CaseStudy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import DataFrame, Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPython Notebook 내 그래프를 직접 출력하는 설정\n",
    "\n",
    "% 기호로 시작하면 IPython \"매직\" 명령 (교재 p.86 \"매직\" 명령 참조)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "usa.gov 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = 'data/pydata-book/usagov_bitly_data2012-03-16-1331923249.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "경로 구분자는 어떤 운영체제에서든 / 로 하는 것을 권장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some_dir\n",
      "ew_item\n"
     ]
    }
   ],
   "source": [
    "print('some_dir\\new_item')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.TextIOWrapper name='data/pydata-book/usagov_bitly_data2012-03-16-1331923249.txt' mode='r' encoding='UTF-8'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(path).readline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "JSON 문자열을 파이썬 객체로 변환하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "records = [json.loads(line) for line in open(path)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'America/New_York'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records[0]['tz']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'C:\\\\Users\\\\student\\\\Documents\\\\Python Scripts'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " C 드라이브의 볼륨에는 이름이 없습니다.\n",
      " 볼륨 일련 번호: C486-D054\n",
      "\n",
      " C:\\Users\\student\\Documents\\Python Scripts 디렉터리\n",
      "\n",
      "2015-09-14  오후 03:10    <DIR>          .\n",
      "2015-09-14  오후 03:10    <DIR>          ..\n",
      "2015-09-14  오후 03:02    <DIR>          .ipynb_checkpoints\n",
      "2015-09-14  오전 11:32                22 hello_encoding.txt\n",
      "2015-09-14  오후 02:49                46 my_module.py\n",
      "2015-09-14  오후 02:50               219 my_module.pyc\n",
      "2015-09-14  오후 03:00    <DIR>          pydata\n",
      "2015-09-14  오후 02:59        42,044,366 pydata-book-master.zip\n",
      "2015-09-14  오후 03:10             4,237 사례소개.ipynb\n",
      "2015-09-14  오후 02:41             8,139 파이썬 제어.ipynb\n",
      "2015-09-14  오후 01:57            42,754 파이썬 프로그래밍 기초.ipynb\n",
      "2015-09-14  오후 02:52             3,675 파이썬 함수.ipynb\n",
      "               8개 파일          42,103,458 바이트\n",
      "               4개 디렉터리  456,834,596,864 바이트 남음\n"
     ]
    }
   ],
   "source": [
    "%ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "순수 파이썬으로 시간대 통계 정보 집계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "time_zones = [rec['tz'] for rec in records if 'tz' in rec]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'America/New_York',\n",
       " u'America/Denver',\n",
       " u'America/New_York',\n",
       " u'America/Sao_Paulo',\n",
       " u'America/New_York',\n",
       " u'America/New_York',\n",
       " u'Europe/Warsaw',\n",
       " u'',\n",
       " u'',\n",
       " u'']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_zones[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_counts(sequence):\n",
    "    \"\"\"sequence의 값의 빈도 집계\n",
    "    예: {u'NY': 3, u'Seoul': 2}\"\"\"\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": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NY': 2, 'SEOUL': 3}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = ['NY', 'NY', 'SEOUL', 'SEOUL', 'SEOUL']\n",
    "get_counts(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1251"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = get_counts(time_zones)\n",
    "counts['America/New_York']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{u'': 521,\n",
       " u'Africa/Cairo': 3,\n",
       " u'Africa/Casablanca': 1,\n",
       " u'Africa/Ceuta': 2,\n",
       " u'Africa/Johannesburg': 1,\n",
       " u'Africa/Lusaka': 1,\n",
       " u'America/Anchorage': 5,\n",
       " u'America/Argentina/Buenos_Aires': 1,\n",
       " u'America/Argentina/Cordoba': 1,\n",
       " u'America/Argentina/Mendoza': 1,\n",
       " u'America/Bogota': 3,\n",
       " u'America/Caracas': 1,\n",
       " u'America/Chicago': 400,\n",
       " u'America/Chihuahua': 2,\n",
       " u'America/Costa_Rica': 1,\n",
       " u'America/Denver': 191,\n",
       " u'America/Edmonton': 6,\n",
       " u'America/Guayaquil': 2,\n",
       " u'America/Halifax': 4,\n",
       " u'America/Indianapolis': 20,\n",
       " u'America/La_Paz': 1,\n",
       " u'America/Lima': 1,\n",
       " u'America/Los_Angeles': 382,\n",
       " u'America/Managua': 3,\n",
       " u'America/Mazatlan': 1,\n",
       " u'America/Mexico_City': 15,\n",
       " u'America/Monterrey': 1,\n",
       " u'America/Montevideo': 1,\n",
       " u'America/Montreal': 9,\n",
       " u'America/New_York': 1251,\n",
       " u'America/Phoenix': 20,\n",
       " u'America/Puerto_Rico': 10,\n",
       " u'America/Rainy_River': 25,\n",
       " u'America/Recife': 2,\n",
       " u'America/Santo_Domingo': 1,\n",
       " u'America/Sao_Paulo': 33,\n",
       " u'America/St_Kitts': 1,\n",
       " u'America/Tegucigalpa': 1,\n",
       " u'America/Vancouver': 12,\n",
       " u'America/Winnipeg': 4,\n",
       " u'Asia/Amman': 2,\n",
       " u'Asia/Bangkok': 6,\n",
       " u'Asia/Beirut': 4,\n",
       " u'Asia/Calcutta': 9,\n",
       " u'Asia/Dubai': 4,\n",
       " u'Asia/Harbin': 3,\n",
       " u'Asia/Hong_Kong': 10,\n",
       " u'Asia/Istanbul': 9,\n",
       " u'Asia/Jakarta': 3,\n",
       " u'Asia/Jerusalem': 3,\n",
       " u'Asia/Karachi': 3,\n",
       " u'Asia/Kuala_Lumpur': 3,\n",
       " u'Asia/Kuching': 1,\n",
       " u'Asia/Manila': 1,\n",
       " u'Asia/Nicosia': 1,\n",
       " u'Asia/Novosibirsk': 1,\n",
       " u'Asia/Pontianak': 1,\n",
       " u'Asia/Riyadh': 1,\n",
       " u'Asia/Seoul': 5,\n",
       " u'Asia/Tokyo': 37,\n",
       " u'Asia/Yekaterinburg': 1,\n",
       " u'Australia/NSW': 6,\n",
       " u'Australia/Queensland': 1,\n",
       " u'Chile/Continental': 6,\n",
       " u'Europe/Amsterdam': 22,\n",
       " u'Europe/Athens': 6,\n",
       " u'Europe/Belgrade': 2,\n",
       " u'Europe/Berlin': 28,\n",
       " u'Europe/Bratislava': 3,\n",
       " u'Europe/Brussels': 4,\n",
       " u'Europe/Bucharest': 4,\n",
       " u'Europe/Budapest': 5,\n",
       " u'Europe/Copenhagen': 5,\n",
       " u'Europe/Dublin': 3,\n",
       " u'Europe/Helsinki': 10,\n",
       " u'Europe/Lisbon': 8,\n",
       " u'Europe/Ljubljana': 1,\n",
       " u'Europe/London': 74,\n",
       " u'Europe/Madrid': 35,\n",
       " u'Europe/Malta': 2,\n",
       " u'Europe/Moscow': 10,\n",
       " u'Europe/Oslo': 10,\n",
       " u'Europe/Paris': 14,\n",
       " u'Europe/Prague': 10,\n",
       " u'Europe/Riga': 2,\n",
       " u'Europe/Rome': 27,\n",
       " u'Europe/Skopje': 1,\n",
       " u'Europe/Sofia': 1,\n",
       " u'Europe/Stockholm': 14,\n",
       " u'Europe/Uzhgorod': 1,\n",
       " u'Europe/Vienna': 6,\n",
       " u'Europe/Vilnius': 2,\n",
       " u'Europe/Volgograd': 1,\n",
       " u'Europe/Warsaw': 16,\n",
       " u'Europe/Zurich': 4,\n",
       " u'Pacific/Auckland': 11,\n",
       " u'Pacific/Honolulu': 36}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def top_counts(count_dict, n=10):\n",
    "    value_key_pairs = [(count, tz) for tz, count in count_dict.items()]\n",
    "    # count 값을 기준으로 정렬\n",
    "    value_key_pairs.sort()\n",
    "    return value_key_pairs[-n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1251, u'America/New_York'),\n",
       " (521, u''),\n",
       " (400, u'America/Chicago'),\n",
       " (382, u'America/Los_Angeles'),\n",
       " (191, u'America/Denver'),\n",
       " (74, u'Europe/London'),\n",
       " (37, u'Asia/Tokyo'),\n",
       " (36, u'Pacific/Honolulu'),\n",
       " (35, u'Europe/Madrid'),\n",
       " (33, u'America/Sao_Paulo')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_counts(counts)[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "함수 작성대신 파이썬의 표준 라이브러리를 활용하는 경우"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'America/New_York', 1251),\n",
       " (u'', 521),\n",
       " (u'America/Chicago', 400),\n",
       " (u'America/Los_Angeles', 382),\n",
       " (u'America/Denver', 191),\n",
       " (u'Europe/London', 74),\n",
       " (u'Asia/Tokyo', 37),\n",
       " (u'Pacific/Honolulu', 36),\n",
       " (u'Europe/Madrid', 35),\n",
       " (u'America/Sao_Paulo', 33)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "counts = Counter(time_zones)\n",
    "counts.most_common(10) # 빈도 상위 10개 집계"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas로 수행하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import DataFrame, Series\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_heartbeat_</th>\n",
       "      <th>a</th>\n",
       "      <th>al</th>\n",
       "      <th>c</th>\n",
       "      <th>cy</th>\n",
       "      <th>g</th>\n",
       "      <th>gr</th>\n",
       "      <th>h</th>\n",
       "      <th>hc</th>\n",
       "      <th>hh</th>\n",
       "      <th>kw</th>\n",
       "      <th>l</th>\n",
       "      <th>ll</th>\n",
       "      <th>nk</th>\n",
       "      <th>r</th>\n",
       "      <th>t</th>\n",
       "      <th>tz</th>\n",
       "      <th>u</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Danvers</td>\n",
       "      <td>A6qOVH</td>\n",
       "      <td>MA</td>\n",
       "      <td>wfLQtf</td>\n",
       "      <td>1331822918</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>orofrog</td>\n",
       "      <td>[42.576698, -70.954903]</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...</td>\n",
       "      <td>1331923247</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.ncbi.nlm.nih.gov/pubmed/22415991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Provo</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>1308262393</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>1331923249</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>en-US</td>\n",
       "      <td>US</td>\n",
       "      <td>Washington</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>DC</td>\n",
       "      <td>xxr3Qb</td>\n",
       "      <td>1331919941</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[38.9007, -77.043098]</td>\n",
       "      <td>1</td>\n",
       "      <td>http://t.co/03elZC4Q</td>\n",
       "      <td>1331923250</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://boxer.senate.gov/en/press/releases/0316...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>pt-br</td>\n",
       "      <td>BR</td>\n",
       "      <td>Braz</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>27</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>1331923068</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>[-23.549999, -46.616699]</td>\n",
       "      <td>0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331923249</td>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>MA</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>1273672411</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>1331923251</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>axNK8c</td>\n",
       "      <td>MA</td>\n",
       "      <td>axNK8c</td>\n",
       "      <td>1273672506</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>1331923252</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...</td>\n",
       "      <td>pl-PL,pl;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>PL</td>\n",
       "      <td>Luban</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>77</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1331922854</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[51.116699, 15.2833]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://plus.url.google.com/url?sa=z&amp;n=13319232...</td>\n",
       "      <td>1331923255</td>\n",
       "      <td>Europe/Warsaw</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; rv:2.0.1) Gecko/2...</td>\n",
       "      <td>bg,en-us;q=0.7,en;q=0.3</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1331922854</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/</td>\n",
       "      <td>1331923255</td>\n",
       "      <td></td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Opera/9.80 (X11; Linux zbov; U; en) Presto/2.1...</td>\n",
       "      <td>en-US, en</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1331922854</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n",
       "      <td>1331923254</td>\n",
       "      <td></td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>pt-BR,pt;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>1331923068</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>http://t.co/o1Pd0WeV</td>\n",
       "      <td>1331923255</td>\n",
       "      <td></td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>vNJS4H</td>\n",
       "      <td>WA</td>\n",
       "      <td>u0uD9q</td>\n",
       "      <td>1319563556</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>o_4us71ccioa</td>\n",
       "      <td>[47.5951, -122.332603]</td>\n",
       "      <td>1</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331923258</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.4...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Washington</td>\n",
       "      <td>wG7OIH</td>\n",
       "      <td>DC</td>\n",
       "      <td>A0nRz4</td>\n",
       "      <td>1331815838</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>darrellissa</td>\n",
       "      <td>[38.937599, -77.092796]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://t.co/ND7SoPyo</td>\n",
       "      <td>1331923259</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://oversight.house.gov/wp-content/uploads/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Alexandria</td>\n",
       "      <td>vNJS4H</td>\n",
       "      <td>VA</td>\n",
       "      <td>u0uD9q</td>\n",
       "      <td>1319563556</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>o_4us71ccioa</td>\n",
       "      <td>[38.790901, -77.094704]</td>\n",
       "      <td>1</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331923259</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1331923261</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Marietta</td>\n",
       "      <td>2rOUYc</td>\n",
       "      <td>GA</td>\n",
       "      <td>2rOUYc</td>\n",
       "      <td>1255769846</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[33.953201, -84.5177]</td>\n",
       "      <td>1</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331923262</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://toxtown.nlm.nih.gov/index.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...</td>\n",
       "      <td>zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>HK</td>\n",
       "      <td>Central District</td>\n",
       "      <td>nQvgJp</td>\n",
       "      <td>00</td>\n",
       "      <td>rtrrth</td>\n",
       "      <td>1317318030</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walkeryuen</td>\n",
       "      <td>[22.2833, 114.150002]</td>\n",
       "      <td>1</td>\n",
       "      <td>http://forum2.hkgolden.com/view.aspx?type=BW&amp;m...</td>\n",
       "      <td>1331923263</td>\n",
       "      <td>Asia/Hong_Kong</td>\n",
       "      <td>http://www.ssd.noaa.gov/PS/TROP/TCFP/data/curr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...</td>\n",
       "      <td>zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>HK</td>\n",
       "      <td>Central District</td>\n",
       "      <td>XdUNr</td>\n",
       "      <td>00</td>\n",
       "      <td>qWkgbq</td>\n",
       "      <td>1317318039</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>walkeryuen</td>\n",
       "      <td>[22.2833, 114.150002]</td>\n",
       "      <td>1</td>\n",
       "      <td>http://forum2.hkgolden.com/view.aspx?type=BW&amp;m...</td>\n",
       "      <td>1331923263</td>\n",
       "      <td>Asia/Hong_Kong</td>\n",
       "      <td>http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10.5; r...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Buckfield</td>\n",
       "      <td>zH1BFf</td>\n",
       "      <td>ME</td>\n",
       "      <td>x3jOIv</td>\n",
       "      <td>1331839576</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>andyzieminski</td>\n",
       "      <td>[44.299702, -70.369797]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://t.co/6Cx4ROLs</td>\n",
       "      <td>1331923264</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.usda.gov/wps/portal/usda/usdahome?c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Provo</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>1308262393</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>1331923262</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>it-IT,it;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>IT</td>\n",
       "      <td>Venice</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>20</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1331922854</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[45.438599, 12.3267]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/</td>\n",
       "      <td>1331923264</td>\n",
       "      <td>Europe/Rome</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
       "      <td>es-ES</td>\n",
       "      <td>ES</td>\n",
       "      <td>Alcal</td>\n",
       "      <td>zQ95Hi</td>\n",
       "      <td>51</td>\n",
       "      <td>ytZYWR</td>\n",
       "      <td>1331670549</td>\n",
       "      <td>bitly.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jplnews</td>\n",
       "      <td>[37.516701, -5.9833]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/</td>\n",
       "      <td>1331923265</td>\n",
       "      <td>Africa/Ceuta</td>\n",
       "      <td>http://voyager.jpl.nasa.gov/imagesvideo/uranus...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.6...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Davidsonville</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>MD</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1331922854</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[38.939201, -76.635002]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/</td>\n",
       "      <td>1331923267</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Hockessin</td>\n",
       "      <td>y3ZImz</td>\n",
       "      <td>DE</td>\n",
       "      <td>y3ZImz</td>\n",
       "      <td>1331064158</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[39.785, -75.682297]</td>\n",
       "      <td>0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331923267</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://portal.hud.gov/hudportal/documents/hudd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3)...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Lititz</td>\n",
       "      <td>wWiOiD</td>\n",
       "      <td>PA</td>\n",
       "      <td>wWiOiD</td>\n",
       "      <td>1330217829</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[40.174999, -76.3078]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n",
       "      <td>1331923267</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.tricare.mil/mybenefit/ProfileFilter...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows; U; Windows NT 5.1; es-ES...</td>\n",
       "      <td>es-es,es;q=0.8,en-us;q=0.5,en;q=0.3</td>\n",
       "      <td>ES</td>\n",
       "      <td>Bilbao</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>59</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1331922854</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[43.25, -2.9667]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/</td>\n",
       "      <td>1331923268</td>\n",
       "      <td>Europe/Madrid</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...</td>\n",
       "      <td>en-GB,en;q=0.8,en-US;q=0.6,en-AU;q=0.4</td>\n",
       "      <td>MY</td>\n",
       "      <td>Kuala Lumpur</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>14</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1331922854</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[3.1667, 101.699997]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/</td>\n",
       "      <td>1331923269</td>\n",
       "      <td>Asia/Kuala_Lumpur</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...</td>\n",
       "      <td>ro-RO,ro;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>CY</td>\n",
       "      <td>Nicosia</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>04</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1331922854</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[35.166698, 33.366699]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/?ref=tn_tnmn</td>\n",
       "      <td>1331923268</td>\n",
       "      <td>Asia/Nicosia</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>BR</td>\n",
       "      <td>SPaulo</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>27</td>\n",
       "      <td>zUtuOu</td>\n",
       "      <td>1331923068</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alelex88</td>\n",
       "      <td>[-23.5333, -46.616699]</td>\n",
       "      <td>0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331923269</td>\n",
       "      <td>America/Sao_Paulo</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (iPad; CPU OS 5_0_1 like Mac OS X)...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vNJS4H</td>\n",
       "      <td>NaN</td>\n",
       "      <td>u0uD9q</td>\n",
       "      <td>1319563556</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>o_4us71ccioa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331923270</td>\n",
       "      <td></td>\n",
       "      <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (iPad; U; CPU OS 3_2 like Mac OS X...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FPX0IM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FPX0IL</td>\n",
       "      <td>1331922978</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>twittershare</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>http://t.co/5xlp0B34</td>\n",
       "      <td>1331923270</td>\n",
       "      <td></td>\n",
       "      <td>http://www.ed.gov/news/media-advisories/us-dep...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3530</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.0) AppleWebKit/535.1...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>xVZg4P</td>\n",
       "      <td>CA</td>\n",
       "      <td>wqUkTo</td>\n",
       "      <td>1331908247</td>\n",
       "      <td>go.nasa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nasatwitter</td>\n",
       "      <td>[37.7645, -122.429398]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2Fg...</td>\n",
       "      <td>1331926815</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://www.nasa.gov/multimedia/imagegallery/im...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6...</td>\n",
       "      <td>en-US</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1331922854</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926816</td>\n",
       "      <td></td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3532</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Au3aUS</td>\n",
       "      <td>DC</td>\n",
       "      <td>A9ct6C</td>\n",
       "      <td>1331926420</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ncsha</td>\n",
       "      <td>[38.904202, -77.031998]</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.ncsha.org/</td>\n",
       "      <td>1331926817</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://portal.hud.gov/hudportal/HUD?src=/press...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3533</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (iPad; CPU OS 5_1 like Mac OS X) A...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>b2UtUJ</td>\n",
       "      <td>FL</td>\n",
       "      <td>ieCdgH</td>\n",
       "      <td>1301393171</td>\n",
       "      <td>go.nasa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nasatwitter</td>\n",
       "      <td>[30.279301, -81.585098]</td>\n",
       "      <td>1</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926818</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://apod.nasa.gov/apod/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Frisco</td>\n",
       "      <td>vNJS4H</td>\n",
       "      <td>TX</td>\n",
       "      <td>u0uD9q</td>\n",
       "      <td>1319563556</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>o_4us71ccioa</td>\n",
       "      <td>[33.149899, -96.855499]</td>\n",
       "      <td>1</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926820</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3535</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Houston</td>\n",
       "      <td>zIgLx8</td>\n",
       "      <td>TX</td>\n",
       "      <td>yrPaLt</td>\n",
       "      <td>1331903484</td>\n",
       "      <td>aash.to</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aashto</td>\n",
       "      <td>[29.775499, -95.415199]</td>\n",
       "      <td>1</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926823</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>http://ntl.bts.gov/lib/44000/44300/44374/FHWA-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3536</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (BlackBerry; U; BlackBerry 9800; e...</td>\n",
       "      <td>en-US,en;q=0.5</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>xIcyim</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yG1TTf</td>\n",
       "      <td>1331728309</td>\n",
       "      <td>go.nasa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nasatwitter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>http://t.co/g1VKE8zS</td>\n",
       "      <td>1331926824</td>\n",
       "      <td></td>\n",
       "      <td>http://www.nasa.gov/mission_pages/hurricanes/a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3537</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n",
       "      <td>es-es,es;q=0.8,en-us;q=0.5,en;q=0.3</td>\n",
       "      <td>HN</td>\n",
       "      <td>Tegucigalpa</td>\n",
       "      <td>zCaLwp</td>\n",
       "      <td>08</td>\n",
       "      <td>w63FZW</td>\n",
       "      <td>1331546756</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bufferapp</td>\n",
       "      <td>[14.1, -87.216698]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://t.co/A8TJyibE</td>\n",
       "      <td>1331926825</td>\n",
       "      <td>America/Tegucigalpa</td>\n",
       "      <td>http://apod.nasa.gov/apod/ap120312.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3538</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>qMac9k</td>\n",
       "      <td>CA</td>\n",
       "      <td>qds1Ge</td>\n",
       "      <td>1310473559</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>healthypeople</td>\n",
       "      <td>[34.041599, -118.298798]</td>\n",
       "      <td>0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926825</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://healthypeople.gov/2020/connect/webinars...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3539</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (compatible; Fedora Core 3) FC3 KDE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Bellevue</td>\n",
       "      <td>zu2M5o</td>\n",
       "      <td>WA</td>\n",
       "      <td>zDhdro</td>\n",
       "      <td>1331586192</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>glimtwin</td>\n",
       "      <td>[47.615398, -122.210297]</td>\n",
       "      <td>0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926827</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://www.federalreserve.gov/newsevents/press...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Payson</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>UT</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1331922854</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>[40.014198, -111.738899]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n",
       "      <td>1331926828</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3541</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (X11; U; OpenVMS AlphaServer_ES40;...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Bellevue</td>\n",
       "      <td>zu2M5o</td>\n",
       "      <td>WA</td>\n",
       "      <td>zDhdro</td>\n",
       "      <td>1331586192</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>glimtwin</td>\n",
       "      <td>[47.615398, -122.210297]</td>\n",
       "      <td>0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926828</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://www.federalreserve.gov/newsevents/press...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3542</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Pittsburg</td>\n",
       "      <td>y3reI1</td>\n",
       "      <td>CA</td>\n",
       "      <td>y3reI1</td>\n",
       "      <td>1331926120</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[38.0051, -121.838699]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n",
       "      <td>1331926829</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://www.sba.gov/community/blogs/community-b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3543</th>\n",
       "      <td>1331926831</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3544</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:5.0.1) ...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Wentzville</td>\n",
       "      <td>vNJS4H</td>\n",
       "      <td>MO</td>\n",
       "      <td>u0uD9q</td>\n",
       "      <td>1319563556</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>o_4us71ccioa</td>\n",
       "      <td>[38.790001, -90.854897]</td>\n",
       "      <td>1</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926831</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3545</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...</td>\n",
       "      <td>en-us,en;q=0.5</td>\n",
       "      <td>US</td>\n",
       "      <td>Saint Charles</td>\n",
       "      <td>vNJS4H</td>\n",
       "      <td>IL</td>\n",
       "      <td>u0uD9q</td>\n",
       "      <td>1319563556</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>o_4us71ccioa</td>\n",
       "      <td>[41.9352, -88.290901]</td>\n",
       "      <td>1</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926832</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>https://www.nysdot.gov/rexdesign/design/commun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3546</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>qMac9k</td>\n",
       "      <td>CA</td>\n",
       "      <td>qds1Ge</td>\n",
       "      <td>1310473559</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>healthypeople</td>\n",
       "      <td>[34.041599, -118.298798]</td>\n",
       "      <td>1</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926833</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://healthypeople.gov/2020/connect/webinars...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3547</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Silver Spring</td>\n",
       "      <td>y0jYkg</td>\n",
       "      <td>MD</td>\n",
       "      <td>y0jYkg</td>\n",
       "      <td>1331851811</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[39.052101, -77.014999]</td>\n",
       "      <td>1</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926836</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.epa.gov/otaq/regs/fuels/additive/e1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3548</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Mcgehee</td>\n",
       "      <td>y5rMac</td>\n",
       "      <td>AR</td>\n",
       "      <td>xANY6O</td>\n",
       "      <td>1331916302</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>twitterfeed</td>\n",
       "      <td>[33.628399, -91.356903]</td>\n",
       "      <td>1</td>\n",
       "      <td>https://twitter.com/fdarecalls/status/18069759...</td>\n",
       "      <td>1331926836</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>http://www.fda.gov/Safety/Recalls/ucm296326.htm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3549</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>sv-SE,sv;q=0.8,en-US;q=0.6,en;q=0.4</td>\n",
       "      <td>SE</td>\n",
       "      <td>Sollefte</td>\n",
       "      <td>eH8wu</td>\n",
       "      <td>24</td>\n",
       "      <td>7dtjei</td>\n",
       "      <td>1260316355</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tweetdeckapi</td>\n",
       "      <td>[63.166698, 17.266701]</td>\n",
       "      <td>1</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926834</td>\n",
       "      <td>Europe/Stockholm</td>\n",
       "      <td>http://www.nasa.gov/mission_pages/WISE/main/in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3550</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Conshohocken</td>\n",
       "      <td>A00b72</td>\n",
       "      <td>PA</td>\n",
       "      <td>yGSwzn</td>\n",
       "      <td>1331917632</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>addthis</td>\n",
       "      <td>[40.0798, -75.2855]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.linkedin.com/home?trk=hb_tab_home_top</td>\n",
       "      <td>1331926837</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.nlm.nih.gov/medlineplus/news/fullst...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3551</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wcndER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zkpJBR</td>\n",
       "      <td>1331922854</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bnjacobs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>http://plus.url.google.com/url?sa=z&amp;n=13319268...</td>\n",
       "      <td>1331926837</td>\n",
       "      <td></td>\n",
       "      <td>http://www.nasa.gov/mission_pages/nustar/main/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3552</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Decatur</td>\n",
       "      <td>rqgJuE</td>\n",
       "      <td>AL</td>\n",
       "      <td>xcz8vt</td>\n",
       "      <td>1331227417</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bootsnall</td>\n",
       "      <td>[34.572701, -86.940598]</td>\n",
       "      <td>0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926839</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>http://travel.state.gov/passport/passport_5535...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3553</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>MA</td>\n",
       "      <td>9b6kNl</td>\n",
       "      <td>1273672411</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>1331926840</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3554</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ...</td>\n",
       "      <td>en-us</td>\n",
       "      <td>US</td>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>axNK8c</td>\n",
       "      <td>MA</td>\n",
       "      <td>axNK8c</td>\n",
       "      <td>1273672506</td>\n",
       "      <td>bit.ly</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[42.286499, -71.714699]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/selco/</td>\n",
       "      <td>1331926840</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.shrewsbury-ma.gov/egov/gallery/1341...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3555</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
       "      <td>en</td>\n",
       "      <td>US</td>\n",
       "      <td>Paramus</td>\n",
       "      <td>e5SvKE</td>\n",
       "      <td>NJ</td>\n",
       "      <td>fqPSr9</td>\n",
       "      <td>1301298479</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tweetdeckapi</td>\n",
       "      <td>[40.9445, -74.07]</td>\n",
       "      <td>1</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926841</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://www.fda.gov/AdvisoryCommittees/Committe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3556</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...</td>\n",
       "      <td>en-US,en;q=0.8</td>\n",
       "      <td>US</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>jQLtP4</td>\n",
       "      <td>OK</td>\n",
       "      <td>jQLtP4</td>\n",
       "      <td>1307530247</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[35.4715, -97.518997]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.facebook.com/l.php?u=http%3A%2F%2F1...</td>\n",
       "      <td>1331926844</td>\n",
       "      <td>America/Chicago</td>\n",
       "      <td>http://www.okc.gov/PublicNotificationSystem/Fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3557</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GoogleMaps/RochesterNY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Provo</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>UT</td>\n",
       "      <td>mwszkS</td>\n",
       "      <td>1308262393</td>\n",
       "      <td>j.mp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[40.218102, -111.613297]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.AwareMap.com/</td>\n",
       "      <td>1331926846</td>\n",
       "      <td>America/Denver</td>\n",
       "      <td>http://www.monroecounty.gov/etc/911/rss.php</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3558</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GoogleProducer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>zjtI4X</td>\n",
       "      <td>CA</td>\n",
       "      <td>zjtI4X</td>\n",
       "      <td>1327528527</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[37.419201, -122.057404]</td>\n",
       "      <td>0</td>\n",
       "      <td>direct</td>\n",
       "      <td>1331926847</td>\n",
       "      <td>America/Los_Angeles</td>\n",
       "      <td>http://www.ahrq.gov/qual/qitoolkit/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3559</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
       "      <td>en-US</td>\n",
       "      <td>US</td>\n",
       "      <td>Mc Lean</td>\n",
       "      <td>qxKrTK</td>\n",
       "      <td>VA</td>\n",
       "      <td>qxKrTK</td>\n",
       "      <td>1312897670</td>\n",
       "      <td>1.usa.gov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitly</td>\n",
       "      <td>[38.935799, -77.162102]</td>\n",
       "      <td>0</td>\n",
       "      <td>http://t.co/OEEEvwjU</td>\n",
       "      <td>1331926849</td>\n",
       "      <td>America/New_York</td>\n",
       "      <td>http://herndon-va.gov/Content/public_safety/Pu...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3560 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      _heartbeat_                                                  a  \\\n",
       "0             NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "1             NaN                             GoogleMaps/RochesterNY   \n",
       "2             NaN  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...   \n",
       "3             NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...   \n",
       "4             NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "5             NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "6             NaN  Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...   \n",
       "7             NaN  Mozilla/5.0 (Windows NT 6.1; rv:2.0.1) Gecko/2...   \n",
       "8             NaN  Opera/9.80 (X11; Linux zbov; U; en) Presto/2.1...   \n",
       "9             NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "10            NaN  Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...   \n",
       "11            NaN  Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.4...   \n",
       "12            NaN  Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...   \n",
       "13     1331923261                                                NaN   \n",
       "14            NaN  Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US...   \n",
       "15            NaN  Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...   \n",
       "16            NaN  Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...   \n",
       "17            NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10.5; r...   \n",
       "18            NaN                             GoogleMaps/RochesterNY   \n",
       "19            NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "20            NaN  Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...   \n",
       "21            NaN  Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10.6...   \n",
       "22            NaN  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...   \n",
       "23            NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_3)...   \n",
       "24            NaN  Mozilla/5.0 (Windows; U; Windows NT 5.1; es-ES...   \n",
       "25            NaN  Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...   \n",
       "26            NaN  Mozilla/5.0 (Windows NT 6.1) AppleWebKit/535.1...   \n",
       "27            NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...   \n",
       "28            NaN  Mozilla/5.0 (iPad; CPU OS 5_0_1 like Mac OS X)...   \n",
       "29            NaN  Mozilla/5.0 (iPad; U; CPU OS 3_2 like Mac OS X...   \n",
       "...           ...                                                ...   \n",
       "3530          NaN  Mozilla/5.0 (Windows NT 6.0) AppleWebKit/535.1...   \n",
       "3531          NaN  Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6...   \n",
       "3532          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...   \n",
       "3533          NaN  Mozilla/5.0 (iPad; CPU OS 5_1 like Mac OS X) A...   \n",
       "3534          NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...   \n",
       "3535          NaN  Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/...   \n",
       "3536          NaN  Mozilla/5.0 (BlackBerry; U; BlackBerry 9800; e...   \n",
       "3537          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...   \n",
       "3538          NaN  Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...   \n",
       "3539          NaN    Mozilla/5.0 (compatible; Fedora Core 3) FC3 KDE   \n",
       "3540          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "3541          NaN  Mozilla/5.0 (X11; U; OpenVMS AlphaServer_ES40;...   \n",
       "3542          NaN  Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...   \n",
       "3543   1331926831                                                NaN   \n",
       "3544          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64; rv:5.0.1) ...   \n",
       "3545          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64; rv:10.0.2)...   \n",
       "3546          NaN  Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...   \n",
       "3547          NaN  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...   \n",
       "3548          NaN  Mozilla/5.0 (iPhone; CPU iPhone OS 5_1 like Ma...   \n",
       "3549          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "3550          NaN  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...   \n",
       "3551          NaN  Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...   \n",
       "3552          NaN  Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US...   \n",
       "3553          NaN  Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ...   \n",
       "3554          NaN  Mozilla/4.0 (compatible; MSIE 7.0; Windows NT ...   \n",
       "3555          NaN  Mozilla/4.0 (compatible; MSIE 9.0; Windows NT ...   \n",
       "3556          NaN  Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...   \n",
       "3557          NaN                             GoogleMaps/RochesterNY   \n",
       "3558          NaN                                     GoogleProducer   \n",
       "3559          NaN  Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...   \n",
       "\n",
       "                                          al     c                cy       g  \\\n",
       "0                             en-US,en;q=0.8    US           Danvers  A6qOVH   \n",
       "1                                        NaN    US             Provo  mwszkS   \n",
       "2                                      en-US    US        Washington  xxr3Qb   \n",
       "3                                      pt-br    BR              Braz  zCaLwp   \n",
       "4                             en-US,en;q=0.8    US        Shrewsbury  9b6kNl   \n",
       "5                             en-US,en;q=0.8    US        Shrewsbury  axNK8c   \n",
       "6        pl-PL,pl;q=0.8,en-US;q=0.6,en;q=0.4    PL             Luban  wcndER   \n",
       "7                    bg,en-us;q=0.7,en;q=0.3  None               NaN  wcndER   \n",
       "8                                  en-US, en  None               NaN  wcndER   \n",
       "9        pt-BR,pt;q=0.8,en-US;q=0.6,en;q=0.4  None               NaN  zCaLwp   \n",
       "10                            en-us,en;q=0.5    US           Seattle  vNJS4H   \n",
       "11                            en-us,en;q=0.5    US        Washington  wG7OIH   \n",
       "12                            en-us,en;q=0.5    US        Alexandria  vNJS4H   \n",
       "13                                       NaN   NaN               NaN     NaN   \n",
       "14                            en-us,en;q=0.5    US          Marietta  2rOUYc   \n",
       "15       zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4    HK  Central District  nQvgJp   \n",
       "16       zh-TW,zh;q=0.8,en-US;q=0.6,en;q=0.4    HK  Central District   XdUNr   \n",
       "17                            en-us,en;q=0.5    US         Buckfield  zH1BFf   \n",
       "18                                       NaN    US             Provo  mwszkS   \n",
       "19       it-IT,it;q=0.8,en-US;q=0.6,en;q=0.4    IT            Venice  wcndER   \n",
       "20                                     es-ES    ES             Alcal  zQ95Hi   \n",
       "21                            en-us,en;q=0.5    US     Davidsonville  wcndER   \n",
       "22                                     en-us    US         Hockessin  y3ZImz   \n",
       "23                                     en-us    US            Lititz  wWiOiD   \n",
       "24       es-es,es;q=0.8,en-us;q=0.5,en;q=0.3    ES            Bilbao  wcndER   \n",
       "25    en-GB,en;q=0.8,en-US;q=0.6,en-AU;q=0.4    MY      Kuala Lumpur  wcndER   \n",
       "26       ro-RO,ro;q=0.8,en-US;q=0.6,en;q=0.4    CY           Nicosia  wcndER   \n",
       "27                            en-US,en;q=0.8    BR            SPaulo  zCaLwp   \n",
       "28                                     en-us  None               NaN  vNJS4H   \n",
       "29                                     en-us  None               NaN  FPX0IM   \n",
       "...                                      ...   ...               ...     ...   \n",
       "3530                          en-US,en;q=0.8    US     San Francisco  xVZg4P   \n",
       "3531                                   en-US  None               NaN  wcndER   \n",
       "3532                          en-us,en;q=0.5    US        Washington  Au3aUS   \n",
       "3533                                   en-us    US      Jacksonville  b2UtUJ   \n",
       "3534                                   en-us    US            Frisco  vNJS4H   \n",
       "3535                                   en-us    US           Houston  zIgLx8   \n",
       "3536                          en-US,en;q=0.5  None               NaN  xIcyim   \n",
       "3537     es-es,es;q=0.8,en-us;q=0.5,en;q=0.3    HN       Tegucigalpa  zCaLwp   \n",
       "3538                                   en-us    US       Los Angeles  qMac9k   \n",
       "3539                                     NaN    US          Bellevue  zu2M5o   \n",
       "3540                          en-US,en;q=0.8    US            Payson  wcndER   \n",
       "3541                                     NaN    US          Bellevue  zu2M5o   \n",
       "3542                                   en-us    US         Pittsburg  y3reI1   \n",
       "3543                                     NaN   NaN               NaN     NaN   \n",
       "3544                          en-us,en;q=0.5    US        Wentzville  vNJS4H   \n",
       "3545                          en-us,en;q=0.5    US     Saint Charles  vNJS4H   \n",
       "3546                                   en-us    US       Los Angeles  qMac9k   \n",
       "3547                                   en-us    US     Silver Spring  y0jYkg   \n",
       "3548                                   en-us    US           Mcgehee  y5rMac   \n",
       "3549     sv-SE,sv;q=0.8,en-US;q=0.6,en;q=0.4    SE          Sollefte   eH8wu   \n",
       "3550                                   en-us    US      Conshohocken  A00b72   \n",
       "3551                          en-US,en;q=0.8  None               NaN  wcndER   \n",
       "3552                                     NaN    US           Decatur  rqgJuE   \n",
       "3553                                   en-us    US        Shrewsbury  9b6kNl   \n",
       "3554                                   en-us    US        Shrewsbury  axNK8c   \n",
       "3555                                      en    US           Paramus  e5SvKE   \n",
       "3556                          en-US,en;q=0.8    US     Oklahoma City  jQLtP4   \n",
       "3557                                     NaN    US             Provo  mwszkS   \n",
       "3558                                     NaN    US     Mountain View  zjtI4X   \n",
       "3559                                   en-US    US           Mc Lean  qxKrTK   \n",
       "\n",
       "       gr       h          hc           hh   kw              l  \\\n",
       "0      MA  wfLQtf  1331822918    1.usa.gov  NaN        orofrog   \n",
       "1      UT  mwszkS  1308262393         j.mp  NaN          bitly   \n",
       "2      DC  xxr3Qb  1331919941    1.usa.gov  NaN          bitly   \n",
       "3      27  zUtuOu  1331923068    1.usa.gov  NaN       alelex88   \n",
       "4      MA  9b6kNl  1273672411       bit.ly  NaN          bitly   \n",
       "5      MA  axNK8c  1273672506       bit.ly  NaN          bitly   \n",
       "6      77  zkpJBR  1331922854    1.usa.gov  NaN       bnjacobs   \n",
       "7     NaN  zkpJBR  1331922854    1.usa.gov  NaN       bnjacobs   \n",
       "8     NaN  zkpJBR  1331922854    1.usa.gov  NaN       bnjacobs   \n",
       "9     NaN  zUtuOu  1331923068    1.usa.gov  NaN       alelex88   \n",
       "10     WA  u0uD9q  1319563556    1.usa.gov  NaN   o_4us71ccioa   \n",
       "11     DC  A0nRz4  1331815838    1.usa.gov  NaN    darrellissa   \n",
       "12     VA  u0uD9q  1319563556    1.usa.gov  NaN   o_4us71ccioa   \n",
       "13    NaN     NaN         NaN          NaN  NaN            NaN   \n",
       "14     GA  2rOUYc  1255769846    1.usa.gov  NaN          bitly   \n",
       "15     00  rtrrth  1317318030         j.mp  NaN     walkeryuen   \n",
       "16     00  qWkgbq  1317318039         j.mp  NaN     walkeryuen   \n",
       "17     ME  x3jOIv  1331839576    1.usa.gov  NaN  andyzieminski   \n",
       "18     UT  mwszkS  1308262393    1.usa.gov  NaN          bitly   \n",
       "19     20  zkpJBR  1331922854    1.usa.gov  NaN       bnjacobs   \n",
       "20     51  ytZYWR  1331670549    bitly.com  NaN        jplnews   \n",
       "21     MD  zkpJBR  1331922854    1.usa.gov  NaN       bnjacobs   \n",
       "22     DE  y3ZImz  1331064158    1.usa.gov  NaN          bitly   \n",
       "23     PA  wWiOiD  1330217829    1.usa.gov  NaN          bitly   \n",
       "24     59  zkpJBR  1331922854    1.usa.gov  NaN       bnjacobs   \n",
       "25     14  zkpJBR  1331922854    1.usa.gov  NaN       bnjacobs   \n",
       "26     04  zkpJBR  1331922854    1.usa.gov  NaN       bnjacobs   \n",
       "27     27  zUtuOu  1331923068    1.usa.gov  NaN       alelex88   \n",
       "28    NaN  u0uD9q  1319563556    1.usa.gov  NaN   o_4us71ccioa   \n",
       "29    NaN  FPX0IL  1331922978    1.usa.gov  NaN   twittershare   \n",
       "...   ...     ...         ...          ...  ...            ...   \n",
       "3530   CA  wqUkTo  1331908247  go.nasa.gov  NaN    nasatwitter   \n",
       "3531  NaN  zkpJBR  1331922854    1.usa.gov  NaN       bnjacobs   \n",
       "3532   DC  A9ct6C  1331926420    1.usa.gov  NaN          ncsha   \n",
       "3533   FL  ieCdgH  1301393171  go.nasa.gov  NaN    nasatwitter   \n",
       "3534   TX  u0uD9q  1319563556    1.usa.gov  NaN   o_4us71ccioa   \n",
       "3535   TX  yrPaLt  1331903484      aash.to  NaN         aashto   \n",
       "3536  NaN  yG1TTf  1331728309  go.nasa.gov  NaN    nasatwitter   \n",
       "3537   08  w63FZW  1331546756    1.usa.gov  NaN      bufferapp   \n",
       "3538   CA  qds1Ge  1310473559    1.usa.gov  NaN  healthypeople   \n",
       "3539   WA  zDhdro  1331586192       bit.ly  NaN       glimtwin   \n",
       "3540   UT  zkpJBR  1331922854    1.usa.gov  NaN       bnjacobs   \n",
       "3541   WA  zDhdro  1331586192    1.usa.gov  NaN       glimtwin   \n",
       "3542   CA  y3reI1  1331926120    1.usa.gov  NaN          bitly   \n",
       "3543  NaN     NaN         NaN          NaN  NaN            NaN   \n",
       "3544   MO  u0uD9q  1319563556    1.usa.gov  NaN   o_4us71ccioa   \n",
       "3545   IL  u0uD9q  1319563556    1.usa.gov  NaN   o_4us71ccioa   \n",
       "3546   CA  qds1Ge  1310473559    1.usa.gov  NaN  healthypeople   \n",
       "3547   MD  y0jYkg  1331851811    1.usa.gov  NaN          bitly   \n",
       "3548   AR  xANY6O  1331916302    1.usa.gov  NaN    twitterfeed   \n",
       "3549   24  7dtjei  1260316355    1.usa.gov  NaN   tweetdeckapi   \n",
       "3550   PA  yGSwzn  1331917632    1.usa.gov  NaN        addthis   \n",
       "3551  NaN  zkpJBR  1331922854    1.usa.gov  NaN       bnjacobs   \n",
       "3552   AL  xcz8vt  1331227417    1.usa.gov  NaN      bootsnall   \n",
       "3553   MA  9b6kNl  1273672411       bit.ly  NaN          bitly   \n",
       "3554   MA  axNK8c  1273672506       bit.ly  NaN          bitly   \n",
       "3555   NJ  fqPSr9  1301298479    1.usa.gov  NaN   tweetdeckapi   \n",
       "3556   OK  jQLtP4  1307530247    1.usa.gov  NaN          bitly   \n",
       "3557   UT  mwszkS  1308262393         j.mp  NaN          bitly   \n",
       "3558   CA  zjtI4X  1327528527    1.usa.gov  NaN          bitly   \n",
       "3559   VA  qxKrTK  1312897670    1.usa.gov  NaN          bitly   \n",
       "\n",
       "                            ll  nk  \\\n",
       "0      [42.576698, -70.954903]   1   \n",
       "1     [40.218102, -111.613297]   0   \n",
       "2        [38.9007, -77.043098]   1   \n",
       "3     [-23.549999, -46.616699]   0   \n",
       "4      [42.286499, -71.714699]   0   \n",
       "5      [42.286499, -71.714699]   0   \n",
       "6         [51.116699, 15.2833]   0   \n",
       "7                          NaN   0   \n",
       "8                          NaN   0   \n",
       "9                          NaN   0   \n",
       "10      [47.5951, -122.332603]   1   \n",
       "11     [38.937599, -77.092796]   0   \n",
       "12     [38.790901, -77.094704]   1   \n",
       "13                         NaN NaN   \n",
       "14       [33.953201, -84.5177]   1   \n",
       "15       [22.2833, 114.150002]   1   \n",
       "16       [22.2833, 114.150002]   1   \n",
       "17     [44.299702, -70.369797]   0   \n",
       "18    [40.218102, -111.613297]   0   \n",
       "19        [45.438599, 12.3267]   0   \n",
       "20        [37.516701, -5.9833]   0   \n",
       "21     [38.939201, -76.635002]   0   \n",
       "22        [39.785, -75.682297]   0   \n",
       "23       [40.174999, -76.3078]   0   \n",
       "24            [43.25, -2.9667]   0   \n",
       "25        [3.1667, 101.699997]   0   \n",
       "26      [35.166698, 33.366699]   0   \n",
       "27      [-23.5333, -46.616699]   0   \n",
       "28                         NaN   0   \n",
       "29                         NaN   1   \n",
       "...                        ...  ..   \n",
       "3530    [37.7645, -122.429398]   0   \n",
       "3531                       NaN   0   \n",
       "3532   [38.904202, -77.031998]   1   \n",
       "3533   [30.279301, -81.585098]   1   \n",
       "3534   [33.149899, -96.855499]   1   \n",
       "3535   [29.775499, -95.415199]   1   \n",
       "3536                       NaN   0   \n",
       "3537        [14.1, -87.216698]   0   \n",
       "3538  [34.041599, -118.298798]   0   \n",
       "3539  [47.615398, -122.210297]   0   \n",
       "3540  [40.014198, -111.738899]   0   \n",
       "3541  [47.615398, -122.210297]   0   \n",
       "3542    [38.0051, -121.838699]   0   \n",
       "3543                       NaN NaN   \n",
       "3544   [38.790001, -90.854897]   1   \n",
       "3545     [41.9352, -88.290901]   1   \n",
       "3546  [34.041599, -118.298798]   1   \n",
       "3547   [39.052101, -77.014999]   1   \n",
       "3548   [33.628399, -91.356903]   1   \n",
       "3549    [63.166698, 17.266701]   1   \n",
       "3550       [40.0798, -75.2855]   0   \n",
       "3551                       NaN   0   \n",
       "3552   [34.572701, -86.940598]   0   \n",
       "3553   [42.286499, -71.714699]   0   \n",
       "3554   [42.286499, -71.714699]   0   \n",
       "3555         [40.9445, -74.07]   1   \n",
       "3556     [35.4715, -97.518997]   0   \n",
       "3557  [40.218102, -111.613297]   0   \n",
       "3558  [37.419201, -122.057404]   0   \n",
       "3559   [38.935799, -77.162102]   0   \n",
       "\n",
       "                                                      r           t  \\\n",
       "0     http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...  1331923247   \n",
       "1                              http://www.AwareMap.com/  1331923249   \n",
       "2                                  http://t.co/03elZC4Q  1331923250   \n",
       "3                                                direct  1331923249   \n",
       "4                   http://www.shrewsbury-ma.gov/selco/  1331923251   \n",
       "5                   http://www.shrewsbury-ma.gov/selco/  1331923252   \n",
       "6     http://plus.url.google.com/url?sa=z&n=13319232...  1331923255   \n",
       "7                              http://www.facebook.com/  1331923255   \n",
       "8     http://www.facebook.com/l.php?u=http%3A%2F%2F1...  1331923254   \n",
       "9                                  http://t.co/o1Pd0WeV  1331923255   \n",
       "10                                               direct  1331923258   \n",
       "11                                 http://t.co/ND7SoPyo  1331923259   \n",
       "12                                               direct  1331923259   \n",
       "13                                                  NaN         NaN   \n",
       "14                                               direct  1331923262   \n",
       "15    http://forum2.hkgolden.com/view.aspx?type=BW&m...  1331923263   \n",
       "16    http://forum2.hkgolden.com/view.aspx?type=BW&m...  1331923263   \n",
       "17                                 http://t.co/6Cx4ROLs  1331923264   \n",
       "18                             http://www.AwareMap.com/  1331923262   \n",
       "19                             http://www.facebook.com/  1331923264   \n",
       "20                             http://www.facebook.com/  1331923265   \n",
       "21                             http://www.facebook.com/  1331923267   \n",
       "22                                               direct  1331923267   \n",
       "23    http://www.facebook.com/l.php?u=http%3A%2F%2F1...  1331923267   \n",
       "24                             http://www.facebook.com/  1331923268   \n",
       "25                             http://www.facebook.com/  1331923269   \n",
       "26                 http://www.facebook.com/?ref=tn_tnmn  1331923268   \n",
       "27                                               direct  1331923269   \n",
       "28                                               direct  1331923270   \n",
       "29                                 http://t.co/5xlp0B34  1331923270   \n",
       "...                                                 ...         ...   \n",
       "3530  http://www.facebook.com/l.php?u=http%3A%2F%2Fg...  1331926815   \n",
       "3531                                             direct  1331926816   \n",
       "3532                              http://www.ncsha.org/  1331926817   \n",
       "3533                                             direct  1331926818   \n",
       "3534                                             direct  1331926820   \n",
       "3535                                             direct  1331926823   \n",
       "3536                               http://t.co/g1VKE8zS  1331926824   \n",
       "3537                               http://t.co/A8TJyibE  1331926825   \n",
       "3538                                             direct  1331926825   \n",
       "3539                                             direct  1331926827   \n",
       "3540  http://www.facebook.com/l.php?u=http%3A%2F%2F1...  1331926828   \n",
       "3541                                             direct  1331926828   \n",
       "3542  http://www.facebook.com/l.php?u=http%3A%2F%2F1...  1331926829   \n",
       "3543                                                NaN         NaN   \n",
       "3544                                             direct  1331926831   \n",
       "3545                                             direct  1331926832   \n",
       "3546                                             direct  1331926833   \n",
       "3547                                             direct  1331926836   \n",
       "3548  https://twitter.com/fdarecalls/status/18069759...  1331926836   \n",
       "3549                                             direct  1331926834   \n",
       "3550   http://www.linkedin.com/home?trk=hb_tab_home_top  1331926837   \n",
       "3551  http://plus.url.google.com/url?sa=z&n=13319268...  1331926837   \n",
       "3552                                             direct  1331926839   \n",
       "3553                http://www.shrewsbury-ma.gov/selco/  1331926840   \n",
       "3554                http://www.shrewsbury-ma.gov/selco/  1331926840   \n",
       "3555                                             direct  1331926841   \n",
       "3556  http://www.facebook.com/l.php?u=http%3A%2F%2F1...  1331926844   \n",
       "3557                           http://www.AwareMap.com/  1331926846   \n",
       "3558                                             direct  1331926847   \n",
       "3559                               http://t.co/OEEEvwjU  1331926849   \n",
       "\n",
       "                       tz                                                  u  \n",
       "0        America/New_York        http://www.ncbi.nlm.nih.gov/pubmed/22415991  \n",
       "1          America/Denver        http://www.monroecounty.gov/etc/911/rss.php  \n",
       "2        America/New_York  http://boxer.senate.gov/en/press/releases/0316...  \n",
       "3       America/Sao_Paulo            http://apod.nasa.gov/apod/ap120312.html  \n",
       "4        America/New_York  http://www.shrewsbury-ma.gov/egov/gallery/1341...  \n",
       "5        America/New_York  http://www.shrewsbury-ma.gov/egov/gallery/1341...  \n",
       "6           Europe/Warsaw  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "7                          http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "8                          http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "9                                    http://apod.nasa.gov/apod/ap120312.html  \n",
       "10    America/Los_Angeles  https://www.nysdot.gov/rexdesign/design/commun...  \n",
       "11       America/New_York  http://oversight.house.gov/wp-content/uploads/...  \n",
       "12       America/New_York  https://www.nysdot.gov/rexdesign/design/commun...  \n",
       "13                    NaN                                                NaN  \n",
       "14       America/New_York               http://toxtown.nlm.nih.gov/index.php  \n",
       "15         Asia/Hong_Kong  http://www.ssd.noaa.gov/PS/TROP/TCFP/data/curr...  \n",
       "16         Asia/Hong_Kong  http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc...  \n",
       "17       America/New_York  http://www.usda.gov/wps/portal/usda/usdahome?c...  \n",
       "18         America/Denver        http://www.monroecounty.gov/etc/911/rss.php  \n",
       "19            Europe/Rome  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "20           Africa/Ceuta  http://voyager.jpl.nasa.gov/imagesvideo/uranus...  \n",
       "21       America/New_York  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "22       America/New_York  http://portal.hud.gov/hudportal/documents/hudd...  \n",
       "23       America/New_York  http://www.tricare.mil/mybenefit/ProfileFilter...  \n",
       "24          Europe/Madrid  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "25      Asia/Kuala_Lumpur  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "26           Asia/Nicosia  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "27      America/Sao_Paulo            http://apod.nasa.gov/apod/ap120312.html  \n",
       "28                         https://www.nysdot.gov/rexdesign/design/commun...  \n",
       "29                         http://www.ed.gov/news/media-advisories/us-dep...  \n",
       "...                   ...                                                ...  \n",
       "3530  America/Los_Angeles  http://www.nasa.gov/multimedia/imagegallery/im...  \n",
       "3531                       http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "3532     America/New_York  http://portal.hud.gov/hudportal/HUD?src=/press...  \n",
       "3533     America/New_York                         http://apod.nasa.gov/apod/  \n",
       "3534      America/Chicago  https://www.nysdot.gov/rexdesign/design/commun...  \n",
       "3535      America/Chicago  http://ntl.bts.gov/lib/44000/44300/44374/FHWA-...  \n",
       "3536                       http://www.nasa.gov/mission_pages/hurricanes/a...  \n",
       "3537  America/Tegucigalpa            http://apod.nasa.gov/apod/ap120312.html  \n",
       "3538  America/Los_Angeles  http://healthypeople.gov/2020/connect/webinars...  \n",
       "3539  America/Los_Angeles  http://www.federalreserve.gov/newsevents/press...  \n",
       "3540       America/Denver  http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "3541  America/Los_Angeles  http://www.federalreserve.gov/newsevents/press...  \n",
       "3542  America/Los_Angeles  http://www.sba.gov/community/blogs/community-b...  \n",
       "3543                  NaN                                                NaN  \n",
       "3544      America/Chicago  https://www.nysdot.gov/rexdesign/design/commun...  \n",
       "3545      America/Chicago  https://www.nysdot.gov/rexdesign/design/commun...  \n",
       "3546  America/Los_Angeles  http://healthypeople.gov/2020/connect/webinars...  \n",
       "3547     America/New_York  http://www.epa.gov/otaq/regs/fuels/additive/e1...  \n",
       "3548      America/Chicago    http://www.fda.gov/Safety/Recalls/ucm296326.htm  \n",
       "3549     Europe/Stockholm  http://www.nasa.gov/mission_pages/WISE/main/in...  \n",
       "3550     America/New_York  http://www.nlm.nih.gov/medlineplus/news/fullst...  \n",
       "3551                       http://www.nasa.gov/mission_pages/nustar/main/...  \n",
       "3552      America/Chicago  http://travel.state.gov/passport/passport_5535...  \n",
       "3553     America/New_York  http://www.shrewsbury-ma.gov/egov/gallery/1341...  \n",
       "3554     America/New_York  http://www.shrewsbury-ma.gov/egov/gallery/1341...  \n",
       "3555     America/New_York  http://www.fda.gov/AdvisoryCommittees/Committe...  \n",
       "3556      America/Chicago  http://www.okc.gov/PublicNotificationSystem/Fo...  \n",
       "3557       America/Denver        http://www.monroecounty.gov/etc/911/rss.php  \n",
       "3558  America/Los_Angeles                http://www.ahrq.gov/qual/qitoolkit/  \n",
       "3559     America/New_York  http://herndon-va.gov/Content/public_safety/Pu...  \n",
       "\n",
       "[3560 rows x 18 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = DataFrame(records)\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 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: 528.4+ KB\n"
     ]
    }
   ],
   "source": [
    "frame.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "시간대 (다시) 집계해 보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "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",
       "Europe/Berlin                       28\n",
       "Europe/Rome                         27\n",
       "America/Rainy_River                 25\n",
       "Europe/Amsterdam                    22\n",
       "America/Indianapolis                20\n",
       "America/Phoenix                     20\n",
       "Europe/Warsaw                       16\n",
       "America/Mexico_City                 15\n",
       "Europe/Stockholm                    14\n",
       "Europe/Paris                        14\n",
       "America/Vancouver                   12\n",
       "Pacific/Auckland                    11\n",
       "Europe/Prague                       10\n",
       "Europe/Helsinki                     10\n",
       "Asia/Hong_Kong                      10\n",
       "America/Puerto_Rico                 10\n",
       "Europe/Oslo                         10\n",
       "Europe/Moscow                       10\n",
       "Asia/Calcutta                        9\n",
       "America/Montreal                     9\n",
       "                                  ... \n",
       "Europe/Belgrade                      2\n",
       "Asia/Pontianak                       1\n",
       "America/Mazatlan                     1\n",
       "Europe/Skopje                        1\n",
       "America/Argentina/Cordoba            1\n",
       "Europe/Sofia                         1\n",
       "America/Argentina/Mendoza            1\n",
       "America/St_Kitts                     1\n",
       "America/Santo_Domingo                1\n",
       "America/Monterrey                    1\n",
       "Asia/Novosibirsk                     1\n",
       "America/Argentina/Buenos_Aires       1\n",
       "Europe/Volgograd                     1\n",
       "America/Caracas                      1\n",
       "Australia/Queensland                 1\n",
       "Europe/Ljubljana                     1\n",
       "Asia/Riyadh                          1\n",
       "Asia/Manila                          1\n",
       "Africa/Casablanca                    1\n",
       "Africa/Johannesburg                  1\n",
       "Asia/Yekaterinburg                   1\n",
       "America/Lima                         1\n",
       "Asia/Kuching                         1\n",
       "Africa/Lusaka                        1\n",
       "America/Tegucigalpa                  1\n",
       "America/La_Paz                       1\n",
       "Asia/Nicosia                         1\n",
       "Europe/Uzhgorod                      1\n",
       "America/Costa_Rica                   1\n",
       "America/Montevideo                   1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['tz'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "없는 필드와 비어있는 값 처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clean_tz = frame['tz'].fillna('Missing')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "불리언 필터로 비어있는 값을 처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'selection' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-68e11a955d77>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mselection\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mselection\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'selection' is not defined"
     ]
    }
   ],
   "source": [
    "del selection\n",
    "selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "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                   Unknown\n",
       "8                   Unknown\n",
       "9                   Unknown\n",
       "10      America/Los_Angeles\n",
       "11         America/New_York\n",
       "12         America/New_York\n",
       "13                  Missing\n",
       "14         America/New_York\n",
       "15           Asia/Hong_Kong\n",
       "16           Asia/Hong_Kong\n",
       "17         America/New_York\n",
       "18           America/Denver\n",
       "19              Europe/Rome\n",
       "20             Africa/Ceuta\n",
       "21         America/New_York\n",
       "22         America/New_York\n",
       "23         America/New_York\n",
       "24            Europe/Madrid\n",
       "25        Asia/Kuala_Lumpur\n",
       "26             Asia/Nicosia\n",
       "27        America/Sao_Paulo\n",
       "28                  Unknown\n",
       "29                  Unknown\n",
       "               ...         \n",
       "3530    America/Los_Angeles\n",
       "3531                Unknown\n",
       "3532       America/New_York\n",
       "3533       America/New_York\n",
       "3534        America/Chicago\n",
       "3535        America/Chicago\n",
       "3536                Unknown\n",
       "3537    America/Tegucigalpa\n",
       "3538    America/Los_Angeles\n",
       "3539    America/Los_Angeles\n",
       "3540         America/Denver\n",
       "3541    America/Los_Angeles\n",
       "3542    America/Los_Angeles\n",
       "3543                Missing\n",
       "3544        America/Chicago\n",
       "3545        America/Chicago\n",
       "3546    America/Los_Angeles\n",
       "3547       America/New_York\n",
       "3548        America/Chicago\n",
       "3549       Europe/Stockholm\n",
       "3550       America/New_York\n",
       "3551                Unknown\n",
       "3552        America/Chicago\n",
       "3553       America/New_York\n",
       "3554       America/New_York\n",
       "3555       America/New_York\n",
       "3556        America/Chicago\n",
       "3557         America/Denver\n",
       "3558    America/Los_Angeles\n",
       "3559       America/New_York\n",
       "Name: tz, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_tz[selection]= 'Unknown'\n",
    "clean_tz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz_counts = clean_tz.value_counts()\n",
    "tz_counts[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPython Notebook 내에서 그래프 출력 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdb92908>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MBiYC5ZW8H1VautfczKyPKZVKlEql95ZbW1sL6bcZEutASXNzy3dExFlk5zMvk/QKWdKp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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdd2c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tz_counts[:10].plot(kind='barh', rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "어떤 브라우저인가?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'GoogleMaps/RochesterNY'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "없는 값은 떨구기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agents = [a.split()[0] for a in frame['a'].dropna()]\n",
    "results = Series(agents)\n",
    "results[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.value_counts()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "윈도우 Vs. 비-윈도우"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...\n",
       "1                               GoogleMaps/RochesterNY\n",
       "2    Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...\n",
       "3    Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...\n",
       "4    Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...\n",
       "5    Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...\n",
       "6    Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.1...\n",
       "7    Mozilla/5.0 (Windows NT 6.1; rv:2.0.1) Gecko/2...\n",
       "8    Opera/9.80 (X11; Linux zbov; U; en) Presto/2.1...\n",
       "9    Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...\n",
       "Name: a, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame['a'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cframe = frame[frame.a.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        True\n",
       "1        True\n",
       "2        True\n",
       "3        True\n",
       "4        True\n",
       "5        True\n",
       "6        True\n",
       "7        True\n",
       "8        True\n",
       "9        True\n",
       "10       True\n",
       "11       True\n",
       "12       True\n",
       "13      False\n",
       "14       True\n",
       "15       True\n",
       "16       True\n",
       "17       True\n",
       "18       True\n",
       "19       True\n",
       "20       True\n",
       "21       True\n",
       "22       True\n",
       "23       True\n",
       "24       True\n",
       "25       True\n",
       "26       True\n",
       "27       True\n",
       "28       True\n",
       "29       True\n",
       "        ...  \n",
       "3530     True\n",
       "3531     True\n",
       "3532     True\n",
       "3533     True\n",
       "3534     True\n",
       "3535     True\n",
       "3536     True\n",
       "3537     True\n",
       "3538     True\n",
       "3539     True\n",
       "3540     True\n",
       "3541     True\n",
       "3542     True\n",
       "3543    False\n",
       "3544     True\n",
       "3545     True\n",
       "3546     True\n",
       "3547     True\n",
       "3548     True\n",
       "3549     True\n",
       "3550     True\n",
       "3551     True\n",
       "3552     True\n",
       "3553     True\n",
       "3554     True\n",
       "3555     True\n",
       "3556     True\n",
       "3557     True\n",
       "3558     True\n",
       "3559     True\n",
       "Name: a, dtype: bool"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame[\"a\"] == frame.a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cframe['a'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Windows', 'Not Windows', 'Windows', 'Not Windows', 'Windows'], \n",
       "      dtype='|S11')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "operating_system = np.where(cframe['a'].str.contains('Windows'),\n",
    "         'Windows', 'Not Windows')\n",
    "operating_system[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "시간대별 운영체제 분류"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "by_tz_os = cframe.groupby(['tz', operating_system])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x000000000DB8A5F8>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_tz_os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Cairo</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Casablanca</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Ceuta</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Johannesburg</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa/Lusaka</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Anchorage</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Buenos_Aires</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Cordoba</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Argentina/Mendoza</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Not Windows  Windows\n",
       "tz                                                  \n",
       "                                        245      276\n",
       "Africa/Cairo                              0        3\n",
       "Africa/Casablanca                         0        1\n",
       "Africa/Ceuta                              0        2\n",
       "Africa/Johannesburg                       0        1\n",
       "Africa/Lusaka                             0        1\n",
       "America/Anchorage                         4        1\n",
       "America/Argentina/Buenos_Aires            1        0\n",
       "America/Argentina/Cordoba                 0        1\n",
       "America/Argentina/Mendoza                 0        1"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_counts = by_tz_os.size().unstack().fillna(0)\n",
    "agg_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexer = agg_counts.sum(1).argsort()\n",
    "indexer[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "count_subset = agg_counts.take(indexer)[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xed3f5c0>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MK65IGtyEfe4Hvp7bpxvQFXgPeFtST+DwtG1LoENELClWr6e9GcDRkjqlUfrRwAxJ2wEf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\nR9aStpa0aN3eXjM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TuXNnxo8fX7vPihUruOWWW7jgggvYfPPN6d27N2effTbXX399bTzF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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xed21e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_subset.plot(kind='barh', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "비율을 비교해서 살펴보기 쉽도록 정규화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>0.393939</td>\n",
       "      <td>0.606061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/Madrid</th>\n",
       "      <td>0.457143</td>\n",
       "      <td>0.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pacific/Honolulu</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia/Tokyo</th>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.945946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe/London</th>\n",
       "      <td>0.581081</td>\n",
       "      <td>0.418919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Denver</th>\n",
       "      <td>0.691099</td>\n",
       "      <td>0.308901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Los_Angeles</th>\n",
       "      <td>0.340314</td>\n",
       "      <td>0.659686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/Chicago</th>\n",
       "      <td>0.287500</td>\n",
       "      <td>0.712500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>0.470250</td>\n",
       "      <td>0.529750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>America/New_York</th>\n",
       "      <td>0.270983</td>\n",
       "      <td>0.729017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Not Windows   Windows\n",
       "tz                                        \n",
       "America/Sao_Paulo       0.393939  0.606061\n",
       "Europe/Madrid           0.457143  0.542857\n",
       "Pacific/Honolulu        0.000000  1.000000\n",
       "Asia/Tokyo              0.054054  0.945946\n",
       "Europe/London           0.581081  0.418919\n",
       "America/Denver          0.691099  0.308901\n",
       "America/Los_Angeles     0.340314  0.659686\n",
       "America/Chicago         0.287500  0.712500\n",
       "                        0.470250  0.529750\n",
       "America/New_York        0.270983  0.729017"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normed_subset = count_subset.div(count_subset.sum(1), axis=0)\n",
    "normed_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xec83cf8>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0mJlZnQXAwrWrYp0OwJI2Jwume0gKoBNZsHoQyAfPlbntlaw6bgFDI+LTgnoB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\nxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeddd9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normed_subset.plot(kind='barh', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 영화 평점 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pydata/ch02/movielens\\\\users.dat'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'pydata/ch02/movielens'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users=pd.read_csv(path+'/users.dat',\n",
    "           sep='::',\n",
    "           header=None,\n",
    "           names=['user_id', 'gender', 'age', 'occupation', 'zip'])\n",
    "users[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>661</td>\n",
       "      <td>3</td>\n",
       "      <td>978302109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamp\n",
       "0        1      1193       5  978300760\n",
       "1        1       661       3  978302109\n",
       "2        1       914       3  978301968"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path+'/ratings.dat',\n",
    "                     sep='::',\n",
    "                     names=['user_id', 'movie_id', 'rating', 'timestamp'])\n",
    "ratings[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3873</th>\n",
       "      <td>3943</td>\n",
       "      <td>Bamboozled (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3874</th>\n",
       "      <td>3944</td>\n",
       "      <td>Bootmen (2000)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3875</th>\n",
       "      <td>3945</td>\n",
       "      <td>Digimon: The Movie (2000)</td>\n",
       "      <td>Adventure|Animation|Children's</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3876</th>\n",
       "      <td>3946</td>\n",
       "      <td>Get Carter (2000)</td>\n",
       "      <td>Action|Drama|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3877</th>\n",
       "      <td>3947</td>\n",
       "      <td>Get Carter (1971)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3878</th>\n",
       "      <td>3948</td>\n",
       "      <td>Meet the Parents (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3879</th>\n",
       "      <td>3949</td>\n",
       "      <td>Requiem for a Dream (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3880</th>\n",
       "      <td>3950</td>\n",
       "      <td>Tigerland (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3881</th>\n",
       "      <td>3951</td>\n",
       "      <td>Two Family House (2000)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3882</th>\n",
       "      <td>3952</td>\n",
       "      <td>Contender, The (2000)</td>\n",
       "      <td>Drama|Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id                       title                          genres\n",
       "3873      3943           Bamboozled (2000)                          Comedy\n",
       "3874      3944              Bootmen (2000)                    Comedy|Drama\n",
       "3875      3945   Digimon: The Movie (2000)  Adventure|Animation|Children's\n",
       "3876      3946           Get Carter (2000)           Action|Drama|Thriller\n",
       "3877      3947           Get Carter (1971)                        Thriller\n",
       "3878      3948     Meet the Parents (2000)                          Comedy\n",
       "3879      3949  Requiem for a Dream (2000)                           Drama\n",
       "3880      3950            Tigerland (2000)                           Drama\n",
       "3881      3951     Two Family House (2000)                           Drama\n",
       "3882      3952       Contender, The (2000)                  Drama|Thriller"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv(path+'/movies.dat',\n",
    "                    sep='::',\n",
    "                    names=['movie_id', 'title', 'genres'],\n",
    "                    encoding='latin-1')\n",
    "movies[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame 병합하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>zip</th>\n",
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       "  </thead>\n",
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       "      <td>1193</td>\n",
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       "      <td>978300760</td>\n",
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       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
       "      <td>1193</td>\n",
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       "      <td>978298413</td>\n",
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       "      <td>16</td>\n",
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       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
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       "      <td>12</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978220179</td>\n",
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       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>32793</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978199279</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>22903</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978158471</td>\n",
       "      <td>M</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>95350</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "      <td>1193</td>\n",
       "      <td>4</td>\n",
       "      <td>978156168</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>95825</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>982730936</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48073</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978136709</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>10023</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>28</td>\n",
       "      <td>1193</td>\n",
       "      <td>3</td>\n",
       "      <td>978125194</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>14607</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978557765</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>55421</td>\n",
       "      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
       "5       18      1193       4  978156168      F   18           3  95825   \n",
       "6       19      1193       5  982730936      M    1          10  48073   \n",
       "7       24      1193       5  978136709      F   25           7  10023   \n",
       "8       28      1193       3  978125194      F   25           1  14607   \n",
       "9       33      1193       5  978557765      M   45           3  55421   \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",
       "5  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "6  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "7  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "8  One Flew Over the Cuckoo's Nest (1975)  Drama  \n",
       "9  One Flew Over the Cuckoo's Nest (1975)  Drama  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(pd.merge(ratings, users), movies)\n",
    "data[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "열 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     2\n",
       "2    12\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['user_id'][:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "특정 행 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ix[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분석목표: 성별에 따른 각 영화의 평균 평점 구하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings = data.pivot_table('rating', aggfunc='mean',\n",
    "                index='title',\n",
    "                columns='gender')\n",
    "mean_ratings[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분석목표: 평점이 250건 이상인 영화만 추려내기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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",
       "1-900 (1994)                           2\n",
       "10 Things I Hate About You (1999)    700\n",
       "101 Dalmatians (1961)                565\n",
       "101 Dalmatians (1996)                364\n",
       "12 Angry Men (1957)                  616\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_by_title = data.groupby('title').size()\n",
    "ratings_by_title[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "제목만 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u''burbs, The (1989)', u'10 Things I Hate About You (1999)',\n",
       "       u'101 Dalmatians (1961)', u'101 Dalmatians (1996)',\n",
       "       u'12 Angry Men (1957)', u'13th Warrior, The (1999)',\n",
       "       u'2 Days in the Valley (1996)', u'20,000 Leagues Under the Sea (1954)',\n",
       "       u'2001: A Space Odyssey (1968)', u'2010 (1984)', \n",
       "       ...\n",
       "       u'X-Men (2000)', u'Year of Living Dangerously (1982)',\n",
       "       u'Yellow Submarine (1968)', u'You've Got Mail (1998)',\n",
       "       u'Young Frankenstein (1974)', u'Young Guns (1988)',\n",
       "       u'Young Guns II (1990)', u'Young Sherlock Holmes (1985)',\n",
       "       u'Zero Effect (1998)', u'eXistenZ (1999)'],\n",
       "      dtype='object', name=u'title', length=1216)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_titles = ratings_by_title.index[ratings_by_title >= 250]\n",
    "active_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>13th Warrior, The (1999)</th>\n",
       "      <td>3.112000</td>\n",
       "      <td>3.168000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Days in the Valley (1996)</th>\n",
       "      <td>3.488889</td>\n",
       "      <td>3.244813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20,000 Leagues Under the Sea (1954)</th>\n",
       "      <td>3.670103</td>\n",
       "      <td>3.709205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001: A Space Odyssey (1968)</th>\n",
       "      <td>3.825581</td>\n",
       "      <td>4.129738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010 (1984)</th>\n",
       "      <td>3.446809</td>\n",
       "      <td>3.413712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "13th Warrior, The (1999)             3.112000  3.168000\n",
       "2 Days in the Valley (1996)          3.488889  3.244813\n",
       "20,000 Leagues Under the Sea (1954)  3.670103  3.709205\n",
       "2001: A Space Odyssey (1968)         3.825581  4.129738\n",
       "2010 (1984)                          3.446809  3.413712"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings = mean_ratings.ix[active_titles]\n",
    "mean_ratings[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_female_ratings = mean_ratings.sort_index(by='F', ascending=False)\n",
    "top_female_ratings[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 평점 차이 구하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분석목표: 남녀 평균 평점 차이"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>'burbs, The (1989)</th>\n",
       "      <td>2.793478</td>\n",
       "      <td>2.962085</td>\n",
       "      <td>0.168607</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",
       "      <td>-0.334586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1961)</th>\n",
       "      <td>3.791444</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>-0.291444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101 Dalmatians (1996)</th>\n",
       "      <td>3.240000</td>\n",
       "      <td>2.911215</td>\n",
       "      <td>-0.328785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12 Angry Men (1957)</th>\n",
       "      <td>4.184397</td>\n",
       "      <td>4.328421</td>\n",
       "      <td>0.144024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13th Warrior, The (1999)</th>\n",
       "      <td>3.112000</td>\n",
       "      <td>3.168000</td>\n",
       "      <td>0.056000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 Days in the Valley (1996)</th>\n",
       "      <td>3.488889</td>\n",
       "      <td>3.244813</td>\n",
       "      <td>-0.244076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20,000 Leagues Under the Sea (1954)</th>\n",
       "      <td>3.670103</td>\n",
       "      <td>3.709205</td>\n",
       "      <td>0.039102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001: A Space Odyssey (1968)</th>\n",
       "      <td>3.825581</td>\n",
       "      <td>4.129738</td>\n",
       "      <td>0.304156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010 (1984)</th>\n",
       "      <td>3.446809</td>\n",
       "      <td>3.413712</td>\n",
       "      <td>-0.033097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                                      F         M      diff\n",
       "title                                                            \n",
       "'burbs, The (1989)                   2.793478  2.962085  0.168607\n",
       "10 Things I Hate About You (1999)    3.646552  3.311966 -0.334586\n",
       "101 Dalmatians (1961)                3.791444  3.500000 -0.291444\n",
       "101 Dalmatians (1996)                3.240000  2.911215 -0.328785\n",
       "12 Angry Men (1957)                  4.184397  4.328421  0.144024\n",
       "13th Warrior, The (1999)             3.112000  3.168000  0.056000\n",
       "2 Days in the Valley (1996)          3.488889  3.244813 -0.244076\n",
       "20,000 Leagues Under the Sea (1954)  3.670103  3.709205  0.039102\n",
       "2001: A Space Odyssey (1968)         3.825581  4.129738  0.304156\n",
       "2010 (1984)                          3.446809  3.413712 -0.033097"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']\n",
    "mean_ratings[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "diff 열을 기준으로 인덱스 정렬 (오름차순)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>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",
       "    <tr>\n",
       "      <th>French Kiss (1995)</th>\n",
       "      <td>3.535714</td>\n",
       "      <td>3.056962</td>\n",
       "      <td>-0.478752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Little Shop of Horrors, The (1960)</th>\n",
       "      <td>3.650000</td>\n",
       "      <td>3.179688</td>\n",
       "      <td>-0.470312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guys and Dolls (1955)</th>\n",
       "      <td>4.051724</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>-0.468391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mary Poppins (1964)</th>\n",
       "      <td>4.197740</td>\n",
       "      <td>3.730594</td>\n",
       "      <td>-0.467147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patch Adams (1998)</th>\n",
       "      <td>3.473282</td>\n",
       "      <td>3.008746</td>\n",
       "      <td>-0.464536</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\n",
       "French Kiss (1995)                     3.535714  3.056962 -0.478752\n",
       "Little Shop of Horrors, The (1960)     3.650000  3.179688 -0.470312\n",
       "Guys and Dolls (1955)                  4.051724  3.583333 -0.468391\n",
       "Mary Poppins (1964)                    4.197740  3.730594 -0.467147\n",
       "Patch Adams (1998)                     3.473282  3.008746 -0.464536"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff = mean_ratings.sort_index(by='diff')\n",
    "sorted_by_diff[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "남성들이 선호하는 영화 15개를 내림차순으로 표시"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    <tr>\n",
       "      <th>Porky's (1981)</th>\n",
       "      <td>2.296875</td>\n",
       "      <td>2.836364</td>\n",
       "      <td>0.539489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Animal House (1978)</th>\n",
       "      <td>3.628906</td>\n",
       "      <td>4.167192</td>\n",
       "      <td>0.538286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exorcist, The (1973)</th>\n",
       "      <td>3.537634</td>\n",
       "      <td>4.067239</td>\n",
       "      <td>0.529605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fright Night (1985)</th>\n",
       "      <td>2.973684</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barb Wire (1996)</th>\n",
       "      <td>1.585366</td>\n",
       "      <td>2.100386</td>\n",
       "      <td>0.515020</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\n",
       "Porky's (1981)                          2.296875  2.836364  0.539489\n",
       "Animal House (1978)                     3.628906  4.167192  0.538286\n",
       "Exorcist, The (1973)                    3.537634  4.067239  0.529605\n",
       "Fright Night (1985)                     2.973684  3.500000  0.526316\n",
       "Barb Wire (1996)                        1.585366  2.100386  0.515020"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_diff[::-1][:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분석 목표: 호불호가 갈리는 영화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "$1,000,000 Duck (1971)               1.092563\n",
       "'Night Mother (1986)                 1.118636\n",
       "'Til There Was You (1997)            1.020159\n",
       "'burbs, The (1989)                   1.107760\n",
       "...And Justice for All (1979)        0.878110\n",
       "1-900 (1994)                         0.707107\n",
       "10 Things I Hate About You (1999)    0.989815\n",
       "101 Dalmatians (1961)                0.982103\n",
       "101 Dalmatians (1996)                1.098717\n",
       "12 Angry Men (1957)                  0.812731\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_std_by_title = data.groupby('title')['rating'].std()\n",
    "rating_std_by_title[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "평점 정보가 250건 이상인 경우로 한정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "'burbs, The (1989)                     1.107760\n",
       "10 Things I Hate About You (1999)      0.989815\n",
       "101 Dalmatians (1961)                  0.982103\n",
       "101 Dalmatians (1996)                  1.098717\n",
       "12 Angry Men (1957)                    0.812731\n",
       "13th Warrior, The (1999)               1.140421\n",
       "2 Days in the Valley (1996)            0.921592\n",
       "20,000 Leagues Under the Sea (1954)    0.869685\n",
       "2001: A Space Odyssey (1968)           1.042504\n",
       "2010 (1984)                            0.946618\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_std_by_title = rating_std_by_title.ix[active_titles]\n",
    "rating_std_by_title[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "인덱스의 값을 기준으로 내림차순 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_std_by_title.order(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 역대 신생아 이름 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path_prefix = 'pydata/ch02/names'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880 = pd.read_csv(path_prefix+'/yob1880.txt', names=['name', 'sex', 'births'])\n",
    "names1880[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1880년 여아 남아 출생수 집계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "F     90993\n",
       "M    110493\n",
       "Name: births, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names1880.groupby('sex')[\"births\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1880 - 2010년까지 출생수 데이터 가져와 단일한 DataFrame으로 생성하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 버전 스타일 문자열 템플릿"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'yob1880.txt'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'yob%d.txt' % 1880"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 버전 스타일 문자열 템플릿"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'yob1880.txt'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'yob{0}.txt'.format(1880)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "이름: 이성주, 나이: 18\n"
     ]
    }
   ],
   "source": [
    "text = u'이름: {0}, 나이: {1}'.format(u'이성주', 18)\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pieces = []\n",
    "\n",
    "for year in range(1880, 2011):\n",
    "    # 해당 연도의 파일 경로 생성\n",
    "    path = path_prefix + '/yob{0}.txt'.format(year)\n",
    "    # DF 생성\n",
    "    frame = pd.read_csv(path, names=['name', 'sex', 'births'])\n",
    "    # 연도 열 추가\n",
    "    frame['year'] = year\n",
    "    pieces.append(frame)\n",
    "\n",
    "# 각 DataFrame을 합치기\n",
    "names = pd.concat(pieces, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "메모리 관리를 위해 중간에 생성된 객체들을 GC가 수집할 수 있도록 \"해제\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pieces = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "  </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"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>1690779</th>\n",
       "      <td>Zymaire</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690780</th>\n",
       "      <td>Zyonne</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690781</th>\n",
       "      <td>Zyquarius</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690782</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690783</th>\n",
       "      <td>Zzyzx</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name sex  births  year\n",
       "1690779    Zymaire   M       5  2010\n",
       "1690780     Zyonne   M       5  2010\n",
       "1690781  Zyquarius   M       5  2010\n",
       "1690782      Zyran   M       5  2010\n",
       "1690783      Zzyzx   M       5  2010"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1690784 entries, 0 to 1690783\n",
      "Data columns (total 4 columns):\n",
      "name      1690784 non-null object\n",
      "sex       1690784 non-null object\n",
      "births    1690784 non-null int64\n",
      "year      1690784 non-null int64\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 64.5+ MB\n"
     ]
    }
   ],
   "source": [
    "names.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분석목표: 연도별 성별 출생수 합계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births = names.pivot_table('births', aggfunc=sum, \n",
    "                  index='year', columns='sex')\n",
    "total_births[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분석목표: 각 연도별 성별 이름 비율"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_prop(group):\n",
    "    \"\"\"groupby를 통해 묶인 각각의 Group에 대해 연산을 수행\n",
    "    @params\n",
    "    group : group 객체\n",
    "    \"\"\"\n",
    "    # births 열의 값의 자료형을 float로 변경\n",
    "    births = group['births'].astype(float)\n",
    "    \n",
    "    group['prop'] = births / births.sum()\n",
    "    return group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = names.groupby(['year', 'sex']).apply(add_prop)\n",
    "names[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "각 그룹의 비율 합계 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(names.groupby(['year', 'sex'])[\"prop\"].sum(), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "각 연도별 성별 상위 1000개 이름 골라내기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_top1000(group):\n",
    "    top1000 = group.sort_index(by=\"births\", ascending=False)[:1000]\n",
    "    return top1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\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",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</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 rowspan=\"61\" valign=\"top\">1880</th>\n",
       "      <th rowspan=\"61\" valign=\"top\">F</th>\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",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Annie</td>\n",
       "      <td>F</td>\n",
       "      <td>1258</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Clara</td>\n",
       "      <td>F</td>\n",
       "      <td>1226</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.013474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Ella</td>\n",
       "      <td>F</td>\n",
       "      <td>1156</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.012704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Florence</td>\n",
       "      <td>F</td>\n",
       "      <td>1063</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cora</td>\n",
       "      <td>F</td>\n",
       "      <td>1045</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Martha</td>\n",
       "      <td>F</td>\n",
       "      <td>1040</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Laura</td>\n",
       "      <td>F</td>\n",
       "      <td>1012</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.011122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nellie</td>\n",
       "      <td>F</td>\n",
       "      <td>995</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Grace</td>\n",
       "      <td>F</td>\n",
       "      <td>982</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Carrie</td>\n",
       "      <td>F</td>\n",
       "      <td>949</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.010429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maude</td>\n",
       "      <td>F</td>\n",
       "      <td>858</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.009429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mabel</td>\n",
       "      <td>F</td>\n",
       "      <td>808</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Bessie</td>\n",
       "      <td>F</td>\n",
       "      <td>794</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jennie</td>\n",
       "      <td>F</td>\n",
       "      <td>793</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Gertrude</td>\n",
       "      <td>F</td>\n",
       "      <td>787</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Julia</td>\n",
       "      <td>F</td>\n",
       "      <td>783</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hattie</td>\n",
       "      <td>F</td>\n",
       "      <td>769</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Edith</td>\n",
       "      <td>F</td>\n",
       "      <td>768</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.008440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mattie</td>\n",
       "      <td>F</td>\n",
       "      <td>704</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Rose</td>\n",
       "      <td>F</td>\n",
       "      <td>700</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.007693</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>...</th>\n",
       "      <td>Alberta</td>\n",
       "      <td>F</td>\n",
       "      <td>76</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>Katharine</td>\n",
       "      <td>F</td>\n",
       "      <td>76</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Lora</td>\n",
       "      <td>F</td>\n",
       "      <td>74</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Rena</td>\n",
       "      <td>F</td>\n",
       "      <td>74</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Mina</td>\n",
       "      <td>F</td>\n",
       "      <td>73</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Rhoda</td>\n",
       "      <td>F</td>\n",
       "      <td>73</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Mathilda</td>\n",
       "      <td>F</td>\n",
       "      <td>72</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Abbie</td>\n",
       "      <td>F</td>\n",
       "      <td>71</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Eula</td>\n",
       "      <td>F</td>\n",
       "      <td>70</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Dollie</td>\n",
       "      <td>F</td>\n",
       "      <td>69</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Hettie</td>\n",
       "      <td>F</td>\n",
       "      <td>69</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Fanny</td>\n",
       "      <td>F</td>\n",
       "      <td>67</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>Ola</td>\n",
       "      <td>F</td>\n",
       "      <td>67</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Eunice</td>\n",
       "      <td>F</td>\n",
       "      <td>67</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Lenora</td>\n",
       "      <td>F</td>\n",
       "      <td>66</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Adelaide</td>\n",
       "      <td>F</td>\n",
       "      <td>65</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Christina</td>\n",
       "      <td>F</td>\n",
       "      <td>65</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Lelia</td>\n",
       "      <td>F</td>\n",
       "      <td>65</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Nelle</td>\n",
       "      <td>F</td>\n",
       "      <td>65</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Sue</td>\n",
       "      <td>F</td>\n",
       "      <td>65</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Johanna</td>\n",
       "      <td>F</td>\n",
       "      <td>64</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Lilly</td>\n",
       "      <td>F</td>\n",
       "      <td>64</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Lucinda</td>\n",
       "      <td>F</td>\n",
       "      <td>63</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Minerva</td>\n",
       "      <td>F</td>\n",
       "      <td>63</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Lettie</td>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Roxie</td>\n",
       "      <td>F</td>\n",
       "      <td>62</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Cynthia</td>\n",
       "      <td>F</td>\n",
       "      <td>61</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Hulda</td>\n",
       "      <td>F</td>\n",
       "      <td>60</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Hilda</td>\n",
       "      <td>F</td>\n",
       "      <td>60</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Helena</td>\n",
       "      <td>F</td>\n",
       "      <td>60</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name sex  births  year      prop\n",
       "year sex                                           \n",
       "1880 F   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\n",
       "         10       Annie   F    1258  1880  0.013825\n",
       "         11       Clara   F    1226  1880  0.013474\n",
       "         12        Ella   F    1156  1880  0.012704\n",
       "         13    Florence   F    1063  1880  0.011682\n",
       "         14        Cora   F    1045  1880  0.011484\n",
       "         15      Martha   F    1040  1880  0.011429\n",
       "         16       Laura   F    1012  1880  0.011122\n",
       "         17      Nellie   F     995  1880  0.010935\n",
       "         18       Grace   F     982  1880  0.010792\n",
       "         19      Carrie   F     949  1880  0.010429\n",
       "         20       Maude   F     858  1880  0.009429\n",
       "         21       Mabel   F     808  1880  0.008880\n",
       "         22      Bessie   F     794  1880  0.008726\n",
       "         23      Jennie   F     793  1880  0.008715\n",
       "         24    Gertrude   F     787  1880  0.008649\n",
       "         25       Julia   F     783  1880  0.008605\n",
       "         26      Hattie   F     769  1880  0.008451\n",
       "         27       Edith   F     768  1880  0.008440\n",
       "         28      Mattie   F     704  1880  0.007737\n",
       "         29        Rose   F     700  1880  0.007693\n",
       "...                 ...  ..     ...   ...       ...\n",
       "         170    Alberta   F      76  1880  0.000835\n",
       "         171  Katharine   F      76  1880  0.000835\n",
       "         172       Lora   F      74  1880  0.000813\n",
       "         173       Rena   F      74  1880  0.000813\n",
       "         174       Mina   F      73  1880  0.000802\n",
       "         175      Rhoda   F      73  1880  0.000802\n",
       "         176   Mathilda   F      72  1880  0.000791\n",
       "         177      Abbie   F      71  1880  0.000780\n",
       "         178       Eula   F      70  1880  0.000769\n",
       "         179     Dollie   F      69  1880  0.000758\n",
       "         180     Hettie   F      69  1880  0.000758\n",
       "         182      Fanny   F      67  1880  0.000736\n",
       "         183        Ola   F      67  1880  0.000736\n",
       "         181     Eunice   F      67  1880  0.000736\n",
       "         184     Lenora   F      66  1880  0.000725\n",
       "         185   Adelaide   F      65  1880  0.000714\n",
       "         186  Christina   F      65  1880  0.000714\n",
       "         187      Lelia   F      65  1880  0.000714\n",
       "         188      Nelle   F      65  1880  0.000714\n",
       "         189        Sue   F      65  1880  0.000714\n",
       "         190    Johanna   F      64  1880  0.000703\n",
       "         191      Lilly   F      64  1880  0.000703\n",
       "         192    Lucinda   F      63  1880  0.000692\n",
       "         193    Minerva   F      63  1880  0.000692\n",
       "         194     Lettie   F      62  1880  0.000681\n",
       "         195      Roxie   F      62  1880  0.000681\n",
       "         196    Cynthia   F      61  1880  0.000670\n",
       "         199      Hulda   F      60  1880  0.000659\n",
       "         198      Hilda   F      60  1880  0.000659\n",
       "         197     Helena   F      60  1880  0.000659\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top1000 = names.groupby(['year', 'sex']).apply(get_top1000)\n",
    "top1000[:200]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 이름 유행 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "남/여아 각각 분리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\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",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</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 rowspan=\"5\" valign=\"top\">1880</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">M</th>\n",
       "      <th>942</th>\n",
       "      <td>John</td>\n",
       "      <td>M</td>\n",
       "      <td>9655</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.087381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>William</td>\n",
       "      <td>M</td>\n",
       "      <td>9533</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.086277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>James</td>\n",
       "      <td>M</td>\n",
       "      <td>5927</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.053641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>Charles</td>\n",
       "      <td>M</td>\n",
       "      <td>5348</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.048401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>George</td>\n",
       "      <td>M</td>\n",
       "      <td>5126</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.046392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name sex  births  year      prop\n",
       "year sex                                         \n",
       "1880 M   942     John   M    9655  1880  0.087381\n",
       "         943  William   M    9533  1880  0.086277\n",
       "         944    James   M    5927  1880  0.053641\n",
       "         945  Charles   M    5348  1880  0.048401\n",
       "         946   George   M    5126  1880  0.046392"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boys = top1000[top1000['sex'] == 'M']\n",
    "boys[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\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",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</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 rowspan=\"5\" valign=\"top\">1880</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">F</th>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name sex  births  year      prop\n",
       "year sex                                         \n",
       "1880 F   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"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "girls = top1000[top1000['sex']=='F']\n",
    "girls[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분석 목표: 연도별 이름의 출생수 합계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Aaden</th>\n",
       "      <th>Aaliyah</th>\n",
       "      <th>Aarav</th>\n",
       "      <th>Aaron</th>\n",
       "      <th>Aarush</th>\n",
       "      <th>Ab</th>\n",
       "      <th>Abagail</th>\n",
       "      <th>Abb</th>\n",
       "      <th>Abbey</th>\n",
       "      <th>Abbie</th>\n",
       "      <th>...</th>\n",
       "      <th>Zoa</th>\n",
       "      <th>Zoe</th>\n",
       "      <th>Zoey</th>\n",
       "      <th>Zoie</th>\n",
       "      <th>Zola</th>\n",
       "      <th>Zollie</th>\n",
       "      <th>Zona</th>\n",
       "      <th>Zora</th>\n",
       "      <th>Zula</th>\n",
       "      <th>Zuri</th>\n",
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       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>1880</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>58</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>48</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>30</td>\n",
       "      <td>42</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>53</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 6868 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "name  Aaden  Aaliyah  Aarav  Aaron  Aarush  Ab  Abagail  Abb  Abbey  Abbie  \\\n",
       "year                                                                         \n",
       "1880    NaN      NaN    NaN    102     NaN NaN      NaN  NaN    NaN     71   \n",
       "1881    NaN      NaN    NaN     94     NaN NaN      NaN  NaN    NaN     81   \n",
       "1882    NaN      NaN    NaN     85     NaN NaN      NaN  NaN    NaN     80   \n",
       "1883    NaN      NaN    NaN    105     NaN NaN      NaN  NaN    NaN     79   \n",
       "1884    NaN      NaN    NaN     97     NaN NaN      NaN  NaN    NaN     98   \n",
       "1885    NaN      NaN    NaN     88     NaN   6      NaN  NaN    NaN     88   \n",
       "1886    NaN      NaN    NaN     86     NaN NaN      NaN  NaN    NaN     84   \n",
       "1887    NaN      NaN    NaN     78     NaN NaN      NaN  NaN    NaN    104   \n",
       "1888    NaN      NaN    NaN     90     NaN NaN      NaN  NaN    NaN    137   \n",
       "1889    NaN      NaN    NaN     85     NaN NaN      NaN  NaN    NaN    107   \n",
       "\n",
       "name  ...   Zoa  Zoe  Zoey  Zoie  Zola  Zollie  Zona  Zora  Zula  Zuri  \n",
       "year  ...                                                               \n",
       "1880  ...     8   23   NaN   NaN     7     NaN     8    28    27   NaN  \n",
       "1881  ...   NaN   22   NaN   NaN    10     NaN     9    21    27   NaN  \n",
       "1882  ...     8   25   NaN   NaN     9     NaN    17    32    21   NaN  \n",
       "1883  ...   NaN   23   NaN   NaN    10     NaN    11    35    25   NaN  \n",
       "1884  ...    13   31   NaN   NaN    14       6     8    58    27   NaN  \n",
       "1885  ...     6   27   NaN   NaN    12       6    14    48    38   NaN  \n",
       "1886  ...    13   25   NaN   NaN     8     NaN    20    52    43   NaN  \n",
       "1887  ...     9   34   NaN   NaN    23     NaN    28    46    33   NaN  \n",
       "1888  ...    11   42   NaN   NaN    23       7    30    42    45   NaN  \n",
       "1889  ...    14   29   NaN   NaN    22     NaN    29    53    55   NaN  \n",
       "\n",
       "[10 rows x 6868 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_births= top1000.pivot_table('births', aggfunc='sum',\n",
    "                   index='year',\n",
    "                   columns='name')\n",
    "total_births[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "일부 이름만 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>John</th>\n",
       "      <th>Harry</th>\n",
       "      <th>Mary</th>\n",
       "      <th>Marilyn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>9701</td>\n",
       "      <td>2158</td>\n",
       "      <td>7092</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>8795</td>\n",
       "      <td>2002</td>\n",
       "      <td>6948</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>9597</td>\n",
       "      <td>2246</td>\n",
       "      <td>8179</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>8934</td>\n",
       "      <td>2116</td>\n",
       "      <td>8044</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>9427</td>\n",
       "      <td>2338</td>\n",
       "      <td>9253</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>8801</td>\n",
       "      <td>2209</td>\n",
       "      <td>9166</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>9074</td>\n",
       "      <td>2272</td>\n",
       "      <td>9923</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>8165</td>\n",
       "      <td>2070</td>\n",
       "      <td>9935</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>9300</td>\n",
       "      <td>2772</td>\n",
       "      <td>11804</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>8600</td>\n",
       "      <td>2559</td>\n",
       "      <td>11690</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "name  John  Harry   Mary  Marilyn\n",
       "year                             \n",
       "1880  9701   2158   7092      NaN\n",
       "1881  8795   2002   6948      NaN\n",
       "1882  9597   2246   8179      NaN\n",
       "1883  8934   2116   8044      NaN\n",
       "1884  9427   2338   9253      NaN\n",
       "1885  8801   2209   9166      NaN\n",
       "1886  9074   2272   9923      NaN\n",
       "1887  8165   2070   9935      NaN\n",
       "1888  9300   2772  11804      NaN\n",
       "1889  8600   2559  11690      NaN"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_choices = ['John', 'Harry', 'Mary', 'Marilyn']\n",
    "subset = total_births[column_choices]\n",
    "subset[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "일부 이름을 그래프로 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x139175c0>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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NZcuWpVevXowbNw5V5eOPPw5b9S5/slX+IlIJaK+qowBU9YSq7gO6AWO8bmOA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc0e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "각 열을 개별 그래프로 표시"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000000011389E10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000000038B2C198>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000000038FB0780>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000000413E4AC8>], dtype=object)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j1+LA1lxdHOhjjUrvAMYBwcAqci4ObC4io625zwG6OFDdyzZv3sy4ceOoUKEC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lGxYkXi4+PTTI7j4uIICgqiQoUKBAUFpXletGhR9u/fT2RkJHv37iUyMpKjR49y/fXX07ZtWxo2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\n/w9Yd6YRP5vNuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x81e09390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset.plot(subplots=True, figsize=(12,10), grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 다양한 이름을 사용하는 경향?\n",
    "\n",
    "상위 1000개의 이름이 전체에서 차지하는 비율이 감소할수록 다양한 이름을 사용한다고 할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "  </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"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = top1000.pivot_table('prop', aggfunc='xyz', index='year', columns='sex')\n",
    "table[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "상위 1000개 이름의 연도별 성별 비율의 추세를 그래프로 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x4a72db00>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IpCCFt4hIClJ4i4ikIIW3iEgKUniLiKSgNoV3G6rpfDmqprPMzF43sxMT31QR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\nl6LdItFHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x81cd6320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table.plot(yticks=np.linspace(0, 1.2, 13))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분석 목표: 전체 출생수 50%를 차지하는 이름의 개수\n",
    "\n",
    "50%를 차지하는 이름의 개수가 많을 수록 다양한 이름이 등작한다고 할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\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",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</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 rowspan=\"5\" valign=\"top\">2010</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">M</th>\n",
       "      <th>1676644</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>1676645</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>1676646</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>1676647</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>1676648</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name sex  births  year      prop\n",
       "year sex                                             \n",
       "2010 M   1676644    Jacob   M   21875  2010  0.011523\n",
       "         1676645    Ethan   M   17866  2010  0.009411\n",
       "         1676646  Michael   M   17133  2010  0.009025\n",
       "         1676647   Jayden   M   17030  2010  0.008971\n",
       "         1676648  William   M   16870  2010  0.008887"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boys2010 = boys[boys.year == 2010]\n",
    "boys2010[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "비율의 누적 합계 구하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  sex         \n",
       "2010  M    1676644    0.011523\n",
       "           1676645    0.020934\n",
       "           1676646    0.029959\n",
       "           1676647    0.038930\n",
       "           1676648    0.047817\n",
       "           1676649    0.056579\n",
       "           1676650    0.065155\n",
       "           1676651    0.073414\n",
       "           1676652    0.081528\n",
       "           1676653    0.089621\n",
       "Name: prop, dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum = boys2010.sort_index(by='prop', ascending=False)['prop'].cumsum()\n",
    "prop_cumsum[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "누적합계가 0.5와 같거나 큰 경우에 도달하는 인덱스 찾기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([116], dtype=int64)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum.searchsorted(0.5)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5006516075268308"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_cumsum[116]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분석목표: 각 연도별 성별에 대해 50%에 해당하는 이름의 개수 산출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_half_count(group, q=0.5):\n",
    "    # 비율이 높은 순으로 내림차순 정렬\n",
    "    group = group.sort_index(by='prop', ascending=False)\n",
    "    # 배열의 색인이 0부터 시작하기 때문에 마지막에 1을 더해주어야 함.\n",
    "    return group.prop.cumsum().searchsorted(q)[0]+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diversity = top1000.groupby(['year', 'sex']).apply(get_half_count, q=0.4)\n",
    "diversity = diversity.unstack('sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>24</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex    F   M\n",
       "year        \n",
       "1880  24   9\n",
       "1881  25   9\n",
       "1882  25   9\n",
       "1883  26   9\n",
       "1884  26  10\n",
       "1885  27  10\n",
       "1886  28  10\n",
       "1887  28  11\n",
       "1888  29  11\n",
       "1889  29  11"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diversity[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x81ce4828>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B9XLcX49fm2Oiyjn3FXABgJk1Ay4KnPqGI2uz9fD/A39DjJYFCiwP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\n9//eQZSpk5amtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8224b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 마지막 글자의 변화\n",
    "\n",
    "(... 궁금한 것도 많네 ...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "name 열에서 마지막 글자만 추출하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_last_letter(name):\n",
    "    return name[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    y\n",
       "1    a\n",
       "2    a\n",
       "3    h\n",
       "4    e\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_letters = names['name'].apply(get_last_letter)\n",
    "last_letters[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분석 목표: 성별 연도별 마지막 글자에 대한 출생수 합계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"10\" halign=\"left\">F</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1880</th>\n",
       "      <th>1881</th>\n",
       "      <th>1882</th>\n",
       "      <th>1883</th>\n",
       "      <th>1884</th>\n",
       "      <th>1885</th>\n",
       "      <th>1886</th>\n",
       "      <th>1887</th>\n",
       "      <th>1888</th>\n",
       "      <th>1889</th>\n",
       "      <th>...</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>31446</td>\n",
       "      <td>31581</td>\n",
       "      <td>36536</td>\n",
       "      <td>38330</td>\n",
       "      <td>43680</td>\n",
       "      <td>45408</td>\n",
       "      <td>49100</td>\n",
       "      <td>48942</td>\n",
       "      <td>59442</td>\n",
       "      <td>58631</td>\n",
       "      <td>...</td>\n",
       "      <td>39124</td>\n",
       "      <td>38815</td>\n",
       "      <td>37825</td>\n",
       "      <td>38650</td>\n",
       "      <td>36838</td>\n",
       "      <td>36156</td>\n",
       "      <td>34654</td>\n",
       "      <td>32901</td>\n",
       "      <td>31430</td>\n",
       "      <td>28438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>50950</td>\n",
       "      <td>49284</td>\n",
       "      <td>48065</td>\n",
       "      <td>45914</td>\n",
       "      <td>43144</td>\n",
       "      <td>42600</td>\n",
       "      <td>42123</td>\n",
       "      <td>39945</td>\n",
       "      <td>38862</td>\n",
       "      <td>38859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>27113</td>\n",
       "      <td>27238</td>\n",
       "      <td>27697</td>\n",
       "      <td>26778</td>\n",
       "      <td>26078</td>\n",
       "      <td>26635</td>\n",
       "      <td>26864</td>\n",
       "      <td>25318</td>\n",
       "      <td>24048</td>\n",
       "      <td>23125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>609</td>\n",
       "      <td>607</td>\n",
       "      <td>734</td>\n",
       "      <td>810</td>\n",
       "      <td>916</td>\n",
       "      <td>862</td>\n",
       "      <td>1007</td>\n",
       "      <td>1027</td>\n",
       "      <td>1298</td>\n",
       "      <td>1374</td>\n",
       "      <td>...</td>\n",
       "      <td>60838</td>\n",
       "      <td>55829</td>\n",
       "      <td>53391</td>\n",
       "      <td>51754</td>\n",
       "      <td>50670</td>\n",
       "      <td>51410</td>\n",
       "      <td>50595</td>\n",
       "      <td>47910</td>\n",
       "      <td>46172</td>\n",
       "      <td>44398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>33378</td>\n",
       "      <td>34080</td>\n",
       "      <td>40399</td>\n",
       "      <td>41914</td>\n",
       "      <td>48089</td>\n",
       "      <td>49616</td>\n",
       "      <td>53884</td>\n",
       "      <td>54353</td>\n",
       "      <td>66750</td>\n",
       "      <td>66663</td>\n",
       "      <td>...</td>\n",
       "      <td>145395</td>\n",
       "      <td>144651</td>\n",
       "      <td>144769</td>\n",
       "      <td>142098</td>\n",
       "      <td>141123</td>\n",
       "      <td>142999</td>\n",
       "      <td>143698</td>\n",
       "      <td>140966</td>\n",
       "      <td>135496</td>\n",
       "      <td>129012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1758</td>\n",
       "      <td>1817</td>\n",
       "      <td>1819</td>\n",
       "      <td>1904</td>\n",
       "      <td>1985</td>\n",
       "      <td>1968</td>\n",
       "      <td>2090</td>\n",
       "      <td>2195</td>\n",
       "      <td>2212</td>\n",
       "      <td>2255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>44</td>\n",
       "      <td>28</td>\n",
       "      <td>...</td>\n",
       "      <td>2151</td>\n",
       "      <td>2084</td>\n",
       "      <td>2009</td>\n",
       "      <td>1837</td>\n",
       "      <td>1882</td>\n",
       "      <td>1929</td>\n",
       "      <td>2040</td>\n",
       "      <td>2059</td>\n",
       "      <td>2396</td>\n",
       "      <td>2666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>4863</td>\n",
       "      <td>4784</td>\n",
       "      <td>5567</td>\n",
       "      <td>5701</td>\n",
       "      <td>6602</td>\n",
       "      <td>6624</td>\n",
       "      <td>7146</td>\n",
       "      <td>7141</td>\n",
       "      <td>8630</td>\n",
       "      <td>8826</td>\n",
       "      <td>...</td>\n",
       "      <td>85959</td>\n",
       "      <td>88085</td>\n",
       "      <td>88226</td>\n",
       "      <td>89620</td>\n",
       "      <td>92497</td>\n",
       "      <td>98477</td>\n",
       "      <td>99414</td>\n",
       "      <td>100250</td>\n",
       "      <td>99979</td>\n",
       "      <td>98090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>61</td>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>76</td>\n",
       "      <td>84</td>\n",
       "      <td>92</td>\n",
       "      <td>85</td>\n",
       "      <td>105</td>\n",
       "      <td>141</td>\n",
       "      <td>134</td>\n",
       "      <td>...</td>\n",
       "      <td>20980</td>\n",
       "      <td>23610</td>\n",
       "      <td>26011</td>\n",
       "      <td>28500</td>\n",
       "      <td>31317</td>\n",
       "      <td>33558</td>\n",
       "      <td>35231</td>\n",
       "      <td>38151</td>\n",
       "      <td>40912</td>\n",
       "      <td>42956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1069</td>\n",
       "      <td>1088</td>\n",
       "      <td>1203</td>\n",
       "      <td>1094</td>\n",
       "      <td>1291</td>\n",
       "      <td>1241</td>\n",
       "      <td>1254</td>\n",
       "      <td>1381</td>\n",
       "      <td>1416</td>\n",
       "      <td>1459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>k</th>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>42477</td>\n",
       "      <td>42043</td>\n",
       "      <td>42296</td>\n",
       "      <td>41400</td>\n",
       "      <td>42151</td>\n",
       "      <td>42537</td>\n",
       "      <td>42136</td>\n",
       "      <td>39563</td>\n",
       "      <td>37507</td>\n",
       "      <td>35198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l</th>\n",
       "      <td>2541</td>\n",
       "      <td>2911</td>\n",
       "      <td>3527</td>\n",
       "      <td>3848</td>\n",
       "      <td>4808</td>\n",
       "      <td>5144</td>\n",
       "      <td>5721</td>\n",
       "      <td>6175</td>\n",
       "      <td>7900</td>\n",
       "      <td>8395</td>\n",
       "      <td>...</td>\n",
       "      <td>153648</td>\n",
       "      <td>153493</td>\n",
       "      <td>153862</td>\n",
       "      <td>152800</td>\n",
       "      <td>155312</td>\n",
       "      <td>156234</td>\n",
       "      <td>155203</td>\n",
       "      <td>150791</td>\n",
       "      <td>143751</td>\n",
       "      <td>133583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m</th>\n",
       "      <td>58</td>\n",
       "      <td>57</td>\n",
       "      <td>81</td>\n",
       "      <td>86</td>\n",
       "      <td>79</td>\n",
       "      <td>75</td>\n",
       "      <td>103</td>\n",
       "      <td>90</td>\n",
       "      <td>123</td>\n",
       "      <td>137</td>\n",
       "      <td>...</td>\n",
       "      <td>41967</td>\n",
       "      <td>42663</td>\n",
       "      <td>42790</td>\n",
       "      <td>43054</td>\n",
       "      <td>41600</td>\n",
       "      <td>42503</td>\n",
       "      <td>43860</td>\n",
       "      <td>44316</td>\n",
       "      <td>46278</td>\n",
       "      <td>46808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <td>3008</td>\n",
       "      <td>2959</td>\n",
       "      <td>3576</td>\n",
       "      <td>3837</td>\n",
       "      <td>4507</td>\n",
       "      <td>4735</td>\n",
       "      <td>5242</td>\n",
       "      <td>5512</td>\n",
       "      <td>6833</td>\n",
       "      <td>7103</td>\n",
       "      <td>...</td>\n",
       "      <td>616099</td>\n",
       "      <td>630322</td>\n",
       "      <td>663419</td>\n",
       "      <td>676011</td>\n",
       "      <td>686326</td>\n",
       "      <td>720998</td>\n",
       "      <td>741355</td>\n",
       "      <td>733869</td>\n",
       "      <td>715388</td>\n",
       "      <td>688677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>o</th>\n",
       "      <td>30</td>\n",
       "      <td>49</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>74</td>\n",
       "      <td>84</td>\n",
       "      <td>93</td>\n",
       "      <td>97</td>\n",
       "      <td>134</td>\n",
       "      <td>142</td>\n",
       "      <td>...</td>\n",
       "      <td>82146</td>\n",
       "      <td>83180</td>\n",
       "      <td>85423</td>\n",
       "      <td>88822</td>\n",
       "      <td>92001</td>\n",
       "      <td>96350</td>\n",
       "      <td>96895</td>\n",
       "      <td>91485</td>\n",
       "      <td>86423</td>\n",
       "      <td>81025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3419</td>\n",
       "      <td>3157</td>\n",
       "      <td>2982</td>\n",
       "      <td>2841</td>\n",
       "      <td>2768</td>\n",
       "      <td>2721</td>\n",
       "      <td>2739</td>\n",
       "      <td>2637</td>\n",
       "      <td>2595</td>\n",
       "      <td>2409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>602</td>\n",
       "      <td>618</td>\n",
       "      <td>585</td>\n",
       "      <td>523</td>\n",
       "      <td>446</td>\n",
       "      <td>430</td>\n",
       "      <td>431</td>\n",
       "      <td>339</td>\n",
       "      <td>377</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>481</td>\n",
       "      <td>417</td>\n",
       "      <td>590</td>\n",
       "      <td>640</td>\n",
       "      <td>718</td>\n",
       "      <td>799</td>\n",
       "      <td>917</td>\n",
       "      <td>910</td>\n",
       "      <td>1207</td>\n",
       "      <td>1214</td>\n",
       "      <td>...</td>\n",
       "      <td>165377</td>\n",
       "      <td>164821</td>\n",
       "      <td>169878</td>\n",
       "      <td>169452</td>\n",
       "      <td>172069</td>\n",
       "      <td>176490</td>\n",
       "      <td>177207</td>\n",
       "      <td>174632</td>\n",
       "      <td>173200</td>\n",
       "      <td>166064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>1391</td>\n",
       "      <td>1316</td>\n",
       "      <td>1637</td>\n",
       "      <td>1794</td>\n",
       "      <td>2039</td>\n",
       "      <td>2127</td>\n",
       "      <td>2524</td>\n",
       "      <td>2803</td>\n",
       "      <td>3582</td>\n",
       "      <td>3569</td>\n",
       "      <td>...</td>\n",
       "      <td>143791</td>\n",
       "      <td>139595</td>\n",
       "      <td>138632</td>\n",
       "      <td>139642</td>\n",
       "      <td>139913</td>\n",
       "      <td>143232</td>\n",
       "      <td>142155</td>\n",
       "      <td>137056</td>\n",
       "      <td>129861</td>\n",
       "      <td>123670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>2152</td>\n",
       "      <td>2165</td>\n",
       "      <td>2399</td>\n",
       "      <td>2554</td>\n",
       "      <td>2825</td>\n",
       "      <td>2889</td>\n",
       "      <td>3017</td>\n",
       "      <td>3140</td>\n",
       "      <td>3816</td>\n",
       "      <td>3784</td>\n",
       "      <td>...</td>\n",
       "      <td>47688</td>\n",
       "      <td>44991</td>\n",
       "      <td>43765</td>\n",
       "      <td>43870</td>\n",
       "      <td>43369</td>\n",
       "      <td>43553</td>\n",
       "      <td>43437</td>\n",
       "      <td>43846</td>\n",
       "      <td>43674</td>\n",
       "      <td>43398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>380</td>\n",
       "      <td>427</td>\n",
       "      <td>410</td>\n",
       "      <td>444</td>\n",
       "      <td>490</td>\n",
       "      <td>495</td>\n",
       "      <td>511</td>\n",
       "      <td>476</td>\n",
       "      <td>541</td>\n",
       "      <td>469</td>\n",
       "      <td>...</td>\n",
       "      <td>1833</td>\n",
       "      <td>1819</td>\n",
       "      <td>2052</td>\n",
       "      <td>2138</td>\n",
       "      <td>2129</td>\n",
       "      <td>2201</td>\n",
       "      <td>2311</td>\n",
       "      <td>2405</td>\n",
       "      <td>2417</td>\n",
       "      <td>2318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1209</td>\n",
       "      <td>1332</td>\n",
       "      <td>1652</td>\n",
       "      <td>1823</td>\n",
       "      <td>1794</td>\n",
       "      <td>2010</td>\n",
       "      <td>2295</td>\n",
       "      <td>2418</td>\n",
       "      <td>2589</td>\n",
       "      <td>2723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>52265</td>\n",
       "      <td>50103</td>\n",
       "      <td>49079</td>\n",
       "      <td>47556</td>\n",
       "      <td>45464</td>\n",
       "      <td>43217</td>\n",
       "      <td>40251</td>\n",
       "      <td>36937</td>\n",
       "      <td>33181</td>\n",
       "      <td>30656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10691</td>\n",
       "      <td>11009</td>\n",
       "      <td>11718</td>\n",
       "      <td>12399</td>\n",
       "      <td>13025</td>\n",
       "      <td>13992</td>\n",
       "      <td>14306</td>\n",
       "      <td>14834</td>\n",
       "      <td>16640</td>\n",
       "      <td>16352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>10469</td>\n",
       "      <td>10404</td>\n",
       "      <td>12145</td>\n",
       "      <td>12063</td>\n",
       "      <td>13917</td>\n",
       "      <td>13927</td>\n",
       "      <td>14936</td>\n",
       "      <td>14980</td>\n",
       "      <td>17931</td>\n",
       "      <td>17601</td>\n",
       "      <td>...</td>\n",
       "      <td>139109</td>\n",
       "      <td>134557</td>\n",
       "      <td>130569</td>\n",
       "      <td>128367</td>\n",
       "      <td>125190</td>\n",
       "      <td>123707</td>\n",
       "      <td>123397</td>\n",
       "      <td>122633</td>\n",
       "      <td>112922</td>\n",
       "      <td>110425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>106</td>\n",
       "      <td>95</td>\n",
       "      <td>106</td>\n",
       "      <td>141</td>\n",
       "      <td>148</td>\n",
       "      <td>150</td>\n",
       "      <td>202</td>\n",
       "      <td>188</td>\n",
       "      <td>238</td>\n",
       "      <td>277</td>\n",
       "      <td>...</td>\n",
       "      <td>2840</td>\n",
       "      <td>2737</td>\n",
       "      <td>2722</td>\n",
       "      <td>2710</td>\n",
       "      <td>2903</td>\n",
       "      <td>3086</td>\n",
       "      <td>3301</td>\n",
       "      <td>3473</td>\n",
       "      <td>3633</td>\n",
       "      <td>3476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 262 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex       F                                                                 \\\n",
       "year   1880   1881   1882   1883   1884   1885   1886   1887   1888   1889   \n",
       "name                                                                         \n",
       "a     31446  31581  36536  38330  43680  45408  49100  48942  59442  58631   \n",
       "b       NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "c       NaN    NaN      5      5    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "d       609    607    734    810    916    862   1007   1027   1298   1374   \n",
       "e     33378  34080  40399  41914  48089  49616  53884  54353  66750  66663   \n",
       "f       NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "g         7      5     12      8     24     11     18     25     44     28   \n",
       "h      4863   4784   5567   5701   6602   6624   7146   7141   8630   8826   \n",
       "i        61     78     81     76     84     92     85    105    141    134   \n",
       "j       NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "k        13     15     11     17     21     18     27     19     21     22   \n",
       "l      2541   2911   3527   3848   4808   5144   5721   6175   7900   8395   \n",
       "m        58     57     81     86     79     75    103     90    123    137   \n",
       "n      3008   2959   3576   3837   4507   4735   5242   5512   6833   7103   \n",
       "o        30     49     35     47     74     84     93     97    134    142   \n",
       "p       NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "q       NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "r       481    417    590    640    718    799    917    910   1207   1214   \n",
       "s      1391   1316   1637   1794   2039   2127   2524   2803   3582   3569   \n",
       "t      2152   2165   2399   2554   2825   2889   3017   3140   3816   3784   \n",
       "u       380    427    410    444    490    495    511    476    541    469   \n",
       "v       NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "w       NaN      5    NaN    NaN    NaN    NaN      5    NaN    NaN    NaN   \n",
       "x       NaN    NaN    NaN      7    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "y     10469  10404  12145  12063  13917  13927  14936  14980  17931  17601   \n",
       "z       106     95    106    141    148    150    202    188    238    277   \n",
       "\n",
       "sex    ...         M                                                          \\\n",
       "year   ...      2001    2002    2003    2004    2005    2006    2007    2008   \n",
       "name   ...                                                                     \n",
       "a      ...     39124   38815   37825   38650   36838   36156   34654   32901   \n",
       "b      ...     50950   49284   48065   45914   43144   42600   42123   39945   \n",
       "c      ...     27113   27238   27697   26778   26078   26635   26864   25318   \n",
       "d      ...     60838   55829   53391   51754   50670   51410   50595   47910   \n",
       "e      ...    145395  144651  144769  142098  141123  142999  143698  140966   \n",
       "f      ...      1758    1817    1819    1904    1985    1968    2090    2195   \n",
       "g      ...      2151    2084    2009    1837    1882    1929    2040    2059   \n",
       "h      ...     85959   88085   88226   89620   92497   98477   99414  100250   \n",
       "i      ...     20980   23610   26011   28500   31317   33558   35231   38151   \n",
       "j      ...      1069    1088    1203    1094    1291    1241    1254    1381   \n",
       "k      ...     42477   42043   42296   41400   42151   42537   42136   39563   \n",
       "l      ...    153648  153493  153862  152800  155312  156234  155203  150791   \n",
       "m      ...     41967   42663   42790   43054   41600   42503   43860   44316   \n",
       "n      ...    616099  630322  663419  676011  686326  720998  741355  733869   \n",
       "o      ...     82146   83180   85423   88822   92001   96350   96895   91485   \n",
       "p      ...      3419    3157    2982    2841    2768    2721    2739    2637   \n",
       "q      ...       602     618     585     523     446     430     431     339   \n",
       "r      ...    165377  164821  169878  169452  172069  176490  177207  174632   \n",
       "s      ...    143791  139595  138632  139642  139913  143232  142155  137056   \n",
       "t      ...     47688   44991   43765   43870   43369   43553   43437   43846   \n",
       "u      ...      1833    1819    2052    2138    2129    2201    2311    2405   \n",
       "v      ...      1209    1332    1652    1823    1794    2010    2295    2418   \n",
       "w      ...     52265   50103   49079   47556   45464   43217   40251   36937   \n",
       "x      ...     10691   11009   11718   12399   13025   13992   14306   14834   \n",
       "y      ...    139109  134557  130569  128367  125190  123707  123397  122633   \n",
       "z      ...      2840    2737    2722    2710    2903    3086    3301    3473   \n",
       "\n",
       "sex                   \n",
       "year    2009    2010  \n",
       "name                  \n",
       "a      31430   28438  \n",
       "b      38862   38859  \n",
       "c      24048   23125  \n",
       "d      46172   44398  \n",
       "e     135496  129012  \n",
       "f       2212    2255  \n",
       "g       2396    2666  \n",
       "h      99979   98090  \n",
       "i      40912   42956  \n",
       "j       1416    1459  \n",
       "k      37507   35198  \n",
       "l     143751  133583  \n",
       "m      46278   46808  \n",
       "n     715388  688677  \n",
       "o      86423   81025  \n",
       "p       2595    2409  \n",
       "q        377     342  \n",
       "r     173200  166064  \n",
       "s     129861  123670  \n",
       "t      43674   43398  \n",
       "u       2417    2318  \n",
       "v       2589    2723  \n",
       "w      33181   30656  \n",
       "x      16640   16352  \n",
       "y     112922  110425  \n",
       "z       3633    3476  \n",
       "\n",
       "[26 rows x 262 columns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_letters_table = names.pivot_table('births', aggfunc=sum,\n",
    "                 index=last_letters,\n",
    "                 columns=['sex', 'year'])\n",
    "last_letters_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "특정 연도만 대표적으로 추출하기\n",
    "\n",
    "1910, 1960, 2010년"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>1880</th>\n",
       "      <th>1881</th>\n",
       "      <th>1882</th>\n",
       "      <th>1883</th>\n",
       "      <th>1884</th>\n",
       "      <th>1885</th>\n",
       "      <th>1886</th>\n",
       "      <th>1887</th>\n",
       "      <th>1888</th>\n",
       "      <th>1889</th>\n",
       "      <th>...</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>31446</td>\n",
       "      <td>31581</td>\n",
       "      <td>36536</td>\n",
       "      <td>38330</td>\n",
       "      <td>43680</td>\n",
       "      <td>45408</td>\n",
       "      <td>49100</td>\n",
       "      <td>48942</td>\n",
       "      <td>59442</td>\n",
       "      <td>58631</td>\n",
       "      <td>...</td>\n",
       "      <td>673418</td>\n",
       "      <td>681001</td>\n",
       "      <td>702628</td>\n",
       "      <td>710441</td>\n",
       "      <td>727357</td>\n",
       "      <td>753391</td>\n",
       "      <td>752779</td>\n",
       "      <td>723405</td>\n",
       "      <td>698477</td>\n",
       "      <td>670605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>394</td>\n",
       "      <td>332</td>\n",
       "      <td>344</td>\n",
       "      <td>601</td>\n",
       "      <td>369</td>\n",
       "      <td>367</td>\n",
       "      <td>409</td>\n",
       "      <td>408</td>\n",
       "      <td>435</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>539</td>\n",
       "      <td>607</td>\n",
       "      <td>649</td>\n",
       "      <td>663</td>\n",
       "      <td>613</td>\n",
       "      <td>633</td>\n",
       "      <td>775</td>\n",
       "      <td>926</td>\n",
       "      <td>931</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 131 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year   1880   1881   1882   1883   1884   1885   1886   1887   1888   1889  \\\n",
       "name                                                                         \n",
       "a     31446  31581  36536  38330  43680  45408  49100  48942  59442  58631   \n",
       "b       NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "c       NaN    NaN      5      5    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "year   ...      2001    2002    2003    2004    2005    2006    2007    2008  \\\n",
       "name   ...                                                                     \n",
       "a      ...    673418  681001  702628  710441  727357  753391  752779  723405   \n",
       "b      ...       394     332     344     601     369     367     409     408   \n",
       "c      ...       539     607     649     663     613     633     775     926   \n",
       "\n",
       "year    2009    2010  \n",
       "name                  \n",
       "a     698477  670605  \n",
       "b        435     450  \n",
       "c        931     946  \n",
       "\n",
       "[3 rows x 131 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_letters_table['F'][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "indices are out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-164-904f0f31d848>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlast_letters_table\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1910\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1960\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2010\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1789\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1790\u001b[0m             \u001b[1;31m# either boolean or fancy integer index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1791\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1792\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1793\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.pyc\u001b[0m in \u001b[0;36m_getitem_array\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1834\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1835\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1836\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1837\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1838\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indices, axis, convert, is_copy)\u001b[0m\n\u001b[0;32m   1356\u001b[0m         new_data = self._data.take(indices,\n\u001b[0;32m   1357\u001b[0m                                    \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_block_manager_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1358\u001b[1;33m                                    convert=True, verify=True)\n\u001b[0m\u001b[0;32m   1359\u001b[0m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1360\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mtake\u001b[1;34m(self, indexer, axis, verify, convert)\u001b[0m\n\u001b[0;32m   3264\u001b[0m         \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3265\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3266\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaybe_convert_indices\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3267\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3268\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mverify\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36mmaybe_convert_indices\u001b[1;34m(indices, n)\u001b[0m\n\u001b[0;32m   1709\u001b[0m     \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mindices\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mindices\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1710\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1711\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"indices are out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1712\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mindices\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1713\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: indices are out-of-bounds"
     ]
    }
   ],
   "source": [
    "last_letters_table[[1910, 1960, 2010]] # 색인이 계층적이라 오류!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "계층 색인의 year 부분의 일부만 선택해 열의 색인을 재구성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>name</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</td>\n",
       "      <td>691247</td>\n",
       "      <td>670605</td>\n",
       "      <td>977</td>\n",
       "      <td>5204</td>\n",
       "      <td>28438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>694</td>\n",
       "      <td>450</td>\n",
       "      <td>411</td>\n",
       "      <td>3912</td>\n",
       "      <td>38859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>5</td>\n",
       "      <td>49</td>\n",
       "      <td>946</td>\n",
       "      <td>482</td>\n",
       "      <td>15476</td>\n",
       "      <td>23125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>6750</td>\n",
       "      <td>3729</td>\n",
       "      <td>2607</td>\n",
       "      <td>22111</td>\n",
       "      <td>262112</td>\n",
       "      <td>44398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>133569</td>\n",
       "      <td>435013</td>\n",
       "      <td>313833</td>\n",
       "      <td>28655</td>\n",
       "      <td>178823</td>\n",
       "      <td>129012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex        F                      M                \n",
       "year    1910    1960    2010   1910    1960    2010\n",
       "name                                               \n",
       "a     108376  691247  670605    977    5204   28438\n",
       "b        NaN     694     450    411    3912   38859\n",
       "c          5      49     946    482   15476   23125\n",
       "d       6750    3729    2607  22111  262112   44398\n",
       "e     133569  435013  313833  28655  178823  129012"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtable = last_letters_table.reindex(columns=[1910, 1960, 2010], level='year')\n",
    "subtable[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분석 목표: 각 글자가 차지하는 비율 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>name</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex          F                             M                    \n",
       "year      1910      1960      2010      1910      1960      2010\n",
       "name                                                            \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"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = subtable/subtable.sum().astype(float)\n",
    "letter_prop[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "성별 마지막 글자 그래프 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x842824a8>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FPhMnTmT9+vV1y1q/fj077LDDO4595ZVXitTVUCVJklrSpk2b+Mu//EvGjh3L3LlzgcqY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\n/wNM3N6yGt/E+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x84405fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "letter_prop['M'].plot(kind='bar', figsize=(10,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x825bf978>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hSpIkqQBDlSRJUgGGKkmSpAIMVZIkSQUYqiRJkgowVEmSJBVgqJIkSSrAUCVJ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\nJEkqwFAlSZJUgKFKkiSpgP8fXQWbbunB9EQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x49db7080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "letter_prop['F'].plot(kind='bar', figsize=(10,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "분석 목표: 특정 글자 (d/n/y)의 시대별 추세 \n",
    "\n",
    "1880-2010년 전체에 대해 각 글자의 비율 변화 산출 필요"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th colspan=\"10\" halign=\"left\">F</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>1880</th>\n",
       "      <th>1881</th>\n",
       "      <th>1882</th>\n",
       "      <th>1883</th>\n",
       "      <th>1884</th>\n",
       "      <th>1885</th>\n",
       "      <th>1886</th>\n",
       "      <th>1887</th>\n",
       "      <th>1888</th>\n",
       "      <th>1889</th>\n",
       "      <th>...</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>0.345587</td>\n",
       "      <td>0.343440</td>\n",
       "      <td>0.338764</td>\n",
       "      <td>0.341251</td>\n",
       "      <td>0.338550</td>\n",
       "      <td>0.341270</td>\n",
       "      <td>0.339703</td>\n",
       "      <td>0.335258</td>\n",
       "      <td>0.332764</td>\n",
       "      <td>0.328706</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020162</td>\n",
       "      <td>0.020019</td>\n",
       "      <td>0.019177</td>\n",
       "      <td>0.019505</td>\n",
       "      <td>0.018481</td>\n",
       "      <td>0.017635</td>\n",
       "      <td>0.016747</td>\n",
       "      <td>0.016189</td>\n",
       "      <td>0.015927</td>\n",
       "      <td>0.014980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026256</td>\n",
       "      <td>0.025418</td>\n",
       "      <td>0.024368</td>\n",
       "      <td>0.023171</td>\n",
       "      <td>0.021645</td>\n",
       "      <td>0.020778</td>\n",
       "      <td>0.020357</td>\n",
       "      <td>0.019655</td>\n",
       "      <td>0.019693</td>\n",
       "      <td>0.020470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013972</td>\n",
       "      <td>0.014048</td>\n",
       "      <td>0.014042</td>\n",
       "      <td>0.013514</td>\n",
       "      <td>0.013083</td>\n",
       "      <td>0.012991</td>\n",
       "      <td>0.012983</td>\n",
       "      <td>0.012458</td>\n",
       "      <td>0.012186</td>\n",
       "      <td>0.012181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.006693</td>\n",
       "      <td>0.006601</td>\n",
       "      <td>0.006806</td>\n",
       "      <td>0.007211</td>\n",
       "      <td>0.007100</td>\n",
       "      <td>0.006478</td>\n",
       "      <td>0.006967</td>\n",
       "      <td>0.007035</td>\n",
       "      <td>0.007266</td>\n",
       "      <td>0.007703</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031352</td>\n",
       "      <td>0.028794</td>\n",
       "      <td>0.027069</td>\n",
       "      <td>0.026118</td>\n",
       "      <td>0.025420</td>\n",
       "      <td>0.025075</td>\n",
       "      <td>0.024451</td>\n",
       "      <td>0.023574</td>\n",
       "      <td>0.023398</td>\n",
       "      <td>0.023387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.366819</td>\n",
       "      <td>0.370616</td>\n",
       "      <td>0.374582</td>\n",
       "      <td>0.373159</td>\n",
       "      <td>0.372722</td>\n",
       "      <td>0.372896</td>\n",
       "      <td>0.372802</td>\n",
       "      <td>0.372324</td>\n",
       "      <td>0.373675</td>\n",
       "      <td>0.373736</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074927</td>\n",
       "      <td>0.074603</td>\n",
       "      <td>0.073396</td>\n",
       "      <td>0.071710</td>\n",
       "      <td>0.070799</td>\n",
       "      <td>0.069748</td>\n",
       "      <td>0.069445</td>\n",
       "      <td>0.069362</td>\n",
       "      <td>0.068663</td>\n",
       "      <td>0.067959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 262 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sex          F                                                              \\\n",
       "year      1880      1881      1882      1883      1884      1885      1886   \n",
       "name                                                                         \n",
       "a     0.345587  0.343440  0.338764  0.341251  0.338550  0.341270  0.339703   \n",
       "b          NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "c          NaN       NaN  0.000046  0.000045       NaN       NaN       NaN   \n",
       "d     0.006693  0.006601  0.006806  0.007211  0.007100  0.006478  0.006967   \n",
       "e     0.366819  0.370616  0.374582  0.373159  0.372722  0.372896  0.372802   \n",
       "\n",
       "sex                                   ...            M                      \\\n",
       "year      1887      1888      1889    ...         2001      2002      2003   \n",
       "name                                  ...                                    \n",
       "a     0.335258  0.332764  0.328706    ...     0.020162  0.020019  0.019177   \n",
       "b          NaN       NaN       NaN    ...     0.026256  0.025418  0.024368   \n",
       "c          NaN       NaN       NaN    ...     0.013972  0.014048  0.014042   \n",
       "d     0.007035  0.007266  0.007703    ...     0.031352  0.028794  0.027069   \n",
       "e     0.372324  0.373675  0.373736    ...     0.074927  0.074603  0.073396   \n",
       "\n",
       "sex                                                                         \n",
       "year      2004      2005      2006      2007      2008      2009      2010  \n",
       "name                                                                        \n",
       "a     0.019505  0.018481  0.017635  0.016747  0.016189  0.015927  0.014980  \n",
       "b     0.023171  0.021645  0.020778  0.020357  0.019655  0.019693  0.020470  \n",
       "c     0.013514  0.013083  0.012991  0.012983  0.012458  0.012186  0.012181  \n",
       "d     0.026118  0.025420  0.025075  0.024451  0.023574  0.023398  0.023387  \n",
       "e     0.071710  0.070799  0.069748  0.069445  0.069362  0.068663  0.067959  \n",
       "\n",
       "[5 rows x 262 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letter_prop = last_letters_table / last_letters_table.sum().astype(float)\n",
    "letter_prop[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d/n/y로 끝나는 남자 이름의 시대별 비율"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</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": [
       "name         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": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = ['d', 'n', 'y']\n",
    "cols = 'M'\n",
    "letter_dny_male = letter_prop.ix[rows, cols]\n",
    "# 행열 전환\n",
    "letter_dny_male = letter_dny_male.T\n",
    "letter_dny_male[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x845f5d30>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8Oe9J1kav5cH2D/Jcs+e48PwLqXteXYoFnX5rL29weZZ+XnJqMtEJ0VQpU+Vv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\n+Jw/PJH+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x84f1da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "letter_dny_male.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 남자 이름과 여자 이름이 시대에 따라 바뀌는 경우?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "상위 1000개 이름 전체 목록 구하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mary', 'Anna', 'Emma', 'Elizabeth', 'Minnie', 'Margaret', 'Ida',\n",
       "       'Alice', 'Bertha', 'Sarah'], dtype=object)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_names = top1000.name.unique()\n",
    "all_names[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "문자열 'lesl'로 시작하는 이름 찾기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'a' in 'abc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'lesl' in 'lesley'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'lesl' in 'mary'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_lesl = np.array(['lesl' in name.lower() for name in all_names])\n",
    "name_lesl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Leslie', 'Lesley', 'Leslee', 'Lesli', 'Lesly'], dtype=object)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lesley_like = all_names[name_lesl]\n",
    "lesley_like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "상위 1000개의 이름 중 해당 이름들만 추출\n",
    "\n",
    "Series.isin() : \"이 중에 하나\" --> True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\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",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>sex</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 rowspan=\"2\" valign=\"top\">1880</th>\n",
       "      <th>F</th>\n",
       "      <th>654</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>1108</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>79</td>\n",
       "      <td>1880</td>\n",
       "      <td>0.000715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1881</th>\n",
       "      <th>F</th>\n",
       "      <th>2523</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>11</td>\n",
       "      <td>1881</td>\n",
       "      <td>0.000120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>3072</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>92</td>\n",
       "      <td>1881</td>\n",
       "      <td>0.000913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1882</th>\n",
       "      <th>F</th>\n",
       "      <th>4593</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>9</td>\n",
       "      <td>1882</td>\n",
       "      <td>0.000083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
       "      <th>5081</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>122</td>\n",
       "      <td>1882</td>\n",
       "      <td>0.001073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5865</th>\n",
       "      <td>Lesley</td>\n",
       "      <td>M</td>\n",
       "      <td>6</td>\n",
       "      <td>1882</td>\n",
       "      <td>0.000053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1883</th>\n",
       "      <th>F</th>\n",
       "      <th>6850</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>7</td>\n",
       "      <td>1883</td>\n",
       "      <td>0.000062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">M</th>\n",
       "      <th>7225</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>120</td>\n",
       "      <td>1883</td>\n",
       "      <td>0.001147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8093</th>\n",
       "      <td>Lesley</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>1883</td>\n",
       "      <td>0.000048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1884</th>\n",
       "      <th>F</th>\n",
       "      <th>8697</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>1884</td>\n",
       "      <td>0.000116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>9432</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>125</td>\n",
       "      <td>1884</td>\n",
       "      <td>0.001092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1885</th>\n",
       "      <th>F</th>\n",
       "      <th>11161</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>10</td>\n",
       "      <td>1885</td>\n",
       "      <td>0.000075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>11751</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>122</td>\n",
       "      <td>1885</td>\n",
       "      <td>0.001132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1886</th>\n",
       "      <th>F</th>\n",
       "      <th>13601</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>1886</td>\n",
       "      <td>0.000055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>14132</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>136</td>\n",
       "      <td>1886</td>\n",
       "      <td>0.001228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1887</th>\n",
       "      <th>F</th>\n",
       "      <th>15806</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>12</td>\n",
       "      <td>1887</td>\n",
       "      <td>0.000082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>16524</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>166</td>\n",
       "      <td>1887</td>\n",
       "      <td>0.001637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1888</th>\n",
       "      <th>F</th>\n",
       "      <th>18030</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>23</td>\n",
       "      <td>1888</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>19074</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>175</td>\n",
       "      <td>1888</td>\n",
       "      <td>0.001448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1889</th>\n",
       "      <th>F</th>\n",
       "      <th>20690</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>23</td>\n",
       "      <td>1889</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>21737</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>155</td>\n",
       "      <td>1889</td>\n",
       "      <td>0.001402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1890</th>\n",
       "      <th>F</th>\n",
       "      <th>23332</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>20</td>\n",
       "      <td>1890</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>24372</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>181</td>\n",
       "      <td>1890</td>\n",
       "      <td>0.001630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1891</th>\n",
       "      <th>F</th>\n",
       "      <th>25928</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>28</td>\n",
       "      <td>1891</td>\n",
       "      <td>0.000151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>27068</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>164</td>\n",
       "      <td>1891</td>\n",
       "      <td>0.001621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1892</th>\n",
       "      <th>F</th>\n",
       "      <th>28704</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>22</td>\n",
       "      <td>1892</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>29851</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>207</td>\n",
       "      <td>1892</td>\n",
       "      <td>0.001696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1893</th>\n",
       "      <th>F</th>\n",
       "      <th>31576</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>26</td>\n",
       "      <td>1893</td>\n",
       "      <td>0.000122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <th>32765</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>M</td>\n",
       "      <td>185</td>\n",
       "      <td>1893</td>\n",
       "      <td>0.001647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2000</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>1332261</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>3619</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.001995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1332560</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>742</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.000409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1332601</th>\n",
       "      <td>Lesley</td>\n",
       "      <td>F</td>\n",
       "      <td>658</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.000363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2001</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>1362012</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>3610</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.002007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362300</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>801</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.000445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362452</th>\n",
       "      <td>Lesley</td>\n",
       "      <td>F</td>\n",
       "      <td>509</td>\n",
       "      <td>2001</td>\n",
       "      <td>0.000283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2002</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>1392272</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>3520</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.001962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1392586</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>717</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.000400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1392743</th>\n",
       "      <td>Lesley</td>\n",
       "      <td>F</td>\n",
       "      <td>471</td>\n",
       "      <td>2002</td>\n",
       "      <td>0.000262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2003</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>1422818</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>3635</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.001992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423091</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>838</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.000459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423330</th>\n",
       "      <td>Lesley</td>\n",
       "      <td>F</td>\n",
       "      <td>451</td>\n",
       "      <td>2003</td>\n",
       "      <td>0.000247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2004</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>1453982</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>3497</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.001908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454295</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>747</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.000408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454500</th>\n",
       "      <td>Lesley</td>\n",
       "      <td>F</td>\n",
       "      <td>450</td>\n",
       "      <td>2004</td>\n",
       "      <td>0.000245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2005</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>1486010</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>3120</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.001692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1486308</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>783</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.000425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1486623</th>\n",
       "      <td>Lesley</td>\n",
       "      <td>F</td>\n",
       "      <td>381</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.000207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2006</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>1518523</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>3035</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.001600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1518834</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>761</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.000401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1519161</th>\n",
       "      <td>Lesley</td>\n",
       "      <td>F</td>\n",
       "      <td>370</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.000195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2007</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>1552581</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>2689</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.001403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552882</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>765</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.000399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553271</th>\n",
       "      <td>Lesley</td>\n",
       "      <td>F</td>\n",
       "      <td>351</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.000183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2008</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
       "      <th>1587484</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>2323</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.001233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587788</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>699</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.000371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2009</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
       "      <th>1622503</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>1975</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.001081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622845</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>598</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.000327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2010</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">F</th>\n",
       "      <th>1657142</th>\n",
       "      <td>Leslie</td>\n",
       "      <td>F</td>\n",
       "      <td>1558</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657525</th>\n",
       "      <td>Lesly</td>\n",
       "      <td>F</td>\n",
       "      <td>502</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.000285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name sex  births  year      prop\n",
       "year sex                                            \n",
       "1880 F   654      Leslie   F       8  1880  0.000088\n",
       "     M   1108     Leslie   M      79  1880  0.000715\n",
       "1881 F   2523     Leslie   F      11  1881  0.000120\n",
       "     M   3072     Leslie   M      92  1881  0.000913\n",
       "1882 F   4593     Leslie   F       9  1882  0.000083\n",
       "     M   5081     Leslie   M     122  1882  0.001073\n",
       "         5865     Lesley   M       6  1882  0.000053\n",
       "1883 F   6850     Leslie   F       7  1883  0.000062\n",
       "     M   7225     Leslie   M     120  1883  0.001147\n",
       "         8093     Lesley   M       5  1883  0.000048\n",
       "1884 F   8697     Leslie   F      15  1884  0.000116\n",
       "     M   9432     Leslie   M     125  1884  0.001092\n",
       "1885 F   11161    Leslie   F      10  1885  0.000075\n",
       "     M   11751    Leslie   M     122  1885  0.001132\n",
       "1886 F   13601    Leslie   F       8  1886  0.000055\n",
       "     M   14132    Leslie   M     136  1886  0.001228\n",
       "1887 F   15806    Leslie   F      12  1887  0.000082\n",
       "     M   16524    Leslie   M     166  1887  0.001637\n",
       "1888 F   18030    Leslie   F      23  1888  0.000129\n",
       "     M   19074    Leslie   M     175  1888  0.001448\n",
       "1889 F   20690    Leslie   F      23  1889  0.000129\n",
       "     M   21737    Leslie   M     155  1889  0.001402\n",
       "1890 F   23332    Leslie   F      20  1890  0.000105\n",
       "     M   24372    Leslie   M     181  1890  0.001630\n",
       "1891 F   25928    Leslie   F      28  1891  0.000151\n",
       "     M   27068    Leslie   M     164  1891  0.001621\n",
       "1892 F   28704    Leslie   F      22  1892  0.000104\n",
       "     M   29851    Leslie   M     207  1892  0.001696\n",
       "1893 F   31576    Leslie   F      26  1893  0.000122\n",
       "     M   32765    Leslie   M     185  1893  0.001647\n",
       "...                  ...  ..     ...   ...       ...\n",
       "2000 F   1332261  Leslie   F    3619  2000  0.001995\n",
       "         1332560   Lesly   F     742  2000  0.000409\n",
       "         1332601  Lesley   F     658  2000  0.000363\n",
       "2001 F   1362012  Leslie   F    3610  2001  0.002007\n",
       "         1362300   Lesly   F     801  2001  0.000445\n",
       "         1362452  Lesley   F     509  2001  0.000283\n",
       "2002 F   1392272  Leslie   F    3520  2002  0.001962\n",
       "         1392586   Lesly   F     717  2002  0.000400\n",
       "         1392743  Lesley   F     471  2002  0.000262\n",
       "2003 F   1422818  Leslie   F    3635  2003  0.001992\n",
       "         1423091   Lesly   F     838  2003  0.000459\n",
       "         1423330  Lesley   F     451  2003  0.000247\n",
       "2004 F   1453982  Leslie   F    3497  2004  0.001908\n",
       "         1454295   Lesly   F     747  2004  0.000408\n",
       "         1454500  Lesley   F     450  2004  0.000245\n",
       "2005 F   1486010  Leslie   F    3120  2005  0.001692\n",
       "         1486308   Lesly   F     783  2005  0.000425\n",
       "         1486623  Lesley   F     381  2005  0.000207\n",
       "2006 F   1518523  Leslie   F    3035  2006  0.001600\n",
       "         1518834   Lesly   F     761  2006  0.000401\n",
       "         1519161  Lesley   F     370  2006  0.000195\n",
       "2007 F   1552581  Leslie   F    2689  2007  0.001403\n",
       "         1552882   Lesly   F     765  2007  0.000399\n",
       "         1553271  Lesley   F     351  2007  0.000183\n",
       "2008 F   1587484  Leslie   F    2323  2008  0.001233\n",
       "         1587788   Lesly   F     699  2008  0.000371\n",
       "2009 F   1622503  Leslie   F    1975  2009  0.001081\n",
       "         1622845   Lesly   F     598  2009  0.000327\n",
       "2010 F   1657142  Leslie   F    1558  2010  0.000886\n",
       "         1657525   Lesly   F     502  2010  0.000285\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lesley_like_names = top1000[top1000['name'].isin(lesley_like)]\n",
    "lesley_like_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "대상 이름의 전체 기간에 대한 출생수 합계 산출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "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": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_lesley_births = lesley_like_names.groupby('name').births.sum()\n",
    "total_lesley_births"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "성별과 연도별로 lesl를 포함하는 이름의 출생수 합계 산출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total_lesley_year_sex = lesley_like_names.pivot_table('births', aggfunc='sum', \n",
    "                               index='year', columns='sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>8</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>11</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>9</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex    F    M\n",
       "year         \n",
       "1880   8   79\n",
       "1881  11   92\n",
       "1882   9  128"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_lesley_year_sex[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "해당 이름들이 성별에서 차지하는 비율 정규화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>0.091954</td>\n",
       "      <td>0.908046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>0.106796</td>\n",
       "      <td>0.893204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>0.065693</td>\n",
       "      <td>0.934307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>0.053030</td>\n",
       "      <td>0.946970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>0.107143</td>\n",
       "      <td>0.892857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex          F         M\n",
       "year                    \n",
       "1880  0.091954  0.908046\n",
       "1881  0.106796  0.893204\n",
       "1882  0.065693  0.934307\n",
       "1883  0.053030  0.946970\n",
       "1884  0.107143  0.892857"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normed_total_lesley = total_lesley_year_sex.div(total_lesley_year_sex.sum(1), axis=0)\n",
    "normed_total_lesley[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "시대별 이름의 성별 변화 추이 그래프 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x84fccd68>"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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IY489xhdffBFnFl1W0pq38livLnyV+0vdz/81/L90t7F9O/TuDbt3OzAwlWqO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\nSmVD/w+NOybUeOGAEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x84416080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normed_total_lesley.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}