 'America/New_York')]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_counts(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('America/New_York', 1251),\n",
       " ('', 521),\n",
       " ('America/Chicago', 400),\n",
       " ('America/Los_Angeles', 382),\n",
       " ('America/Denver', 191),\n",
       " ('Europe/London', 74),\n",
       " ('Asia/Tokyo', 37),\n",
       " ('Pacific/Honolulu', 36),\n",
       " ('Europe/Madrid', 35),\n",
       " ('America/Sao_Paulo', 33)]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "counts = Counter(time_zones)\n",
    "counts.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting Time Zones with pandas\n",
    "#### This is the second (easier way) of getting the time-zone data.\n",
    "#### Creating DataFrame from original set of records is easy as passing list of records to 'pandas.DataFrame'.\n",
    "#### The output of 'info' is a 'summary view', shown for large DataFrame. We can do a 'value_counts' after replacing missing values using 'fillna'.\n",
    "#### The empty strings can be replaced with 'Unknown' string through Boolean indexing.\n",
    "#### After the data munging, we can create a horizontal bar plot through 'seaborn' to visualize this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3560 entries, 0 to 3559\n",
      "Data columns (total 18 columns):\n",
      "_heartbeat_    120 non-null float64\n",
      "a              3440 non-null object\n",
      "al             3094 non-null object\n",
      "c              2919 non-null object\n",
      "cy             2919 non-null object\n",
      "g              3440 non-null object\n",
      "gr             2919 non-null object\n",
      "h              3440 non-null object\n",
      "hc             3440 non-null float64\n",
      "hh             3440 non-null object\n",
      "kw             93 non-null object\n",
      "l              3440 non-null object\n",
      "ll             2919 non-null object\n",
      "nk             3440 non-null float64\n",
      "r              3440 non-null object\n",
      "t              3440 non-null float64\n",
      "tz             3440 non-null object\n",
      "u              3440 non-null object\n",
      "dtypes: float64(4), object(14)\n",
      "memory usage: 500.7+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "frame = pd.DataFrame(records)\n",
    "frame.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     America/New_York\n",
       "1       America/Denver\n",
       "2     America/New_York\n",
       "3    America/Sao_Paulo\n",
       "4     America/New_York\n",
       "5     America/New_York\n",
       "6        Europe/Warsaw\n",
       "7                     \n",
       "8                     \n",
       "9                     \n",
       "Name: tz, dtype: object"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['tz'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "                        521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Europe/London            74\n",
       "Asia/Tokyo               37\n",
       "Pacific/Honolulu         36\n",
       "Europe/Madrid            35\n",
       "America/Sao_Paulo        33\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts = frame['tz'].value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "America/New_York       1251\n",
       "Unknown                 521\n",
       "America/Chicago         400\n",
       "America/Los_Angeles     382\n",
       "America/Denver          191\n",
       "Missing                 120\n",
       "Europe/London            74\n",
       "Asia/Tokyo               37\n",
       "Pacific/Honolulu         36\n",
       "Europe/Madrid            35\n",
       "Name: tz, dtype: int64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_tz = frame['tz'].fillna('Missing')\n",
    "clean_tz[clean_tz == ''] = 'Unknown'\n",
    "tz_counts = clean_tz.value_counts()\n",
    "\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20395c61978>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "subset = tz_counts[:10]\n",
    "sns.barplot(y = subset.index, x = subset.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The field 'a' contains information on browser, device, or application used to perform URL shortening.\n",
    "#### Parsing the data from the strings may seem daunting. One strategy is to split off first token from the string (which shows the browser capability) and make a strategy summary of user behaviour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GoogleMaps/RochesterNY'"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               Mozilla/5.0\n",
       "1    GoogleMaps/RochesterNY\n",
       "2               Mozilla/4.0\n",
       "3               Mozilla/5.0\n",
       "4               Mozilla/5.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.Series([x.split()[0] for x in frame.a.dropna()])\n",
    "results[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mozilla/5.0                 2594\n",
       "Mozilla/4.0                  601\n",
       "GoogleMaps/RochesterNY       121\n",
       "Opera/9.80                    34\n",
       "TEST_INTERNET_AGENT           24\n",
       "GoogleProducer                21\n",
       "Mozilla/6.0                    5\n",
       "BlackBerry8520/5.0.0.681       4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.value_counts()[:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Next we may need to classify the top time zones into Windows and non-Windows users.\n",
    "#### If a user is on Windows, the string 'Windows' will be in the agent string. We will exclude agents that are missing from the data.\n",
    "#### Then we compute a value on whether each row is Windows or not and group the data by time zone and os column.\n",
    "#### The group counts, just like 'value_counts' function, can be computed with 'size'. This result can be reshaped into a table with 'unstack'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "cframe = frame[frame.a.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0        Windows\n",
       "1    Not Windows\n",
       "2        Windows\n",
       "3    Not Windows\n",
       "4        Windows\n",
       "Name: os, dtype: object"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "cframe['os'] = np.where(cframe['a'].str.contains('Windows'),\n",
    "                       'Windows', 'Not Windows')\n",
    "\n",
    "cframe['os'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Cairo</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Casablanca</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Ceuta</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Johannesburg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Lusaka</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Anchorage</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Buenos_Aires</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Cordoba</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Mendoza</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                              Not Windows  Windows\n",
       "tz                                                  \n",
       "                                      245.0    276.0\n",
       "Africa/Cairo                            0.0      3.0\n",
       "Africa/Casablanca                       0.0      1.0\n",
       "Africa/Ceuta                            0.0      2.0\n",
       "Africa/Johannesburg                     0.0      1.0\n",
       "Africa/Lusaka                           0.0      1.0\n",
       "America/Anchorage                       4.0      1.0\n",
       "America/Argentina/Buenos_Aires          1.0      0.0\n",
       "America/Argentina/Cordoba               0.0      1.0\n",
       "America/Argentina/Mendoza               0.0      1.0"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_tz_os = cframe.groupby(['tz', 'os'])\n",
    "\n",
    "agg_counts = by_tz_os.size().unstack().fillna(0)\n",
    "\n",
    "agg_counts[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After this, we can select top overall time zones. We can construct an indirect index array from the row counts in the DataFrame.\n",
    "#### Then we can use 'take' to select the rows in that order, then slice off last 10 rows i.e. largest values.\n",
    "#### pandas has a convinient method to replace this, 'nlargest', which does the same thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "                                  24\n",
       "Africa/Cairo                      20\n",
       "Africa/Casablanca                 21\n",
       "Africa/Ceuta                      92\n",
       "Africa/Johannesburg               87\n",
       "Africa/Lusaka                     53\n",
       "America/Anchorage                 54\n",
       "America/Argentina/Buenos_Aires    57\n",
       "America/Argentina/Cordoba         26\n",
       "America/Argentina/Mendoza         55\n",
       "dtype: int64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To sort in ascending order\n",
    "indexer = agg_counts.sum(1).argsort()\n",
    "\n",
    "indexer[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>os</th>\n",
       "      <th>Not Windows</th>\n",
       "      <th>Windows</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tz</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>America/Sao_Paulo</th>\n",
       "      <td>13.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/Madrid</th>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific/Honolulu</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Tokyo</th>\n",
       "      <td>2.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/London</th>\n",
       "      <td>43.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>132.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>130.0</td>\n",
       "      <td>252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>115.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>245.0</td>\n",
       "      <td>276.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>339.0</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "os                   Not Windows  Windows\n",
       "tz                                       \n",
       "America/Sao_Paulo           13.0     20.0\n",
       "Europe/Madrid               16.0     19.0\n",
       "Pacific/Honolulu             0.0     36.0\n",
       "Asia/Tokyo                   2.0     35.0\n",
       "Europe/London               43.0     31.0\n",
       "America/Denver             132.0     59.0\n",
       "America/Los_Angeles        130.0    252.0\n",
       "America/Chicago            115.0    285.0\n",
       "                           245.0    276.0\n",
       "America/New_York           339.0    912.0"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_subset = agg_counts.take(indexer[-10:])\n",
    "count_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tz\n",
       "America/New_York       1251.0\n",
       "                        521.0\n",
       "America/Chicago         400.0\n",
       "America/Los_Angeles     382.0\n",
       "America/Denver          191.0\n",
       "Europe/London            74.0\n",
       "Asia/Tokyo               37.0\n",
       "Pacific/Honolulu         36.0\n",
       "Europe/Madrid            35.0\n",
       "America/Sao_Paulo        33.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts.sum(1).nlargest(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can then prepare this data for a stacked barplot. We can do this by passing an additional argument to seaborn's 'barplot' function.\n",
    "#### The first plot that we create does not make it easy to see relative percentage of Windows users in smaller groups. So, we will normalize the group percentages to 1. Then we can implement a new bar plot with this percentage data.\n",
    "#### The normalized sum could be achieved more efficiently using 'transform' method with 'groupby'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tz</th>\n",
       "      <th>os</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>Windows</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pacific/Honolulu</td>\n",
       "      <td>Windows</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Asia/Tokyo</td>\n",
       "      <td>Windows</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Not Windows</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Europe/London</td>\n",
       "      <td>Windows</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  tz           os  total\n",
       "0  America/Sao_Paulo  Not Windows   13.0\n",
       "1  America/Sao_Paulo      Windows   20.0\n",
       "2      Europe/Madrid  Not Windows   16.0\n",
       "3      Europe/Madrid      Windows   19.0\n",
       "4   Pacific/Honolulu  Not Windows    0.0\n",
       "5   Pacific/Honolulu      Windows   36.0\n",
       "6         Asia/Tokyo  Not Windows    2.0\n",
       "7         Asia/Tokyo      Windows   35.0\n",
       "8      Europe/London  Not Windows   43.0\n",
       "9      Europe/London      Windows   31.0"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rearrange data for plotting\n",
    "count_subset = count_subset.stack()\n",
    "count_subset.name = 'total'\n",
    "count_subset = count_subset.reset_index()\n",
    "\n",
    "count_subset[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20395d13358>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='total', y='tz', hue='os', data=count_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "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": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20395c97eb8>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='normed_total', y='tz', hue='os', data=results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = count_subset.groupby('tz')\n",
    "results2 = count_subset.total / g.total.transform('sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MovieLens 1M Dataset\n",
    "#### GroupLens Research provides a number of collections of movie ratings data gathered from users of MovieLens.\n",
    "#### The data provides movie ratings, movie metadata (genre and year) and demographic data (age, zip code, gender and occupation).\n",
    "#### Such data is often useful in development of recommendation systems using machine learning algorithms.\n",
    "#### Although ML is not the focus here, we can use techniques to slice and dice the data to get the exact form we need.\n",
    "#### The MovieLens 1M dataset has 1 million ratings collected from 6000 users on 4000 movies.\n",
    "#### It's spread across 3 tables - ratings, user information, and movie information.\n",
    "#### The first step is to load the tables into a DataFrame using 'pandas.read_table'. You can verify success by checking first few rows of each dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Smaller display\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, engine='python')\n",
    "\n",
    "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
    "ratings = pd.read_table('datasets/movielens/ratings.dat', sep='::',\n",
    "                       header=None, names=rnames, engine='python')\n",
    "\n",
    "mnames = ['movie_id', 'title', 'genres']\n",
    "movies = pd.read_table('datasets/movielens/movies.dat', sep='::',\n",
    "                       header=None, names=mnames, engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>55117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>7</td>\n",
       "      <td>02460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>55455</td>\n",
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       "   user_id gender  age  occupation    zip\n",
       "0        1      F    1          10  48067\n",
       "1        2      M   56          16  70072\n",
       "2        3      M   25          15  55117\n",
       "3        4      M   45           7  02460\n",
       "4        5      M   25          20  55455"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users[:5]"
   ]
  },
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   "execution_count": 192,
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       "   user_id  movie_id  rating  timestamp\n",
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   ],
   "source": [
    "ratings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
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       "   movie_id                               title                        genres\n",
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       "2         3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3         4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4         5  Father of the Bride Part II (1995)                        Comedy"
      ]
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   "source": [
    "movies[:5]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 194,
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   "outputs": [
    {
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       "         user_id  movie_id  rating  timestamp\n",
       "0              1      1193       5  978300760\n",
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       "\n",
       "[1000209 rows x 4 columns]"
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     },
     "execution_count": 194,
     "metadata": {},
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   "source": [
    "ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The ages and occupations in 'users' are encoded as integers which indicate to groups in the README file.\n",
    "#### Analyzing data across 3 tables is not easy. eg - Compute mean ratings for a movie by sex and age.\n",
    "#### The task will be easier with all the 3 tables merged into a single one. This can be achieved with pandas's 'merge' function.\n",
    "#### We first merge ratings with users and then merge that result with movies. pandas infers which columns to use as the merge (join) keys based on overlapping names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "         user_id  movie_id  rating  timestamp gender  age  occupation    zip  \\\n",
       "0              1      1193       5  978300760      F    1          10  48067   \n",
       "1              2      1193       5  978298413      M   56          16  70072   \n",
       "2             12      1193       4  978220179      M   25          12  32793   \n",
       "3             15      1193       4  978199279      M   25           7  22903   \n",
       "4             17      1193       5  978158471      M   50           1  95350   \n",
       "...          ...       ...     ...        ...    ...  ...         ...    ...   \n",
       "1000204     5949      2198       5  958846401      M   18          17  47901   \n",
       "1000205     5675      2703       3  976029116      M   35          14  30030   \n",
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       "1000207     5851      3607       5  957756608      F   18          20  55410   \n",
       "1000208     5938      2909       4  957273353      M   25           1  35401   \n",
       "\n",
       "                                               title                genres  \n",
       "0             One Flew Over the Cuckoo's Nest (1975)                 Drama  \n",
       "1             One Flew Over the Cuckoo's Nest (1975)                 Drama  \n",
       "2             One Flew Over the Cuckoo's Nest (1975)                 Drama  \n",
       "3             One Flew Over the Cuckoo's Nest (1975)                 Drama  \n",
       "4             One Flew Over the Cuckoo's Nest (1975)                 Drama  \n",
       "...                                              ...                   ...  \n",
       "1000204                           Modulations (1998)           Documentary  \n",
       "1000205                        Broken Vessels (1998)                 Drama  \n",
       "1000206                            White Boys (1999)                 Drama  \n",
       "1000207                     One Little Indian (1973)  Comedy|Drama|Western  \n",
       "1000208  Five Wives, Three Secretaries and Me (1998)           Documentary  \n",
       "\n",
       "[1000209 rows x 10 columns]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(pd.merge(ratings, users), movies)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                                            1\n",
       "movie_id                                        1193\n",
       "rating                                             5\n",
       "timestamp                                  978300760\n",
       "gender                                             F\n",
       "age                                                1\n",
       "occupation                                        10\n",
       "zip                                            48067\n",
       "title         One Flew Over the Cuckoo's Nest (1975)\n",
       "genres                                         Drama\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To get mean movie ratings grouped by gender, we use the 'pivot_table' method. This will return DataFrame with movie titles as row labels (index) and gender as column labels.\n",
    "#### We can filter the data by accepting movies that received at least 250 ratings (arbitrary). To get this, we group data by title and use 'size()' to get Series of group sizes for each title.The index of titles can be used to select rows from 'mean_ratings'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>$1,000,000 Duck (1971)</th>\n",
       "      <td>3.375000</td>\n",
       "      <td>2.761905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Night Mother (1986)</th>\n",
       "      <td>3.388889</td>\n",
       "      <td>3.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Til There Was You (1997)</th>\n",
       "      <td>2.675676</td>\n",
       "      <td>2.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...And Justice for All (1979)</th>\n",
       "      <td>3.828571</td>\n",
       "      <td>3.689024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                F         M\n",
       "title                                            \n",
       "$1,000,000 Duck (1971)         3.375000  2.761905\n",
       "'Night Mother (1986)           3.388889  3.352941\n",
       "'Til There Was You (1997)      2.675676  2.733333\n",
       "'burbs, The (1989)             2.793478  2.962085\n",
       "...And Justice for All (1979)  3.828571  3.689024"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings = data.pivot_table('rating', index='title',\n",
    "                               columns = 'gender', aggfunc='mean')\n",
    "mean_ratings[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "$1,000,000 Duck (1971)            37\n",
       "'Night Mother (1986)              70\n",
       "'Til There Was You (1997)         52\n",
       "'burbs, The (1989)               303\n",
       "...And Justice for All (1979)    199\n",
       "dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_by_title = data.groupby('title').size()\n",
    "ratings_by_title[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([''burbs, The (1989)', '10 Things I Hate About You (1999)',\n",
       "       '101 Dalmatians (1961)', '101 Dalmatians (1996)', '12 Angry Men (1957)',\n",
       "       '13th Warrior, The (1999)', '2 Days in the Valley (1996)',\n",
       "       '20,000 Leagues Under the Sea (1954)', '2001: A Space Odyssey (1968)',\n",
       "       '2010 (1984)',\n",
       "       ...\n",
       "       'X-Men (2000)', 'Year of Living Dangerously (1982)',\n",
       "       'Yellow Submarine (1968)', 'You've Got Mail (1998)',\n",
       "       'Young Frankenstein (1974)', 'Young Guns (1988)',\n",
       "       'Young Guns II (1990)', 'Young Sherlock Holmes (1985)',\n",
       "       'Zero Effect (1998)', 'eXistenZ (1999)'],\n",
       "      dtype='object', name='title', length=1216)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_titles = ratings_by_title.index[ratings_by_title >= 250]\n",
    "active_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 Things I Hate About You (1999)</th>\n",
       "      <td>3.646552</td>\n",
       "      <td>3.311966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1961)</th>\n",
       "      <td>3.791444</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>3.240000</td>\n",
       "      <td>2.911215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.184397</td>\n",
       "      <td>4.328421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Guns (1988)</th>\n",
       "      <td>3.371795</td>\n",
       "      <td>3.425620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Guns II (1990)</th>\n",
       "      <td>2.934783</td>\n",
       "      <td>2.904025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Young Sherlock Holmes (1985)</th>\n",
       "      <td>3.514706</td>\n",
       "      <td>3.363344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zero Effect (1998)</th>\n",
       "      <td>3.864407</td>\n",
       "      <td>3.723140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eXistenZ (1999)</th>\n",
       "      <td>3.098592</td>\n",
       "      <td>3.289086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1216 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                    F         M\n",
       "title                                                \n",
       "'burbs, The (1989)                 2.793478  2.962085\n",
       "10 Things I Hate About You (1999)  3.646552  3.311966\n",
       "101 Dalmatians (1961)              3.791444  3.500000\n",
       "101 Dalmatians (1996)              3.240000  2.911215\n",
       "12 Angry Men (1957)                4.184397  4.328421\n",
       "...                                     ...       ...\n",
       "Young Guns (1988)                  3.371795  3.425620\n",
       "Young Guns II (1990)               2.934783  2.904025\n",
       "Young Sherlock Holmes (1985)       3.514706  3.363344\n",
       "Zero Effect (1998)                 3.864407  3.723140\n",
       "eXistenZ (1999)                    3.098592  3.289086\n",
       "\n",
       "[1216 rows x 2 columns]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select rows on index\n",
    "mean_ratings = mean_ratings.loc[active_titles]\n",
    "mean_ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measuring Rating Disagreement\n",
    "#### To see top films among female viewers, we can sort the 'F' column in descending order.\n",
    "#### But to find movies that are most divisive between male and female viewers, you need to add a column containing the difference ('diff') in means and then sort by it.\n",
    "#### Sorting by 'diff' shows the greatest rating difference so that we can see which ones were preferred by women."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Close Shave, A (1995)</th>\n",
       "      <td>4.644444</td>\n",
       "      <td>4.473795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrong Trousers, The (1993)</th>\n",
       "      <td>4.588235</td>\n",
       "      <td>4.478261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)</th>\n",
       "      <td>4.572650</td>\n",
       "      <td>4.464589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wallace &amp; Gromit: The Best of Aardman Animation (1996)</th>\n",
       "      <td>4.563107</td>\n",
       "      <td>4.385075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schindler's List (1993)</th>\n",
       "      <td>4.562602</td>\n",
       "      <td>4.491415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shawshank Redemption, The (1994)</th>\n",
       "      <td>4.539075</td>\n",
       "      <td>4.560625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grand Day Out, A (1992)</th>\n",
       "      <td>4.537879</td>\n",
       "      <td>4.293255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>To Kill a Mockingbird (1962)</th>\n",
       "      <td>4.536667</td>\n",
       "      <td>4.372611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creature Comforts (1990)</th>\n",
       "      <td>4.513889</td>\n",
       "      <td>4.272277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usual Suspects, The (1995)</th>\n",
       "      <td>4.513317</td>\n",
       "      <td>4.518248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                                     F         M\n",
       "title                                                                 \n",
       "Close Shave, A (1995)                               4.644444  4.473795\n",
       "Wrong Trousers, The (1993)                          4.588235  4.478261\n",
       "Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)       4.572650  4.464589\n",
       "Wallace & Gromit: The Best of Aardman Animation...  4.563107  4.385075\n",
       "Schindler's List (1993)                             4.562602  4.491415\n",
       "Shawshank Redemption, The (1994)                    4.539075  4.560625\n",
       "Grand Day Out, A (1992)                             4.537879  4.293255\n",
       "To Kill a Mockingbird (1962)                        4.536667  4.372611\n",
       "Creature Comforts (1990)                            4.513889  4.272277\n",
       "Usual Suspects, The (1995)                          4.513317  4.518248"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_female_ratings = mean_ratings.sort_values(by='F', ascending=False)\n",
    "top_female_ratings[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dirty Dancing (1987)</th>\n",
       "      <td>3.790378</td>\n",
       "      <td>2.959596</td>\n",
       "      <td>-0.830782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumpin' Jack Flash (1986)</th>\n",
       "      <td>3.254717</td>\n",
       "      <td>2.578358</td>\n",
       "      <td>-0.676359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grease (1978)</th>\n",
       "      <td>3.975265</td>\n",
       "      <td>3.367041</td>\n",
       "      <td>-0.608224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Little Women (1994)</th>\n",
       "      <td>3.870588</td>\n",
       "      <td>3.321739</td>\n",
       "      <td>-0.548849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steel Magnolias (1989)</th>\n",
       "      <td>3.901734</td>\n",
       "      <td>3.365957</td>\n",
       "      <td>-0.535777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anastasia (1997)</th>\n",
       "      <td>3.800000</td>\n",
       "      <td>3.281609</td>\n",
       "      <td>-0.518391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rocky Horror Picture Show, The (1975)</th>\n",
       "      <td>3.673016</td>\n",
       "      <td>3.160131</td>\n",
       "      <td>-0.512885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Color Purple, The (1985)</th>\n",
       "      <td>4.158192</td>\n",
       "      <td>3.659341</td>\n",
       "      <td>-0.498851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age of Innocence, The (1993)</th>\n",
       "      <td>3.827068</td>\n",
       "      <td>3.339506</td>\n",
       "      <td>-0.487561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free Willy (1993)</th>\n",
       "      <td>2.921348</td>\n",
       "      <td>2.438776</td>\n",
       "      <td>-0.482573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                        F         M      diff\n",
       "title                                                              \n",
       "Dirty Dancing (1987)                   3.790378  2.959596 -0.830782\n",
       "Jumpin' Jack Flash (1986)              3.254717  2.578358 -0.676359\n",
       "Grease (1978)                          3.975265  3.367041 -0.608224\n",
       "Little Women (1994)                    3.870588  3.321739 -0.548849\n",
       "Steel Magnolias (1989)                 3.901734  3.365957 -0.535777\n",
       "Anastasia (1997)                       3.800000  3.281609 -0.518391\n",
       "Rocky Horror Picture Show, The (1975)  3.673016  3.160131 -0.512885\n",
       "Color Purple, The (1985)               4.158192  3.659341 -0.498851\n",
       "Age of Innocence, The (1993)           3.827068  3.339506 -0.487561\n",
       "Free Willy (1993)                      2.921348  2.438776 -0.482573"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']\n",
    "\n",
    "sorted_by_diff = mean_ratings.sort_values(by='diff')\n",
    "sorted_by_diff[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reversing the order of rows and slicing top 10 would give us movies preferred by men that women didn't like.\n",
    "#### To get movies that elicited most disagreement among viewers independent of their gender, can be achieved measuring the variance or standard deviation of the ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Good, The Bad and The Ugly, The (1966)</th>\n",
       "      <td>3.494949</td>\n",
       "      <td>4.221300</td>\n",
       "      <td>0.726351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky Fried Movie, The (1977)</th>\n",
       "      <td>2.878788</td>\n",
       "      <td>3.555147</td>\n",
       "      <td>0.676359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dumb &amp; Dumber (1994)</th>\n",
       "      <td>2.697987</td>\n",
       "      <td>3.336595</td>\n",
       "      <td>0.638608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longest Day, The (1962)</th>\n",
       "      <td>3.411765</td>\n",
       "      <td>4.031447</td>\n",
       "      <td>0.619682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cable Guy, The (1996)</th>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.863787</td>\n",
       "      <td>0.613787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evil Dead II (Dead By Dawn) (1987)</th>\n",
       "      <td>3.297297</td>\n",
       "      <td>3.909283</td>\n",
       "      <td>0.611985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hidden, The (1987)</th>\n",
       "      <td>3.137931</td>\n",
       "      <td>3.745098</td>\n",
       "      <td>0.607167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rocky III (1982)</th>\n",
       "      <td>2.361702</td>\n",
       "      <td>2.943503</td>\n",
       "      <td>0.581801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caddyshack (1980)</th>\n",
       "      <td>3.396135</td>\n",
       "      <td>3.969737</td>\n",
       "      <td>0.573602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>For a Few Dollars More (1965)</th>\n",
       "      <td>3.409091</td>\n",
       "      <td>3.953795</td>\n",
       "      <td>0.544704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                         F         M      diff\n",
       "title                                                               \n",
       "Good, The Bad and The Ugly, The (1966)  3.494949  4.221300  0.726351\n",
       "Kentucky Fried Movie, The (1977)        2.878788  3.555147  0.676359\n",
       "Dumb & Dumber (1994)                    2.697987  3.336595  0.638608\n",
       "Longest Day, The (1962)                 3.411765  4.031447  0.619682\n",
       "Cable Guy, The (1996)                   2.250000  2.863787  0.613787\n",
       "Evil Dead II (Dead By Dawn) (1987)      3.297297  3.909283  0.611985\n",
       "Hidden, The (1987)                      3.137931  3.745098  0.607167\n",
       "Rocky III (1982)                        2.361702  2.943503  0.581801\n",
       "Caddyshack (1980)                       3.396135  3.969737  0.573602\n",
       "For a Few Dollars More (1965)           3.409091  3.953795  0.544704"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff[::-1][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Dumb & Dumber (1994)                     1.321333\n",
       "Blair Witch Project, The (1999)          1.316368\n",
       "Natural Born Killers (1994)              1.307198\n",
       "Tank Girl (1995)                         1.277695\n",
       "Rocky Horror Picture Show, The (1975)    1.260177\n",
       "Eyes Wide Shut (1999)                    1.259624\n",
       "Evita (1996)                             1.253631\n",
       "Billy Madison (1995)                     1.249970\n",
       "Fear and Loathing in Las Vegas (1998)    1.246408\n",
       "Bicentennial Man (1999)                  1.245533\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Std Dev of ratings grouped by title\n",
    "rating_std_by_title = data.groupby('title')['rating'].std()\n",
    "\n",
    "# Filter down active_titles\n",
    "rating_std_by_title = rating_std_by_title.loc[active_titles]\n",
    "\n",
    "# Order in descending\n",
    "rating_std_by_title.sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## US Baby Names 1880-2010\n",
    "#### The US Social Security Administration (SSA) has made available the frequency of baby names from 1880 to present.\n",
    "#### To load and use this dataset, we need to do some data-wrangling. There are many things we may want to do with this dataset:\n",
    "####     1. Visualize proportions of babies given a certain name over time.\n",
    "####     2. Determine relative rank of a name.\n",
    "####     3. Determine most popular names each year and names whose popularity has increased or declined the most.\n",
    "####     4. Analyzing trends in names - vowels, consonants, length, etc.\n",
    "####     5. Analyze external sources of trends - biblical names, celebrities, demographic changes.\n",
    "#### The SSA releases 1 data file per year, containing total number of births for each sex/year combination.\n",
    "#### After downloading and unzipping the folder, we can see a series of files named in the format 'yob1880.txt'. We can use 'more' command (or 'head' in Unix) to check first few lines in the file.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mary,F,7065\n",
      "Anna,F,2604\n",
      "Emma,F,2003\n",
      "Elizabeth,F,1939\n",
      "Minnie,F,1746\n",
      "Margaret,F,1578\n",
      "Ida,F,1472\n",
      "Alice,F,1414\n",
      "Bertha,F,1320\n",
      "Sarah,F,1288\n",
      "Annie,F,1258\n",
      "Clara,F,1226\n",
      "Ella,F,1156\n",
      "Florence,F,1063\n",
      "Cora,F,1045\n",
      "Martha,F,1040\n",
      "Laura,F,1012\n",
      "Nellie,F,995\n",
      "Grace,F,982\n",
      "Carrie,F,949\n",
      "Maude,F,858\n",
      "Mabel,F,808\n",
      "Bessie,F,794\n",
      "Jennie,F,793\n",
      "Gertrude,F,787\n",
      "Julia,F,783\n",
      "Hattie,F,769\n",
      "Edith,F,768\n",
      "Mattie,F,704\n",
      "Rose,F,700\n",
      "Catherine,F,688\n",
      "Lillian,F,672\n",
      "Ada,F,652\n",
      "Lillie,F,647\n",
      "Helen,F,636\n",
      "Jessie,F,635\n",
      "Louise,F,635\n",
      "Ethel,F,633\n",
      "Lula,F,621\n",
      "Myrtle,F,615\n",
      "Eva,F,614\n",
      "Frances,F,605\n",
      "Lena,F,603\n",
      "Lucy,F,591\n",
      "Edna,F,588\n",
      "Maggie,F,582\n",
      "Pearl,F,569\n",
      "Daisy,F,564\n",
      "Fannie,F,560\n",
      "Josephine,F,544\n",
      "Dora,F,524\n",
      "Rosa,F,507\n",
      "Katherine,F,502\n",
      "Agnes,F,473\n",
      "Marie,F,471\n",
      "Nora,F,471\n",
      "May,F,462\n",
      "Mamie,F,436\n",
      "Blanche,F,427\n",
      "Stella,F,414\n",
      "Ellen,F,411\n",
      "Nancy,F,411\n",
      "Effie,F,406\n",
      "Sallie,F,404\n",
      "Nettie,F,403\n",
      "Della,F,391\n",
      "Lizzie,F,388\n",
      "Flora,F,365\n",
      "Susie,F,361\n",
      "Maud,F,345\n",
      "Mae,F,344\n",
      "Etta,F,323\n",
      "Harriet,F,319\n",
      "Sadie,F,317\n",
      "Caroline,F,306\n",
      "Katie,F,303\n",
      "Lydia,F,302\n",
      "Elsie,F,301\n",
      "Kate,F,299\n",
      "Susan,F,286\n",
      "Mollie,F,283\n",
      "Alma,F,277\n",
      "Addie,F,274\n",
      "Georgia,F,259\n",
      "Eliza,F,252\n",
      "Lulu,F,249\n",
      "Nannie,F,248\n",
      "Lottie,F,245\n",
      "Amanda,F,241\n",
      "Belle,F,238\n",
      "Charlotte,F,237\n",
      "Rebecca,F,236\n",
      "Ruth,F,234\n",
      "Viola,F,229\n",
      "Olive,F,224\n",
      "Amelia,F,221\n",
      "Hannah,F,221\n",
      "Jane,F,215\n",
      "Virginia,F,213\n",
      "Emily,F,210\n",
      "Matilda,F,210\n",
      "Irene,F,204\n",
      "Kathryn,F,204\n",
      "Esther,F,198\n",
      "Willie,F,192\n",
      "Henrietta,F,191\n",
      "Ollie,F,183\n",
      "Amy,F,167\n",
      "Rachel,F,166\n",
      "Sara,F,165\n",
      "Estella,F,162\n",
      "Theresa,F,153\n",
      "Augusta,F,151\n",
      "Ora,F,149\n",
      "Pauline,F,144\n",
      "Josie,F,141\n",
      "Lola,F,138\n",
      "Sophia,F,138\n",
      "Leona,F,137\n",
      "Anne,F,136\n",
      "Mildred,F,132\n",
      "Ann,F,131\n",
      "Beulah,F,131\n",
      "Callie,F,131\n",
      "Lou,F,131\n",
      "Delia,F,129\n",
      "Eleanor,F,129\n",
      "Barbara,F,127\n",
      "Iva,F,127\n",
      "Louisa,F,126\n",
      "Maria,F,125\n",
      "Mayme,F,124\n",
      "Evelyn,F,122\n",
      "Estelle,F,119\n",
      "Nina,F,119\n",
      "Betty,F,117\n",
      "Marion,F,115\n",
      "Bettie,F,113\n",
      "Dorothy,F,112\n",
      "Luella,F,111\n",
      "Inez,F,106\n",
      "Lela,F,106\n",
      "Rosie,F,106\n",
      "Allie,F,105\n",
      "Millie,F,105\n",
      "Janie,F,96\n",
      "Cornelia,F,94\n",
      "Victoria,F,93\n",
      "Ruby,F,92\n",
      "Winifred,F,92\n",
      "Alta,F,91\n",
      "Celia,F,90\n",
      "Christine,F,89\n",
      "Beatrice,F,87\n",
      "Birdie,F,85\n",
      "Harriett,F,83\n",
      "Mable,F,83\n",
      "Myra,F,83\n",
      "Sophie,F,83\n",
      "Tillie,F,83\n",
      "Isabel,F,81\n",
      "Sylvia,F,81\n",
      "Carolyn,F,80\n",
      "Isabelle,F,80\n",
      "Leila,F,80\n",
      "Sally,F,80\n",
      "Ina,F,79\n",
      "Essie,F,78\n",
      "Bertie,F,77\n",
      "Nell,F,77\n",
      "Alberta,F,76\n",
      "Katharine,F,76\n",
      "Lora,F,74\n",
      "Rena,F,74\n",
      "Mina,F,73\n",
      "Rhoda,F,73\n",
      "Mathilda,F,72\n",
      "Abbie,F,71\n",
      "Eula,F,70\n",
      "Dollie,F,69\n",
      "Hettie,F,69\n",
      "Eunice,F,67\n",
      "Fanny,F,67\n",
      "Ola,F,67\n",
      "Lenora,F,66\n",
      "Adelaide,F,65\n",
      "Christina,F,65\n",
      "Lelia,F,65\n",
      "Nelle,F,65\n",
      "Sue,F,65\n",
      "Johanna,F,64\n",
      "Lilly,F,64\n",
      "Lucinda,F,63\n",
      "Minerva,F,63\n",
      "Lettie,F,62\n",
      "Roxie,F,62\n",
      "Cynthia,F,61\n",
      "Helena,F,60\n",
      "Hilda,F,60\n",
      "Hulda,F,60\n",
      "Bernice,F,59\n",
      "Genevieve,F,59\n",
      "Jean,F,59\n",
      "Cordelia,F,58\n",
      "Marian,F,56\n",
      "Francis,F,55\n",
      "Jeanette,F,55\n",
      "Adeline,F,54\n",
      "Gussie,F,54\n",
      "Leah,F,54\n",
      "Lois,F,53\n",
      "Lura,F,53\n",
      "Mittie,F,53\n",
      "Hallie,F,51\n",
      "Isabella,F,50\n",
      "Olga,F,50\n",
      "Phoebe,F,50\n",
      "Teresa,F,50\n",
      "Hester,F,49\n",
      "Lida,F,49\n",
      "Lina,F,49\n",
      "Winnie,F,49\n",
      "Claudia,F,48\n",
      "Marguerite,F,48\n",
      "Vera,F,48\n",
      "Cecelia,F,47\n",
      "Bess,F,46\n",
      "Emilie,F,46\n",
      "John,F,46\n",
      "Rosetta,F,46\n",
      "Verna,F,46\n",
      "Myrtie,F,45\n",
      "Cecilia,F,44\n",
      "Elva,F,44\n",
      "Olivia,F,44\n",
      "Ophelia,F,44\n",
      "Georgie,F,43\n",
      "Elnora,F,42\n",
      "Violet,F,42\n",
      "Adele,F,41\n",
      "Lily,F,41\n",
      "Linnie,F,41\n",
      "Loretta,F,41\n",
      "Madge,F,41\n",
      "Polly,F,41\n",
      "Virgie,F,41\n",
      "Eugenia,F,40\n",
      "Lucile,F,40\n",
      "Lucille,F,40\n",
      "Mabelle,F,39\n",
      "Rosalie,F,39\n",
      "Kittie,F,38\n",
      "Meta,F,37\n",
      "Angie,F,36\n",
      "Dessie,F,36\n",
      "Georgiana,F,36\n",
      "Lila,F,36\n",
      "Regina,F,36\n",
      "Selma,F,36\n",
      "Wilhelmina,F,36\n",
      "Bridget,F,35\n",
      "Lilla,F,35\n",
      "Malinda,F,35\n",
      "Vina,F,35\n",
      "Freda,F,34\n",
      "Gertie,F,34\n",
      "Jeannette,F,34\n",
      "Louella,F,34\n",
      "Mandy,F,34\n",
      "Roberta,F,34\n",
      "Cassie,F,33\n",
      "Corinne,F,33\n",
      "Ivy,F,33\n",
      "Melissa,F,33\n",
      "Lyda,F,32\n",
      "Naomi,F,32\n",
      "Norma,F,32\n",
      "Bell,F,31\n",
      "Margie,F,31\n",
      "Nona,F,31\n",
      "Zella,F,31\n",
      "Dovie,F,30\n",
      "Elvira,F,30\n",
      "Erma,F,30\n",
      "Irma,F,30\n",
      "Leota,F,30\n",
      "William,F,30\n",
      "Artie,F,29\n",
      "Blanch,F,29\n",
      "Charity,F,29\n",
      "Janet,F,29\n",
      "Lorena,F,29\n",
      "Lucretia,F,29\n",
      "Orpha,F,29\n",
      "Alvina,F,28\n",
      "Annette,F,28\n",
      "Catharine,F,28\n",
      "Elma,F,28\n",
      "Geneva,F,28\n",
      "Lee,F,28\n",
      "Leora,F,28\n",
      "Lona,F,28\n",
      "Miriam,F,28\n",
      "Zora,F,28\n",
      "Linda,F,27\n",
      "Octavia,F,27\n",
      "Sudie,F,27\n",
      "Zula,F,27\n",
      "Adella,F,26\n",
      "Alpha,F,26\n",
      "Frieda,F,26\n",
      "George,F,26\n",
      "Joanna,F,26\n",
      "Leonora,F,26\n",
      "Priscilla,F,26\n",
      "Tennie,F,26\n",
      "Angeline,F,25\n",
      "Docia,F,25\n",
      "Ettie,F,25\n",
      "Flossie,F,25\n",
      "Hanna,F,25\n",
      "Letha,F,25\n",
      "Minta,F,25\n",
      "Retta,F,25\n",
      "Rosella,F,25\n",
      "Adah,F,24\n",
      "Berta,F,24\n",
      "Elisabeth,F,24\n",
      "Elise,F,24\n",
      "Goldie,F,24\n",
      "Leola,F,24\n",
      "Margret,F,24\n",
      "Adaline,F,23\n",
      "Floy,F,23\n",
      "Idella,F,23\n",
      "Juanita,F,23\n",
      "Lenna,F,23\n",
      "Lucie,F,23\n",
      "Missouri,F,23\n",
      "Nola,F,23\n",
      "Zoe,F,23\n",
      "Eda,F,22\n",
      "Isabell,F,22\n",
      "James,F,22\n",
      "Julie,F,22\n",
      "Letitia,F,22\n",
      "Madeline,F,22\n",
      "Malissa,F,22\n",
      "Mariah,F,22\n",
      "Pattie,F,22\n",
      "Vivian,F,22\n",
      "Almeda,F,21\n",
      "Aurelia,F,21\n",
      "Claire,F,21\n",
      "Dolly,F,21\n",
      "Hazel,F,21\n",
      "Jannie,F,21\n",
      "Kathleen,F,21\n",
      "Kathrine,F,21\n",
      "Lavinia,F,21\n",
      "Marietta,F,21\n",
      "Melvina,F,21\n",
      "Ona,F,21\n",
      "Pinkie,F,21\n",
      "Samantha,F,21\n",
      "Susanna,F,21\n",
      "Chloe,F,20\n",
      "Donnie,F,20\n",
      "Elsa,F,20\n",
      "Gladys,F,20\n",
      "Matie,F,20\n",
      "Pearle,F,20\n",
      "Vesta,F,20\n",
      "Vinnie,F,20\n",
      "Antoinette,F,19\n",
      "Clementine,F,19\n",
      "Edythe,F,19\n",
      "Harriette,F,19\n",
      "Libbie,F,19\n",
      "Lilian,F,19\n",
      "Lue,F,19\n",
      "Lutie,F,19\n",
      "Magdalena,F,19\n",
      "Meda,F,19\n",
      "Rita,F,19\n",
      "Tena,F,19\n",
      "Zelma,F,19\n",
      "Adelia,F,18\n",
      "Annetta,F,18\n",
      "Antonia,F,18\n",
      "Dona,F,18\n",
      "Elizebeth,F,18\n",
      "Georgianna,F,18\n",
      "Gracie,F,18\n",
      "Iona,F,18\n",
      "Lessie,F,18\n",
      "Leta,F,18\n",
      "Liza,F,18\n",
      "Mertie,F,18\n",
      "Molly,F,18\n",
      "Neva,F,18\n",
      "Oma,F,18\n",
      "Alida,F,17\n",
      "Alva,F,17\n",
      "Cecile,F,17\n",
      "Cleo,F,17\n",
      "Donna,F,17\n",
      "Ellie,F,17\n",
      "Ernestine,F,17\n",
      "Evie,F,17\n",
      "Frankie,F,17\n",
      "Helene,F,17\n",
      "Minna,F,17\n",
      "Myrta,F,17\n",
      "Prudence,F,17\n",
      "Queen,F,17\n",
      "Rilla,F,17\n",
      "Savannah,F,17\n",
      "Tessie,F,17\n",
      "Tina,F,17\n",
      "Agatha,F,16\n",
      "America,F,16\n",
      "Anita,F,16\n",
      "Arminta,F,16\n",
      "Dorothea,F,16\n",
      "Ira,F,16\n",
      "Luvenia,F,16\n",
      "Marjorie,F,16\n",
      "Maybelle,F,16\n",
      "Mellie,F,16\n",
      "Nan,F,16\n",
      "Pearlie,F,16\n",
      "Sidney,F,16\n",
      "Velma,F,16\n",
      "Clare,F,15\n",
      "Constance,F,15\n",
      "Dixie,F,15\n",
      "Ila,F,15\n",
      "Iola,F,15\n",
      "Jimmie,F,15\n",
      "Louvenia,F,15\n",
      "Lucia,F,15\n",
      "Ludie,F,15\n",
      "Luna,F,15\n",
      "Metta,F,15\n",
      "Patsy,F,15\n",
      "Phebe,F,15\n",
      "Sophronia,F,15\n",
      "Adda,F,14\n",
      "Avis,F,14\n",
      "Betsy,F,14\n",
      "Bonnie,F,14\n",
      "Cecil,F,14\n",
      "Cordie,F,14\n",
      "Emmaline,F,14\n",
      "Ethelyn,F,14\n",
      "Hortense,F,14\n",
      "June,F,14\n",
      "Louie,F,14\n",
      "Lovie,F,14\n",
      "Marcella,F,14\n",
      "Melinda,F,14\n",
      "Mona,F,14\n",
      "Odessa,F,14\n",
      "Veronica,F,14\n",
      "Aimee,F,13\n",
      "Annabel,F,13\n",
      "Ava,F,13\n",
      "Bella,F,13\n",
      "Carolina,F,13\n",
      "Cathrine,F,13\n",
      "Christena,F,13\n",
      "Clyde,F,13\n",
      "Dena,F,13\n",
      "Dolores,F,13\n",
      "Eleanore,F,13\n",
      "Elmira,F,13\n",
      "Fay,F,13\n",
      "Frank,F,13\n",
      "Jenny,F,13\n",
      "Kizzie,F,13\n",
      "Lonnie,F,13\n",
      "Loula,F,13\n",
      "Magdalene,F,13\n",
      "Mettie,F,13\n",
      "Mintie,F,13\n",
      "Peggy,F,13\n",
      "Reba,F,13\n",
      "Serena,F,13\n",
      "Vida,F,13\n",
      "Zada,F,13\n",
      "Abigail,F,12\n",
      "Celestine,F,12\n",
      "Celina,F,12\n",
      "Claudie,F,12\n",
      "Clemmie,F,12\n",
      "Connie,F,12\n",
      "Daisie,F,12\n",
      "Deborah,F,12\n",
      "Dessa,F,12\n",
      "Easter,F,12\n",
      "Eddie,F,12\n",
      "Emelia,F,12\n",
      "Emmie,F,12\n",
      "Imogene,F,12\n",
      "India,F,12\n",
      "Jeanne,F,12\n",
      "Joan,F,12\n",
      "Lenore,F,12\n",
      "Liddie,F,12\n",
      "Lotta,F,12\n",
      "Mame,F,12\n",
      "Nevada,F,12\n",
      "Rachael,F,12\n",
      "Sina,F,12\n",
      "Willa,F,12\n",
      "Aline,F,11\n",
      "Beryl,F,11\n",
      "Charles,F,11\n",
      "Daisey,F,11\n",
      "Dorcas,F,11\n",
      "Edmonia,F,11\n",
      "Effa,F,11\n",
      "Eldora,F,11\n",
      "Eloise,F,11\n",
      "Emmer,F,11\n",
      "Era,F,11\n",
      "Gena,F,11\n",
      "Henry,F,11\n",
      "Iris,F,11\n",
      "Izora,F,11\n",
      "Lennie,F,11\n",
      "Lissie,F,11\n",
      "Mallie,F,11\n",
      "Malvina,F,11\n",
      "Mathilde,F,11\n",
      "Mazie,F,11\n",
      "Queenie,F,11\n",
      "Robert,F,11\n",
      "Rosina,F,11\n",
      "Salome,F,11\n",
      "Theodora,F,11\n",
      "Therese,F,11\n",
      "Vena,F,11\n",
      "Wanda,F,11\n",
      "Wilda,F,11\n",
      "Altha,F,10\n",
      "Anastasia,F,10\n",
      "Besse,F,10\n",
      "Bird,F,10\n",
      "Birtie,F,10\n",
      "Clarissa,F,10\n",
      "Claude,F,10\n",
      "Delilah,F,10\n",
      "Diana,F,10\n",
      "Emelie,F,10\n",
      "Erna,F,10\n",
      "Fern,F,10\n",
      "Florida,F,10\n",
      "Frona,F,10\n",
      "Hilma,F,10\n",
      "Joseph,F,10\n",
      "Juliet,F,10\n",
      "Leonie,F,10\n",
      "Lugenia,F,10\n",
      "Mammie,F,10\n",
      "Manda,F,10\n",
      "Manerva,F,10\n",
      "Manie,F,10\n",
      "Nella,F,10\n",
      "Paulina,F,10\n",
      "Philomena,F,10\n",
      "Rae,F,10\n",
      "Selina,F,10\n",
      "Sena,F,10\n",
      "Theodosia,F,10\n",
      "Tommie,F,10\n",
      "Una,F,10\n",
      "Vernie,F,10\n",
      "Adela,F,9\n",
      "Althea,F,9\n",
      "Amalia,F,9\n",
      "Amber,F,9\n",
      "Angelina,F,9\n",
      "Annabelle,F,9\n",
      "Anner,F,9\n",
      "Arie,F,9\n",
      "Clarice,F,9\n",
      "Corda,F,9\n",
      "Corrie,F,9\n",
      "Dell,F,9\n",
      "Dellar,F,9\n",
      "Donie,F,9\n",
      "Doris,F,9\n",
      "Elda,F,9\n",
      "Elinor,F,9\n",
      "Emeline,F,9\n",
      "Emilia,F,9\n",
      "Esta,F,9\n",
      "Estell,F,9\n",
      "Etha,F,9\n",
      "Fred,F,9\n",
      "Hope,F,9\n",
      "Indiana,F,9\n",
      "Ione,F,9\n",
      "Jettie,F,9\n",
      "Johnnie,F,9\n",
      "Josiephine,F,9\n",
      "Kitty,F,9\n",
      "Lavina,F,9\n",
      "Leda,F,9\n",
      "Letta,F,9\n",
      "Mahala,F,9\n",
      "Marcia,F,9\n",
      "Margarette,F,9\n",
      "Maudie,F,9\n",
      "Maye,F,9\n",
      "Norah,F,9\n",
      "Oda,F,9\n",
      "Patty,F,9\n",
      "Paula,F,9\n",
      "Permelia,F,9\n",
      "Rosalia,F,9\n",
      "Roxanna,F,9\n",
      "Sula,F,9\n",
      "Vada,F,9\n",
      "Winnifred,F,9\n",
      "Adline,F,8\n",
      "Almira,F,8\n",
      "Alvena,F,8\n",
      "Arizona,F,8\n",
      "Becky,F,8\n",
      "Bennie,F,8\n",
      "Bernadette,F,8\n",
      "Camille,F,8\n",
      "Cordia,F,8\n",
      "Corine,F,8\n",
      "Dicie,F,8\n",
      "Dove,F,8\n",
      "Drusilla,F,8\n",
      "Elena,F,8\n",
      "Elenora,F,8\n",
      "Elmina,F,8\n",
      "Ethyl,F,8\n",
      "Evalyn,F,8\n",
      "Evelina,F,8\n",
      "Faye,F,8\n",
      "Huldah,F,8\n",
      "Idell,F,8\n",
      "Inga,F,8\n",
      "Irena,F,8\n",
      "Jewell,F,8\n",
      "Kattie,F,8\n",
      "Lavenia,F,8\n",
      "Leslie,F,8\n",
      "Lovina,F,8\n",
      "Lulie,F,8\n",
      "Magnolia,F,8\n",
      "Margeret,F,8\n",
      "Margery,F,8\n",
      "Media,F,8\n",
      "Millicent,F,8\n",
      "Nena,F,8\n",
      "Ocie,F,8\n",
      "Orilla,F,8\n",
      "Osie,F,8\n",
      "Pansy,F,8\n",
      "Ray,F,8\n",
      "Rosia,F,8\n",
      "Rowena,F,8\n",
      "Shirley,F,8\n",
      "Tabitha,F,8\n",
      "Thomas,F,8\n",
      "Verdie,F,8\n",
      "Walter,F,8\n",
      "Zetta,F,8\n",
      "Zoa,F,8\n",
      "Zona,F,8\n",
      "Albertina,F,7\n",
      "Albina,F,7\n",
      "Alyce,F,7\n",
      "Amie,F,7\n",
      "Angela,F,7\n",
      "Annis,F,7\n",
      "Carol,F,7\n",
      "Carra,F,7\n",
      "Clarence,F,7\n",
      "Clarinda,F,7\n",
      "Delphia,F,7\n",
      "Dillie,F,7\n",
      "Doshie,F,7\n",
      "Drucilla,F,7\n",
      "Etna,F,7\n",
      "Eugenie,F,7\n",
      "Eulalia,F,7\n",
      "Eve,F,7\n",
      "Felicia,F,7\n",
      "Florance,F,7\n",
      "Fronie,F,7\n",
      "Geraldine,F,7\n",
      "Gina,F,7\n",
      "Glenna,F,7\n",
      "Grayce,F,7\n",
      "Hedwig,F,7\n",
      "Jessica,F,7\n",
      "Jossie,F,7\n",
      "Katheryn,F,7\n",
      "Katy,F,7\n",
      "Lea,F,7\n",
      "Leanna,F,7\n",
      "Leitha,F,7\n",
      "Leone,F,7\n",
      "Lidie,F,7\n",
      "Loma,F,7\n",
      "Lular,F,7\n",
      "Magdalen,F,7\n",
      "Maymie,F,7\n",
      "Minervia,F,7\n",
      "Muriel,F,7\n",
      "Neppie,F,7\n",
      "Olie,F,7\n",
      "Onie,F,7\n",
      "Osa,F,7\n",
      "Otelia,F,7\n",
      "Paralee,F,7\n",
      "Patience,F,7\n",
      "Rella,F,7\n",
      "Rillie,F,7\n",
      "Rosanna,F,7\n",
      "Theo,F,7\n",
      "Tilda,F,7\n",
      "Tishie,F,7\n",
      "Tressa,F,7\n",
      "Viva,F,7\n",
      "Yetta,F,7\n",
      "Zena,F,7\n",
      "Zola,F,7\n",
      "Abby,F,6\n",
      "Aileen,F,6\n",
      "Alba,F,6\n",
      "Alda,F,6\n",
      "Alla,F,6\n",
      "Alverta,F,6\n",
      "Ara,F,6\n",
      "Ardelia,F,6\n",
      "Ardella,F,6\n",
      "Arrie,F,6\n",
      "Arvilla,F,6\n",
      "Augustine,F,6\n",
      "Aurora,F,6\n",
      "Bama,F,6\n",
      "Bena,F,6\n",
      "Byrd,F,6\n",
      "Calla,F,6\n",
      "Camilla,F,6\n",
      "Carey,F,6\n",
      "Carlotta,F,6\n",
      "Celestia,F,6\n",
      "Cherry,F,6\n",
      "Cinda,F,6\n",
      "Classie,F,6\n",
      "Claudine,F,6\n",
      "Clemie,F,6\n",
      "Clifford,F,6\n",
      "Clyda,F,6\n",
      "Creola,F,6\n",
      "Debbie,F,6\n",
      "Dee,F,6\n",
      "Dinah,F,6\n",
      "Doshia,F,6\n",
      "Ednah,F,6\n",
      "Edyth,F,6\n",
      "Eleanora,F,6\n",
      "Electa,F,6\n",
      "Eola,F,6\n",
      "Erie,F,6\n",
      "Eudora,F,6\n",
      "Euphemia,F,6\n",
      "Evalena,F,6\n",
      "Evaline,F,6\n",
      "Faith,F,6\n",
      "Fidelia,F,6\n",
      "Freddie,F,6\n",
      "Golda,F,6\n",
      "Harry,F,6\n",
      "Helma,F,6\n",
      "Hermine,F,6\n",
      "Hessie,F,6\n",
      "Ivah,F,6\n",
      "Janette,F,6\n",
      "Jennette,F,6\n",
      "Joella,F,6\n",
      "Kathryne,F,6\n",
      "Lacy,F,6\n",
      "Lanie,F,6\n",
      "Lauretta,F,6\n",
      "Leana,F,6\n",
      "Leatha,F,6\n",
      "Leo,F,6\n",
      "Liller,F,6\n",
      "Lillis,F,6\n",
      "Louetta,F,6\n",
      "Madie,F,6\n",
      "Mai,F,6\n",
      "Martina,F,6\n",
      "Maryann,F,6\n",
      "Melva,F,6\n",
      "Mena,F,6\n",
      "Mercedes,F,6\n",
      "Merle,F,6\n",
      "Mima,F,6\n",
      "Minda,F,6\n",
      "Monica,F,6\n",
      "Nealie,F,6\n",
      "Netta,F,6\n",
      "Nolia,F,6\n",
      "Nonie,F,6\n",
      "Odelia,F,6\n",
      "Ottilie,F,6\n",
      "Phyllis,F,6\n",
      "Robbie,F,6\n",
      "Sabina,F,6\n",
      "Sada,F,6\n",
      "Sammie,F,6\n",
      "Suzanne,F,6\n",
      "Sybilla,F,6\n",
      "Thea,F,6\n",
      "Tressie,F,6\n",
      "Vallie,F,6\n",
      "Venie,F,6\n",
      "Viney,F,6\n",
      "Wilhelmine,F,6\n",
      "Winona,F,6\n",
      "Zelda,F,6\n",
      "Zilpha,F,6\n",
      "Adelle,F,5\n",
      "Adina,F,5\n",
      "Adrienne,F,5\n",
      "Albertine,F,5\n",
      "Alys,F,5\n",
      "Ana,F,5\n",
      "Araminta,F,5\n",
      "Arthur,F,5\n",
      "Birtha,F,5\n",
      "Bulah,F,5\n",
      "Caddie,F,5\n",
      "Celie,F,5\n",
      "Charlotta,F,5\n",
      "Clair,F,5\n",
      "Concepcion,F,5\n",
      "Cordella,F,5\n",
      "Corrine,F,5\n",
      "Delila,F,5\n",
      "Delphine,F,5\n",
      "Dosha,F,5\n",
      "Edgar,F,5\n",
      "Elaine,F,5\n",
      "Elisa,F,5\n",
      "Ellar,F,5\n",
      "Elmire,F,5\n",
      "Elvina,F,5\n",
      "Ena,F,5\n",
      "Estie,F,5\n",
      "Etter,F,5\n",
      "Fronnie,F,5\n",
      "Genie,F,5\n",
      "Georgina,F,5\n",
      "Glenn,F,5\n",
      "Gracia,F,5\n",
      "Guadalupe,F,5\n",
      "Gwendolyn,F,5\n",
      "Hassie,F,5\n",
      "Honora,F,5\n",
      "Icy,F,5\n",
      "Isa,F,5\n",
      "Isadora,F,5\n",
      "Jesse,F,5\n",
      "Jewel,F,5\n",
      "Joe,F,5\n",
      "Johannah,F,5\n",
      "Juana,F,5\n",
      "Judith,F,5\n",
      "Judy,F,5\n",
      "Junie,F,5\n",
      "Lavonia,F,5\n",
      "Lella,F,5\n",
      "Lemma,F,5\n",
      "Letty,F,5\n",
      "Linna,F,5\n",
      "Littie,F,5\n",
      "Lollie,F,5\n",
      "Lorene,F,5\n",
      "Louis,F,5\n",
      "Love,F,5\n",
      "Lovisa,F,5\n",
      "Lucina,F,5\n",
      "Lynn,F,5\n",
      "Madora,F,5\n",
      "Mahalia,F,5\n",
      "Manervia,F,5\n",
      "Manuela,F,5\n",
      "Margarett,F,5\n",
      "Margaretta,F,5\n",
      "Margarita,F,5\n",
      "Marilla,F,5\n",
      "Mignon,F,5\n",
      "Mozella,F,5\n",
      "Natalie,F,5\n",
      "Nelia,F,5\n",
      "Nolie,F,5\n",
      "Omie,F,5\n",
      "Opal,F,5\n",
      "Ossie,F,5\n",
      "Ottie,F,5\n",
      "Ottilia,F,5\n",
      "Parthenia,F,5\n",
      "Penelope,F,5\n",
      "Pinkey,F,5\n",
      "Pollie,F,5\n",
      "Rennie,F,5\n",
      "Reta,F,5\n",
      "Roena,F,5\n",
      "Rosalee,F,5\n",
      "Roseanna,F,5\n",
      "Ruthie,F,5\n",
      "Sabra,F,5\n",
      "Sannie,F,5\n",
      "Selena,F,5\n",
      "Sibyl,F,5\n",
      "Tella,F,5\n",
      "Tempie,F,5\n",
      "Tennessee,F,5\n",
      "Teressa,F,5\n",
      "Texas,F,5\n",
      "Theda,F,5\n",
      "Thelma,F,5\n",
      "Thursa,F,5\n",
      "Ula,F,5\n",
      "Vannie,F,5\n",
      "Verona,F,5\n",
      "Vertie,F,5\n",
      "Wilma,F,5\n",
      "John,M,9655\n",
      "William,M,9533\n",
      "James,M,5927\n",
      "Charles,M,5348\n",
      "George,M,5126\n",
      "Frank,M,3242\n",
      "Joseph,M,2632\n",
      "Thomas,M,2534\n",
      "Henry,M,2444\n",
      "Robert,M,2416\n",
      "Edward,M,2364\n",
      "Harry,M,2152\n",
      "Walter,M,1755\n",
      "Arthur,M,1599\n",
      "Fred,M,1569\n",
      "Albert,M,1493\n",
      "Samuel,M,1024\n",
      "David,M,869\n",
      "Louis,M,828\n",
      "Joe,M,731\n",
      "Charlie,M,730\n",
      "Clarence,M,730\n",
      "Richard,M,728\n",
      "Andrew,M,644\n",
      "Daniel,M,643\n",
      "Ernest,M,615\n",
      "Will,M,588\n",
      "Jesse,M,569\n",
      "Oscar,M,544\n",
      "Lewis,M,517\n",
      "Peter,M,496\n",
      "Benjamin,M,490\n",
      "Frederick,M,483\n",
      "Willie,M,476\n",
      "Alfred,M,469\n",
      "Sam,M,457\n",
      "Roy,M,440\n",
      "Herbert,M,424\n",
      "Jacob,M,404\n",
      "Tom,M,399\n",
      "Elmer,M,373\n",
      "Carl,M,372\n",
      "Lee,M,361\n",
      "Howard,M,357\n",
      "Martin,M,357\n",
      "Michael,M,354\n",
      "Bert,M,348\n",
      "Herman,M,347\n",
      "Jim,M,345\n",
      "Francis,M,344\n",
      "Harvey,M,344\n",
      "Earl,M,335\n",
      "Eugene,M,328\n",
      "Ralph,M,317\n",
      "Ed,M,310\n",
      "Claude,M,309\n",
      "Edwin,M,309\n",
      "Ben,M,305\n",
      "Charley,M,305\n",
      "Paul,M,301\n",
      "Edgar,M,283\n",
      "Isaac,M,274\n",
      "Otto,M,271\n",
      "Luther,M,260\n",
      "Lawrence,M,257\n",
      "Ira,M,249\n",
      "Patrick,M,248\n",
      "Guy,M,239\n",
      "Oliver,M,234\n",
      "Theodore,M,232\n",
      "Hugh,M,224\n",
      "Clyde,M,221\n",
      "Alexander,M,211\n",
      "August,M,210\n",
      "Floyd,M,206\n",
      "Homer,M,205\n",
      "Jack,M,204\n",
      "Leonard,M,200\n",
      "Horace,M,199\n",
      "Marion,M,189\n",
      "Philip,M,186\n",
      "Allen,M,184\n",
      "Archie,M,183\n",
      "Stephen,M,176\n",
      "Chester,M,168\n",
      "Willis,M,166\n",
      "Raymond,M,165\n",
      "Rufus,M,163\n",
      "Warren,M,158\n",
      "Jessie,M,154\n",
      "Milton,M,149\n",
      "Alex,M,147\n",
      "Leo,M,147\n",
      "Julius,M,143\n",
      "Ray,M,142\n",
      "Sidney,M,142\n",
      "Bernard,M,140\n",
      "Dan,M,140\n",
      "Jerry,M,136\n",
      "Calvin,M,134\n",
      "Perry,M,134\n",
      "Dave,M,131\n",
      "Anthony,M,130\n",
      "Eddie,M,129\n",
      "Amos,M,128\n",
      "Dennis,M,128\n",
      "Clifford,M,127\n",
      "Leroy,M,124\n",
      "Wesley,M,123\n",
      "Alonzo,M,122\n",
      "Garfield,M,122\n",
      "Franklin,M,120\n",
      "Emil,M,119\n",
      "Leon,M,118\n",
      "Nathan,M,114\n",
      "Harold,M,113\n",
      "Matthew,M,113\n",
      "Levi,M,112\n",
      "Moses,M,111\n",
      "Everett,M,110\n",
      "Lester,M,109\n",
      "Winfield,M,108\n",
      "Adam,M,104\n",
      "Lloyd,M,104\n",
      "Mack,M,104\n",
      "Fredrick,M,103\n",
      "Jay,M,103\n",
      "Jess,M,103\n",
      "Melvin,M,103\n",
      "Noah,M,103\n",
      "Aaron,M,102\n",
      "Alvin,M,102\n",
      "Norman,M,102\n",
      "Gilbert,M,101\n",
      "Elijah,M,100\n",
      "Victor,M,100\n",
      "Gus,M,99\n",
      "Nelson,M,99\n",
      "Jasper,M,98\n",
      "Silas,M,98\n",
      "Jake,M,96\n",
      "Christopher,M,95\n",
      "Mike,M,95\n",
      "Percy,M,94\n",
      "Adolph,M,93\n",
      "Maurice,M,93\n",
      "Cornelius,M,92\n",
      "Felix,M,92\n",
      "Reuben,M,92\n",
      "Wallace,M,91\n",
      "Claud,M,90\n",
      "Roscoe,M,90\n",
      "Sylvester,M,89\n",
      "Earnest,M,88\n",
      "Hiram,M,88\n",
      "Otis,M,88\n",
      "Simon,M,88\n",
      "Willard,M,88\n",
      "Irvin,M,86\n",
      "Mark,M,85\n",
      "Jose,M,84\n",
      "Wilbur,M,82\n",
      "Abraham,M,81\n",
      "Virgil,M,81\n",
      "Clinton,M,79\n",
      "Elbert,M,79\n",
      "Leslie,M,79\n",
      "Marshall,M,78\n",
      "Owen,M,78\n",
      "Wiley,M,78\n",
      "Anton,M,77\n",
      "Morris,M,77\n",
      "Manuel,M,75\n",
      "Phillip,M,75\n",
      "Augustus,M,74\n",
      "Emmett,M,74\n",
      "Eli,M,73\n",
      "Nicholas,M,73\n",
      "Wilson,M,72\n",
      "Alva,M,70\n",
      "Harley,M,70\n",
      "Newton,M,70\n",
      "Timothy,M,70\n",
      "Marvin,M,69\n",
      "Ross,M,69\n",
      "Curtis,M,68\n",
      "Edmund,M,67\n",
      "Jeff,M,66\n",
      "Elias,M,65\n",
      "Harrison,M,65\n",
      "Stanley,M,65\n",
      "Columbus,M,64\n",
      "Lon,M,64\n",
      "Ora,M,64\n",
      "Ollie,M,63\n",
      "Pearl,M,62\n",
      "Russell,M,62\n",
      "Solomon,M,62\n",
      "Arch,M,61\n",
      "Asa,M,60\n",
      "Clayton,M,60\n",
      "Enoch,M,60\n",
      "Irving,M,60\n",
      "Mathew,M,60\n",
      "Nathaniel,M,60\n",
      "Scott,M,60\n",
      "Hubert,M,59\n",
      "Lemuel,M,59\n",
      "Andy,M,58\n",
      "Ellis,M,58\n",
      "Emanuel,M,57\n",
      "Joshua,M,57\n",
      "Millard,M,56\n",
      "Vernon,M,56\n",
      "Wade,M,56\n",
      "Cyrus,M,54\n",
      "Miles,M,54\n",
      "Rudolph,M,54\n",
      "Sherman,M,54\n",
      "Austin,M,53\n",
      "Bill,M,53\n",
      "Chas,M,53\n",
      "Lonnie,M,53\n",
      "Monroe,M,53\n",
      "Byron,M,52\n",
      "Edd,M,52\n",
      "Emery,M,52\n",
      "Grant,M,52\n",
      "Jerome,M,52\n",
      "Max,M,52\n",
      "Mose,M,52\n",
      "Steve,M,52\n",
      "Gordon,M,51\n",
      "Abe,M,50\n",
      "Pete,M,50\n",
      "Chris,M,49\n",
      "Clark,M,49\n",
      "Gustave,M,49\n",
      "Orville,M,49\n",
      "Lorenzo,M,48\n",
      "Bruce,M,47\n",
      "Marcus,M,47\n",
      "Preston,M,47\n",
      "Bob,M,46\n",
      "Dock,M,46\n",
      "Donald,M,46\n",
      "Jackson,M,46\n",
      "Cecil,M,45\n",
      "Barney,M,44\n",
      "Delbert,M,44\n",
      "Edmond,M,44\n",
      "Anderson,M,43\n",
      "Christian,M,43\n",
      "Glenn,M,43\n",
      "Jefferson,M,43\n",
      "Luke,M,43\n",
      "Neal,M,43\n",
      "Burt,M,42\n",
      "Ike,M,42\n",
      "Myron,M,42\n",
      "Tony,M,42\n",
      "Conrad,M,41\n",
      "Joel,M,41\n",
      "Matt,M,41\n",
      "Riley,M,41\n",
      "Vincent,M,41\n",
      "Emory,M,40\n",
      "Isaiah,M,40\n",
      "Nick,M,40\n",
      "Ezra,M,39\n",
      "Green,M,39\n",
      "Juan,M,39\n",
      "Clifton,M,38\n",
      "Lucius,M,38\n",
      "Porter,M,38\n",
      "Arnold,M,37\n",
      "Bud,M,37\n",
      "Jeremiah,M,37\n",
      "Taylor,M,37\n",
      "Forrest,M,36\n",
      "Roland,M,36\n",
      "Spencer,M,35\n",
      "Burton,M,34\n",
      "Don,M,34\n",
      "Emmet,M,34\n",
      "Gustav,M,33\n",
      "Louie,M,33\n",
      "Morgan,M,33\n",
      "Ned,M,33\n",
      "Van,M,33\n",
      "Ambrose,M,32\n",
      "Chauncey,M,32\n",
      "Elisha,M,32\n",
      "Ferdinand,M,32\n",
      "General,M,32\n",
      "Julian,M,32\n",
      "Kenneth,M,32\n",
      "Mitchell,M,32\n",
      "Allie,M,31\n",
      "Josh,M,31\n",
      "Judson,M,31\n",
      "Lyman,M,31\n",
      "Napoleon,M,31\n",
      "Pedro,M,31\n",
      "Berry,M,30\n",
      "Dewitt,M,30\n",
      "Ervin,M,30\n",
      "Forest,M,30\n",
      "Lynn,M,30\n",
      "Pink,M,30\n",
      "Ruben,M,30\n",
      "Sanford,M,30\n",
      "Ward,M,30\n",
      "Douglas,M,29\n",
      "Ole,M,29\n",
      "Omer,M,29\n",
      "Ulysses,M,29\n",
      "Walker,M,29\n",
      "Wilbert,M,29\n",
      "Adelbert,M,28\n",
      "Benjiman,M,28\n",
      "Ivan,M,28\n",
      "Jonas,M,28\n",
      "Major,M,28\n",
      "Abner,M,27\n",
      "Archibald,M,27\n",
      "Caleb,M,27\n",
      "Clint,M,27\n",
      "Dudley,M,27\n",
      "Granville,M,27\n",
      "King,M,27\n",
      "Mary,M,27\n",
      "Merton,M,27\n",
      "Antonio,M,26\n",
      "Bennie,M,26\n",
      "Carroll,M,26\n",
      "Freeman,M,26\n",
      "Josiah,M,26\n",
      "Milo,M,26\n",
      "Royal,M,26\n",
      "Dick,M,25\n",
      "Earle,M,25\n",
      "Elza,M,25\n",
      "Emerson,M,25\n",
      "Fletcher,M,25\n",
      "Judge,M,25\n",
      "Laurence,M,25\n",
      "Neil,M,25\n",
      "Roger,M,25\n",
      "Seth,M,25\n",
      "Glen,M,24\n",
      "Hugo,M,24\n",
      "Jimmie,M,24\n",
      "Johnnie,M,24\n",
      "Washington,M,24\n",
      "Elwood,M,23\n",
      "Gust,M,23\n",
      "Harmon,M,23\n",
      "Jordan,M,23\n",
      "Simeon,M,23\n",
      "Wayne,M,23\n",
      "Wilber,M,23\n",
      "Clem,M,22\n",
      "Evan,M,22\n",
      "Frederic,M,22\n",
      "Irwin,M,22\n",
      "Junius,M,22\n",
      "Lafayette,M,22\n",
      "Loren,M,22\n",
      "Madison,M,22\n",
      "Mason,M,22\n",
      "Orval,M,22\n",
      "Abram,M,21\n",
      "Aubrey,M,21\n",
      "Elliott,M,21\n",
      "Hans,M,21\n",
      "Karl,M,21\n",
      "Minor,M,21\n",
      "Wash,M,21\n",
      "Wilfred,M,21\n",
      "Allan,M,20\n",
      "Alphonse,M,20\n",
      "Dallas,M,20\n",
      "Dee,M,20\n",
      "Isiah,M,20\n",
      "Jason,M,20\n",
      "Johnny,M,20\n",
      "Lawson,M,20\n",
      "Lew,M,20\n",
      "Micheal,M,20\n",
      "Orin,M,20\n",
      "Addison,M,19\n",
      "Cal,M,19\n",
      "Erastus,M,19\n",
      "Francisco,M,19\n",
      "Hardy,M,19\n",
      "Lucien,M,19\n",
      "Randolph,M,19\n",
      "Stewart,M,19\n",
      "Vern,M,19\n",
      "Wilmer,M,19\n",
      "Zack,M,19\n",
      "Adrian,M,18\n",
      "Alvah,M,18\n",
      "Bertram,M,18\n",
      "Clay,M,18\n",
      "Ephraim,M,18\n",
      "Fritz,M,18\n",
      "Giles,M,18\n",
      "Grover,M,18\n",
      "Harris,M,18\n",
      "Isom,M,18\n",
      "Jesus,M,18\n",
      "Johnie,M,18\n",
      "Jonathan,M,18\n",
      "Lucian,M,18\n",
      "Malcolm,M,18\n",
      "Merritt,M,18\n",
      "Otho,M,18\n",
      "Perley,M,18\n",
      "Rolla,M,18\n",
      "Sandy,M,18\n",
      "Tomas,M,18\n",
      "Wilford,M,18\n",
      "Adolphus,M,17\n",
      "Angus,M,17\n",
      "Arther,M,17\n",
      "Carlos,M,17\n",
      "Cary,M,17\n",
      "Cassius,M,17\n",
      "Davis,M,17\n",
      "Hamilton,M,17\n",
      "Harve,M,17\n",
      "Israel,M,17\n",
      "Leander,M,17\n",
      "Melville,M,17\n",
      "Merle,M,17\n",
      "Murray,M,17\n",
      "Pleasant,M,17\n",
      "Sterling,M,17\n",
      "Steven,M,17\n",
      "Axel,M,16\n",
      "Boyd,M,16\n",
      "Bryant,M,16\n",
      "Clement,M,16\n",
      "Erwin,M,16\n",
      "Ezekiel,M,16\n",
      "Foster,M,16\n",
      "Frances,M,16\n",
      "Geo,M,16\n",
      "Houston,M,16\n",
      "Issac,M,16\n",
      "Jules,M,16\n",
      "Larkin,M,16\n",
      "Mat,M,16\n",
      "Morton,M,16\n",
      "Orlando,M,16\n",
      "Pierce,M,16\n",
      "Prince,M,16\n",
      "Rollie,M,16\n",
      "Rollin,M,16\n",
      "Sim,M,16\n",
      "Stuart,M,16\n",
      "Wilburn,M,16\n",
      "Bennett,M,15\n",
      "Casper,M,15\n",
      "Christ,M,15\n",
      "Dell,M,15\n",
      "Egbert,M,15\n",
      "Elmo,M,15\n",
      "Fay,M,15\n",
      "Gabriel,M,15\n",
      "Hector,M,15\n",
      "Horatio,M,15\n",
      "Lige,M,15\n",
      "Saul,M,15\n",
      "Smith,M,15\n",
      "Squire,M,15\n",
      "Tobe,M,15\n",
      "Tommie,M,15\n",
      "Wyatt,M,15\n",
      "Alford,M,14\n",
      "Alma,M,14\n",
      "Alton,M,14\n",
      "Andres,M,14\n",
      "Burl,M,14\n",
      "Cicero,M,14\n",
      "Dean,M,14\n",
      "Dorsey,M,14\n",
      "Enos,M,14\n",
      "Howell,M,14\n",
      "Lou,M,14\n",
      "Loyd,M,14\n",
      "Mahlon,M,14\n",
      "Nat,M,14\n",
      "Omar,M,14\n",
      "Oran,M,14\n",
      "Parker,M,14\n",
      "Raleigh,M,14\n",
      "Reginald,M,14\n",
      "Rubin,M,14\n",
      "Seymour,M,14\n",
      "Wm,M,14\n",
      "Young,M,14\n",
      "Benjamine,M,13\n",
      "Carey,M,13\n",
      "Carlton,M,13\n",
      "Eldridge,M,13\n",
      "Elzie,M,13\n",
      "Garrett,M,13\n",
      "Isham,M,13\n",
      "Johnson,M,13\n",
      "Larry,M,13\n",
      "Logan,M,13\n",
      "Merrill,M,13\n",
      "Mont,M,13\n",
      "Oren,M,13\n",
      "Pierre,M,13\n",
      "Rex,M,13\n",
      "Rodney,M,13\n",
      "Ted,M,13\n",
      "Webster,M,13\n",
      "West,M,13\n",
      "Wheeler,M,13\n",
      "Willam,M,13\n",
      "Al,M,12\n",
      "Aloysius,M,12\n",
      "Alvie,M,12\n",
      "Anna,M,12\n",
      "Art,M,12\n",
      "Augustine,M,12\n",
      "Bailey,M,12\n",
      "Benjaman,M,12\n",
      "Beverly,M,12\n",
      "Bishop,M,12\n",
      "Clair,M,12\n",
      "Cloyd,M,12\n",
      "Coleman,M,12\n",
      "Dana,M,12\n",
      "Duncan,M,12\n",
      "Dwight,M,12\n",
      "Emile,M,12\n",
      "Evert,M,12\n",
      "Henderson,M,12\n",
      "Hunter,M,12\n",
      "Jean,M,12\n",
      "Lem,M,12\n",
      "Luis,M,12\n",
      "Mathias,M,12\n",
      "Maynard,M,12\n",
      "Miguel,M,12\n",
      "Mortimer,M,12\n",
      "Nels,M,12\n",
      "Norris,M,12\n",
      "Pat,M,12\n",
      "Phil,M,12\n",
      "Rush,M,12\n",
      "Santiago,M,12\n",
      "Sol,M,12\n",
      "Sydney,M,12\n",
      "Thaddeus,M,12\n",
      "Thornton,M,12\n",
      "Tim,M,12\n",
      "Travis,M,12\n",
      "Truman,M,12\n",
      "Watson,M,12\n",
      "Webb,M,12\n",
      "Wellington,M,12\n",
      "Winfred,M,12\n",
      "Wylie,M,12\n",
      "Alec,M,11\n",
      "Basil,M,11\n",
      "Baxter,M,11\n",
      "Bertrand,M,11\n",
      "Buford,M,11\n",
      "Burr,M,11\n",
      "Cleveland,M,11\n",
      "Colonel,M,11\n",
      "Dempsey,M,11\n",
      "Early,M,11\n",
      "Ellsworth,M,11\n",
      "Fate,M,11\n",
      "Finley,M,11\n",
      "Gabe,M,11\n",
      "Garland,M,11\n",
      "Gerald,M,11\n",
      "Herschel,M,11\n",
      "Hezekiah,M,11\n",
      "Justus,M,11\n",
      "Lindsey,M,11\n",
      "Marcellus,M,11\n",
      "Olaf,M,11\n",
      "Olin,M,11\n",
      "Pablo,M,11\n",
      "Rolland,M,11\n",
      "Turner,M,11\n",
      "Verne,M,11\n",
      "Volney,M,11\n",
      "Williams,M,11\n",
      "Almon,M,10\n",
      "Alois,M,10\n",
      "Alonza,M,10\n",
      "Anson,M,10\n",
      "Authur,M,10\n",
      "Benton,M,10\n",
      "Billie,M,10\n",
      "Cornelious,M,10\n",
      "Darius,M,10\n",
      "Denis,M,10\n",
      "Dillard,M,10\n",
      "Doctor,M,10\n",
      "Elvin,M,10\n",
      "Emma,M,10\n",
      "Eric,M,10\n",
      "Evans,M,10\n",
      "Gideon,M,10\n",
      "Haywood,M,10\n",
      "Hilliard,M,10\n",
      "Hosea,M,10\n",
      "Lincoln,M,10\n",
      "Lonzo,M,10\n",
      "Lucious,M,10\n",
      "Lum,M,10\n",
      "Malachi,M,10\n",
      "Newt,M,10\n",
      "Noel,M,10\n",
      "Orie,M,10\n",
      "Palmer,M,10\n",
      "Pinkney,M,10\n",
      "Shirley,M,10\n",
      "Sumner,M,10\n",
      "Terry,M,10\n",
      "Urban,M,10\n",
      "Uriah,M,10\n",
      "Valentine,M,10\n",
      "Waldo,M,10\n",
      "Warner,M,10\n",
      "Wong,M,10\n",
      "Zeb,M,10\n",
      "Abel,M,9\n",
      "Alden,M,9\n",
      "Archer,M,9\n",
      "Avery,M,9\n",
      "Carson,M,9\n",
      "Cullen,M,9\n",
      "Doc,M,9\n",
      "Eben,M,9\n",
      "Elige,M,9\n",
      "Elizabeth,M,9\n",
      "Elmore,M,9\n",
      "Ernst,M,9\n",
      "Finis,M,9\n",
      "Freddie,M,9\n",
      "Godfrey,M,9\n",
      "Guss,M,9\n",
      "Hamp,M,9\n",
      "Hermann,M,9\n",
      "Isadore,M,9\n",
      "Isreal,M,9\n",
      "Jones,M,9\n",
      "June,M,9\n",
      "Lacy,M,9\n",
      "Lafe,M,9\n",
      "Leland,M,9\n",
      "Llewellyn,M,9\n",
      "Ludwig,M,9\n",
      "Manford,M,9\n",
      "Maxwell,M,9\n",
      "Minnie,M,9\n",
      "Obie,M,9\n",
      "Octave,M,9\n",
      "Orrin,M,9\n",
      "Ossie,M,9\n",
      "Oswald,M,9\n",
      "Park,M,9\n",
      "Parley,M,9\n",
      "Ramon,M,9\n",
      "Rice,M,9\n",
      "Stonewall,M,9\n",
      "Theo,M,9\n",
      "Tillman,M,9\n",
      "Addie,M,8\n",
      "Aron,M,8\n",
      "Ashley,M,8\n",
      "Bernhard,M,8\n",
      "Bertie,M,8\n",
      "Berton,M,8\n",
      "Buster,M,8\n",
      "Butler,M,8\n",
      "Carleton,M,8\n",
      "Carrie,M,8\n",
      "Clara,M,8\n",
      "Clarance,M,8\n",
      "Clare,M,8\n",
      "Crawford,M,8\n",
      "Danial,M,8\n",
      "Dayton,M,8\n",
      "Dolphus,M,8\n",
      "Elder,M,8\n",
      "Ephriam,M,8\n",
      "Fayette,M,8\n",
      "Felipe,M,8\n",
      "Fernando,M,8\n",
      "Flem,M,8\n",
      "Florence,M,8\n",
      "Ford,M,8\n",
      "Harlan,M,8\n",
      "Hayes,M,8\n",
      "Henery,M,8\n",
      "Hoy,M,8\n",
      "Huston,M,8\n",
      "Ida,M,8\n",
      "Ivory,M,8\n",
      "Jonah,M,8\n",
      "Justin,M,8\n",
      "Lenard,M,8\n",
      "Leopold,M,8\n",
      "Lionel,M,8\n",
      "Manley,M,8\n",
      "Marquis,M,8\n",
      "Marshal,M,8\n",
      "Mart,M,8\n",
      "Odie,M,8\n",
      "Olen,M,8\n",
      "Oral,M,8\n",
      "Orley,M,8\n",
      "Otha,M,8\n",
      "Press,M,8\n",
      "Price,M,8\n",
      "Quincy,M,8\n",
      "Randall,M,8\n",
      "Rich,M,8\n",
      "Richmond,M,8\n",
      "Romeo,M,8\n",
      "Russel,M,8\n",
      "Rutherford,M,8\n",
      "Shade,M,8\n",
      "Shelby,M,8\n",
      "Solon,M,8\n",
      "Thurman,M,8\n",
      "Tilden,M,8\n",
      "Troy,M,8\n",
      "Woodson,M,8\n",
      "Worth,M,8\n",
      "Aden,M,7\n",
      "Alcide,M,7\n",
      "Alf,M,7\n",
      "Algie,M,7\n",
      "Arlie,M,7\n",
      "Bart,M,7\n",
      "Bedford,M,7\n",
      "Benito,M,7\n",
      "Billy,M,7\n",
      "Bird,M,7\n",
      "Birt,M,7\n",
      "Bruno,M,7\n",
      "Burley,M,7\n",
      "Chancy,M,7\n",
      "Claus,M,7\n",
      "Cliff,M,7\n",
      "Clovis,M,7\n",
      "Connie,M,7\n",
      "Creed,M,7\n",
      "Delos,M,7\n",
      "Duke,M,7\n",
      "Eber,M,7\n",
      "Eligah,M,7\n",
      "Elliot,M,7\n",
      "Elton,M,7\n",
      "Emmitt,M,7\n",
      "Gene,M,7\n",
      "Golden,M,7\n",
      "Hal,M,7\n",
      "Hardin,M,7\n",
      "Harman,M,7\n",
      "Hervey,M,7\n",
      "Hollis,M,7\n",
      "Ivey,M,7\n",
      "Jennie,M,7\n",
      "Len,M,7\n",
      "Lindsay,M,7\n",
      "Lonie,M,7\n",
      "Lyle,M,7\n",
      "Mac,M,7\n",
      "Mal,M,7\n",
      "Math,M,7\n",
      "Miller,M,7\n",
      "Orson,M,7\n",
      "Osborne,M,7\n",
      "Percival,M,7\n",
      "Pleas,M,7\n",
      "Ples,M,7\n",
      "Rafael,M,7\n",
      "Raoul,M,7\n",
      "Roderick,M,7\n",
      "Rose,M,7\n",
      "Shelton,M,7\n",
      "Sid,M,7\n",
      "Theron,M,7\n",
      "Tobias,M,7\n",
      "Toney,M,7\n",
      "Tyler,M,7\n",
      "Vance,M,7\n",
      "Vivian,M,7\n",
      "Walton,M,7\n",
      "Watt,M,7\n",
      "Weaver,M,7\n",
      "Wilton,M,7\n",
      "Adolf,M,6\n",
      "Albin,M,6\n",
      "Albion,M,6\n",
      "Allison,M,6\n",
      "Alpha,M,6\n",
      "Alpheus,M,6\n",
      "Anastacio,M,6\n",
      "Andre,M,6\n",
      "Annie,M,6\n",
      "Arlington,M,6\n",
      "Armand,M,6\n",
      "Asberry,M,6\n",
      "Asbury,M,6\n",
      "Asher,M,6\n",
      "Augustin,M,6\n",
      "Auther,M,6\n",
      "Author,M,6\n",
      "Ballard,M,6\n",
      "Blas,M,6\n",
      "Caesar,M,6\n",
      "Candido,M,6\n",
      "Cato,M,6\n",
      "Clarke,M,6\n",
      "Clemente,M,6\n",
      "Colin,M,6\n",
      "Commodore,M,6\n",
      "Cora,M,6\n",
      "Coy,M,6\n",
      "Cruz,M,6\n",
      "Curt,M,6\n",
      "Damon,M,6\n",
      "Davie,M,6\n",
      "Delmar,M,6\n",
      "Dexter,M,6\n",
      "Dora,M,6\n",
      "Doss,M,6\n",
      "Drew,M,6\n",
      "Edson,M,6\n",
      "Elam,M,6\n",
      "Elihu,M,6\n",
      "Eliza,M,6\n",
      "Elsie,M,6\n",
      "Erie,M,6\n",
      "Ernie,M,6\n",
      "Ethel,M,6\n",
      "Ferd,M,6\n",
      "Friend,M,6\n",
      "Garry,M,6\n",
      "Gary,M,6\n",
      "Grace,M,6\n",
      "Gustaf,M,6\n",
      "Hallie,M,6\n",
      "Hampton,M,6\n",
      "Harrie,M,6\n",
      "Hattie,M,6\n",
      "Hence,M,6\n",
      "Hillard,M,6\n",
      "Hollie,M,6\n",
      "Holmes,M,6\n",
      "Hope,M,6\n",
      "Hyman,M,6\n",
      "Ishmael,M,6\n",
      "Jarrett,M,6\n",
      "Jessee,M,6\n",
      "Joeseph,M,6\n",
      "Junious,M,6\n",
      "Kirk,M,6\n",
      "Levy,M,6\n",
      "Mervin,M,6\n",
      "Michel,M,6\n",
      "Milford,M,6\n",
      "Mitchel,M,6\n",
      "Nellie,M,6\n",
      "Noble,M,6\n",
      "Obed,M,6\n",
      "Oda,M,6\n",
      "Orren,M,6\n",
      "Ottis,M,6\n",
      "Rafe,M,6\n",
      "Redden,M,6\n",
      "Reese,M,6\n",
      "Rube,M,6\n",
      "Ruby,M,6\n",
      "Rupert,M,6\n",
      "Salomon,M,6\n",
      "Sammie,M,6\n",
      "Sanders,M,6\n",
      "Soloman,M,6\n",
      "Stacy,M,6\n",
      "Stanford,M,6\n",
      "Stanton,M,6\n",
      "Thad,M,6\n",
      "Titus,M,6\n",
      "Tracy,M,6\n",
      "Vernie,M,6\n",
      "Wendell,M,6\n",
      "Wilhelm,M,6\n",
      "Willian,M,6\n",
      "Yee,M,6\n",
      "Zeke,M,6\n",
      "Ab,M,5\n",
      "Abbott,M,5\n",
      "Agustus,M,5\n",
      "Albertus,M,5\n",
      "Almer,M,5\n",
      "Alphonso,M,5\n",
      "Alvia,M,5\n",
      "Artie,M,5\n",
      "Arvid,M,5\n",
      "Ashby,M,5\n",
      "Augusta,M,5\n",
      "Aurthur,M,5\n",
      "Babe,M,5\n",
      "Baldwin,M,5\n",
      "Barnett,M,5\n",
      "Bartholomew,M,5\n",
      "Barton,M,5\n",
      "Bernie,M,5\n",
      "Blaine,M,5\n",
      "Boston,M,5\n",
      "Brad,M,5\n",
      "Bradford,M,5\n",
      "Bradley,M,5\n",
      "Brooks,M,5\n",
      "Buck,M,5\n",
      "Budd,M,5\n",
      "Ceylon,M,5\n",
      "Chalmers,M,5\n",
      "Chesley,M,5\n",
      "Chin,M,5\n",
      "Cleo,M,5\n",
      "Crockett,M,5\n",
      "Cyril,M,5\n",
      "Daisy,M,5\n",
      "Denver,M,5\n",
      "Dow,M,5\n",
      "Duff,M,5\n",
      "Edie,M,5\n",
      "Edith,M,5\n",
      "Elick,M,5\n",
      "Elie,M,5\n",
      "Eliga,M,5\n",
      "Eliseo,M,5\n",
      "Elroy,M,5\n",
      "Ely,M,5\n",
      "Ennis,M,5\n",
      "Enrique,M,5\n",
      "Erasmus,M,5\n",
      "Esau,M,5\n",
      "Everette,M,5\n",
      "Firman,M,5\n",
      "Fleming,M,5\n",
      "Flora,M,5\n",
      "Gardner,M,5\n",
      "Gee,M,5\n",
      "Gorge,M,5\n",
      "Gottlieb,M,5\n",
      "Gregorio,M,5\n",
      "Gregory,M,5\n",
      "Gustavus,M,5\n",
      "Halsey,M,5\n",
      "Handy,M,5\n",
      "Hardie,M,5\n",
      "Harl,M,5\n",
      "Hayden,M,5\n",
      "Hays,M,5\n",
      "Hermon,M,5\n",
      "Hershel,M,5\n",
      "Holly,M,5\n",
      "Hosteen,M,5\n",
      "Hoyt,M,5\n",
      "Hudson,M,5\n",
      "Huey,M,5\n",
      "Humphrey,M,5\n",
      "Hunt,M,5\n",
      "Hyrum,M,5\n",
      "Irven,M,5\n",
      "Isam,M,5\n",
      "Ivy,M,5\n",
      "Jabez,M,5\n",
      "Jewel,M,5\n",
      "Jodie,M,5\n",
      "Judd,M,5\n",
      "Julious,M,5\n",
      "Justice,M,5\n",
      "Katherine,M,5\n",
      "Kelly,M,5\n",
      "Kit,M,5\n",
      "Knute,M,5\n",
      "Lavern,M,5\n",
      "Lawyer,M,5\n",
      "Layton,M,5\n",
      "Leonidas,M,5\n",
      "Lewie,M,5\n",
      "Lillie,M,5\n",
      "Linwood,M,5\n",
      "Loran,M,5\n",
      "Lorin,M,5\n",
      "Mace,M,5\n",
      "Malcom,M,5\n",
      "Manly,M,5\n",
      "Manson,M,5\n",
      "Matthias,M,5\n",
      "Mattie,M,5\n",
      "Merida,M,5\n",
      "Miner,M,5\n",
      "Montgomery,M,5\n",
      "Moroni,M,5\n",
      "Murdock,M,5\n",
      "Myrtle,M,5\n",
      "Nate,M,5\n",
      "Nathanial,M,5\n",
      "Nimrod,M,5\n",
      "Nora,M,5\n",
      "Norval,M,5\n",
      "Nova,M,5\n",
      "Orion,M,5\n",
      "Orla,M,5\n",
      "Orrie,M,5\n",
      "Payton,M,5\n",
      "Philo,M,5\n",
      "Phineas,M,5\n",
      "Presley,M,5\n",
      "Ransom,M,5\n",
      "Reece,M,5\n",
      "Rene,M,5\n",
      "Roswell,M,5\n",
      "Rowland,M,5\n",
      "Sampson,M,5\n",
      "Samual,M,5\n",
      "Santos,M,5\n",
      "Schuyler,M,5\n",
      "Sheppard,M,5\n",
      "Spurgeon,M,5\n",
      "Starling,M,5\n",
      "Sylvanus,M,5\n",
      "Theadore,M,5\n",
      "Theophile,M,5\n",
      "Tilmon,M,5\n",
      "Tommy,M,5\n",
      "Unknown,M,5\n",
      "Vann,M,5\n",
      "Wes,M,5\n",
      "Winston,M,5\n",
      "Wood,M,5\n",
      "Woodie,M,5\n",
      "Worthy,M,5\n",
      "Wright,M,5\n",
      "York,M,5\n",
      "Zachariah,M,5\n"
     ]
    }
   ],
   "source": [
    "!more /e datasets\\babynames\\yob1880.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The file is in a comma-seperated form and so can be loaded into a DataFrame with 'pandas.read_csv'.\n",
    "#### The file only contains names that have at least 5 occurences in each year. So for simplicity, we can use sum of births by sex as total number of births each year.\n",
    "#### Since data is split into files by year, we need to assemble them all into a single DataFrame using 'pandas.concat' and add a 'year' field.\n",
    "#### concat glues DataFrame objects row-wise by default. Also, we do not need the original row numbers, so we will pass 'ignore_index=True' when concatenating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births\n",
       "0       Mary   F    7065\n",
       "1       Anna   F    2604\n",
       "2       Emma   F    2003\n",
       "3  Elizabeth   F    1939\n",
       "4     Minnie   F    1746"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880 = pd.read_csv('datasets/babynames/yob1880.txt',\n",
    "                       names=['name', 'sex', 'births'])\n",
    "names1880[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110493\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        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"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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 in a single DataFrame\n",
    "names = pd.concat(pieces, ignore_index=True)\n",
    "names[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With a large DataFrame in hand, we can aggregate the data by year and sex level using either 'groupby' or 'pivot_table'.\n",
    "#### Next, we will insert a column 'prop' which contains fraction of babies havin a name, relative to total number of births. eg - prop of 0.2 means 2 out of every 100 babies were given that name.\n",
    "#### It is always good to have a sanity check while doing group operations like this. eg - checking the 'prop' column sums to 1 within all groups. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1896468</td>\n",
       "      <td>2050234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1916888</td>\n",
       "      <td>2069242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1883645</td>\n",
       "      <td>2032310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1827643</td>\n",
       "      <td>1973359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1759010</td>\n",
       "      <td>1898382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex         F        M\n",
       "year                  \n",
       "2006  1896468  2050234\n",
       "2007  1916888  2069242\n",
       "2008  1883645  2032310\n",
       "2009  1827643  1973359\n",
       "2010  1759010  1898382"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births = names.pivot_table('births', index='year',\n",
    "                                columns = 'sex', aggfunc=sum)\n",
    "\n",
    "total_births.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2039d3dc198>"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_births.plot(title='Total births by sex and year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.017342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.016177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.015540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year      prop\n",
       "0       Mary   F    7065  1880  0.077643\n",
       "1       Anna   F    2604  1880  0.028618\n",
       "2       Emma   F    2003  1880  0.022013\n",
       "3  Elizabeth   F    1939  1880  0.021309\n",
       "4     Minnie   F    1746  1880  0.019188\n",
       "5   Margaret   F    1578  1880  0.017342\n",
       "6        Ida   F    1472  1880  0.016177\n",
       "7      Alice   F    1414  1880  0.015540\n",
       "8     Bertha   F    1320  1880  0.014507\n",
       "9      Sarah   F    1288  1880  0.014155"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_prop(group):\n",
    "    group['prop'] = group.births / group.births.sum()\n",
    "    return group\n",
    "\n",
    "names = names.groupby(['year', 'sex']).apply(add_prop)\n",
    "names[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex\n",
       "1880  F      1.0\n",
       "      M      1.0\n",
       "1881  F      1.0\n",
       "      M      1.0\n",
       "1882  F      1.0\n",
       "            ... \n",
       "2008  M      1.0\n",
       "2009  F      1.0\n",
       "      M      1.0\n",
       "2010  F      1.0\n",
       "      M      1.0\n",
       "Name: prop, Length: 262, dtype: float64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check\n",
    "names.groupby(['year', 'sex']).prop.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We will extract a subset of top 1000 names for each sex/year combination for further analysis.\n",
    "#### This can be done by a group operation or with a more manual approach.\n",
    "#### The final dataset is a bit smaller and will be used for further investigations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group operation approach\n",
    "def get_top1000(group):\n",
    "    return group.sort_values(by='births', ascending=False)[:1000]\n",
    "\n",
    "grouped = names.groupby(['year', 'sex'])\n",
    "top1000 = grouped.apply(get_top1000)\n",
    "# Drop group index as it is not needed\n",
    "top1000.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\npieces=[]\\nfor year, group in names.groupby(['year', 'sex']):\\n    pieces.append(group.sort_values(by='births', ascending=False)[:1000])\\n    \\ntop1000 = pd.concat(pieces, ignore_index=True)\\n\""
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# More manual approach\n",
    "'''\n",
    "pieces=[]\n",
    "for year, group in names.groupby(['year', 'sex']):\n",
    "    pieces.append(group.sort_values(by='births', ascending=False)[:1000])\n",
    "    \n",
    "top1000 = pd.concat(pieces, ignore_index=True)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.077643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.028618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.022013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.021309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.019188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Margaret</td>\n",
       "      <td>F</td>\n",
       "      <td>1578</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.017342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ida</td>\n",
       "      <td>F</td>\n",
       "      <td>1472</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.016177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alice</td>\n",
       "      <td>F</td>\n",
       "      <td>1414</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.015540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bertha</td>\n",
       "      <td>F</td>\n",
       "      <td>1320</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sarah</td>\n",
       "      <td>F</td>\n",
       "      <td>1288</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.014155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name sex  births  year      prop\n",
       "0       Mary   F    7065  1880  0.077643\n",
       "1       Anna   F    2604  1880  0.028618\n",
       "2       Emma   F    2003  1880  0.022013\n",
       "3  Elizabeth   F    1939  1880  0.021309\n",
       "4     Minnie   F    1746  1880  0.019188\n",
       "5   Margaret   F    1578  1880  0.017342\n",
       "6        Ida   F    1472  1880  0.016177\n",
       "7      Alice   F    1414  1880  0.015540\n",
       "8     Bertha   F    1320  1880  0.014507\n",
       "9      Sarah   F    1288  1880  0.014155"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing Naming Trends\n",
    "#### With the dataset of top 1000 names in hand, we can start analyzing various naming trends that we find interesting.\n",
    "#### Splitting the names into boy and girl portions is easy. But getting a time Series (eg - number of babies with a certain name each year) will require data munging.\n",
    "#### We will first form a pivot table of total number of births by year and name. We can then plot this data with the 'plot' method. We will only focus on a few names.\n",
    "#### We can conclude that the names that we plotted have grown out of favor. But there can be other factors as we will see later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "boys = top1000[top1000.sex == 'M']\n",
    "girls = top1000[top1000.sex == 'F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 131 entries, 1880 to 2010\n",
      "Columns: 6868 entries, Aaden to Zuri\n",
      "dtypes: float64(6868)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "total_births = top1000.pivot_table('births', index='year',\n",
    "                                  columns = 'name',\n",
    "                                  aggfunc=sum)\n",
    "\n",
    "total_births.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000203B283C4E0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E0B57A58>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E0ABA0B8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E0A90B38>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": {},
   "source": [
    "### Measuring Increase in Naming Diversity\n",
    "#### From previous analyses, we concluded that fewer parents are choosing common names for their children.\n",
    "#### We can explore this hypothesis further and confirm it with data. One measure is the proportion of births represented by top 1000 names, which we aggregate and plot by year and sex.\n",
    "#### We can see the plot that there is a decrease in the total proportion of the top 1000 names along the years. Which means that parents are choosing other names for their babies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.998702</td>\n",
       "      <td>0.995646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.997596</td>\n",
       "      <td>0.998566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.993156</td>\n",
       "      <td>0.994539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>0.992251</td>\n",
       "      <td>0.995501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>0.989504</td>\n",
       "      <td>0.995035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>0.988279</td>\n",
       "      <td>0.996697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>0.984241</td>\n",
       "      <td>0.992429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>0.984061</td>\n",
       "      <td>0.994981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex          F         M\n",
       "year                    \n",
       "1880  1.000000  0.997375\n",
       "1881  1.000000  1.000000\n",
       "1882  0.998702  0.995646\n",
       "1883  0.997596  0.998566\n",
       "1884  0.993156  0.994539\n",
       "1885  0.992251  0.995501\n",
       "1886  0.989504  0.995035\n",
       "1887  0.988279  0.996697\n",
       "1888  0.984241  0.992429\n",
       "1889  0.984061  0.994981"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = top1000.pivot_table('prop', index='year',\n",
    "                           columns = 'sex', aggfunc=sum)\n",
    "table[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203df24dc18>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "#### Another interesting measure is the number of distinct names in order of popularity from lowest to highest in the top 50% births.\n",
    "#### It is a little tricky to compute so we will only focus on boys' names. We will sort 'prop' in descending order. \n",
    "#### Then we need to know the most popular names to reach 50%. We can use a for loop but Vectorized NumPy array is more clever.\n",
    "#### Taking the cumulative sum ('cumsum') of the 'prop' and then calling 'searchsorted' returns position in cumulative sum at which 0.5 would need to be inserted to keep it in sorted order.\n",
    "#### When comparing the year 2010, where the number of names that added up to 50% of total names is 117, the year 1990 has just 25 names that make up 50%. This shows an increase in naming diversity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>prop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>260877</th>\n",
       "      <td>Jacob</td>\n",
       "      <td>M</td>\n",
       "      <td>21875</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.011523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260878</th>\n",
       "      <td>Ethan</td>\n",
       "      <td>M</td>\n",
       "      <td>17866</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260879</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>17133</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260880</th>\n",
       "      <td>Jayden</td>\n",
       "      <td>M</td>\n",
       "      <td>17030</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260881</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>16870</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260882</th>\n",
       "      <td>Alexander</td>\n",
       "      <td>M</td>\n",
       "      <td>16634</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260883</th>\n",
       "      <td>Noah</td>\n",
       "      <td>M</td>\n",
       "      <td>16281</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260884</th>\n",
       "      <td>Daniel</td>\n",
       "      <td>M</td>\n",
       "      <td>15679</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260885</th>\n",
       "      <td>Aiden</td>\n",
       "      <td>M</td>\n",
       "      <td>15403</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260886</th>\n",
       "      <td>Anthony</td>\n",
       "      <td>M</td>\n",
       "      <td>15364</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.008093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name sex  births  year      prop\n",
       "260877      Jacob   M   21875  2010  0.011523\n",
       "260878      Ethan   M   17866  2010  0.009411\n",
       "260879    Michael   M   17133  2010  0.009025\n",
       "260880     Jayden   M   17030  2010  0.008971\n",
       "260881    William   M   16870  2010  0.008887\n",
       "260882  Alexander   M   16634  2010  0.008762\n",
       "260883       Noah   M   16281  2010  0.008576\n",
       "260884     Daniel   M   15679  2010  0.008259\n",
       "260885      Aiden   M   15403  2010  0.008114\n",
       "260886    Anthony   M   15364  2010  0.008093"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = boys[boys.year == 2010]\n",
    "df[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260877    0.011523\n",
       "260878    0.020934\n",
       "260879    0.029959\n",
       "260880    0.038930\n",
       "260881    0.047817\n",
       "260882    0.056579\n",
       "260883    0.065155\n",
       "260884    0.073414\n",
       "260885    0.081528\n",
       "260886    0.089621\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum = df.sort_values(by='prop', ascending=False).prop.cumsum()\n",
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Arrays are 0-indexed, so total would be 117\n",
    "prop_cumsum.values.searchsorted(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": "markdown",
   "metadata": {},
   "source": [
    "#### We can apply this operation to all the years for both sexes, by applying groupby on those fields and apply a function returning count for each group.\n",
    "#### The result has 2 time series, one for each sex, indexed by year. This can be inspected better as a plot.\n",
    "#### From the plot we can see that girl names are more diverse than boy names, and continue to be so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>38</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex    F   M\n",
       "year        \n",
       "1880  38  14\n",
       "1881  38  14\n",
       "1882  38  15\n",
       "1883  39  15\n",
       "1884  39  16"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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')\n",
    "\n",
    "diversity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203df199f60>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity.plot(title=\"Number of popular names in top 50%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The \"Last Letter\" Revolution\n",
    "#### Recent research has shown that the distribution of boy names by final letter has changed significantly over last 100 years.\n",
    "#### To verify this, we will aggregate the full dataset by year, sex and final letter.\n",
    "#### The we select 3 years (1910, 1960 and 2010) which span the history and check their first few rows.\n",
    "#### Next, we normailze the table by total births to compute a new table having proportion of total births for each sex ending in each letter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting last letter\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": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>108376.0</td>\n",
       "      <td>691247.0</td>\n",
       "      <td>670605.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>5204.0</td>\n",
       "      <td>28438.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>694.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>3912.0</td>\n",
       "      <td>38859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>15476.0</td>\n",
       "      <td>23125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6750.0</td>\n",
       "      <td>3729.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>22111.0</td>\n",
       "      <td>262112.0</td>\n",
       "      <td>44398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>133569.0</td>\n",
       "      <td>435013.0</td>\n",
       "      <td>313833.0</td>\n",
       "      <td>28655.0</td>\n",
       "      <td>178823.0</td>\n",
       "      <td>129012.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                            M                    \n",
       "year             1910      1960      2010     1910      1960      2010\n",
       "last_letter                                                           \n",
       "a            108376.0  691247.0  670605.0    977.0    5204.0   28438.0\n",
       "b                 NaN     694.0     450.0    411.0    3912.0   38859.0\n",
       "c                 5.0      49.0     946.0    482.0   15476.0   23125.0\n",
       "d              6750.0    3729.0    2607.0  22111.0  262112.0   44398.0\n",
       "e            133569.0  435013.0  313833.0  28655.0  178823.0  129012.0"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')\n",
    "subtable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex  year\n",
       "F    1910     396416.0\n",
       "     1960    2022062.0\n",
       "     2010    1759010.0\n",
       "M    1910     194198.0\n",
       "     1960    2132588.0\n",
       "     2010    1898382.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">F</th>\n",
       "      <th colspan=\"3\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "      <th>1910</th>\n",
       "      <th>1960</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_letter</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.273390</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.002116</td>\n",
       "      <td>0.001834</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.002482</td>\n",
       "      <td>0.007257</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.017028</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>0.001482</td>\n",
       "      <td>0.113858</td>\n",
       "      <td>0.122908</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.336941</td>\n",
       "      <td>0.215133</td>\n",
       "      <td>0.178415</td>\n",
       "      <td>0.147556</td>\n",
       "      <td>0.083853</td>\n",
       "      <td>0.067959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000783</td>\n",
       "      <td>0.004325</td>\n",
       "      <td>0.001188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000374</td>\n",
       "      <td>0.002250</td>\n",
       "      <td>0.009488</td>\n",
       "      <td>0.001404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.051529</td>\n",
       "      <td>0.036224</td>\n",
       "      <td>0.075852</td>\n",
       "      <td>0.045562</td>\n",
       "      <td>0.037907</td>\n",
       "      <td>0.051670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>0.001526</td>\n",
       "      <td>0.039965</td>\n",
       "      <td>0.031734</td>\n",
       "      <td>0.000844</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>0.022628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex                 F                             M                    \n",
       "year             1910      1960      2010      1910      1960      2010\n",
       "last_letter                                                            \n",
       "a            0.273390  0.341853  0.381240  0.005031  0.002440  0.014980\n",
       "b                 NaN  0.000343  0.000256  0.002116  0.001834  0.020470\n",
       "c            0.000013  0.000024  0.000538  0.002482  0.007257  0.012181\n",
       "d            0.017028  0.001844  0.001482  0.113858  0.122908  0.023387\n",
       "e            0.336941  0.215133  0.178415  0.147556  0.083853  0.067959\n",
       "f                 NaN  0.000010  0.000055  0.000783  0.004325  0.001188\n",
       "g            0.000144  0.000157  0.000374  0.002250  0.009488  0.001404\n",
       "h            0.051529  0.036224  0.075852  0.045562  0.037907  0.051670\n",
       "i            0.001526  0.039965  0.031734  0.000844  0.000603  0.022628\n",
       "j                 NaN       NaN  0.000090       NaN       NaN  0.000769"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = subtable / subtable.sum()\n",
    "\n",
    "letter_prop[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With the proportions in hand, we can make bar plots for each sex broken down by year.\n",
    "#### From the plot, we can see that boy names ending in 'n' have increased since 1960s.\n",
    "#### Now we will focus on just 3 letters (d, n and y) on the entire dataset for boy names. We will normalize by year and sex, select a subset of the letters and transpose each column into time series.\n",
    "#### With the time series, we can plot trends over time with 'plot' method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203deffcb70>"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>last_letter</th>\n",
       "      <th>d</th>\n",
       "      <th>n</th>\n",
       "      <th>y</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>0.083055</td>\n",
       "      <td>0.153213</td>\n",
       "      <td>0.075760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.083247</td>\n",
       "      <td>0.153214</td>\n",
       "      <td>0.077451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.085340</td>\n",
       "      <td>0.149560</td>\n",
       "      <td>0.077537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.084066</td>\n",
       "      <td>0.151646</td>\n",
       "      <td>0.079144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.086120</td>\n",
       "      <td>0.149915</td>\n",
       "      <td>0.080405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "last_letter         d         n         y\n",
       "year                                     \n",
       "1880         0.083055  0.153213  0.075760\n",
       "1881         0.083247  0.153214  0.077451\n",
       "1882         0.085340  0.149560  0.077537\n",
       "1883         0.084066  0.151646  0.079144\n",
       "1884         0.086120  0.149915  0.080405"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for trends in boy names ending in d, n and y\n",
    "letter_prop = table / table.sum()\n",
    "\n",
    "dny_ts = letter_prop.loc[['d', 'n', 'y'], 'M'].T\n",
    "dny_ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203deeb6b00>"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dny_ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boy Names that became Girl Names\n",
    "#### A fun trend is looking at names that were more popular with one sex in earlier years but 'changed sexes' in the present. eg - Lesley or Leslie.\n",
    "#### Going to the top1000 DataFrame, we first compute a list of names in the dataset that start with 'lesl'.\n",
    "#### Then we can filter down to the names and sum births grouped by name to see relative frequencies.\n",
    "#### Next, we aggregate by sex and year and normalize within year. This can be plotted as breakdown by sex over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "632     Leslie\n",
       "2294    Lesley\n",
       "4262    Leslee\n",
       "4728     Lesli\n",
       "6103     Lesly\n",
       "dtype: object"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Leslee      1082\n",
       "Lesley     35022\n",
       "Lesli        929\n",
       "Leslie    370429\n",
       "Lesly      10067\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered = top1000[top1000.name.isin(lesley_like)]\n",
    "filtered.groupby('name').births.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex     F   M\n",
       "year         \n",
       "2006  1.0 NaN\n",
       "2007  1.0 NaN\n",
       "2008  1.0 NaN\n",
       "2009  1.0 NaN\n",
       "2010  1.0 NaN"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = filtered.pivot_table('births', index='year',\n",
    "                            columns='sex', aggfunc='sum')\n",
    "table = table.div(table.sum(1), axis=0)\n",
    "\n",
    "table.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203da055cf8>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JSFiWJHcz3L9/n23atKGbmxurVKnCv/zlL9Tr9fTx8WG/fv3Yv39/ent7U6/X88yZM1RKcfjw4czIyKC7uzvffffdPO3l/CIA4MyZMws9bk69kSNHMiwsjF5eXvnmuOdcE2jZsmW+C8aLFi0iAAYGBvLOnTucPXu2KdkBYFxcnOVOkg26f/++6Q9zcHAwn3vuOTZu3Jg1a9aki4uLaWqlq6sr33nnHa3DLdPatWvHunXrslGjRqZPpjnu3LnDJUuWyOylUibJ3Uw3b9409fJyxsj79etHHx8fBgQEsFevXqa6OcMgkydPLnAe+cWLF6mUIgDTTRcFSUlJ4SuvvGKaRplzIfZhS5cuLXRoYe3ataZxzsTERLq4uNDJyYk1atR4rGmTZdnSpUvZokULtm/fnj179uSQIUM4btw4Ll26lFlZWezWrRtr167tMOfDGo4fP87t27fz/v37nDdvXp5lPIQ2JLk/hgsXLnDWrFmm3vO3335r6oHnvv39wYMHfPrpp01lBS1q1LFjR/r7+5vVm7l16xaXLVvGK1eulPg1vPTSS8xZ4VEY5Hz6cfR50cK+mJvc7eqbmIqrTp06GDVqFHQ6HQCgXbt2prLc37Ho5uaGJUuWwMPDAzVq1ECdOnXytfXjjz/it99+g1KqyONWqlQJvXv3ho+PT4lfw6BBgwDArr/N/XG9+OKLcHFxwfLly7UOpUxaunQpli5dqnUYopiU4Q9B6QsNDWVsbKwmxy4KSXh7e+P27du4desWXF1d85Rv2LABt27dQu/evTWKMD+S2LRpEzp06AAnJ/mbnaNbt244cuQILly4YNYfXHu3atUqtGvXDlWrVsWGDRtw/PhxvPnmmyhfvjwAw/so5zw9/fTT8PT0lK9XtDFKqTiSoUXVkyxQAKUUXn31VfTq1StfYgeATp062VRiBwwxh4WFSWJ/SO/evXHp0iXs3btX61A099VXX6Fnz5748MMPAQBbt27FmDFjEBAQgLfeegutWrVCy5YtkZaWhps3b+LgwYPo0KGDxlGLYjNn7MYaP7Y05i7sV0pKCnU6HSdNmqR1KJo5duwY+/btSwDs0aNHnhvotm/fblojqU2bNty4cSNJ8scffyQA7t69W6uwRSFg5pi7DMsIu1e7dm106NAB8+fP1zqUUvfRRx9h4sSJ8PDwQEREBKZMmQIXF5d89ZhrOCYzM9P0iTUzMxPOzs6lGrN4NHOHZeR/Tdg9X19fXLlyReswNDFq1CjUqlUL3bt3h5eXV6H1cl+PcHZ2xpQpU1ChQgVJ7GWY/M8Ju+fr64tjx45pHYYmKlasiMGDBz/WPkopTJ482UoRidIiV9+E3XPEnnt6ejo6duyIX3/9VetQhEYkuQu75+fnhzt37uDOnTtah1Jqli9fjs2bN8v0TwcmyV3YPV9fXwBwmN47SXz55ZcICgpCWFiY1uEIjZiV3JVS4Uqpk0qpM0qpcQWU+yulYpRSB5RSh5VScpuksBmOlty3bt2K2NhYjBo1SnruDqzI5K6U0gH4CkAXAI0B9FNKNX6o2kQAy0g+DaAvgK8tHagQxeVoyX3KlCnw9vZ+7Aupwr6YM1umBYAzJM8BgFLqJwA9AOSefkAAlYyPKwO4askghSgJe07uJLFx40ZUrVoVoaGhIIlhw4ZBr9ejXLlyWocnNGROcvcFcDnX8wQALR+qMwXABqXUKADlAXQsqCGl1FAAQwHA39//cWMVolg8PDxQpUoVJCQkaB2KRaWlpeH5559HXFwcdDodZsyYgcjISLzyyitahyZsgDlj7gUN2j18W2s/APNJ+gF4AcB/lFL52iY5j2QoydDq1as/frRCFJOfn5/d9dw9PDzQunVr/Otf/8JLL72EsWPHon379khLS9M6NGEDzOm5JwConeu5H/IPu/wVQDgAkNyllHIH4AXgD0sEKURJ2dNc9z/++APJyckICgrC7NmzAQB//etfMWfOHGzcuFEuogoA5vXc9wFooJQKVEq5wnDBNOqhOpcAdAAApVQjAO4AEi0ZqBAlYS/JnSTeeusttG7dGrdv3zZtV0ph1KhRiIqKkrF2AcCMnjvJLKXUSAC/AtAB+IHkUaXUNBhWJ4sC8B6Afyml3oFhyOZ1arUimRAF8PX1xY0bN5CVlVUm10sZNWoUVq9eDZK4dOkSPv30U1SqVKnoHYXDMutdTjIaQPRD2yblenwMQJuH9xPCVvj6+kKv1+P69evw8/PTOpzHkp6ejrt376Jq1aoICQlBvXr1MGbMGK3DEjau7HVhhCiG3NMhy1pyd3Nzw7///W+twxBljCR34RByErqtj7snJydj1apVuHLlCvr06YNz586hZs2aaN68udahiTJGkrtwCDk9d1ud657zZRmnTp3Cm2++CQCYPHkyXF1d0aJFC2zbtk3jCEVZIwuHCYfg5eUFV1dXm+u5Z2dnY9iwYRg5ciQAoEWLFjh06BCuX7+OmTNn4tlnn8WsWbM0jlKURZLchUNQSsHHx8fmkvuOHTswd+5c0/deOjk54cknn0TNmjURGRmJmJgYPP3001qHKcogSe7CYdjiXPcjR44AAP7+97/LzUfCoiS5C4fh5+dnc2Pu8fHxqFKliumagBCWIsldOIzAwEBcvHgR2dnZWodicuTIEYSEhEivXViczJYRDqNu3brIzMzE5cuXERAQoHU4AIC33noLrq6uWoch7JAkd+Ew6tWrBwA4d+6czST3gQMHah2CsFMyLCMcRk5yP3v2rMaRGFy7dg3Hjh2zqWEiYT8kuQuH4efnBxcXF5tJ7osWLUJwcDBu3bqldSjCDklyFw5Dp9MhMDDQZpL7kSNH4OPjg6pVq2odirBDktyFQ6lbt67NJPf4+HiEhIRoHYawU5LchUOpV68ezp49C62/biArKwvHjh1DkyZNNI1D2C9J7sKh1KtXD7dv30ZycrKmcZw9exbp6enScxdWI1MhhUPJPWOmWrVqmsXh4+OD1atXo1mzZprFIOyb9NyFQ7GF6ZCpqalwcnJCt27d4OPjo1kcwr5JchcOJTAwEIB2yT0uLg7NmjVDRESEJscXjkOSu3AoHh4eqFWrlibJ/ddff8Vzzz2HrKws0xdyCGEtktyFw6lXrx7OnTtXqsdcuXIlunfvjieeeAL79u1Dq1atSvX4wvFIchcOJ2c6ZGm5du0aXn/9dTzzzDP4/fffUbNmzVI7tnBcktyFw6lXrx6uXLmC+/fvl8rxatWqhRMnTmDdunWoUqVKqRxTCEnuwuHUrVsXQOlcVE1JSQFJ+Pr6olKlSlY/nhA5JLkLh9OyZUsAwO+//27V45BE586d8dprr1n1OEIURJK7cDj169dH/fr1sX79eqse57///S/27duHDh06WPU4QhREkrtwSOHh4YiJicGDBw+s0j5JzJgxAw0aNJAv5BCakOQuHFJ4eDjS0tKwbds2q7S/c+dO7Nu3D2PGjIFOp7PKMYR4FFlbRjik9u3bw83NDevXr0dYWJjF2583bx48PT0xaNAgi7ctiiczMxMJCQlW+7Rmae7u7qYvmCkOpdXSp6GhoYyNjdXk2EIAQKdOnXDlyhUcPXrU4m3fu3cP8fHxpou3Qnvnz59HxYoVUa1aNSiltA7nkUgiKSkJd+7cMS2ZkUMpFUcytKg2ZFhGOKzw8HAcO3YMly5dsnjb5cuXl8RuYx48eFAmEjsAKKVQrVq1En3KkOQuHFaXLl0AGGa1WFL79u2xbt06i7YpLKMsJPYcJY3VrOSulApXSp1USp1RSo0rpE4fpdQxpdRRpdTiEkUlRCkICgpCq1atMHbsWGzYsMEibSYmJmLLli04ceKERdoToriKTO5KKR2ArwB0AdAYQD+lVOOH6jQAMB5AG5LBAMZYIVYhLEophTVr1iAoKAg9evTApk2bStxmzvh9cHBwidsSoiTM6bm3AHCG5DmSGQB+AtDjoTpDAHxFMgUASP5h2TCFsI5q1aph06ZNqFu3LoYMGVLi9iS526d79+6ha9eueOqppxASEoKlS5ciLi4O7dq1Q/PmzdG5c2dcu3YNWVlZpgXiAGD8+PGYMGGCJjGbk9x9AVzO9TzBuC23JwA8oZTaoZTarZQKt1SAQlibl5cX+vTpgwsXLiA9Pb1EbR09ehSVK1eWb1iyM+vXr4ePjw8OHTqE+Ph4hIeHY9SoUVixYgXi4uLwxhtvYMKECXB2dsb8+fMxfPhwbNy4EevXr8fkyZM1idmcee4Fjeo/PH/SGUADAO0B+AHYppQKIZmapyGlhgIYCgD+/v6PHawQ1pIz3ezixYt44oknit1OhQoV0LFjxzJ14U4UrUmTJoiMjMTf/vY3dOvWDZ6enoiPjzfdI5GdnY1atWoBMHxqGzhwILp3745du3bB1dVVk5jNSe4JAGrneu4H4GoBdXaTzARwXil1EoZkvy93JZLzAMwDDPPcixu0EJaWk9zPnz9fouT+ySefWCokYUOeeOIJxMXFITo6GuPHj0dYWBiCg4Oxa9euAusfOXIEVapUwY0bN0o50v8xZ1hmH4AGSqlApZQrgL4Aoh6q818AfwIApZQXDMM0pftVN0KUQEBAAADgwoULmsYhbNPVq1fh4eGBAQMGIDIyEnv27EFiYqIpuWdmZpqut/z8889ISkrC1q1bMXr0aKSmpj6qaaspsudOMkspNRLArwB0AH4geVQpNQ1ALMkoY1knpdQxANkA3ieZZM3AhbAkHx8fuLi44Pz588VuIyYmBkOHDsWKFSvw1FNPWTA6obUjR47g/fffh5OTE1xcXPDNN9/A2dkZo0ePxq1bt5CVlYUxY8agZs2aGDduHDZv3ozatWtj5MiRiIiIwIIFC0o9ZrPWliEZDSD6oW2Tcj0mgHeNP0KUOTqdDnXq1ClRcj9y5AjOnDmDGjVqWDAyYQs6d+6Mzp0759u+devWfNtOnTplejx69GirxvUocoeqEEYBAQElGpY5evQoqlatCm9vb8sFJUQxSXIXwigwMLBEPfejR48iODhYZsoImyDJXQijwMBAJCYm4u7du4+9L0lTchfCFkhyF8IoZ8bMxYsXH3vfBw8eoFevXujYsaOFoxKieCS5C2GUe667uWJiYjBy5EiUK1cO3333HXr16mWt8IR4LJLchTDKSe7mXlSdO3cuwsLCEB0dXayhHCGsSZK7EEY1atRAuXLliuy537t3D6NHj8awYcPQqVMnHDx4EBUqVCilKEVZptPp0LRpU9OPNW+ak+9QFcJIKYWAgIAik3t4eDi2b9+O0aNH47PPPoOzs/waCfOUK1cOBw8eLJVjybtSiFwCAwNNvalr166hYsWK+XrlkyZNgpubG55//nkNIhTCPJLchcglMDAQO3fuRGZmJoKDg5GamoqoqChcvHgRN27cwNSpU00rAQrxuO7fv4+mTZsCMLzXVq1aZbVjSXIXIpeAgACkpqbi7t27+O677zBgwAB0794dANCjRw/o9XrodDqNoxSW0L59+3zb+vTpg7fffhtpaWl44YUX8pW//vrreP3113Hz5k28/PLLecpyvqDjUWRYRgiN5MyYOXfuHHr27InU1FR8/PHHuHTpEubMmSOJXZQZyrDmV+kLDQ1lbGysJscWojAXL15EQEAAunbtimHDhqFbt25ahyQs5Pjx42jUqJGmMVSoUOGxps0WFLNSKo5kaFH7ylRIIXKpU6cOAgMDER0dbdbHbCFslSR3IR4SFBQEkgWOyQpREqV5s5skdyEe4uLiAgByY5Io0yS5C/GQnIXDCvt+TCHKAknuQuSSkZGBzMxM1KhRA5s3b9Y6HCGKTZK7ELm4urri6NGj6Nu3L3bs2IEHDx5g9+7dWLFihdahCfFYJLkLUYCwsDA8ePAAPXv2xLPPPovevXvjiy++0DosIcwmNzEJkcvMmTOxbds2LFq0CDqdDuvXr8fIkSNx9epVvPPOOyhfvjyGDBmidZhCFEl67kLksmfPHpw6dQqVKlXCihUrsGPHDsyaNQuLFy9Gly5d8NZbbyEiIgK3bt3SOlRRBimlMHDgQNPzrKwsVK9e3So3y0lyFyKX8+fPm5YgeOmll9C6dWsAhrH4lStX4u2338bs2bMRFBSENWvWaBmqKIPKly+P+Ph43L9/HwAN8pHnAAAgAElEQVSwceNG+Pr6WuVYktyFyOXChQum5P6wcuXKYc6cOdi7dy+8vb3x4osv4rPPPoNWS3iIsqlLly5Yu3YtAGDJkiXo16+fVY4jyV0Io9u3byM5Odn0RdmFCQ0NxY4dO9CzZ09ERkZiwIAB2LdvnyR5YZa+ffvip59+woMHD3D48GG0bNnSKseRC6pCGN2+fRt/+tOf0KRJkyLrenh4YNmyZZg8eTJmzpyJxYsXIyQkBJs2bULNmjVLIVpREmPGjLH40rtNmzY1a0bVk08+iQsXLmDJkiUFLitsKdJzF8LIz88Pv/32G7p06WJWfScnJ0yfPh3Xr1/H559/jvj4eKxbt87KUQp78OKLLyIyMtJqQzKA9NyFKLEqVapg5MiRGDduHI4ePap1OMIMWt+z8MYbb6By5cpo0qSJ1VYflZ67EEbjxo1DixYtirWvs7MzgoKCJLkLs/j5+SEiIsKqx5DkLoTRsWPH8ODBg2LvHxwcLMldPFJBS/62b9/eKtNqJbkLYfSoaZDmCA4OxqVLl3Dnzh0LRiVE8UhyFwIASZw/f77IaZCPEhwcDMDwCUAIrZmV3JVS4Uqpk0qpM0qpcY+o97JSikqpIr/fTwhbkpSUhLt375a45w5AhmaETSgyuSuldAC+AtAFQGMA/ZRSjQuoVxHAaAB7LB2kENaWmZmJ1157DaGhxe+XBAYGwt3dXZK7DStLN5qVNFZzeu4tAJwheY5kBoCfAPQooN50AJ8AKP4VKSE0UqtWLSxYsABt27Ytdhs6nQ6NGzeW5G6j3N3dkZSUVCYSPEkkJSXB3d292G2YM8/dF8DlXM8TAOS5X1Yp9TSA2iTXKKUiix2NEBpJT0+Hq6srlFIlaic4OBi//fabhaISluTn54eEhAQkJiZqHYpZ3N3d4efnV+z9zUnuBb3bTX/6lFJOAD4H8HqRDSk1FMBQAPD39zcvQiGsTK/Xo2fPnjhy5AguXbpUoraCg4Pxn//8B6mpqahSpYqFIhSW4OLiUqJrKmWNOcMyCQBq53ruB+BqrucVAYQA+F0pdQFAKwBRBV1UJTmPZCjJ0OrVqxc/aiEsRK/XY+jQoYiOjsawYcNK3J7MmBG2wpzkvg9AA6VUoFLKFUBfAFE5hSRvkfQiGUAyAMBuAC+SjLVKxEJYUEREBL7//ntMmDAB48ePL3F7MmNG2IoikzvJLAAjAfwK4DiAZSSPKqWmKaVetHaAQljLjRs3MGfOHLz11luYPn16icfbAaBOnTooX768xVccFOJxmTXPnWQ0ySdI1iP5D+O2SSSjCqjbXnrtoizIysrC6NGjMXLkSIskdsCwUmR4eDiWLFmCtLQ0i7QpRHHIHarCYfn6+uLLL79ESEiIRdsdM2YMUlJSsHDhQou2K8TjkOQuHFJ2djZ2796N7Oxsi7fdpk0bhIaG4osvvoBer7d4+0KYQ5K7cEhxcXFo3bo1VqxYYfG2lVJ45513cPLkSaxfv97i7QthDknuwiGtW7cOSil07NjRKu337t0bvr6++Oc//2mV9oUoiiR34ZDWr1+PFi1aoFq1alZp38XFBREREdi8eTN27NhhlWMI8SiS3IXDSUpKwp49exAeHm7V44wYMQLe3t4YP358mVjPRNgXSe7C4Wzfvh0kERYWZtXjeHh4YNKkSdi2bZuMvYtSp7TqUYSGhjI2VqbDi9J3+/ZtHD58GM2bN0e5cuWseqyMjAw0atQIFStWxP79++HkJP0pUTJKqTiSRa5NLe804XAqVaqEtm3bWj2xA4CrqyumTZuGQ4cO4W9/+5sMz4hSY86qkELYlcWLF6N69epWH5bJ8eqrr2Lnzp349NNPcefOHXz11VfQ6XSlcmzhuCS5C4czadIkhIaGllpyV0phzpw5qFy5MmbMmIEbN27g3//+tywJLKxKhmWEQ8nKysLFixdRr169Uj2uUgofffQRvvjiC6xZswbNmzdHXFxcqcYgHIskd+FQLl26hKysrFJP7jkiIiKwdetWZGRkoFOnTlZZ/kAIQJK7cDBnz54FANStW1ezGFq3bo3p06cjOTkZZ86c0SwOYd8kuQuHkpPcteq558hZiTI+Pl7TOIT9kuQuHMqQIUNw/vx5+Pr6ahpH48aNoZTCkSNHNI1D2C+ZLSMcik6nQ0BAgNZhwMPDA/Xq1ZOeu7Aa6bkLh/Lhhx/i559/1joMAECTJk0kuQurkeQuHAZJfPzxx/j999+1DgWAYdz99OnTuH//vtahCDskyV04jMTERNy9e1fzi6k5mjRpAr1ejxMnTmgdirBDktyFw7CVmTI5cmbMyEVVYQ2S3IXDOHfuHADbSe4NGjSAq6urjLsLq5DkLhxGYmIinJ2dERgYqHUoAABnZ2c0atRIeu7CKiS5C4cxZswYpKWlwd3dXetQTEJCQqTnLqxCkrtwKC4uLlqHkEeTJk2QkJCA1NRUrUMRdkaSu3AYw4cPx3fffad1GHnkXFSNiorSOBJhbyS5C4exaNEimxsCadu2LZ544gkMGjQI3bp1w+7du6HX6wEAhw8fxscff4ykpCSNoxRlkSw/IBzC7du3cffuXc3XlHlY5cqVcfjwYcyaNQvTp09H69at4e3tDW9vbxw8eBAAcPLkSfzwww8aRyrKGum5C4eQkJAAADaX3AHAzc0N77//Pi5evIhFixbh+eefh4eHB7788ksMGzYM8+fPx6FDh7QOU5Qx0nMXDuHKlSsAAD8/P40jKZynpyf69++P/v37m7alpKRg2bJliIyMxIYNG6CU0jBCUZZIz104hPT0dPj6+tp0ci+Ip6cnJk2ahE2bNmHGjBn48ccfsWvXLq3DEmWAIqnJgUNDQxkbG6vJsYUoSzIyMhAaGprnZqeNGzeiY8eOGkYltKKUiiMZWlQ9s3ruSqlwpdRJpdQZpdS4AsrfVUodU0odVkptVkrVKU7QQoj8XF1dsXfvXpw4cQLHjx9HUFAQBg8eLHPjxSMVmdyVUjoAXwHoAqAxgH5KqcYPVTsAIJTkkwBWAPjE0oEKURLvvfceRo0apXUYxebu7o6GDRsiKCgICxcuxLVr1xAREZGnzqFDh/DVV19Bq0/jwraYc0G1BYAzJM8BgFLqJwA9ABzLqUAyJlf93QAGWDJIIUpqy5YtqF69utZhWMQzzzyDCRMmYNq0aXB1dcWECROwYcMGjB49Gunp6ahTpw66deumdZhCY+Ykd18Al3M9TwDQ8hH1/wpgXUmCEsLSrly5gqZNm2odhsVMnDgRycnJmDdvHn744Qfo9XqEhYXhzJkzmDJlCrp27SozaxycOWPuBb1DCvzcp5QaACAUwMxCyocqpWKVUrGJiYnmRylECWRmZuLGjRs2Oce9uFxcXDB79mycPXsWERER+Pjjj7Fu3TpMnDgRcXFxiI6O1jpEobEiZ8sopVoDmEKys/H5eAAgOeOheh0BzAbQjuQfRR1YZsuI0nL58mX4+/tj7ty5GDp0qNbhWFVmZiYaNmyIatWqYe/evdJ7t0OWnC2zD0ADpVSgUsoVQF8AeVY5Uko9DWAugBfNSexClKYHDx6gVatWaNCggdahWJ2LiwsmTpyI2NhYrF+/XutwhIbMmueulHoBwBcAdAB+IPkPpdQ0ALEko5RSmwA0AXDNuMslki8+qk3puQthHZmZmQgMDERwcDB+/fVXrcMRFmZuz92s5QdIRgOIfmjbpFyP5W4KIWyEi4sL3n77bUyYMAHHjx9Ho0aNtA5JaECWHxB278MPP0SrVq20DqNUDRkyBG5ubpgzZ47WoQiNSHIXdu/48eP44w/HuhRUvXp19OvXDwsWLJA7WR2UJHdh965cuWJX0yDNNWrUKNy7dw9fffXVY++7bds2bN++3QpRidIiyV3YvYSEBIdM7s2aNUNYWBgmTpyIdu3aYcuWLWbtt2LFCvzpT39C//79ZSmDMkySu7BrmZmZDttzBwzfzTpr1iycPn0a7du3x+zZs/OUp6Wl4csvv0SPHj0wbdo0zJo1C3379kWFChVw6dIlnD17VqPIRUnJl3UUYsmSJUhNTcXw4cO1DkWUQGpqKl566SWEhYVpHYom3N3dMWrUKLz55pvo378/Ro8ejeTkZLRs2RK//fYb5s+fj8TERNSpUwerV68GSTz77LOYPXs2mjdvjk2bNqF+/fpavwxRDLKeeyFy7uzT6/Vl5i6/jIwMuLq6ah2GsFFZWVl444038J///AeAYcpkWFgYxo8fj7Zt2yIlJQXx8fFo3rw5ypUrh4CAADzzzDNYsWKFxpGL3Cy6nrsjGj9+PADg1KlTebbfuXMHkydPxv3797UIq1B79uyBm5ub2eOqjiAzMxMnT57UOgyb4ezsjPnz52PFihXYuHEjUlJSsHbtWrRt2xaA4VufnnvuOXh4eEAphQ4dOuC3335Ddna2xpGL4pDkDkMSOHHiRJ5tAwcOBADs2LEjz/aRI0fiww8/LPQLiwcPHvxYc6pTU1ORlZX1mBHn98033wAAFi1aVOK27EVMTAyCgoKwYcMGrUOxGU5OTujVqxc6duyI8uXLP7Jux44dkZKSgoMHD5ZSdMKSJLkDmDp1Kho1amR6Ey9YsAD79u2Dp6cndu7caaq3ePFiLFy4EMOGDcPKlSuxd+/ePO3cuXMH8+fPx549e/CoVS9J4t///jf+/Oc/o2rVqpg6dWqB9a5du4b09PQCy5KTk5GZmQnA8P2gq1evBgB4e3ub/8LLuAsXLmDSpEmYMWMG5s6dazoHOZYvX44KFSrg+eef1yjCsq1Dhw4AgE2bNgEwnG/pxZchJDX5ad68OW1FYmIiAXDUqFEkyeDgYHbu3JkbN27kuXPnSJKpqamsXLky27Rpw6SkJCqlOHXq1Dzt/PDDDwTAX3755ZHHO3fuHNu0acPAwEDWq1ePDRs2zFfnwIEDLFeuHPv3719gGy+//DLDw8Op1+t5/PhxBgQEcP369cV5+WXS2rVrWaVKFcKw/DQBsH79+qbyn3/+mdWqVeOrr76qYZRlX0hICNu2bcvhw4cTAKdMmWIq0+v1vHHjhobROSYY1vQqMsc6dHJfvnw5L126RJLs27cvPT09mZCQQAD86KOP8tT98ssvCYCxsbEkycaNG/OFF17IU6ddu3Zs0KAB9Xp9kcfOzMykXq/nrFmzCIBnzpwxld28eZMBAQEEQGdn5wJ/gb744gsC4Ny5c0mS2dnZzM7Opl6v5+3btx/vRJRBv//+O1u2bMmzZ88yLS2Nly9f5qlTp0iSycnJLFeuHAFw1apVGkdato0ZM4YAqJSij48PfXx8mJmZSZL87rvvqNPpePjwYY2jdCyS3M3QqFEjdu/enSS5YcMGAuArr7xCANyxYwfv3r3LuXPncv/+/Tx06BCnT59u2vf111+nl5eXKZHr9XpOmjSJ3377LTds2MBevXoxKysr3zGvX7+eJ1lfvXqV69at44MHD0gakn7Hjh3p5ubG1atX8/jx46a6t2/f5ptvvsnr168zOzubf/7zn6nT6Xjy5ElTneeee45/+ctfLHuibNSj/ogeO3aMs2fPNiUiUTyHDx9mly5duG3bNq5atcr0yTQjI8PUARk6dKjWYToUu0/ut2/f5ttvv83Tp0/nK8vIyOBrr73GQ4cOFbp/zlBMTg89KyuL/v7+BMBy5coxPT2d9+7do7OzM8eNG5dv/2+++YYAePbs2XxlP/30EwFw165d+cree+89enh4FNq7Tk9P56effsqFCxfme71t27alTqczDb9cuHDBNCRx/fp1kuRbb73FihUrMj09vdDXXpZ99913HDZsmOmPoSg9GRkZ9Pb2Zrdu3Th//nwCYKNGjejh4cHk5GStw3MYdp3c9Xo9+/btSwAcO3ZsvvKdO3cSAH18fApt45dffiEAbt261bQtJSWFvXr1YocOHUzbnnnmGQJgXFxcnv33799PLy8vxsTEMD09nWvWrGFGRgZJw7CKk5MTJ0+enGef9PR0enl5sWfPnnm2nz17lhMnTuSdO3fyxZmRkcFevXqxatWq1Ol0XLZsWZ7yn3/+mREREabnOb2rmJiYQl97WZWdnc2GDRuyRYsWWofisP7+97/TycmJderU4VNPPcX9+/cTAP/5z39qHZrDsOvkPmfOHFOv4eOPP85XnjOO3a5du0I/uo8dO5aurq68f/9+vrLcvd6ePXsSACMjI/PU0ev1prY/+OADAmB0dLSpvFWrVmzZsmWefZYsWZKvHklu2rSJANi5c2cuWrQoXzxdu3alTqfj0qVLC3wtud2+fZvOzs58//33i6xrK5KTk/njjz8yOzv7kfXWr19PAAWeI1E6zp49a/q0mPN+bNOmDevXr1/k/5+wDLtN7levXqWbmxu7detW6JtpwIABrFWr1iPbadOmDZ999tkijxcTE8PKlSvzwoULBZbv3buXOp2OgwYNyrN9ypQpBMD4+HiSZGRkJJVSrFu3br6x+PT0dFasWJEA+Prrr+c7RkpKymNdtOrcuTMDAgLKzC/bgAEDCIDffvttvrKcP6Dx8fF84okn6O3tbbdDTmVF165dGRwcbHof53RaIiMjuW3bthINmd24cYMffPABU1NT85WlpqZy7ty5BZY5ErtN7qTh4mdSUhJJw1j5w+N98+bNM42lFzZVKzU11TS7ori+//57AqC3tzdTUlLylJ0+fZqhoaGmsfBly5bxgw8+4NWrVwtsq3fv3vTw8GBCQkKJYiLJLVu2cNWqVQVe0LU0vV7PESNGcMSIEfk+JaWnp3PJkiXcsGFDofsfOHCASimWK1eOlSpV4pUrV0xl165dY6NGjfj7779z9uzZVErlm8UkSt+9e/fyXDNKT09n+/btTT16X19frl27tlht9+nThwD49ttv59l+5MgRNmjQgABYt25d7t27l6mpqVy4cCEXLFhQotdT1th1cs+tcePGhc5lnjdvHnU6XZ6EYUlRUVEEwNWrV5e4revXr5fJKWUrV67M84lDr9ebZhbVqlWLADhp0qRC99+/fz/Dw8O5d+9e+vv7c8uWLaayESNG0NnZ2TRNNCkpqcx8GnFEf/zxB1euXMng4GACYP/+/bl8+XKeP3/erP3XrVtHAAwICKBSivv27SNpmKDg4eFBb29vfvvtt/T396eLiwvd3NxMf1AcacqrwyT3/v3708vLy/RLf/PmTd68eZMkeerUKQLgzJkz8+yzdOlSTps2rcSJQq/Xm45lay5dusTp06dbdVZJamoqa9WqxaZNm5qmHIaFhZl+4cLCwrhmzRpT2datW3nkyBHT/g/39HPqXbt2jWvXrqWLiwuHDRtmtfiFdTx48IATJkygq6ur6b0wcOBA04SDhIQETp06lRMmTODkyZO5fPlyXrlyhYGBgWzYsCH/+OMPent7s3nz5nznnXcIgG3atDF96k1KSuKIESMYERHBbdu2sVmzZqxatapFPvWWBQ6T3BcuXJhnNsuHH35IpRRv3bpFkmzdujUDAgLyJLmuXbsyKCjIIse3VTm9oOL0aE6fPs3vvvuOcXFxj5wn/uqrr9LJyYl79+41bfvxxx/5/fff8/Lly3nqZmdnMzg4mBUrVmRUVBQ/++wzPvfccwW2P3nyZAKgu7u7w/zC2qP79+9z7969HDt2LAGwa9euXLRoET09PQmATk5Oee4wzj3La/HixaZto0aNeuR1lhMnTtDDw4Pt27fnokWLOGvWLH722Wf87LPP+N///tesmwrLEodJ7tevX88zX71Hjx55buffuHFjnt57eno6PT09+eabb1rk+LYqMzOTNWrUYK9evR5Zb926daZrDxkZGZwxY0aej7sPD3ktXbqUDx48oF6vZ8OGDfNMwyzKpUuX+OSTT5raDg8PZ2JiYr56iYmJnDVrFleuXGl228K2ffPNN1RKEQBbtmxpes9lZmZyx44dnDx5Mr/88ktTfb1ezw8++IA//fSTWe3nXP8q6Oe1117jvXv3rPK6tOAwyZ0kmzdvzgYNGjAzM5Pe3t4cOHBgnvKuXbuyTp06vH//Pnv16kUAxb7gU5a89957pjsIcz7J5LZ48WI6Ozub5t1//fXXBMCePXvywIEDXLJkSZ758qtXryYAzpo1iyS5ffv2x75oe/v2bY4bN86h1sERBlFRUfz888+tdtfw6dOneeLECd64cYO3bt1iamoqp0yZQqUUn3rqKdPkhrLOoZJ7bGwsY2JiePnyZQLg7Nmz85RfunSJiYmJHD9+PAHw888/t9ixbdn9+/c5duxYOjk58YMPPiBp6Hl7eXmxbt26VEqxXbt2psT/yy+/FHhx+O7du/z888/p7+/P4OBgmYooypS1a9fS3d2d4eHhdnFB3qGSe47o6GgC4J49ewosv3HjBv/1r39Z/Li2bs+ePbx79y5J8ujRoxw+fDj79+/PyMhIpqWlFbl/zmqXALh9+3ZrhyuExeV8KrWHjp25yd2uvmbvzJkzGDZsGNauXQs3NzeLtu3ISGL48OHw8/PDxIkTtQ5HiMdGEi+99BLWr1+PXbt2oVmzZlqHVGzmfs2eXSV3IYQozM2bN/HUU08hJSUFH374ISIiIqDT6bQO67HJd6gKIUQuXl5e2LNnDzp06ID33nsPzz77LJKTk7UOy2okuQshHIafnx+ioqKwZMkS1KtXD56enlqHZDUyLCOEEGWIDMsIIYQDk+QuhBB2yKzkrpQKV0qdVEqdUUqNK6DcTSm11Fi+RykVYOlAhRBCmK/I5K6U0gH4CkAXAI0B9FNKNX6o2l8BpJCsD+BzAB9bOlAhhBDmM6fn3gLAGZLnSGYA+AlAj4fq9ACwwPh4BYAOSilluTCFEEI8DnOSuy+Ay7meJxi3FViHZBaAWwCqWSJAIYQQj8+c5F5QD/zh+ZPm1IFSaqhSKlYpFZuYmGhOfEIIIYrBnOSeAKB2rud+AK4WVkcp5QygMoB8t36RnEcylGRo9erVixexEEKIIjmbUWcfgAZKqUAAVwD0BfDqQ3WiAAwCsAvAywB+YxF3R8XFxd1USl18/JABAF4AbhZzX61J7NqQ2EtfWY0bsO3Y65hTqcjkTjJLKTUSwK8AdAB+IHlUKTUNhqUnowB8D+A/SqkzMPTY+5rRbrG77kqpWHPu0LJFErs2JPbSV1bjBsp27DnM6bmDZDSA6Ie2Tcr1+AGA3pYNTQghRHHJHapCCGGHympyn6d1ACUgsWtDYi99ZTVuoGzHDkDDVSGFEEJYT1ntuQshhHgEm0nuSqkflFJ/KKXic21rqpTarZQ6aLz5qYVxe2Wl1Gql1CGl1FGl1OBc+wxSSp02/gzSMPanlFK7lFJHjLFWylU23rjI2kmlVOdc2x+5QJuWcSulwpRSccbtcUqpP+fap7lx+xml1KzSWHricc+5sdxfKXVXKRWZa1upnvPixK6UetJYdtRY7m7cbtPnXSnlopRaYNx+XCk1Ptc+pf1er62UijHGcVQpFWHcXlUptdGYLzYqpTyN25XxnJ5RSh1WSjXL1Vap55hiMedbtEvjB8DzAJoBiM+1bQOALsbHLwD43fj47wA+Nj6uDsP0S1cAVQGcM/7raXzsqVHs+wC0Mz5+A8B04+PGAA4BcAMQCOAsDFNMdcbHdY2v5RCAxjYU99MAfIyPQwBcybXPXgCtYbhTeV3O/5mtxJ6rfCWA5QAijc9L/ZwX47w7AzgM4Cnj82oAdGXhvMNwP8xPxsceAC4ACNDovV4LQDPj44oAThl/Fz8BMM64fRz+l1deMJ5TBaAVgD3G7ZrkmOL82EzPneRW5L+rlQByejCV8b87YwmgorGnUsG4XxaAzgA2kkwmmQJgI4BwjWJvCGCr8fFGAL2Mj3vA8IZPJ3kewBkYFmczZ4E2zeImeYBkzvk/CsBdGZZ6rgWgEsldNLz7FwJ4yZpxP27sAKCUegmGX8SjueqX+jkHHjv2TgAOkzxk3DeJZHYZOe8EUF4Z7lovByADwG1o816/RnK/8fEdAMdhWBMr96KHC/C/c9gDwEIa7AZQxXjONckxxWEzyb0QYwDMVEpdBvApgJyPdXMANIIh2R8BEEFSD/MWOSst8QBeND7ujf8t4VBYjLYSe2Fx59YLwAGS6TDEmJCrzObOuVKqPIC/AZj6UH1bOedA4ef9CQBUSv2qlNqvlBpr3G7z5x2GFWLvAbgG4BKAT0kmQ+PzrgzfN/E0gD0AapK8Bhj+AACoYaxm67+nRbL15D4cwDskawN4B4Y7YQHDX8+DAHwANAUwxzjOZ9YCZqXkDQAjlFJxMHwMzDBuLyxGW4m9sLgBAEqpYBjW638rZ1MBbdjaOZ8K4HOSdx+qXxZidwbQFkB/479/UUp1QNmIvQWAbBh+TwMBvKeUqgsNY1dKVYBheG4MyduPqlrANlv6PS2SWXeoamgQgAjj4+UAvjM+Hgzg/4wfR88opc4DCILhr2j7XPv7Afi9VCJ9CMkTMHykhlLqCQBdjUWPWoitqAXarO4RcUMp5QdgFYDXSJ41bk6AIdYcmsQNPDL2lgBeVkp9AqAKAL1S6gGAONjAOQeKfL9sIXnTWBYNw5j3Itj+eX8VwHqSmQD+UErtABAKQ8+31M+7UsoFhsT+I8mfjZtvKKVqkbxmHHb5w7i9sN9Tm8kxRdJ60D/3DwwXW3JfqDkOoL3xcQcAccbH3wCYYnxcE4YFzbxguMhxHoYLHZ7Gx1U1ir2G8V8nGMZD3zA+D0beC6rnYLjA5Gx8HIj/XWQKtqG4qxhj6lVAG/tguOiUc2HvBVs65w/tMwX/u6CqyTl/zPPuCWA/DBcknQFsAtC1LJx3GIbC/m2MrzyAYwCe1OK8G2NYCOCLh7bPRN4Lqp8YH3dF3guqe43bNcsxj/2atQ4g10leAsPYXCYMfx3/CsPH0Djjf/4eAM2NdX1gmElzBIbxvgG52nkDhouUZwAM1jD2CBiuyJ8C8H8w3jBmrD8BhtkCJ5FrhgMMV+hPGbfR1J4AAAIlSURBVMsm2FLcACbCMH56MNdPzi91qPH/4SwM10OULcX+0H5TYEzuWpzzYr5fBsBwITg+J/mUhfMOw2SH5cbYjwF4X8P3elsYhk8O53r/vgDD7KPNAE4b/61qrK9g+HrRszDkmdBcbZV6jinOj9yhKoQQdsjWL6gKIYQoBknuQghhhyS5CyGEHZLkLoQQdkiSuxBC2CFJ7kIIYYckuQtRAkopndYxCFEQSe7CYSilpues4218/g+l1Gil1PtKqX3Gdbun5ir/rzKsXX9UKTU01/a7SqlpSqk9MCy5K4TNkeQuHMn3MKxXBKWUE4C+AG4AaADDIldNATRXSj1vrP8GyeYw3Ak6WilVzbi9PAy337ckub00X4AQ5rL1hcOEsBiSF5RSSUqpp2FYk+gAgGdgWPTqgLFaBRiS/VYYEvpfjNtrG7cnwbDS4crSjF2IxyXJXTia7wC8DsAbwA8wLEg3g+Tc3JWUUu0BdATQmmSaUup3AO7G4gcks0srYCGKQ4ZlhKNZBcM35zwD4FfjzxvGdb6hlPJVStWA4Zu/UoyJPQiGlQGFKDOk5y4cCskMpVQMgFRj73uDUqoRgF3G75e+C8MqjOsBDFNKHYZh9c7dWsUsRHHIqpDCoRgvpO4H0Jvkaa3jEcJaZFhGOAylVGMY1uDeLIld2DvpuQshhB2SnrsQQtghSe5CCGGHJLkLIYQdkuQuhBB2SJK7EELYIUnuQghhh/4f0IjOaSDxpsAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(style={'M': 'k-', 'F': 'k--'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Federal Election Commission Database\n",
    "#### The US Federal Election Comission publishes data on contribution to political campaigns. This includes contributor names, occupation and employer, address and amount.\n",
    "#### We will focus on the 2012 US Presidential election. It can be easily downloaded with 'pandas.read_csv'.\n",
    "#### The objective is to extract informative statistics about donors and patterns in campaign contributions.\n",
    "#### It would be good to include political affiliations, which are not originally included in the data. We can get unique political candidates using 'unique'.\n",
    "#### Then we can indicate party affiliation using a dict mapping and the 'map' method to convert political candidates to political parties. This will be added as a new column 'party'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adity\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2785: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1001731 entries, 0 to 1001730\n",
      "Data columns (total 16 columns):\n",
      "cmte_id              1001731 non-null object\n",
      "cand_id              1001731 non-null object\n",
      "cand_nm              1001731 non-null object\n",
      "contbr_nm            1001731 non-null object\n",
      "contbr_city          1001712 non-null object\n",
      "contbr_st            1001727 non-null object\n",
      "contbr_zip           1001620 non-null object\n",
      "contbr_employer      988002 non-null object\n",
      "contbr_occupation    993301 non-null object\n",
      "contb_receipt_amt    1001731 non-null float64\n",
      "contb_receipt_dt     1001731 non-null object\n",
      "receipt_desc         14166 non-null object\n",
      "memo_cd              92482 non-null object\n",
      "memo_text            97770 non-null object\n",
      "form_tp              1001731 non-null object\n",
      "file_num             1001731 non-null int64\n",
      "dtypes: float64(1), int64(1), object(14)\n",
      "memory usage: 122.3+ MB\n"
     ]
    }
   ],
   "source": [
    "fec = pd.read_csv('datasets/fec/P00000001-ALL.csv')\n",
    "fec.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cmte_id             C00431445\n",
       "cand_id             P80003338\n",
       "cand_nm         Obama, Barack\n",
       "contbr_nm         ELLMAN, IRA\n",
       "contbr_city             TEMPE\n",
       "                    ...      \n",
       "receipt_desc              NaN\n",
       "memo_cd                   NaN\n",
       "memo_text                 NaN\n",
       "form_tp                 SA17A\n",
       "file_num               772372\n",
       "Name: 123456, Length: 16, dtype: object"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.iloc[123456]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachmann, Michelle', 'Romney, Mitt', 'Obama, Barack',\n",
       "       \"Roemer, Charles E. 'Buddy' III\", 'Pawlenty, Timothy',\n",
       "       'Johnson, Gary Earl', 'Paul, Ron', 'Santorum, Rick',\n",
       "       'Cain, Herman', 'Gingrich, Newt', 'McCotter, Thaddeus G',\n",
       "       'Huntsman, Jon', 'Perry, Rick'], dtype=object)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_cands = fec.cand_nm.unique()\n",
    "unique_cands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "parties = {'Bachmann, Michelle': 'Republican',\n",
    "           'Cain, Herman': 'Republican',\n",
    "           'Gingrich, Newt': 'Republican',\n",
    "           'Huntsman, Jon': 'Republican',\n",
    "           'Johnson, Gary Earl': 'Republican',\n",
    "           'McCotter, Thaddeus G': 'Republican',\n",
    "           'Obama, Barack': 'Democrat',\n",
    "           'Paul, Ron': 'Republican',\n",
    "           'Pawlenty, Timothy': 'Republican',\n",
    "           'Perry, Rick': 'Republican',\n",
    "           \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n",
    "           'Romney, Mitt': 'Republican',\n",
    "           'Santorum, Rick': 'Republican'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Democrat      593746\n",
       "Republican    407985\n",
       "Name: party, dtype: int64"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec['party'] = fec.cand_nm.map(parties)\n",
    "\n",
    "fec['party'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Before doing any analysis, we need to consider a few simplifications.\n",
    "#### First, there contributions and refunds (negative contributions in dataset). We will consider only positive contributions.\n",
    "#### Second, we will only consider the two main candidates - Barack Obama and Mitt Romney."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     991475\n",
       "False     10256\n",
       "Name: contb_receipt_amt, dtype: int64"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(fec.contb_receipt_amt > 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "fec = fec[fec.contb_receipt_amt > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "fec_mrbo = fec[fec.cand_nm.isin(['Obama, Barack', 'Romney, Mitt'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Donation Statistics by Occupation and Employer\n",
    "#### Donation by occupation is an oft-studied statistic. eg - lawyers tend to donate to Democrats, while business executives to Republicans.\n",
    "#### We can see this trend in the data. First we will get the total donations by occupations.\n",
    "#### These occupations refer to some basic job types, or are variants of the same thing. We can use a mapping function to clean up by mapping one occupation to another.\n",
    "#### The trick is using 'dict.get' to allow occupations with no mapping to 'pass through'.\n",
    "#### The same technique can be used for employers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RETIRED                                   233990\n",
       "INFORMATION REQUESTED                      35107\n",
       "ATTORNEY                                   34286\n",
       "HOMEMAKER                                  29931\n",
       "PHYSICIAN                                  23432\n",
       "INFORMATION REQUESTED PER BEST EFFORTS     21138\n",
       "ENGINEER                                   14334\n",
       "TEACHER                                    13990\n",
       "CONSULTANT                                 13273\n",
       "PROFESSOR                                  12555\n",
       "Name: contbr_occupation, dtype: int64"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fec.contbr_occupation.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "occ_mapping = {\n",
    "    'INFORMATION REQUESTED PER BEST EFFORTS' : 'NOT PROVIDED',\n",
    "    'INFORMATION REQUESTED' : 'NOT PROVIDED',\n",
    "    'INFORMATION REQUESTED (BEST EFFORTS)' : 'NOT PROVIDED',\n",
    "    'C.E.O.': 'CEO'\n",
    "}\n",
    "\n",
    "# If no mapping, 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": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_mapping = {\n",
    "    'INFORMATION REQUESTED PER BEST EFFORTS' : 'NOT PROVIDED',\n",
    "    'INFORMATION REQUESTED' : 'NOT PROVIDED',\n",
    "    'SELF' : 'SELF-EMPLOYED',\n",
    "    'SELF EMPLOYED' : 'SELF-EMPLOYED'\n",
    "}\n",
    "\n",
    "f = lambda x: emp_mapping.get(x, x)\n",
    "fec.contbr_employer = fec.contbr_employer.map(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can use 'pivot_table' and aggregate by party and occupation, then filter down to subset that donated at least 2 billion overall.\n",
    "#### This data would be easier to look at as a bar plot.\n",
    "#### Next topic of interest is the top donor occupations or top companies that donated to either of the candidates.\n",
    "#### For this we can group by candidate name and use 'nlargest'. Then we aggregate by occupation and employer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>party</th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Republican</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATTORNEY</th>\n",
       "      <td>11141982.97</td>\n",
       "      <td>7477194.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CEO</th>\n",
       "      <td>2074974.79</td>\n",
       "      <td>4211040.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONSULTANT</th>\n",
       "      <td>2459912.71</td>\n",
       "      <td>2544725.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENGINEER</th>\n",
       "      <td>951525.55</td>\n",
       "      <td>1818373.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXECUTIVE</th>\n",
       "      <td>1355161.05</td>\n",
       "      <td>4138850.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOMEMAKER</th>\n",
       "      <td>4248875.80</td>\n",
       "      <td>13634275.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INVESTOR</th>\n",
       "      <td>884133.00</td>\n",
       "      <td>2431768.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAWYER</th>\n",
       "      <td>3160478.87</td>\n",
       "      <td>391224.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANAGER</th>\n",
       "      <td>762883.22</td>\n",
       "      <td>1444532.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOT PROVIDED</th>\n",
       "      <td>4866973.96</td>\n",
       "      <td>20565473.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "party                 Democrat   Republican\n",
       "contbr_occupation                          \n",
       "ATTORNEY           11141982.97   7477194.43\n",
       "CEO                 2074974.79   4211040.52\n",
       "CONSULTANT          2459912.71   2544725.45\n",
       "ENGINEER             951525.55   1818373.70\n",
       "EXECUTIVE           1355161.05   4138850.09\n",
       "HOMEMAKER           4248875.80  13634275.78\n",
       "INVESTOR             884133.00   2431768.92\n",
       "LAWYER              3160478.87    391224.32\n",
       "MANAGER              762883.22   1444532.37\n",
       "NOT PROVIDED        4866973.96  20565473.01"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_occupation = fec.pivot_table('contb_receipt_amt',\n",
    "                               index='contbr_occupation',\n",
    "                               columns='party', aggfunc='sum')\n",
    "\n",
    "over_2mm = by_occupation[by_occupation.sum(1) > 2000000]\n",
    "over_2mm[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20395e32dd8>"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "over_2mm.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "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": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm        contbr_occupation    \n",
       "Obama, Barack  RETIRED                  25305116.38\n",
       "               ATTORNEY                 11141982.97\n",
       "               INFORMATION REQUESTED     4866973.96\n",
       "               HOMEMAKER                 4248875.80\n",
       "               PHYSICIAN                 3735124.94\n",
       "                                           ...     \n",
       "Romney, Mitt   HOMEMAKER                 8147446.22\n",
       "               ATTORNEY                  5364718.82\n",
       "               PRESIDENT                 2491244.89\n",
       "               EXECUTIVE                 2300947.03\n",
       "               C.E.O.                    1968386.11\n",
       "Name: contb_receipt_amt, Length: 14, dtype: float64"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby('cand_nm')\n",
    "\n",
    "grouped.apply(get_top_amounts, 'contbr_occupation', n=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cand_nm        contbr_employer      \n",
       "Obama, Barack  RETIRED                  22694358.85\n",
       "               SELF-EMPLOYED            17080985.96\n",
       "               NOT EMPLOYED              8586308.70\n",
       "               INFORMATION REQUESTED     5053480.37\n",
       "               HOMEMAKER                 2605408.54\n",
       "                                           ...     \n",
       "Romney, Mitt   CREDIT SUISSE              281150.00\n",
       "               MORGAN STANLEY             267266.00\n",
       "               GOLDMAN SACH & CO.         238250.00\n",
       "               BARCLAYS CAPITAL           162750.00\n",
       "               H.I.G. CAPITAL             139500.00\n",
       "Name: contb_receipt_amt, Length: 20, dtype: float64"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.apply(get_top_amounts, 'contbr_employer', n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bucketing Donation Amounts\n",
    "#### A useful way to analyze data is to use the 'cut' function to discretize contributor amounts into buckets of contribution size.\n",
    "#### We can then group the data by the 2 candidates' name and bin label to get histogram by donation size.\n",
    "#### Through this histogram, we can see that Obama received much larger samll donations than Romney.\n",
    "#### We can sum the contribution amounts and normalize within buckets to see percentage of total donations by candidate. We will exclude the largest donations from the bar plot as they  are not made by individuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "411      (10, 100]\n",
       "412    (100, 1000]\n",
       "413    (100, 1000]\n",
       "414      (10, 100]\n",
       "415      (10, 100]\n",
       "416      (10, 100]\n",
       "417    (100, 1000]\n",
       "418      (10, 100]\n",
       "419    (100, 1000]\n",
       "420      (10, 100]\n",
       "Name: contb_receipt_amt, dtype: category\n",
       "Categories (8, interval[int64]): [(0, 1] < (1, 10] < (10, 100] < (100, 1000] < (1000, 10000] < (10000, 100000] < (100000, 1000000] < (1000000, 10000000]]"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>493.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 10]</th>\n",
       "      <td>40070.0</td>\n",
       "      <td>3681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10, 100]</th>\n",
       "      <td>372280.0</td>\n",
       "      <td>31853.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>153991.0</td>\n",
       "      <td>43357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 10000]</th>\n",
       "      <td>22284.0</td>\n",
       "      <td>26186.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10000, 100000]</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000, 1000000]</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000000, 10000000]</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                       493.0          77.0\n",
       "(1, 10]                    40070.0        3681.0\n",
       "(10, 100]                 372280.0       31853.0\n",
       "(100, 1000]               153991.0       43357.0\n",
       "(1000, 10000]              22284.0       26186.0\n",
       "(10000, 100000]                2.0           1.0\n",
       "(100000, 1000000]              3.0           NaN\n",
       "(1000000, 10000000]            4.0           NaN"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby(['cand_nm', labels])\n",
    "grouped.size().unstack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contb_receipt_amt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1]</th>\n",
       "      <td>0.805182</td>\n",
       "      <td>0.194818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 10]</th>\n",
       "      <td>0.918767</td>\n",
       "      <td>0.081233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10, 100]</th>\n",
       "      <td>0.910769</td>\n",
       "      <td>0.089231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 1000]</th>\n",
       "      <td>0.710176</td>\n",
       "      <td>0.289824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000, 10000]</th>\n",
       "      <td>0.447326</td>\n",
       "      <td>0.552674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10000, 100000]</th>\n",
       "      <td>0.823120</td>\n",
       "      <td>0.176880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100000, 1000000]</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1000000, 10000000]</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm              Obama, Barack  Romney, Mitt\n",
       "contb_receipt_amt                               \n",
       "(0, 1]                    0.805182      0.194818\n",
       "(1, 10]                   0.918767      0.081233\n",
       "(10, 100]                 0.910769      0.089231\n",
       "(100, 1000]               0.710176      0.289824\n",
       "(1000, 10000]             0.447326      0.552674\n",
       "(10000, 100000]           0.823120      0.176880\n",
       "(100000, 1000000]         1.000000           NaN\n",
       "(1000000, 10000000]       1.000000           NaN"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket_sums = grouped.contb_receipt_amt.sum().unstack(0)\n",
    "\n",
    "normed_sums = bucket_sums.div(bucket_sums.sum(axis=1), axis=0)\n",
    "\n",
    "normed_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203da060630>"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normed_sums[:-2].plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Donation Statistics by State\n",
    "#### Aggregating by candidate and state is pretty normal. If we divide each row by total contribution amount, we get relative percentage of total donations by state for each candidate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_st</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>281840.15</td>\n",
       "      <td>86204.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>543123.48</td>\n",
       "      <td>527303.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>359247.28</td>\n",
       "      <td>105556.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>1506476.98</td>\n",
       "      <td>1888436.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>23824984.24</td>\n",
       "      <td>11237636.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>2132429.49</td>\n",
       "      <td>1506714.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>2068291.26</td>\n",
       "      <td>3499475.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>4373538.80</td>\n",
       "      <td>1025137.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>336669.14</td>\n",
       "      <td>82712.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>7318178.58</td>\n",
       "      <td>8338458.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm    Obama, Barack  Romney, Mitt\n",
       "contbr_st                             \n",
       "AK             281840.15      86204.24\n",
       "AL             543123.48     527303.51\n",
       "AR             359247.28     105556.00\n",
       "AZ            1506476.98    1888436.23\n",
       "CA           23824984.24   11237636.60\n",
       "CO            2132429.49    1506714.12\n",
       "CT            2068291.26    3499475.45\n",
       "DC            4373538.80    1025137.50\n",
       "DE             336669.14      82712.00\n",
       "FL            7318178.58    8338458.81"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = fec_mrbo.groupby(['cand_nm', 'contbr_st'])\n",
    "totals = grouped.contb_receipt_amt.sum().unstack(0).fillna(0)\n",
    "totals = totals[totals.sum(1) > 100000]\n",
    "\n",
    "totals[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cand_nm</th>\n",
       "      <th>Obama, Barack</th>\n",
       "      <th>Romney, Mitt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contbr_st</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AK</th>\n",
       "      <td>0.765778</td>\n",
       "      <td>0.234222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>0.507390</td>\n",
       "      <td>0.492610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>0.772902</td>\n",
       "      <td>0.227098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>0.443745</td>\n",
       "      <td>0.556255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>0.679498</td>\n",
       "      <td>0.320502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>0.585970</td>\n",
       "      <td>0.414030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CT</th>\n",
       "      <td>0.371476</td>\n",
       "      <td>0.628524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>0.810113</td>\n",
       "      <td>0.189887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>0.802776</td>\n",
       "      <td>0.197224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>0.467417</td>\n",
       "      <td>0.532583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cand_nm    Obama, Barack  Romney, Mitt\n",
       "contbr_st                             \n",
       "AK              0.765778      0.234222\n",
       "AL              0.507390      0.492610\n",
       "AR              0.772902      0.227098\n",
       "AZ              0.443745      0.556255\n",
       "CA              0.679498      0.320502\n",
       "CO              0.585970      0.414030\n",
       "CT              0.371476      0.628524\n",
       "DC              0.810113      0.189887\n",
       "DE              0.802776      0.197224\n",
       "FL              0.467417      0.532583"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent = totals.div(totals.sum(1), axis=0)\n",
    "percent[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## USDA Food Database\n",
    "#### The US Department of Agriculture makes available a database of food nutrient information. This is available in JSON format.\n",
    "#### Each food has number of identifying attributes with 2 lists of nutrients and portion sizes. This format is not amenable to analysis, so a little wrangling is needed to get better form.\n",
    "#### You can load data with any JSON library of your choosing, we will use the built-in 'json' module.\n",
    "#### each entry is a dict having all the data for a single food. The 'nutrients' field is a list of dicts, one for each nutrient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6636"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "db = json.load(open('datasets/usda_food/database.json'))\n",
    "len(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['id', 'description', 'tags', 'manufacturer', 'group', 'portions', 'nutrients'])"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'value': 25.18,\n",
       " 'units': 'g',\n",
       " 'description': 'Protein',\n",
       " 'group': 'Composition'}"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db[0]['nutrients'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>description</th>\n",
       "      <th>group</th>\n",
       "      <th>units</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>25.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>29.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>3.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>3.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kcal</td>\n",
       "      <td>376.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>39.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kJ</td>\n",
       "      <td>1573.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   description        group units    value\n",
       "0                      Protein  Composition     g    25.18\n",
       "1            Total lipid (fat)  Composition     g    29.20\n",
       "2  Carbohydrate, by difference  Composition     g     3.06\n",
       "3                          Ash        Other     g     3.28\n",
       "4                       Energy       Energy  kcal   376.00\n",
       "5                        Water  Composition     g    39.28\n",
       "6                       Energy       Energy    kJ  1573.00"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients = pd.DataFrame(db[0]['nutrients'])\n",
    "nutrients[:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### When converting list of dicts to DataFrame, we can specify list of fields to extract. Here it will be - food names, group, ID, and manufaturer.\n",
    "#### We can see the distribution of food groups with 'value_counts'.\n",
    "#### To analyse nutrient data, its easier to assemble nutrients for each food into large table. But this will include several steps.\n",
    "#### First, convert each list of food nutrient into DataFrame, adding a new column for food 'id'. Then append the DataFrame to a list. These can be concatenated together with 'concat'.\n",
    "#### We notice duplicates in this DataFrame, so dropping them will make things easier.\n",
    "#### Since 'group' and 'description' are in both the objects, they need to be renamed for clarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>description</th>\n",
       "      <th>group</th>\n",
       "      <th>id</th>\n",
       "      <th>manufacturer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cheese, caraway</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1008</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cheese, cheddar</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1009</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cheese, edam</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1018</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cheese, feta</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1019</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cheese, mozzarella, part skim milk</td>\n",
       "      <td>Dairy and Egg Products</td>\n",
       "      <td>1028</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          description                   group    id  \\\n",
       "0                     Cheese, caraway  Dairy and Egg Products  1008   \n",
       "1                     Cheese, cheddar  Dairy and Egg Products  1009   \n",
       "2                        Cheese, edam  Dairy and Egg Products  1018   \n",
       "3                        Cheese, feta  Dairy and Egg Products  1019   \n",
       "4  Cheese, mozzarella, part skim milk  Dairy and Egg Products  1028   \n",
       "\n",
       "  manufacturer  \n",
       "0               \n",
       "1               \n",
       "2               \n",
       "3               \n",
       "4               "
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_keys = ['description', 'group', 'id', 'manufacturer']\n",
    "\n",
    "info = pd.DataFrame(db, columns = info_keys)\n",
    "info[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6636 entries, 0 to 6635\n",
      "Data columns (total 4 columns):\n",
      "description     6636 non-null object\n",
      "group           6636 non-null object\n",
      "id              6636 non-null int64\n",
      "manufacturer    5195 non-null object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 207.5+ KB\n"
     ]
    }
   ],
   "source": [
    "info.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vegetables and Vegetable Products    812\n",
       "Beef Products                        618\n",
       "Baked Products                       496\n",
       "Breakfast Cereals                    403\n",
       "Fast Foods                           365\n",
       "Legumes and Legume Products          365\n",
       "Lamb, Veal, and Game Products        345\n",
       "Sweets                               341\n",
       "Fruits and Fruit Juices              328\n",
       "Pork Products                        328\n",
       "Name: group, dtype: int64"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(info.group)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>description</th>\n",
       "      <th>group</th>\n",
       "      <th>units</th>\n",
       "      <th>value</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>25.18</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>29.20</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>3.06</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>3.28</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kcal</td>\n",
       "      <td>376.00</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Water</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>39.28</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kJ</td>\n",
       "      <td>1573.00</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fiber, total dietary</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Calcium, Ca</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>673.00</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Iron, Fe</td>\n",
       "      <td>Elements</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   description        group units    value    id\n",
       "0                      Protein  Composition     g    25.18  1008\n",
       "1            Total lipid (fat)  Composition     g    29.20  1008\n",
       "2  Carbohydrate, by difference  Composition     g     3.06  1008\n",
       "3                          Ash        Other     g     3.28  1008\n",
       "4                       Energy       Energy  kcal   376.00  1008\n",
       "5                        Water  Composition     g    39.28  1008\n",
       "6                       Energy       Energy    kJ  1573.00  1008\n",
       "7         Fiber, total dietary  Composition     g     0.00  1008\n",
       "8                  Calcium, Ca     Elements    mg   673.00  1008\n",
       "9                     Iron, Fe     Elements    mg     0.64  1008"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This piece of code is not present in the book. \n",
    "# But it is necessary for further steps.\n",
    "nutrient_list = []\n",
    "\n",
    "for i in range(len(db)):\n",
    "    data = pd.DataFrame(db[i]['nutrients'])\n",
    "    data['id'] = db[i]['id']\n",
    "    nutrient_list.append(data)\n",
    "    \n",
    "nutrients = pd.concat(nutrient_list)\n",
    "nutrients[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 389355 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      "description    389355 non-null object\n",
      "group          389355 non-null object\n",
      "units          389355 non-null object\n",
      "value          389355 non-null float64\n",
      "id             389355 non-null int64\n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 17.8+ MB\n"
     ]
    }
   ],
   "source": [
    "nutrient.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14179"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nutrients.duplicated().sum() #Number of duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "nutrients = nutrients.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6636 entries, 0 to 6635\n",
      "Data columns (total 4 columns):\n",
      "food            6636 non-null object\n",
      "fgroup          6636 non-null object\n",
      "id              6636 non-null int64\n",
      "manufacturer    5195 non-null object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 207.5+ KB\n"
     ]
    }
   ],
   "source": [
    "col_mapping = {'description': 'food',\n",
    "              'group': 'fgroup'}\n",
    "\n",
    "info = info.rename(columns=col_mapping, copy=False)\n",
    "info.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nutrient</th>\n",
       "      <th>nutgroup</th>\n",
       "      <th>units</th>\n",
       "      <th>value</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Protein</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>25.180</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total lipid (fat)</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>29.200</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carbohydrate, by difference</td>\n",
       "      <td>Composition</td>\n",
       "      <td>g</td>\n",
       "      <td>3.060</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ash</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>3.280</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Energy</td>\n",
       "      <td>Energy</td>\n",
       "      <td>kcal</td>\n",
       "      <td>376.000</td>\n",
       "      <td>1008</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>45</th>\n",
       "      <td>Vitamin B-12, added</td>\n",
       "      <td>Vitamins</td>\n",
       "      <td>mcg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Cholesterol</td>\n",
       "      <td>Other</td>\n",
       "      <td>mg</td>\n",
       "      <td>0.000</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Fatty acids, total saturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.072</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Fatty acids, total monounsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.028</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Fatty acids, total polyunsaturated</td>\n",
       "      <td>Other</td>\n",
       "      <td>g</td>\n",
       "      <td>0.041</td>\n",
       "      <td>43546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>375176 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              nutrient     nutgroup units    value     id\n",
       "0                              Protein  Composition     g   25.180   1008\n",
       "1                    Total lipid (fat)  Composition     g   29.200   1008\n",
       "2          Carbohydrate, by difference  Composition     g    3.060   1008\n",
       "3                                  Ash        Other     g    3.280   1008\n",
       "4                               Energy       Energy  kcal  376.000   1008\n",
       "..                                 ...          ...   ...      ...    ...\n",
       "45                 Vitamin B-12, added     Vitamins   mcg    0.000  43546\n",
       "46                         Cholesterol        Other    mg    0.000  43546\n",
       "47        Fatty acids, total saturated        Other     g    0.072  43546\n",
       "48  Fatty acids, total monounsaturated        Other     g    0.028  43546\n",
       "49  Fatty acids, total polyunsaturated        Other     g    0.041  43546\n",
       "\n",
       "[375176 rows x 5 columns]"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_mapping = {'description': 'nutrient',\n",
    "              'group': 'nutgroup'}\n",
    "\n",
    "nutrients = nutrients.rename(columns=col_mapping, copy=False)\n",
    "nutrients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With both the DataFrames ready, we can merge 'info' with 'nutrients'.\n",
    "#### We can make bar plot of median values of food group and nutrient type.\n",
    "#### With some cleverness, we can also find which food is most dense in each nutrient.\n",
    "#### We will display just 1 nutrient group - 'Amino Acid'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 375176 entries, 0 to 375175\n",
      "Data columns (total 8 columns):\n",
      "nutrient        375176 non-null object\n",
      "nutgroup        375176 non-null object\n",
      "units           375176 non-null object\n",
      "value           375176 non-null float64\n",
      "id              375176 non-null int64\n",
      "food            375176 non-null object\n",
      "fgroup          375176 non-null object\n",
      "manufacturer    293054 non-null object\n",
      "dtypes: float64(1), int64(1), object(6)\n",
      "memory usage: 25.8+ MB\n"
     ]
    }
   ],
   "source": [
    "ndata = pd.merge(nutrients, info, on='id', how='outer')\n",
    "ndata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nutrient                 Alcohol, ethyl\n",
       "nutgroup                          Other\n",
       "units                                 g\n",
       "value                                 0\n",
       "id                                 1159\n",
       "food            Cheese, goat, soft type\n",
       "fgroup           Dairy and Egg Products\n",
       "manufacturer                           \n",
       "Name: 3000, dtype: object"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndata.iloc[3000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203e9e6e358>"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result  = ndata.groupby(['nutrient', 'fgroup'])['value'].quantile(0.5)\n",
    "#result\n",
    "result['Zinc, Zn'].sort_values().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "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 food a little smaller\n",
    "max_foods.food = max_foods.food.str[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nutrient\n",
       "Alanine                          Gelatins, dry powder, unsweetened\n",
       "Arginine                              Seeds, sesame flour, low-fat\n",
       "Aspartic acid                                  Soy protein isolate\n",
       "Cystine               Seeds, cottonseed flour, low fat (glandless)\n",
       "Glutamic acid                                  Soy protein isolate\n",
       "                                       ...                        \n",
       "Serine           Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Threonine        Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Tryptophan        Sea lion, Steller, meat with fat (Alaska Native)\n",
       "Tyrosine         Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Valine           Soy protein isolate, PROTEIN TECHNOLOGIES INTE...\n",
       "Name: food, Length: 19, dtype: object"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_foods.loc['Amino Acids']['food']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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