{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "- 이예영" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ch. 2 Introductory Examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. usa.gov data from bit.ly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 웹에 저장되어 있는 txt 파일을 url로 접근해 가져오기 위한 urllib2 모듈 활용" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pathUrl2 = 'https://raw.githubusercontent.com/pydata/pydata-book/master/ch02/usagov_bitly_data2012-03-16-1331923249.txt'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import urllib" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "response = urllib.urlopen(pathUrl2)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "responseLines = response.readlines()" ] }, { "cell_type": "code", "execution_count": 6, "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": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "responseLines[0]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "records = [json.loads(line) for line in responseLines]\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{u'a': u'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11',\n", " u'al': u'en-US,en;q=0.8',\n", " u'c': u'US',\n", " u'cy': u'Danvers',\n", " u'g': u'A6qOVH',\n", " u'gr': u'MA',\n", " u'h': u'wfLQtf',\n", " u'hc': 1331822918,\n", " u'hh': u'1.usa.gov',\n", " u'l': u'orofrog',\n", " u'll': [42.576698, -70.954903],\n", " u'nk': 1,\n", " u'r': u'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n", " u't': 1331923247,\n", " u'tz': u'America/New_York',\n", " u'u': u'http://www.ncbi.nlm.nih.gov/pubmed/22415991'}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "records[0]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'America/New_York'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "records[0]['tz']" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "America/New_York\n" ] } ], "source": [ "print records[0]['tz']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) 순수 파이썬으로 표준시간대 세어보기 (Time zone 카운팅)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "ename": "KeyError", "evalue": "'tz'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtime_zones\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrec\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'tz'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrecords\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mKeyError\u001b[0m: 'tz'" ] } ], "source": [ "time_zones=[rec['tz'] for rec in records]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "time_zones = [rec['tz'] for rec in records if 'tz' in rec]" ] }, { "cell_type": "code", "execution_count": 17, "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": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "time_zones[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 파이썬 표준 라이브러리 collections.Counter 클래스 이용" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "counts = Counter(time_zones)" ] }, { "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": [ "counts.most_common(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "2) pandas로 표준시간대 세어보기" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pandas import DataFrame, Series" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd; \n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'0.16.1'" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.__version__" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "frame = DataFrame(records)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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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": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frame" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 America/New_York\n", "1 America/Denver\n", "2 America/New_York\n", "3 America/Sao_Paulo\n", "4 America/New_York\n", "5 America/New_York\n", "6 Europe/Warsaw\n", "7 \n", "8 \n", "9 \n", "Name: tz, dtype: object" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frame['tz'][:10]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tz_counts = frame['tz'].value_counts()\n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "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", "dtype: int64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tz_counts[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- plot 결과가 ipython notebook 페이지에 안보일때 아래명령어 수행" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EQlS3JkLDEMJ8KBHunV5ZbPtp21dXxis6Ly1U/k7/NeRoHDFldp73QKTJv3gh7W8nTbYd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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tz_counts[:10].plot(kind='barh', rot=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- url 축약하는데 다음과같은 정보가 담은 필드 존재" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'GoogleMaps/RochesterNY'" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frame['a'][1]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/20100101 Firefox/10.0.2'" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frame['a'][50]" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'Mozilla/5.0 (Linux; U; Android 2.2.2; en-us; LG-P925/V10e Build/FRG83G) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1'" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frame['a'][51]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "result = Series([x.split()[0] for x in frame.a.dropna()])" ] }, { "cell_type": "code", "execution_count": 41, "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": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result[:5]" ] }, { "cell_type": "code", "execution_count": 42, "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", "GoogleProducer 21\n", "Mozilla/6.0 5\n", "BlackBerry8520/5.0.0.681 4\n", "dtype: int64" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.value_counts()[:8]" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cframe = frame[frame.a.notnull()]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "operating_system = np.where(cframe['a'].str.contains('Windows'), 'Windows', 'Not Windows')" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['Windows', 'Not Windows', 'Windows', 'Not Windows', 'Windows'], \n", " dtype='|S11')" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "operating_system[:5] \n", "#각 행이 윈도우인지 아닌지 검사" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "by_tz_os = cframe.groupby(['tz', operating_system])\n", "#표준시간대와 운영체제 데이터를 그룹으로 묶기" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 그룹별 합계는 size함수로 계산한다" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "tz \n", " Not Windows 245\n", " Windows 276\n", "Africa/Cairo Windows 3\n", "Africa/Casablanca Windows 1\n", "Africa/Ceuta Windows 2\n", "Africa/Johannesburg Windows 1\n", "Africa/Lusaka Windows 1\n", "America/Anchorage Not Windows 4\n", " Windows 1\n", "America/Argentina/Buenos_Aires Not Windows 1\n", "America/Argentina/Cordoba Windows 1\n", "America/Argentina/Mendoza Windows 1\n", "America/Bogota Not Windows 1\n", " Windows 2\n", "America/Caracas Windows 1\n", "America/Chicago Not Windows 115\n", " Windows 285\n", "America/Chihuahua Not Windows 1\n", " Windows 1\n", "America/Costa_Rica Windows 1\n", "America/Denver Not Windows 132\n", " Windows 59\n", "America/Edmonton Not Windows 2\n", " Windows 4\n", "America/Guayaquil Not Windows 2\n", "America/Halifax Not Windows 1\n", " Windows 3\n", "America/Indianapolis Not Windows 8\n", " Windows 12\n", "America/La_Paz Windows 1\n", " ... \n", "Europe/Madrid Not Windows 16\n", " Windows 19\n", "Europe/Malta Windows 2\n", "Europe/Moscow Not Windows 1\n", " Windows 9\n", "Europe/Oslo Not Windows 2\n", " Windows 8\n", "Europe/Paris Not Windows 4\n", " Windows 10\n", "Europe/Prague Not Windows 3\n", " Windows 7\n", "Europe/Riga Not Windows 1\n", " Windows 1\n", "Europe/Rome Not Windows 8\n", " Windows 19\n", "Europe/Skopje Windows 1\n", "Europe/Sofia Windows 1\n", "Europe/Stockholm Not Windows 2\n", " Windows 12\n", "Europe/Uzhgorod Windows 1\n", "Europe/Vienna Not Windows 3\n", " Windows 3\n", "Europe/Vilnius Windows 2\n", "Europe/Volgograd Windows 1\n", "Europe/Warsaw Not Windows 1\n", " Windows 15\n", "Europe/Zurich Not Windows 4\n", "Pacific/Auckland Not Windows 3\n", " Windows 8\n", "Pacific/Honolulu Windows 36\n", "dtype: int64" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "by_tz_os.size()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "agg_counts = by_tz_os.size().unstack().fillna(0)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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Not WindowsWindows
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" ], "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": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "agg_counts[:10]" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "indexer = agg_counts.sum(1).argsort()" ] }, { "cell_type": "code", "execution_count": 51, "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": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "indexer[:10]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "count_subset = agg_counts.take(indexer)[-10:]\n", "#take를 사용해 정렬된 순서그대로 선택, 마지막 10행 잘라내기" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Not WindowsWindows
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America/New_York339912
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" ], "text/plain": [ " Not Windows Windows\n", "tz \n", "America/Sao_Paulo 13 20\n", "Europe/Madrid 16 19\n", "Pacific/Honolulu 0 36\n", "Asia/Tokyo 2 35\n", "Europe/London 43 31\n", "America/Denver 132 59\n", "America/Los_Angeles 130 252\n", "America/Chicago 115 285\n", " 245 276\n", "America/New_York 339 912" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "count_subset" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QBEEQtBhhlIOWoupxqSrrr7J2CP1lU3X9jSLe6BW8ie68ajNc3EEQBI0nYsrL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\nEARB0CSEUQ6CIAiCJuH/Ax3a9rXVzBdvAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "count_subset.plot(kind='barh', stacked=True)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nored_subset = count_subset.div(count_subset.sum(1), axis=0)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nored_subset.plot(kind='barh', stacked=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. MovieLens의 영화평점 데이터" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 웹의 url을 사용하여 파일읽기" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pathUrl3 = 'https://raw.githubusercontent.com/pydata/pydata-book/master/ch02/movielens/users.dat'" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import urllib" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "response = urllib.urlopen(pathUrl3)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "responseLines = response.readlines()\n", "#한줄씩읽음" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['1::F::1::10::48067\\n',\n", " '2::M::56::16::70072\\n',\n", " '3::M::25::15::55117\\n',\n", " 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"cell_type": "code", "execution_count": 74, "metadata": { "collapsed": true }, "outputs": [], "source": [ "unames = ['user_id', 'gender', 'age', 'occupation', 'zip']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- github에서 rqw의 url을 복사하여 다음과 같이 사용" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "users = pd.read_table('https://raw.githubusercontent.com/pydata/pydata-book/master/ch02/movielens/users.dat', \n", " sep='::', header=None, names=unames)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ratings = pd.read_table('https://raw.githubusercontent.com/pydata/pydata-book/master/ch02/movielens/ratings.dat', \n", " sep='::', header=None, names=rnames)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mnames = ['movie_id', 'title', 'genres']" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": true }, "outputs": [], "source": [ "movies = pd.read_table('https://raw.githubusercontent.com/pydata/pydata-book/master/ch02/movielens/movies.dat', \n", " sep='::', header=None, names=mnames)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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movie_idtitlegenres
01Toy Story (1995)Animation|Children's|Comedy
12Jumanji (1995)Adventure|Children's|Fantasy
23Grumpier Old Men (1995)Comedy|Romance
34Waiting to Exhale (1995)Comedy|Drama
45Father of the Bride Part II (1995)Comedy
56Heat (1995)Action|Crime|Thriller
67Sabrina (1995)Comedy|Romance
78Tom and Huck (1995)Adventure|Children's
89Sudden Death (1995)Action
910GoldenEye (1995)Action|Adventure|Thriller
1011American President, The (1995)Comedy|Drama|Romance
1112Dracula: Dead and Loving It (1995)Comedy|Horror
1213Balto (1995)Animation|Children's
1314Nixon (1995)Drama
1415Cutthroat Island (1995)Action|Adventure|Romance
1516Casino (1995)Drama|Thriller
1617Sense and Sensibility (1995)Drama|Romance
1718Four Rooms (1995)Thriller
1819Ace Ventura: When Nature Calls (1995)Comedy
1920Money Train (1995)Action
2021Get Shorty (1995)Action|Comedy|Drama
2122Copycat (1995)Crime|Drama|Thriller
2223Assassins (1995)Thriller
2324Powder (1995)Drama|Sci-Fi
2425Leaving Las Vegas (1995)Drama|Romance
2526Othello (1995)Drama
2627Now and Then (1995)Drama
2728Persuasion (1995)Romance
2829City of Lost Children, The (1995)Adventure|Sci-Fi
2930Shanghai Triad (Yao a yao yao dao waipo qiao) ...Drama
............
38533923Return of the Fly (1959)Horror|Sci-Fi
38543924Pajama Party (1964)Comedy
38553925Stranger Than Paradise (1984)Comedy
38563926Voyage to the Bottom of the Sea (1961)Adventure|Sci-Fi
38573927Fantastic Voyage (1966)Adventure|Sci-Fi
38583928Abbott and Costello Meet Frankenstein (1948)Comedy|Horror
38593929Bank Dick, The (1940)Comedy
38603930Creature From the Black Lagoon, The (1954)Horror
38613931Giant Gila Monster, The (1959)Horror|Sci-Fi
38623932Invisible Man, The (1933)Horror|Sci-Fi
38633933Killer Shrews, The (1959)Horror|Sci-Fi
38643934Kronos (1957)Sci-Fi
38653935Kronos (1973)Horror
38663936Phantom of the Opera, The (1943)Drama|Thriller
38673937Runaway (1984)Sci-Fi|Thriller
38683938Slumber Party Massacre, The (1982)Horror
38693939Slumber Party Massacre II, The (1987)Horror
38703940Slumber Party Massacre III, The (1990)Horror
38713941Sorority House Massacre (1986)Horror
38723942Sorority House Massacre II (1990)Horror
38733943Bamboozled (2000)Comedy
38743944Bootmen (2000)Comedy|Drama
38753945Digimon: The Movie (2000)Adventure|Animation|Children's
38763946Get Carter (2000)Action|Drama|Thriller
38773947Get Carter (1971)Thriller
38783948Meet the Parents (2000)Comedy
38793949Requiem for a Dream (2000)Drama
38803950Tigerland (2000)Drama
38813951Two Family House (2000)Drama
38823952Contender, The (2000)Drama|Thriller
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3883 rows × 3 columns

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" ], "text/plain": [ " movie_id title \\\n", "0 1 Toy Story (1995) \n", "1 2 Jumanji (1995) \n", "2 3 Grumpier Old Men (1995) \n", "3 4 Waiting to Exhale (1995) \n", "4 5 Father of the Bride Part II (1995) \n", "5 6 Heat (1995) \n", "6 7 Sabrina (1995) \n", "7 8 Tom and Huck (1995) \n", "8 9 Sudden Death (1995) \n", "9 10 GoldenEye (1995) \n", "10 11 American President, The (1995) \n", "11 12 Dracula: Dead and Loving It (1995) \n", "12 13 Balto (1995) \n", "13 14 Nixon (1995) \n", "14 15 Cutthroat Island (1995) \n", "15 16 Casino (1995) \n", "16 17 Sense and Sensibility (1995) \n", "17 18 Four Rooms (1995) \n", "18 19 Ace Ventura: When Nature Calls (1995) \n", "19 20 Money Train (1995) \n", "20 21 Get Shorty (1995) \n", "21 22 Copycat (1995) \n", "22 23 Assassins (1995) \n", "23 24 Powder (1995) \n", "24 25 Leaving Las Vegas (1995) \n", "25 26 Othello (1995) \n", "26 27 Now and Then (1995) \n", "27 28 Persuasion (1995) \n", "28 29 City of Lost Children, The (1995) \n", "29 30 Shanghai Triad (Yao a yao yao dao waipo qiao) ... \n", "... ... ... \n", "3853 3923 Return of the Fly (1959) \n", "3854 3924 Pajama Party (1964) \n", "3855 3925 Stranger Than Paradise (1984) \n", "3856 3926 Voyage to the Bottom of the Sea (1961) \n", "3857 3927 Fantastic Voyage (1966) \n", "3858 3928 Abbott and Costello Meet Frankenstein (1948) \n", "3859 3929 Bank Dick, The (1940) \n", "3860 3930 Creature From the Black Lagoon, The (1954) \n", "3861 3931 Giant Gila Monster, The (1959) \n", "3862 3932 Invisible Man, The (1933) \n", "3863 3933 Killer Shrews, The (1959) \n", "3864 3934 Kronos (1957) \n", "3865 3935 Kronos (1973) \n", "3866 3936 Phantom of the Opera, The (1943) \n", "3867 3937 Runaway (1984) \n", "3868 3938 Slumber Party Massacre, The (1982) \n", "3869 3939 Slumber Party Massacre II, The (1987) \n", "3870 3940 Slumber Party Massacre III, The (1990) \n", "3871 3941 Sorority House Massacre (1986) \n", "3872 3942 Sorority House Massacre II (1990) \n", "3873 3943 Bamboozled (2000) \n", "3874 3944 Bootmen (2000) \n", "3875 3945 Digimon: The Movie (2000) \n", "3876 3946 Get Carter (2000) \n", "3877 3947 Get Carter (1971) \n", "3878 3948 Meet the Parents (2000) \n", "3879 3949 Requiem for a Dream (2000) \n", "3880 3950 Tigerland (2000) \n", "3881 3951 Two Family House (2000) \n", "3882 3952 Contender, The (2000) \n", "\n", " genres \n", "0 Animation|Children's|Comedy \n", "1 Adventure|Children's|Fantasy \n", "2 Comedy|Romance \n", "3 Comedy|Drama \n", "4 Comedy \n", "5 Action|Crime|Thriller \n", "6 Comedy|Romance \n", "7 Adventure|Children's \n", "8 Action \n", "9 Action|Adventure|Thriller \n", "10 Comedy|Drama|Romance \n", "11 Comedy|Horror \n", "12 Animation|Children's \n", "13 Drama \n", "14 Action|Adventure|Romance \n", "15 Drama|Thriller \n", "16 Drama|Romance \n", "17 Thriller \n", "18 Comedy \n", "19 Action \n", "20 Action|Comedy|Drama \n", "21 Crime|Drama|Thriller \n", "22 Thriller \n", "23 Drama|Sci-Fi \n", "24 Drama|Romance \n", "25 Drama \n", "26 Drama \n", "27 Romance \n", "28 Adventure|Sci-Fi \n", "29 Drama \n", "... ... \n", "3853 Horror|Sci-Fi \n", "3854 Comedy \n", "3855 Comedy \n", "3856 Adventure|Sci-Fi \n", "3857 Adventure|Sci-Fi \n", "3858 Comedy|Horror \n", "3859 Comedy \n", "3860 Horror \n", "3861 Horror|Sci-Fi \n", "3862 Horror|Sci-Fi \n", "3863 Horror|Sci-Fi \n", "3864 Sci-Fi \n", "3865 Horror \n", "3866 Drama|Thriller \n", "3867 Sci-Fi|Thriller \n", "3868 Horror \n", "3869 Horror \n", "3870 Horror \n", "3871 Horror \n", "3872 Horror \n", "3873 Comedy \n", "3874 Comedy|Drama \n", "3875 Adventure|Animation|Children's \n", "3876 Action|Drama|Thriller \n", "3877 Thriller \n", "3878 Comedy \n", "3879 Drama \n", "3880 Drama \n", "3881 Drama \n", "3882 Drama|Thriller \n", "\n", "[3883 rows x 3 columns]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "movies[:]" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(movies)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2) .pandas의 merge()로 병합하기" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = pd.merge(pd.merge(ratings, users), movies)" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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user_idmovie_idratingtimestampgenderageoccupationziptitlegenres
0111935978300760F11048067One Flew Over the Cuckoo's Nest (1975)Drama
1211935978298413M561670072One Flew Over the Cuckoo's Nest (1975)Drama
21211934978220179M251232793One Flew Over the Cuckoo's Nest (1975)Drama
31511934978199279M25722903One Flew Over the Cuckoo's Nest (1975)Drama
41711935978158471M50195350One Flew Over the Cuckoo's Nest (1975)Drama
51811934978156168F18395825One Flew Over the Cuckoo's Nest (1975)Drama
61911935982730936M11048073One Flew Over the Cuckoo's Nest (1975)Drama
72411935978136709F25710023One Flew Over the Cuckoo's Nest (1975)Drama
82811933978125194F25114607One Flew Over the Cuckoo's Nest (1975)Drama
93311935978557765M45355421One Flew Over the Cuckoo's Nest (1975)Drama
\n", "
" ], "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": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[:10]" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "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": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.ix[0]\n", "#0번째 행의 정보" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3) 성별에 따른 영화의 평균 평점 구하기 (pivot_table 사용)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mean_ratings = data.pivot_table('rating', index='title', columns='gender', aggfunc='mean')\n", "#책의 rows, cols가 index, colums로 바뀜\n", "#aggfunc은 default값이 mean 이며, 값을 어떻게 나타낼지 결정하는 함수" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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genderFM
title
$1,000,000 Duck (1971)3.3750002.761905
'Night Mother (1986)3.3888893.352941
'Til There Was You (1997)2.6756762.733333
'burbs, The (1989)2.7934782.962085
...And Justice for All (1979)3.8285713.689024
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" ], "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": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_ratings[:5]\n", "#성별에 따른 영화 평점 DataFrame으로 객체 생성" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4) 250건 이상의 평점 정보가 있는 영화만 추리기 (size() 사용)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ratings_by_title = data.groupby('title').size()" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "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": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratings_by_title[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 250건 이상의 평점 정보가 있는 영화의 색인은 mean_ratings에서 항목을 선택하기 위해 사용" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": true }, "outputs": [], "source": [ "active_titles = ratings_by_title.index[ratings_by_title >= 250]" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "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": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "active_titles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 250건 이상의 영화에 대한 색인은 mean_ratings에서 항목을 선택하기위해 사용" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mean_ratings = mean_ratings.ix[active_titles]" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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genderFM
title
'burbs, The (1989)2.7934782.962085
10 Things I Hate About You (1999)3.6465523.311966
101 Dalmatians (1961)3.7914443.500000
101 Dalmatians (1996)3.2400002.911215
12 Angry Men (1957)4.1843974.328421
13th Warrior, The (1999)3.1120003.168000
2 Days in the Valley (1996)3.4888893.244813
20,000 Leagues Under the Sea (1954)3.6701033.709205
2001: A Space Odyssey (1968)3.8255814.129738
2010 (1984)3.4468093.413712
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" ], "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": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_ratings[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 여성에게 높은 평점을 받은 영화목록 확인" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": true }, "outputs": [], "source": [ "top_female_ratings = mean_ratings.sort_index(by='F', ascending = False)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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genderFM
title
Close Shave, A (1995)4.6444444.473795
Wrong Trousers, The (1993)4.5882354.478261
Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)4.5726504.464589
Wallace & Gromit: The Best of Aardman Animation (1996)4.5631074.385075
Schindler's List (1993)4.5626024.491415
Shawshank Redemption, The (1994)4.5390754.560625
Grand Day Out, A (1992)4.5378794.293255
To Kill a Mockingbird (1962)4.5366674.372611
Creature Comforts (1990)4.5138894.272277
Usual Suspects, The (1995)4.5133174.518248
It Happened One Night (1934)4.5000004.163934
Rear Window (1954)4.4845364.472991
Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954)4.4811324.576628
Sixth Sense, The (1999)4.4774104.379944
Third Man, The (1949)4.4660194.448276
Some Like It Hot (1959)4.4627454.228769
City Lights (1931)4.4520554.363636
Notorious (1946)4.4487184.211073
Philadelphia Story, The (1940)4.4468094.201729
Life Is Beautiful (La Vita � bella) (1997)4.4223434.286624
Strangers on a Train (1951)4.4148154.262248
Bicycle Thief, The (Ladri di biciclette) (1948)4.4074074.343434
Mr. Smith Goes to Washington (1939)4.4000004.183746
Thin Blue Line, The (1988)4.3965524.213270
Killing Fields, The (1984)4.3780494.211268
Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)4.3766234.464789
Ran (1985)4.3650794.248299
North by Northwest (1959)4.3644584.390641
All About Eve (1950)4.3630574.186992
Manchurian Candidate, The (1962)4.3588244.326050
.........
Rocky III (1982)2.3617022.943503
Congo (1995)2.3473682.217021
House on Haunted Hill, The (1999)2.3333332.299213
Spawn (1997)2.3333332.660287
Blue Lagoon, The (1980)2.3217392.337662
Nightmare on Elm Street Part 2: Freddy's Revenge, A (1985)2.3191492.321586
Inspector Gadget (1999)2.3163272.112727
Porky's (1981)2.2968752.836364
Honey, I Blew Up the Kid (1992)2.2959182.243816
Wild Wild West (1999)2.2754492.131973
Ace Ventura: When Nature Calls (1995)2.2696632.543333
Fly II, The (1989)2.2571432.321285
Cable Guy, The (1996)2.2500002.863787
Island of Dr. Moreau, The (1996)2.2452832.191882
Grease 2 (1982)2.2434781.792553
Superman IV: The Quest for Peace (1987)2.2162161.847458
I Still Know What You Did Last Summer (1998)2.2000002.204444
Eye of the Beholder (1999)2.1818182.115789
Godzilla (1998)2.1754392.343891
Super Mario Bros. (1993)2.1636361.820339
Karate Kid III, The (1989)2.1555562.116183
Teenage Mutant Ninja Turtles II: The Secret of the Ooze (1991)2.1521742.121951
Striptease (1996)2.1500002.193277
Howard the Duck (1986)2.0746272.103542
Anaconda (1997)2.0000002.248447
Avengers, The (1998)1.9152542.017467
Speed 2: Cruise Control (1997)1.9066671.863014
Rocky V (1990)1.8787882.132780
Barb Wire (1996)1.5853662.100386
Battlefield Earth (2000)1.5744681.616949
\n", "

1216 rows × 2 columns

\n", "
" ], "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\n", "It Happened One Night (1934) 4.500000 4.163934\n", "Rear Window (1954) 4.484536 4.472991\n", "Seven Samurai (The Magnificent Seven) (Shichini... 4.481132 4.576628\n", "Sixth Sense, The (1999) 4.477410 4.379944\n", "Third Man, The (1949) 4.466019 4.448276\n", "Some Like It Hot (1959) 4.462745 4.228769\n", "City Lights (1931) 4.452055 4.363636\n", "Notorious (1946) 4.448718 4.211073\n", "Philadelphia Story, The (1940) 4.446809 4.201729\n", "Life Is Beautiful (La Vita 占�bella) (1997) 4.422343 4.286624\n", "Strangers on a Train (1951) 4.414815 4.262248\n", "Bicycle Thief, The (Ladri di biciclette) (1948) 4.407407 4.343434\n", "Mr. Smith Goes to Washington (1939) 4.400000 4.183746\n", "Thin Blue Line, The (1988) 4.396552 4.213270\n", "Killing Fields, The (1984) 4.378049 4.211268\n", "Dr. Strangelove or: How I Learned to Stop Worry... 4.376623 4.464789\n", "Ran (1985) 4.365079 4.248299\n", "North by Northwest (1959) 4.364458 4.390641\n", "All About Eve (1950) 4.363057 4.186992\n", "Manchurian Candidate, The (1962) 4.358824 4.326050\n", "... ... ...\n", "Rocky III (1982) 2.361702 2.943503\n", "Congo (1995) 2.347368 2.217021\n", "House on Haunted Hill, The (1999) 2.333333 2.299213\n", "Spawn (1997) 2.333333 2.660287\n", "Blue Lagoon, The (1980) 2.321739 2.337662\n", "Nightmare on Elm Street Part 2: Freddy's Reveng... 2.319149 2.321586\n", "Inspector Gadget (1999) 2.316327 2.112727\n", "Porky's (1981) 2.296875 2.836364\n", "Honey, I Blew Up the Kid (1992) 2.295918 2.243816\n", "Wild Wild West (1999) 2.275449 2.131973\n", "Ace Ventura: When Nature Calls (1995) 2.269663 2.543333\n", "Fly II, The (1989) 2.257143 2.321285\n", "Cable Guy, The (1996) 2.250000 2.863787\n", "Island of Dr. Moreau, The (1996) 2.245283 2.191882\n", "Grease 2 (1982) 2.243478 1.792553\n", "Superman IV: The Quest for Peace (1987) 2.216216 1.847458\n", "I Still Know What You Did Last Summer (1998) 2.200000 2.204444\n", "Eye of the Beholder (1999) 2.181818 2.115789\n", "Godzilla (1998) 2.175439 2.343891\n", "Super Mario Bros. (1993) 2.163636 1.820339\n", "Karate Kid III, The (1989) 2.155556 2.116183\n", "Teenage Mutant Ninja Turtles II: The Secret of ... 2.152174 2.121951\n", "Striptease (1996) 2.150000 2.193277\n", "Howard the Duck (1986) 2.074627 2.103542\n", "Anaconda (1997) 2.000000 2.248447\n", "Avengers, The (1998) 1.915254 2.017467\n", "Speed 2: Cruise Control (1997) 1.906667 1.863014\n", "Rocky V (1990) 1.878788 2.132780\n", "Barb Wire (1996) 1.585366 2.100386\n", "Battlefield Earth (2000) 1.574468 1.616949\n", "\n", "[1216 rows x 2 columns]" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_female_ratings[:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5) 평점 차이 구하기" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']\n", "#남성 평점과 여성평점 차이를 diff에 넣기" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sorted_by_diff = mean_ratings.sort_index(by='diff')" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
genderFMdiff
title
Dirty Dancing (1987)3.7903782.959596-0.830782
Jumpin' Jack Flash (1986)3.2547172.578358-0.676359
Grease (1978)3.9752653.367041-0.608224
Little Women (1994)3.8705883.321739-0.548849
Steel Magnolias (1989)3.9017343.365957-0.535777
Anastasia (1997)3.8000003.281609-0.518391
Rocky Horror Picture Show, The (1975)3.6730163.160131-0.512885
Color Purple, The (1985)4.1581923.659341-0.498851
Age of Innocence, The (1993)3.8270683.339506-0.487561
Free Willy (1993)2.9213482.438776-0.482573
\n", "
" ], "text/plain": [ "gender F M diff\n", "title \n", "Dirty Dancing (1987) 3.790378 2.959596 -0.830782\n", "Jumpin' Jack Flash (1986) 3.254717 2.578358 -0.676359\n", "Grease (1978) 3.975265 3.367041 -0.608224\n", "Little Women (1994) 3.870588 3.321739 -0.548849\n", "Steel Magnolias (1989) 3.901734 3.365957 -0.535777\n", "Anastasia (1997) 3.800000 3.281609 -0.518391\n", "Rocky Horror Picture Show, The (1975) 3.673016 3.160131 -0.512885\n", "Color Purple, The (1985) 4.158192 3.659341 -0.498851\n", "Age of Innocence, The (1993) 3.827068 3.339506 -0.487561\n", "Free Willy (1993) 2.921348 2.438776 -0.482573" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_by_diff[:10]" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
genderFMdiff
title
Good, The Bad and The Ugly, The (1966)3.4949494.2213000.726351
Kentucky Fried Movie, The (1977)2.8787883.5551470.676359
Dumb & Dumber (1994)2.6979873.3365950.638608
Longest Day, The (1962)3.4117654.0314470.619682
Cable Guy, The (1996)2.2500002.8637870.613787
Evil Dead II (Dead By Dawn) (1987)3.2972973.9092830.611985
Hidden, The (1987)3.1379313.7450980.607167
Rocky III (1982)2.3617022.9435030.581801
Caddyshack (1980)3.3961353.9697370.573602
For a Few Dollars More (1965)3.4090913.9537950.544704
\n", "
" ], "text/plain": [ "gender F M diff\n", "title \n", "Good, The Bad and The Ugly, The (1966) 3.494949 4.221300 0.726351\n", "Kentucky Fried Movie, The (1977) 2.878788 3.555147 0.676359\n", "Dumb & Dumber (1994) 2.697987 3.336595 0.638608\n", "Longest Day, The (1962) 3.411765 4.031447 0.619682\n", "Cable Guy, The (1996) 2.250000 2.863787 0.613787\n", "Evil Dead II (Dead By Dawn) (1987) 3.297297 3.909283 0.611985\n", "Hidden, The (1987) 3.137931 3.745098 0.607167\n", "Rocky III (1982) 2.361702 2.943503 0.581801\n", "Caddyshack (1980) 3.396135 3.969737 0.573602\n", "For a Few Dollars More (1965) 3.409091 3.953795 0.544704" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_by_diff[::-1][:10]\n", "#역순으로 상위 10개만 (남성들이 선호하는 순으로)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 호불호가 극명핳게 나뉘는 영화 찾기" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#평점의 표준편차\n", "rating_std_by_title = data.groupby('title')['rating'].std()" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#active_titles만 선택\n", "rating_std_by_title = rating_std_by_title.ix[active_titles]" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "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": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#내림차순으로 정렬\n", "rating_std_by_title.order(ascending=False)[:10]" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "title\n", "Close Shave, A (1995) 0.667143\n", "Rear Window (1954) 0.688946\n", "Great Escape, The (1963) 0.692585\n", "Shawshank Redemption, The (1994) 0.700443\n", "Wrong Trousers, The (1993) 0.708666\n", "Raiders of the Lost Ark (1981) 0.725647\n", "North by Northwest (1959) 0.732515\n", "Hustler, The (1961) 0.737298\n", "Double Indemnity (1944) 0.740793\n", "Sunset Blvd. (a.k.a. Sunset Boulevard) (1950) 0.740924\n", "Name: rating, dtype: float64" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rating_std_by_title.order(ascending=True)[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3.신생아 이름" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [], "source": [ "names1880 = pd.read_csv('https://raw.githubusercontent.com/pydata/pydata-book/master/ch02/names/yob1880.txt', \n", " names=['name', 'sex', 'births'])" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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namesexbirths
0MaryF7065
1AnnaF2604
2EmmaF2003
3ElizabethF1939
4MinnieF1746
5MargaretF1578
6IdaF1472
7AliceF1414
8BerthaF1320
9SarahF1288
10AnnieF1258
11ClaraF1226
12EllaF1156
13FlorenceF1063
14CoraF1045
15MarthaF1040
16LauraF1012
17NellieF995
18GraceF982
19CarrieF949
20MaudeF858
21MabelF808
22BessieF794
23JennieF793
24GertrudeF787
25JuliaF783
26HattieF769
27EdithF768
28MattieF704
29RoseF700
............
1970PhiloM5
1971PhineasM5
1972PresleyM5
1973RansomM5
1974ReeceM5
1975ReneM5
1976RoswellM5
1977RowlandM5
1978SampsonM5
1979SamualM5
1980SantosM5
1981SchuylerM5
1982SheppardM5
1983SpurgeonM5
1984StarlingM5
1985SylvanusM5
1986TheadoreM5
1987TheophileM5
1988TilmonM5
1989TommyM5
1990UnknownM5
1991VannM5
1992WesM5
1993WinstonM5
1994WoodM5
1995WoodieM5
1996WorthyM5
1997WrightM5
1998YorkM5
1999ZachariahM5
\n", "

2000 rows × 3 columns

\n", "
" ], "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\n", "5 Margaret F 1578\n", "6 Ida F 1472\n", "7 Alice F 1414\n", "8 Bertha F 1320\n", "9 Sarah F 1288\n", "10 Annie F 1258\n", "11 Clara F 1226\n", "12 Ella F 1156\n", "13 Florence F 1063\n", "14 Cora F 1045\n", "15 Martha F 1040\n", "16 Laura F 1012\n", "17 Nellie F 995\n", "18 Grace F 982\n", "19 Carrie F 949\n", "20 Maude F 858\n", "21 Mabel F 808\n", "22 Bessie F 794\n", "23 Jennie F 793\n", "24 Gertrude F 787\n", "25 Julia F 783\n", "26 Hattie F 769\n", "27 Edith F 768\n", "28 Mattie F 704\n", "29 Rose F 700\n", "... ... .. ...\n", "1970 Philo M 5\n", "1971 Phineas M 5\n", "1972 Presley M 5\n", "1973 Ransom M 5\n", "1974 Reece M 5\n", "1975 Rene M 5\n", "1976 Roswell M 5\n", "1977 Rowland M 5\n", "1978 Sampson M 5\n", "1979 Samual M 5\n", "1980 Santos M 5\n", "1981 Schuyler M 5\n", "1982 Sheppard M 5\n", "1983 Spurgeon M 5\n", "1984 Starling M 5\n", "1985 Sylvanus M 5\n", "1986 Theadore M 5\n", "1987 Theophile M 5\n", "1988 Tilmon M 5\n", "1989 Tommy M 5\n", "1990 Unknown M 5\n", "1991 Vann M 5\n", "1992 Wes M 5\n", "1993 Winston M 5\n", "1994 Wood M 5\n", "1995 Woodie M 5\n", "1996 Worthy M 5\n", "1997 Wright M 5\n", "1998 York M 5\n", "1999 Zachariah M 5\n", "\n", "[2000 rows x 3 columns]" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names1880[:]" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "sex\n", "F 90993\n", "M 110493\n", "Name: births, dtype: int64" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names1880.groupby('sex').births.sum()\n", "#성별에 따라 그룹화하여 birth값을 구함 (해당연도의 전체 출생수)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) 연도별로 나누어진 데이터를 DataFrame으로 취함 (panda.concat 사용)" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": true }, "outputs": [], "source": [ "years = range(1880, 2011) #2010년 데이터가 가장 마지막" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pieces=[]" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": true }, "outputs": [], "source": [ "columns = ['name', 'sex', 'births']" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for year in years:\n", " path = 'https://raw.githubusercontent.com/pydata/pydata-book/master/ch02/names/yob%d.txt' % year\n", " frame = pd.read_csv(path, names=columns)\n", " \n", " frame['year'] = year\n", " pieces.append(frame)" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#하나의 DataFrame으로 모든 데이터를 연결시키면\n", "names = pd.concat(pieces, ignore_index=True)\n", "#read_csv를 통해 읽어온 원래 행 순서는 몰라도 되므로 index무시" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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namesexbirthsyear
0MaryF70651880
1AnnaF26041880
2EmmaF20031880
3ElizabethF19391880
4MinnieF17461880
5MargaretF15781880
6IdaF14721880
7AliceF14141880
8BerthaF13201880
9SarahF12881880
10AnnieF12581880
11ClaraF12261880
12EllaF11561880
13FlorenceF10631880
14CoraF10451880
15MarthaF10401880
16LauraF10121880
17NellieF9951880
18GraceF9821880
19CarrieF9491880
20MaudeF8581880
21MabelF8081880
22BessieF7941880
23JennieF7931880
24GertrudeF7871880
25JuliaF7831880
26HattieF7691880
27EdithF7681880
28MattieF7041880
29RoseF7001880
...............
1690754ZaviyonM52010
1690755ZaybrienM52010
1690756ZayshawnM52010
1690757ZayyanM52010
1690758ZealM52010
1690759ZealanM52010
1690760ZechariaM52010
1690761ZeferinoM52010
1690762ZekariahM52010
1690763ZekiM52010
1690764ZeriahM52010
1690765ZeshanM52010
1690766ZhyierM52010
1690767ZildjianM52010
1690768ZinnM52010
1690769ZishanM52010
1690770ZivenM52010
1690771ZmariM52010
1690772ZorenM52010
1690773ZuhaibM52010
1690774ZyeireM52010
1690775ZygmuntM52010
1690776ZykerionM52010
1690777ZylarM52010
1690778ZylinM52010
1690779ZymaireM52010
1690780ZyonneM52010
1690781ZyquariusM52010
1690782ZyranM52010
1690783ZzyzxM52010
\n", "

1690784 rows × 4 columns

\n", "
" ], "text/plain": [ " name sex births year\n", "0 Mary F 7065 1880\n", "1 Anna F 2604 1880\n", "2 Emma F 2003 1880\n", "3 Elizabeth F 1939 1880\n", "4 Minnie F 1746 1880\n", "5 Margaret F 1578 1880\n", "6 Ida F 1472 1880\n", "7 Alice F 1414 1880\n", "8 Bertha F 1320 1880\n", "9 Sarah F 1288 1880\n", "10 Annie F 1258 1880\n", "11 Clara F 1226 1880\n", "12 Ella F 1156 1880\n", "13 Florence F 1063 1880\n", "14 Cora F 1045 1880\n", "15 Martha F 1040 1880\n", "16 Laura F 1012 1880\n", "17 Nellie F 995 1880\n", "18 Grace F 982 1880\n", "19 Carrie F 949 1880\n", "20 Maude F 858 1880\n", "21 Mabel F 808 1880\n", "22 Bessie F 794 1880\n", "23 Jennie F 793 1880\n", "24 Gertrude F 787 1880\n", "25 Julia F 783 1880\n", "26 Hattie F 769 1880\n", "27 Edith F 768 1880\n", "28 Mattie F 704 1880\n", "29 Rose F 700 1880\n", "... ... .. ... ...\n", "1690754 Zaviyon M 5 2010\n", "1690755 Zaybrien M 5 2010\n", "1690756 Zayshawn M 5 2010\n", "1690757 Zayyan M 5 2010\n", "1690758 Zeal M 5 2010\n", "1690759 Zealan M 5 2010\n", "1690760 Zecharia M 5 2010\n", "1690761 Zeferino M 5 2010\n", "1690762 Zekariah M 5 2010\n", "1690763 Zeki M 5 2010\n", "1690764 Zeriah M 5 2010\n", "1690765 Zeshan M 5 2010\n", "1690766 Zhyier M 5 2010\n", "1690767 Zildjian M 5 2010\n", "1690768 Zinn M 5 2010\n", "1690769 Zishan M 5 2010\n", "1690770 Ziven M 5 2010\n", "1690771 Zmari M 5 2010\n", "1690772 Zoren M 5 2010\n", "1690773 Zuhaib M 5 2010\n", "1690774 Zyeire M 5 2010\n", "1690775 Zygmunt M 5 2010\n", "1690776 Zykerion M 5 2010\n", "1690777 Zylar M 5 2010\n", "1690778 Zylin M 5 2010\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\n", "\n", "[1690784 rows x 4 columns]" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 이것을 토대로 groupby and pivot_table을 이용해서 연도나 성별 데이터 수집 가능" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": false }, "outputs": [], "source": [ "total_births = names.pivot_table('births', index='year', \n", " columns='sex', aggfunc=sum)" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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sexFM
year
200618964682050234
200719168882069242
200818836452032310
200918276431973359
201017590101898382
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" ], "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": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "total_births.tail()" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Rznxg38I6TnnHLQPsB2xFqEh7mRkXN0zqesnXDvgEtKKn1TpOclmDz/stTZj4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\nOnAoD4w4yIzHKimL4zSWYs9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5MVNIPuzeCnw0Iv7UdX2MmUo8JWXMFCHpNOCP\nwK/tLMxMxCMMY4wxrfAIwxhjTCvsMIwxxrTCDsMYY0wr7DCMMca0wg7DGGNMK+wwjDHGtOL/hraH\nf9toCHUAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "total_births.plot(title='Total births by sex and year')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2) prop열을 추가해서 전체 출생수에서 차지하는 비율 계산" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#함수 정의\n", "def add_prop(group):\n", " #Integer division floors\n", " births = group.births.astype(float) #birth를 float타입으로\n", " \n", " group['prop'] = births/ births.sum() \n", " #births를 births전체의 합으로 나누면 prop라는 출생률 계산\n", " return group" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": true }, "outputs": [], "source": [ "names = names.groupby(['year', 'sex']).apply(add_prop) \n", "#새로운 열을 추가" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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namesexbirthsyearprop
0MaryF706518800.077643
1AnnaF260418800.028618
2EmmaF200318800.022013
3ElizabethF193918800.021309
4MinnieF174618800.019188
5MargaretF157818800.017342
6IdaF147218800.016177
7AliceF141418800.015540
8BerthaF132018800.014507
9SarahF128818800.014155
10AnnieF125818800.013825
11ClaraF122618800.013474
12EllaF115618800.012704
13FlorenceF106318800.011682
14CoraF104518800.011484
15MarthaF104018800.011429
16LauraF101218800.011122
17NellieF99518800.010935
18GraceF98218800.010792
19CarrieF94918800.010429
20MaudeF85818800.009429
21MabelF80818800.008880
22BessieF79418800.008726
23JennieF79318800.008715
24GertrudeF78718800.008649
25JuliaF78318800.008605
26HattieF76918800.008451
27EdithF76818800.008440
28MattieF70418800.007737
29RoseF70018800.007693
..................
1690754ZaviyonM520100.000003
1690755ZaybrienM520100.000003
1690756ZayshawnM520100.000003
1690757ZayyanM520100.000003
1690758ZealM520100.000003
1690759ZealanM520100.000003
1690760ZechariaM520100.000003
1690761ZeferinoM520100.000003
1690762ZekariahM520100.000003
1690763ZekiM520100.000003
1690764ZeriahM520100.000003
1690765ZeshanM520100.000003
1690766ZhyierM520100.000003
1690767ZildjianM520100.000003
1690768ZinnM520100.000003
1690769ZishanM520100.000003
1690770ZivenM520100.000003
1690771ZmariM520100.000003
1690772ZorenM520100.000003
1690773ZuhaibM520100.000003
1690774ZyeireM520100.000003
1690775ZygmuntM520100.000003
1690776ZykerionM520100.000003
1690777ZylarM520100.000003
1690778ZylinM520100.000003
1690779ZymaireM520100.000003
1690780ZyonneM520100.000003
1690781ZyquariusM520100.000003
1690782ZyranM520100.000003
1690783ZzyzxM520100.000003
\n", "

1690784 rows × 5 columns

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" ], "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\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", "1690754 Zaviyon M 5 2010 0.000003\n", "1690755 Zaybrien M 5 2010 0.000003\n", "1690756 Zayshawn M 5 2010 0.000003\n", "1690757 Zayyan M 5 2010 0.000003\n", "1690758 Zeal M 5 2010 0.000003\n", "1690759 Zealan M 5 2010 0.000003\n", "1690760 Zecharia M 5 2010 0.000003\n", "1690761 Zeferino M 5 2010 0.000003\n", "1690762 Zekariah M 5 2010 0.000003\n", "1690763 Zeki M 5 2010 0.000003\n", "1690764 Zeriah M 5 2010 0.000003\n", "1690765 Zeshan M 5 2010 0.000003\n", "1690766 Zhyier M 5 2010 0.000003\n", "1690767 Zildjian M 5 2010 0.000003\n", "1690768 Zinn M 5 2010 0.000003\n", "1690769 Zishan M 5 2010 0.000003\n", "1690770 Ziven M 5 2010 0.000003\n", "1690771 Zmari M 5 2010 0.000003\n", "1690772 Zoren M 5 2010 0.000003\n", "1690773 Zuhaib M 5 2010 0.000003\n", "1690774 Zyeire M 5 2010 0.000003\n", "1690775 Zygmunt M 5 2010 0.000003\n", "1690776 Zykerion M 5 2010 0.000003\n", "1690777 Zylar M 5 2010 0.000003\n", "1690778 Zylin M 5 2010 0.000003\n", "1690779 Zymaire M 5 2010 0.000003\n", "1690780 Zyonne M 5 2010 0.000003\n", "1690781 Zyquarius M 5 2010 0.000003\n", "1690782 Zyran M 5 2010 0.000003\n", "1690783 Zzyzx M 5 2010 0.000003\n", "\n", "[1690784 rows x 5 columns]" ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3) 모든 그룹에서 prop의 열의 합이 1이 맞는지 확인 (sanity check - np.allclose() 사용)" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.allclose(names.groupby(['year', 'sex']).prop.sum(), 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4) 연도별, 성별에 따른 빈도수가 가장 높은 이름 100개 추출 (그룹연산 사용)" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#함수정의\n", "def get_top100(group):\n", " return group.sort_index(by='births', ascending=False)[:100]\n", " #birth로 내림차순 정렬\n", " \n", "grouped = names.groupby(['year', 'sex']) #year,sex로 그룹화해서 top100 출력" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": true }, "outputs": [], "source": [ "top100 = grouped.apply(get_top100)" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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namesexbirthsyearprop
yearsex
1880F0MaryF706518800.077643
1AnnaF260418800.028618
2EmmaF200318800.022013
3ElizabethF193918800.021309
4MinnieF174618800.019188
5MargaretF157818800.017342
6IdaF147218800.016177
7AliceF141418800.015540
8BerthaF132018800.014507
9SarahF128818800.014155
10AnnieF125818800.013825
11ClaraF122618800.013474
12EllaF115618800.012704
13FlorenceF106318800.011682
14CoraF104518800.011484
15MarthaF104018800.011429
16LauraF101218800.011122
17NellieF99518800.010935
18GraceF98218800.010792
19CarrieF94918800.010429
20MaudeF85818800.009429
21MabelF80818800.008880
22BessieF79418800.008726
23JennieF79318800.008715
24GertrudeF78718800.008649
25JuliaF78318800.008605
26HattieF76918800.008451
27EdithF76818800.008440
28MattieF70418800.007737
29RoseF70018800.007693
........................
2010M1676714XavierM570120100.003003
1676715IanM551020100.002902
1676716ColtonM527020100.002776
1676717DominicM526020100.002771
1676718JuanM521720100.002748
1676719CooperM520620100.002742
1676720JosiahM513820100.002707
1676721LuisM510420100.002689
1676722AydenM509620100.002684
1676723CarsonM506420100.002668
1676724AdamM506220100.002666
1676725NathanielM503920100.002654
1676726BrodyM501520100.002642
1676727TristanM485420100.002557
1676728DiegoM469320100.002472
1676729ParkerM468720100.002469
1676730BlakeM466620100.002458
1676731OliverM463220100.002440
1676732ColeM456220100.002403
1676733CarlosM455920100.002402
1676734JadenM446820100.002354
1676735JesusM442520100.002331
1676736AlexM440920100.002323
1676737AidanM426320100.002246
1676738EricM416320100.002193
1676739HaydenM415120100.002187
1676740BryanM391420100.002062
1676741MaxM381920100.002012
1676742JaxonM380220100.002003
1676743BrianM374420100.001972
\n", "

26200 rows × 5 columns

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" ], "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", "2010 M 1676714 Xavier M 5701 2010 0.003003\n", " 1676715 Ian M 5510 2010 0.002902\n", " 1676716 Colton M 5270 2010 0.002776\n", " 1676717 Dominic M 5260 2010 0.002771\n", " 1676718 Juan M 5217 2010 0.002748\n", " 1676719 Cooper M 5206 2010 0.002742\n", " 1676720 Josiah M 5138 2010 0.002707\n", " 1676721 Luis M 5104 2010 0.002689\n", " 1676722 Ayden M 5096 2010 0.002684\n", " 1676723 Carson M 5064 2010 0.002668\n", " 1676724 Adam M 5062 2010 0.002666\n", " 1676725 Nathaniel M 5039 2010 0.002654\n", " 1676726 Brody M 5015 2010 0.002642\n", " 1676727 Tristan M 4854 2010 0.002557\n", " 1676728 Diego M 4693 2010 0.002472\n", " 1676729 Parker M 4687 2010 0.002469\n", " 1676730 Blake M 4666 2010 0.002458\n", " 1676731 Oliver M 4632 2010 0.002440\n", " 1676732 Cole M 4562 2010 0.002403\n", " 1676733 Carlos M 4559 2010 0.002402\n", " 1676734 Jaden M 4468 2010 0.002354\n", " 1676735 Jesus M 4425 2010 0.002331\n", " 1676736 Alex M 4409 2010 0.002323\n", " 1676737 Aidan M 4263 2010 0.002246\n", " 1676738 Eric M 4163 2010 0.002193\n", " 1676739 Hayden M 4151 2010 0.002187\n", " 1676740 Bryan M 3914 2010 0.002062\n", " 1676741 Max M 3819 2010 0.002012\n", " 1676742 Jaxon M 3802 2010 0.002003\n", " 1676743 Brian M 3744 2010 0.001972\n", "\n", "[26200 rows x 5 columns]" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.이름유행 분석" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) 1000개의 데이터를 남자와 여자로 분리" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": true }, "outputs": [], "source": [ "boys = top100[top100.sex == 'M']" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": true }, "outputs": [], "source": [ "girls = top100[top100.sex == 'F']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 연도와 이름에 대한 전체 출생수를 피벗테이블로 작성" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [], "source": [ "total_births = top100.pivot_table('births', index='year', columns='name', aggfunc=sum)" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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10 rows × 783 columns

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" ], "text/plain": [ "name Aaliyah Aaron Abigail Ada Adam Addie Addison Adrian Agnes \\\n", "year \n", "1880 NaN NaN NaN 652 NaN 274 NaN NaN 473 \n", "1881 NaN NaN NaN 628 NaN 287 NaN NaN 424 \n", "1882 NaN NaN NaN 689 NaN 341 NaN NaN 565 \n", "1883 NaN NaN NaN 778 NaN 362 NaN NaN 623 \n", "1884 NaN NaN NaN 854 NaN 356 NaN NaN 703 \n", "1885 NaN NaN NaN 876 NaN 406 NaN NaN 695 \n", "1886 NaN NaN NaN 915 NaN 417 NaN NaN 779 \n", "1887 NaN NaN NaN 910 NaN 393 NaN NaN 896 \n", "1888 NaN NaN NaN 1116 NaN 455 NaN NaN 1046 \n", "1889 NaN NaN NaN 1005 NaN 448 NaN NaN 1033 \n", "\n", "name Aidan ... Willis Wilma Woodrow Wyatt Xavier Yolanda Yvonne \\\n", "year ... \n", "1880 NaN ... 166 NaN NaN NaN NaN NaN NaN \n", "1881 NaN ... 142 NaN NaN NaN NaN NaN NaN \n", "1882 NaN ... NaN NaN NaN NaN NaN NaN NaN \n", "1883 NaN ... NaN NaN NaN NaN NaN NaN NaN \n", "1884 NaN ... NaN NaN NaN NaN NaN NaN NaN \n", "1885 NaN ... NaN NaN NaN NaN NaN NaN NaN \n", "1886 NaN ... 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NaN NaN NaN NaN NaN NaN NaN \n", "\n", "name Zachary Zoe Zoey \n", "year \n", "1880 NaN NaN NaN \n", "1881 NaN NaN NaN \n", "1882 NaN NaN NaN \n", "1883 NaN NaN NaN \n", "1884 NaN NaN NaN \n", "1885 NaN NaN NaN \n", "1886 NaN NaN NaN \n", "1887 NaN NaN NaN \n", "1888 NaN NaN NaN \n", "1889 NaN NaN NaN \n", "\n", "[10 rows x 783 columns]" ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "total_births[:10]" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": true }, "outputs": [], "source": [ "subset = total_births[['John', 'Harry', 'Mary', 'Marilyn']]" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([,\n", " ,\n", " ,\n", " ], dtype=object)" ] }, "execution_count": 147, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Djq5KmiH9ndzFFTbM2hR5a1jtjXF+SNIlpK/FVgD3A78gfbKeJukY0pizw/Lxj0qaRkqu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\n+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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "subset.plot(subplots=True, figsize=(12,10), grid=False, title='Nuber of births per year')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2) 다양한 이름을 사용하는 경향 파악하기" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "위의 표를 통해서 부모가 아이 이름을 지을 때 흔한 이름은 기피하는것으로 해석할 수 있다." ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": true }, "outputs": [], "source": [ "table = top100.pivot_table('prop', index='year', columns='sex', aggfunc=sum)" ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "sex F M\n", "year \n", "1880 0.765454 0.801381\n", "1881 0.765766 0.802537\n", "1882 0.760688 0.796098\n", "1883 0.755177 0.799650\n", "1884 0.747281 0.792232\n", "1885 0.741334 0.789633\n", "1886 0.736166 0.788870\n", "1887 0.731332 0.790252\n", "1888 0.725104 0.783794\n", "1889 0.718897 0.786346" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table[:10]" ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table.plot(title='Sum of table1000.prop by year and sex', yticks=np.linspace(0, 1.2, 13), xticks=range(1880, 2020, 10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 위 그래프를 통해 실제로 이름의 다양성이 높아지고 있음을 보인다. (비율의 총합이 시간이 흐를수록 감소하고 있음을 보인다.)" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = boys[boys.year == 2010] #2010년에 인기있는 이름순으로 정렬" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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namesexbirthsyearprop
yearsex
2010M1676644JacobM2187520100.011523
1676645EthanM1786620100.009411
1676646MichaelM1713320100.009025
1676647JaydenM1703020100.008971
1676648WilliamM1687020100.008887
1676649AlexanderM1663420100.008762
1676650NoahM1628120100.008576
1676651DanielM1567920100.008259
1676652AidenM1540320100.008114
1676653AnthonyM1536420100.008093
1676654JoshuaM1523820100.008027
1676655MasonM1472820100.007758
1676656ChristopherM1413520100.007446
1676657AndrewM1409320100.007424
1676658DavidM1404220100.007397
1676659MatthewM1395420100.007350
1676660LoganM1394320100.007345
1676661ElijahM1373520100.007235
1676662JamesM1371420100.007224
1676663JosephM1365720100.007194
1676664GabrielM1272220100.006701
1676665BenjaminM1228020100.006469
1676666RyanM1188620100.006261
1676667SamuelM1177620100.006203
1676668JacksonM1169320100.006159
1676669JohnM1142420100.006018
1676670NathanM1126920100.005936
1676671JonathanM1102820100.005809
1676672ChristianM1096520100.005776
1676673LiamM1085220100.005716
..................
...XavierM570120100.003003
1676714IanM551020100.002902
1676715ColtonM527020100.002776
1676716DominicM526020100.002771
1676717JuanM521720100.002748
1676718CooperM520620100.002742
1676719JosiahM513820100.002707
1676720LuisM510420100.002689
1676721AydenM509620100.002684
1676722CarsonM506420100.002668
1676723AdamM506220100.002666
1676724NathanielM503920100.002654
1676725BrodyM501520100.002642
1676726TristanM485420100.002557
1676727DiegoM469320100.002472
1676728ParkerM468720100.002469
1676729BlakeM466620100.002458
1676730OliverM463220100.002440
1676731ColeM456220100.002403
1676732CarlosM455920100.002402
1676733JadenM446820100.002354
1676734JesusM442520100.002331
1676735AlexM440920100.002323
1676736AidanM426320100.002246
1676737EricM416320100.002193
1676738HaydenM415120100.002187
1676739BryanM391420100.002062
1676740MaxM381920100.002012
1676741JaxonM380220100.002003
1676742BrianM374420100.001972
\n", "

100 rows × 5 columns

\n", "
" ], "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\n", " 1676649 Alexander M 16634 2010 0.008762\n", " 1676650 Noah M 16281 2010 0.008576\n", " 1676651 Daniel M 15679 2010 0.008259\n", " 1676652 Aiden M 15403 2010 0.008114\n", " 1676653 Anthony M 15364 2010 0.008093\n", " 1676654 Joshua M 15238 2010 0.008027\n", " 1676655 Mason M 14728 2010 0.007758\n", " 1676656 Christopher M 14135 2010 0.007446\n", " 1676657 Andrew M 14093 2010 0.007424\n", " 1676658 David M 14042 2010 0.007397\n", " 1676659 Matthew M 13954 2010 0.007350\n", " 1676660 Logan M 13943 2010 0.007345\n", " 1676661 Elijah M 13735 2010 0.007235\n", " 1676662 James M 13714 2010 0.007224\n", " 1676663 Joseph M 13657 2010 0.007194\n", " 1676664 Gabriel M 12722 2010 0.006701\n", " 1676665 Benjamin M 12280 2010 0.006469\n", " 1676666 Ryan M 11886 2010 0.006261\n", " 1676667 Samuel M 11776 2010 0.006203\n", " 1676668 Jackson M 11693 2010 0.006159\n", " 1676669 John M 11424 2010 0.006018\n", " 1676670 Nathan M 11269 2010 0.005936\n", " 1676671 Jonathan M 11028 2010 0.005809\n", " 1676672 Christian M 10965 2010 0.005776\n", " 1676673 Liam M 10852 2010 0.005716\n", "... ... .. ... ... ...\n", " 1676714 Xavier M 5701 2010 0.003003\n", " 1676715 Ian M 5510 2010 0.002902\n", " 1676716 Colton M 5270 2010 0.002776\n", " 1676717 Dominic M 5260 2010 0.002771\n", " 1676718 Juan M 5217 2010 0.002748\n", " 1676719 Cooper M 5206 2010 0.002742\n", " 1676720 Josiah M 5138 2010 0.002707\n", " 1676721 Luis M 5104 2010 0.002689\n", " 1676722 Ayden M 5096 2010 0.002684\n", " 1676723 Carson M 5064 2010 0.002668\n", " 1676724 Adam M 5062 2010 0.002666\n", " 1676725 Nathaniel M 5039 2010 0.002654\n", " 1676726 Brody M 5015 2010 0.002642\n", " 1676727 Tristan M 4854 2010 0.002557\n", " 1676728 Diego M 4693 2010 0.002472\n", " 1676729 Parker M 4687 2010 0.002469\n", " 1676730 Blake M 4666 2010 0.002458\n", " 1676731 Oliver M 4632 2010 0.002440\n", " 1676732 Cole M 4562 2010 0.002403\n", " 1676733 Carlos M 4559 2010 0.002402\n", " 1676734 Jaden M 4468 2010 0.002354\n", " 1676735 Jesus M 4425 2010 0.002331\n", " 1676736 Alex M 4409 2010 0.002323\n", " 1676737 Aidan M 4263 2010 0.002246\n", " 1676738 Eric M 4163 2010 0.002193\n", " 1676739 Hayden M 4151 2010 0.002187\n", " 1676740 Bryan M 3914 2010 0.002062\n", " 1676741 Max M 3819 2010 0.002012\n", " 1676742 Jaxon M 3802 2010 0.002003\n", " 1676743 Brian M 3744 2010 0.001972\n", "\n", "[100 rows x 5 columns]" ] }, "execution_count": 154, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3) 전체의 50%가 되기까지 얼마나 많은 이름이 등장하나 (Numpy 이용)" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": true }, "outputs": [], "source": [ "prop_cumsum = df.sort_index(by='prop', ascending=False).prop.cumsum()\n", "#prop의 누계가 0.5가 되는 위치를 구한다." ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "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", " 1676654 0.097648\n", " 1676655 0.105406\n", " 1676656 0.112852\n", " 1676657 0.120276\n", " 1676658 0.127672\n", " 1676659 0.135023\n", " 1676660 0.142368\n", " 1676661 0.149603\n", " 1676662 0.156827\n", " 1676663 0.164021\n", " 1676664 0.170722\n", " 1676665 0.177191\n", " 1676666 0.183452\n", " 1676667 0.189655\n", " 1676668 0.195815\n", " 1676669 0.201832\n", " 1676670 0.207769\n", " 1676671 0.213578\n", " 1676672 0.219354\n", " 1676673 0.225070\n", " ... \n", " 1676714 0.397524\n", " 1676715 0.400426\n", " 1676716 0.403202\n", " 1676717 0.405973\n", " 1676718 0.408721\n", " 1676719 0.411464\n", " 1676720 0.414170\n", " 1676721 0.416859\n", " 1676722 0.419543\n", " 1676723 0.422211\n", " 1676724 0.424877\n", " 1676725 0.427531\n", " 1676726 0.430173\n", " 1676727 0.432730\n", " 1676728 0.435202\n", " 1676729 0.437671\n", " 1676730 0.440129\n", " 1676731 0.442569\n", " 1676732 0.444972\n", " 1676733 0.447374\n", " 1676734 0.449727\n", " 1676735 0.452058\n", " 1676736 0.454381\n", " 1676737 0.456626\n", " 1676738 0.458819\n", " 1676739 0.461006\n", " 1676740 0.463067\n", " 1676741 0.465079\n", " 1676742 0.467082\n", " 1676743 0.469054\n", "Name: prop, dtype: float64" ] }, "execution_count": 157, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prop_cumsum" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(prop_cumsum)" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([3], dtype=int64)" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prop_cumsum.searchsorted(0.03)" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 160, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prop_cumsum.searchsorted(0.03)[0]" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "101" ] }, "execution_count": 161, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prop_cumsum.searchsorted(0.5)[0] + 1 \n", "#색인의 경우 시작을 0부터 하기 때문에 +1을 한다." ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = boys[boys.year == 1900]" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "25" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "in1900 = df.sort_index(by='prop', ascending=False).prop.cumsum()\n", "in1900.searchsorted(0.5)[0] + 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 각 연도와 성별 조합에 적용할 수 있다. \n", "연도와 성별을 Groupby로 묶고 각 그룹에 apply를 사용하여 연산을 적용한다." ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#함수 선언\n", "def get_quantile_count(group, q=0.5):\n", " group = group.sort_index(by='prop', ascending=False)\n", " return group.prop.cumsum().searchsorted(q)[0] + 1\n", "\n", "diversity = top100.groupby(['year', 'sex']).apply(get_quantile_count)\n", "diversity = diversity.unstack('sex')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 연산 결과인 diversity DataFrame은 이제 각 성별에 따라 연도별로 색인된 2개의 시계열 데이터를 담고 있다." ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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sexFM
year
18803814
18813814
18823815
18833915
18843916
\n", "
" ], "text/plain": [ "sex F M\n", "year \n", "1880 38 14\n", "1881 38 14\n", "1882 38 15\n", "1883 39 15\n", "1884 39 16" ] }, "execution_count": 167, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diversity.head() #diversity[:5]와 같은 결과" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "diversity.plot(title = \"Number of popular names in top 50%\")\n", "#numeric searchsorted 는 배열의 위치를 찾는 것이기 때문에 int형이 아님" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. '마지막 글자'의 변환" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) 연도와 성별, 이름의 마지막 글자를 수집해서 확인" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#name 열에서 마지막 글자를 추출\n", "get_last_letter = lambda x : x[-1]\n", "last_letters = names.name.map(get_last_letter)\n", "last_letters.name = 'last_letter'\n", "\n", "table = names.pivot_table('births', index=last_letters, columns=['sex', 'year'], aggfunc=sum)" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ "sex F M \n", "year 1910 1960 2010 1910 1960 2010\n", "last_letter \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": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subtable = table.reindex(columns=[1910, 1960, 2010], level='year')\n", "subtable.head() #subtable[:5]와 같은 효과" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2) 전체 출생수에서 성별로 각각의 마지막 글자가 차지하는 비율 계산\n" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "sex year\n", "F 1910 396416\n", " 1960 2022062\n", " 2010 1759010\n", "M 1910 194198\n", " 1960 2132588\n", " 2010 1898382\n", "dtype: float64" ] }, "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subtable.sum()" ] }, { "cell_type": "code", "execution_count": 172, "metadata": { "collapsed": true }, "outputs": [], "source": [ "letter_prop = subtable / subtable.sum().astype(float)" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(,\n", " )" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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76vMiIiIiuirP/msp5T5V0VFqyb1k5qLcp6ozxn5X+X56pS3nllHtlk49piYi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\nuJBFFBXfsVTjsBpjTA1pllNV4/V+JkK3Klv7PV2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "fig, axes = plt.subplots(2, 1, figsize=(10, 8))\n", "letter_prop['M'].plot(kind='bar', rot=0, ax=axes[0], title='Male'), letter_prop['F'].plot(kind='bar', rot=0, ax=axes[1], title='Female', legend=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "그래프에서 보듯이 남자는 'n'으로 끝나는 이름이 1960이후 증가" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3) 성별로 정규화, 남자아이 이름에서 몇 글자를 선택하여 이름을 열로 하는 시계열 데이터로 변환" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
last_letterdny
year
18800.0830550.1532130.075760
18810.0832470.1532140.077451
18820.0853400.1495600.077537
18830.0840660.1516460.079144
18840.0861200.1499150.080405
\n", "
" ], "text/plain": [ "last_letter d n y\n", "year \n", "1880 0.083055 0.153213 0.075760\n", "1881 0.083247 0.153214 0.077451\n", "1882 0.085340 0.149560 0.077537\n", "1883 0.084066 0.151646 0.079144\n", "1884 0.086120 0.149915 0.080405" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "letter_prop = table / table.sum().astype(float)\n", "dny_ts = letter_prop.ix[['d', 'n', 'y'], 'M'].T\n", "dny_ts.head()" ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 175, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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whjn+v9kl+aX9z4Vz14V/bS99G7hMng65xz/PJgp2fzu7PRh312X7/+7vD04f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\nEhiZ3i+H+9L/h8drvwws1Fr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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dny_ts.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. 남자 이름과 여자 이름이 바뀐 경우" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) lesley 또는 Leslie 라는 이름이 그러한 경우, top1000을 이용하여 'lesl'로 시작하는 이름이 포함된 리스트 생성" ] }, { "cell_type": "code", "execution_count": 178, "metadata": { "collapsed": false }, "outputs": [], "source": [ "all_names = top100.name.unique()\n", "mask = np.array(['lesl' in x.lower() for x in all_names])" ] }, { "cell_type": "code", "execution_count": 179, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['Leslie'], dtype=object)" ] }, "execution_count": 179, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "lesley_like = all_names[mask]\n", "lesley_like" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2)이름들만 추려내어 이름별로 출생수를 구하고 상대 도수 확인" ] }, { "cell_type": "code", "execution_count": 180, "metadata": { "collapsed": true }, "outputs": [], "source": [ "filtered = top100[top100.name.isin(lesley_like)]" ] }, { "cell_type": "code", "execution_count": 181, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "name\n", "Leslie 191481\n", "Name: births, dtype: int64" ] }, "execution_count": 181, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filtered.groupby('name').births.sum()" ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "collapsed": true }, "outputs": [], "source": [ "table = filtered.pivot_table('births', index='year', columns='sex', aggfunc=sum)" ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "collapsed": true }, "outputs": [], "source": [ "table = table.div(table.sum(1), axis=0)" ] }, { "cell_type": "code", "execution_count": 184, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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