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HyeongWookKim/Ch 2. 서울시 범죄 현황 분석.ipynb

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[Ch 2. 서울시 범죄 현황 분석] from "파이썬으로 데이터 주무르기(민형기 지음)"
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Ch 2. 서울시 범죄 현황 분석.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. 서울시 범죄 현황 분석\n",
"<분석 목적>\n",
"- 부유층이 많이 사는 강남 3구(강남, 서초, 송파)의 체감 안전도가 정말로 높은지를 검증해보고자 한다\n",
" - \"강남 3구(강남, 서초, 송파)의 체감 안전도\" 관련 뉴스 기사: https://www.news1.kr/articles/?1911504\n",
"\n",
"<데이터 설명>\n",
"- 1. 서울시 관서별 5대 범죄 발생 검거 현황 데이터: \"02. crime_in_Seoul.csv\"\n",
" - *서울시 경찰서 별로 살인, 강도, 강간, 절도, 폭력이라는 5대 범죄에 대한 발생 건수 및 검거 건수*\n",
" - 관서명: 중부서, 종로서, 남대문서, ...\n",
" - 살인 발생: 살인 사건 발생 건수\n",
" - 살인 검거: 살인 용의자 검거 건수\n",
" - 강도 발생: 강도 사건 발생 건수\n",
" - 강도 검거: 강도 용의자 검거 건수\n",
" - 강간 발생: 강간 사건 발생 건수\n",
" - 강간 검거: 강간 용의자 검거 건수\n",
" - 절도 발생: 절도 사건 발생 건수\n",
" - 절도 검거: 절도 용의자 검거 건수\n",
" - 폭력 발생: 폭력 사건 발생 건수\n",
" - 폭력 검거: 폭력 용의자 검거 건수"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:14.450401Z",
"start_time": "2020-05-28T01:11:13.809117Z"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 데이터 정리하기"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**다운 받은 데이터가 관서별(경찰서별)로 되어있다**\n",
"- 서울시에는 한 구에 하나 또는 두 군데의 경찰서가 위치하고 있으며, 구 이름과 다른 경찰서도 존재한다\n",
"- 경찰서 목록을 소속 구별로 변경해주어야 할 것 같다"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:14.483315Z",
"start_time": "2020-05-28T01:11:14.453393Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>관서명</th>\n",
" <th>살인 발생</th>\n",
" <th>살인 검거</th>\n",
" <th>강도 발생</th>\n",
" <th>강도 검거</th>\n",
" <th>강간 발생</th>\n",
" <th>강간 검거</th>\n",
" <th>절도 발생</th>\n",
" <th>절도 검거</th>\n",
" <th>폭력 발생</th>\n",
" <th>폭력 검거</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>중부서</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>105</td>\n",
" <td>65</td>\n",
" <td>1395</td>\n",
" <td>477</td>\n",
" <td>1355</td>\n",
" <td>1170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>종로서</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>115</td>\n",
" <td>98</td>\n",
" <td>1070</td>\n",
" <td>413</td>\n",
" <td>1278</td>\n",
" <td>1070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>남대문서</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>65</td>\n",
" <td>46</td>\n",
" <td>1153</td>\n",
" <td>382</td>\n",
" <td>869</td>\n",
" <td>794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>서대문서</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>154</td>\n",
" <td>124</td>\n",
" <td>1812</td>\n",
" <td>738</td>\n",
" <td>2056</td>\n",
" <td>1711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>혜화서</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>96</td>\n",
" <td>63</td>\n",
" <td>1114</td>\n",
" <td>424</td>\n",
" <td>1015</td>\n",
" <td>861</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 관서명 살인 발생 살인 검거 강도 발생 강도 검거 강간 발생 강간 검거 절도 발생 절도 검거 폭력 발생 폭력 검거\n",
"0 중부서 2 2 3 2 105 65 1395 477 1355 1170\n",
"1 종로서 3 3 6 5 115 98 1070 413 1278 1070\n",
"2 남대문서 1 0 6 4 65 46 1153 382 869 794\n",
"3 서대문서 2 2 5 4 154 124 1812 738 2056 1711\n",
"4 혜화서 3 2 5 4 96 63 1114 424 1015 861"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 콤마(,)로 천 단위가 구분되어 있으므로, thousands = ','로 설정\n",
"# 한글 인코딩이므로, encoding = 'euc-kr'로 설정\n",
"crime_anal_police = pd.read_csv(\"../data/02. crime_in_Seoul.csv\", thousands = ',',\n",
" encoding = 'euc-kr')\n",
"crime_anal_police.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**경찰서 이름으로 구 정보를 파악해야 한다**\n",
"- 양이 많지 않아서 아래의 사이트를 참고하면서, 직접 수작업으로 해줘도 되긴 한다...그런데 굳이? 그럴 필요는 없어보인다. 왜냐하면 우리에겐 구글 맵스가 있기 때문이다! ^^\n",
" - <위키백과 서울시 관서별 경찰서 목록 사이트>:\n",
" https://ko.wikipedia.org/wiki/%EB%B6%84%EB%A5%98:%EC%84%9C%EC%9A%B8%ED%8A%B9%EB%B3%84%EC%8B%9C%EC%9D%98_%EA%B2%BD%EC%B0%B0%EC%84%9C\n",
"- 구글 맵스를 사용해서 경찰서의 위치(위도, 경도) 정보를 받아온다\n",
" - 구글 지도 API가 유료화된 관계로 아래에 첨부된 사이트를 참고해서 API 키를 받아오자 --> https://pinkwink.kr/1143\n",
" - API 키를 받아왔다면, cmd 창을 켜서 ```pip install googlemaps``` 명령문으로 googlemaps 모듈을 설치하자!!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:14.776530Z",
"start_time": "2020-05-28T01:11:14.486304Z"
}
},
"outputs": [],
"source": [
"import googlemaps"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:14.785506Z",
"start_time": "2020-05-28T01:11:14.779535Z"
}
},
"outputs": [],
"source": [
"# gmaps_key에 구글 지도 API 키 값을 넣어준다\n",
"gmaps_key = \"**********************************\"\n",
"gmaps = googlemaps.Client(key = gmaps_key)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Google Maps를 사용한 단어 검색**\n",
"- 'formatted_address' 항목에 '주소'가 출력\n",
"- 'lng'와 'lat'에 '위도'와 '경도' 정보가 출력\n",
"- 추후에 지도 시각화에서 유용하게 사용할 수 있는 정보!!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:15.042817Z",
"start_time": "2020-05-28T01:11:14.788498Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"126.9895796"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = gmaps.geocode('서울중부경찰서', language = 'ko')\n",
"b = a[0].get(\"geometry\")\n",
"b['location']['lng']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:15.160797Z",
"start_time": "2020-05-28T01:11:15.044811Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[{'access_points': [],\n",
" 'address_components': [{'long_name': '２７',\n",
" 'short_name': '２７',\n",
" 'types': ['premise']},\n",
" {'long_name': '수표로',\n",
" 'short_name': '수표로',\n",
" 'types': ['political', 'sublocality', 'sublocality_level_4']},\n",
" {'long_name': '을지로동',\n",
" 'short_name': '을지로동',\n",
" 'types': ['political', 'sublocality', 'sublocality_level_2']},\n",
" {'long_name': '중구',\n",
" 'short_name': '중구',\n",
" 'types': ['political', 'sublocality', 'sublocality_level_1']},\n",
" {'long_name': '서울특별시',\n",
" 'short_name': '서울특별시',\n",
" 'types': ['administrative_area_level_1', 'political']},\n",
" {'long_name': '대한민국',\n",
" 'short_name': 'KR',\n",
" 'types': ['country', 'political']},\n",
" {'long_name': '100-032',\n",
" 'short_name': '100-032',\n",
" 'types': ['postal_code']}],\n",
" 'formatted_address': '대한민국 서울특별시 중구 을지로동 수표로 27',\n",
" 'geometry': {'location': {'lat': 37.5636465, 'lng': 126.9895796},\n",
" 'location_type': 'ROOFTOP',\n",
" 'viewport': {'northeast': {'lat': 37.56499548029149,\n",
" 'lng': 126.9909285802915},\n",
" 'southwest': {'lat': 37.56229751970849, 'lng': 126.9882306197085}}},\n",
" 'place_id': 'ChIJc-9q5uSifDURLhQmr5wkXmc',\n",
" 'plus_code': {'compound_code': 'HX7Q+FR 대한민국 서울특별시',\n",
" 'global_code': '8Q98HX7Q+FR'},\n",
" 'types': ['establishment', 'point_of_interest', 'police']}]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gmaps.geocode('서울중부경찰서', language = 'ko')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**crime_anal_police 데이터의 '관서명' 형태 변경**\n",
"- 구글 검색에서 주소가 제대로 나오게끔 해주기 위해,\n",
"- 'OO서(ex. 중부서)'의 형태인 '관서명'을 '서울OO경찰서(ex. 서울중부경찰서)'의 형태로 변경"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:15.171765Z",
"start_time": "2020-05-28T01:11:15.162788Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"['서울중부경찰서',\n",
" '서울종로경찰서',\n",
" '서울남대문경찰서',\n",
" '서울서대문경찰서',\n",
" '서울혜화경찰서',\n",
" '서울용산경찰서',\n",
" '서울성북경찰서',\n",
" '서울동대문경찰서',\n",
" '서울마포경찰서',\n",
" '서울영등포경찰서',\n",
" '서울성동경찰서',\n",
" '서울동작경찰서',\n",
" '서울광진경찰서',\n",
" '서울서부경찰서',\n",
" '서울강북경찰서',\n",
" '서울금천경찰서',\n",
" '서울중랑경찰서',\n",
" '서울강남경찰서',\n",
" '서울관악경찰서',\n",
" '서울강서경찰서',\n",
" '서울강동경찰서',\n",
" '서울종암경찰서',\n",
" '서울구로경찰서',\n",
" '서울서초경찰서',\n",
" '서울양천경찰서',\n",
" '서울송파경찰서',\n",
" '서울노원경찰서',\n",
" '서울방배경찰서',\n",
" '서울은평경찰서',\n",
" '서울도봉경찰서',\n",
" '서울수서경찰서']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"station_name = []\n",
"\n",
"for name in crime_anal_police['관서명']:\n",
" # '중부서', '수서서', ... 을 '서울중부경찰서', '서울수서경찰서', ... 의 형태로 변경 \n",
" station_name.append('서울' + str(name[:-1]) + '경찰서')\n",
" \n",
"station_name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**각 경찰서별로 주소, 위도, 경도 데이터를 추출하기**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.634578Z",
"start_time": "2020-05-28T01:11:15.176750Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"서울중부경찰서-->대한민국 서울특별시 중구 을지로동 수표로 27\n",
"서울종로경찰서-->대한민국 서울특별시 종로구 종로1.2.3.4가동 율곡로 46\n",
"서울남대문경찰서-->대한민국 서울특별시 중구 회현동 한강대로 410\n",
"서울서대문경찰서-->대한민국 서울특별시 서대문구 충현동 통일로 113\n",
"서울혜화경찰서-->대한민국 서울특별시 종로구 인의동 창경궁로 112-16\n",
"서울용산경찰서-->대한민국 서울특별시 용산구 원효로1가 백범로 329\n",
"서울성북경찰서-->대한민국 서울특별시 성북구 삼선동5가 301\n",
"서울동대문경찰서-->대한민국 서울특별시 동대문구 청량리동 약령시로21길 29\n",
"서울마포경찰서-->대한민국 서울특별시 마포구 아현동 마포대로 183\n",
"서울영등포경찰서-->대한민국 서울특별시 영등포구 영등포동1가 618-7\n",
"서울성동경찰서-->대한민국 서울특별시 성동구 행당동 왕십리광장로 9\n",
"서울동작경찰서-->대한민국 서울특별시 동작구 노량진1동 노량진로 148\n",
"서울광진경찰서-->대한민국 서울특별시 광진구 구의동 자양로 167\n",
"서울서부경찰서-->대한민국 서울특별시 은평구 대조동 통일로 757\n",
"서울강북경찰서-->대한민국 서울특별시 강북구 번1동 오패산로 406\n",
"서울금천경찰서-->대한민국 서울특별시 금천구 시흥동 190\n",
"서울중랑경찰서-->대한민국 서울특별시 중랑구 묵동 120\n",
"서울강남경찰서-->대한민국 서울특별시 강남구 대치동 998\n",
"서울관악경찰서-->대한민국 서울특별시 관악구 봉천동\n",
"서울강서경찰서-->대한민국 서울특별시 강서구 화곡6동 980-15\n",
"서울강동경찰서-->대한민국 서울특별시 강동구 성내1동 성내로 57\n",
"서울종암경찰서-->대한민국 서울특별시 성북구 종암동 종암로 135\n",
"서울구로경찰서-->대한민국 서울특별시 구로구 구로동 가마산로 235\n",
"서울서초경찰서-->대한민국 서울특별시 서초구 서초3동 반포대로 179\n",
"서울양천경찰서-->대한민국 서울특별시 양천구 신정6동 목동동로 99\n",
"서울송파경찰서-->대한민국 서울특별시 송파구 가락본동 9\n",
"서울노원경찰서-->대한민국 서울특별시 노원구 하계동 노원로 283\n",
"서울방배경찰서-->대한민국 서울특별시 서초구 방배본동 동작대로 204\n",
"서울은평경찰서-->대한민국 서울특별시 은평구 불광동 연서로 365\n",
"서울도봉경찰서-->대한민국 서울특별시 도봉구 창4동 노해로 403\n",
"서울수서경찰서-->대한민국 서울특별시 강남구 개포동 개포로 617\n"
]
}
],
"source": [
"station_address = [] # 주소\n",
"station_lat = [] # 위도\n",
"station_lng = [] # 경도\n",
"\n",
"# for문을 사용해서, 각 경찰서 별로 아래의 작업을 수행\n",
"for name in station_name:\n",
" tmp = gmaps.geocode(name, language = 'ko')\n",
" \n",
" # 주소(address) 정보를 담고 있는 \"formatted_address\" 추출\n",
" station_address.append(tmp[0].get(\"formatted_address\"))\n",
" \n",
" # 위도(lat) 및 경도(lng)의 정보를 담고 있는 \"geometry\" 추출\n",
" tmp_loc = tmp[0].get(\"geometry\")\n",
" \n",
" station_lat.append(tmp_loc['location']['lat'])\n",
" station_lng.append(tmp_loc['location']['lng'])\n",
" \n",
" print(name + '-->' + tmp[0].get(\"formatted_address\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1. 각 경찰서의 주소**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.651529Z",
"start_time": "2020-05-28T01:11:22.642554Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['대한민국 서울특별시 중구 을지로동 수표로 27',\n",
" '대한민국 서울특별시 종로구 종로1.2.3.4가동 율곡로 46',\n",
" '대한민국 서울특별시 중구 회현동 한강대로 410',\n",
" '대한민국 서울특별시 서대문구 충현동 통일로 113',\n",
" '대한민국 서울특별시 종로구 인의동 창경궁로 112-16',\n",
" '대한민국 서울특별시 용산구 원효로1가 백범로 329',\n",
" '대한민국 서울특별시 성북구 삼선동5가 301',\n",
" '대한민국 서울특별시 동대문구 청량리동 약령시로21길 29',\n",
" '대한민국 서울특별시 마포구 아현동 마포대로 183',\n",
" '대한민국 서울특별시 영등포구 영등포동1가 618-7',\n",
" '대한민국 서울특별시 성동구 행당동 왕십리광장로 9',\n",
" '대한민국 서울특별시 동작구 노량진1동 노량진로 148',\n",
" '대한민국 서울특별시 광진구 구의동 자양로 167',\n",
" '대한민국 서울특별시 은평구 대조동 통일로 757',\n",
" '대한민국 서울특별시 강북구 번1동 오패산로 406',\n",
" '대한민국 서울특별시 금천구 시흥동 190',\n",
" '대한민국 서울특별시 중랑구 묵동 120',\n",
" '대한민국 서울특별시 강남구 대치동 998',\n",
" '대한민국 서울특별시 관악구 봉천동',\n",
" '대한민국 서울특별시 강서구 화곡6동 980-15',\n",
" '대한민국 서울특별시 강동구 성내1동 성내로 57',\n",
" '대한민국 서울특별시 성북구 종암동 종암로 135',\n",
" '대한민국 서울특별시 구로구 구로동 가마산로 235',\n",
" '대한민국 서울특별시 서초구 서초3동 반포대로 179',\n",
" '대한민국 서울특별시 양천구 신정6동 목동동로 99',\n",
" '대한민국 서울특별시 송파구 가락본동 9',\n",
" '대한민국 서울특별시 노원구 하계동 노원로 283',\n",
" '대한민국 서울특별시 서초구 방배본동 동작대로 204',\n",
" '대한민국 서울특별시 은평구 불광동 연서로 365',\n",
" '대한민국 서울특별시 도봉구 창4동 노해로 403',\n",
" '대한민국 서울특별시 강남구 개포동 개포로 617']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"station_address"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**2. 각 경찰서의 위도**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.683444Z",
"start_time": "2020-05-28T01:11:22.654521Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[37.5636465,\n",
" 37.5755578,\n",
" 37.5547584,\n",
" 37.5647848,\n",
" 37.5718529,\n",
" 37.5387099,\n",
" 37.5897482,\n",
" 37.58506149999999,\n",
" 37.550814,\n",
" 37.5153176,\n",
" 37.5617309,\n",
" 37.5130866,\n",
" 37.542873,\n",
" 37.6128611,\n",
" 37.63730390000001,\n",
" 37.4568722,\n",
" 37.6145819,\n",
" 37.5094352,\n",
" 37.4743789,\n",
" 37.5516732,\n",
" 37.528511,\n",
" 37.6020592,\n",
" 37.494931,\n",
" 37.4956054,\n",
" 37.5165667,\n",
" 37.5019065,\n",
" 37.6425238,\n",
" 37.4945959,\n",
" 37.6283597,\n",
" 37.6533589,\n",
" 37.49349]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"station_lat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3. 각 경찰서의 경도**"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.698405Z",
"start_time": "2020-05-28T01:11:22.686442Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[126.9895796,\n",
" 126.9848674,\n",
" 126.9734981,\n",
" 126.9667762,\n",
" 126.9989143,\n",
" 126.9659183,\n",
" 127.0161353,\n",
" 127.0457679,\n",
" 126.954028,\n",
" 126.905728,\n",
" 127.0363806,\n",
" 126.9428498,\n",
" 127.083821,\n",
" 126.9274951,\n",
" 127.0273399,\n",
" 126.8970429,\n",
" 127.0815539,\n",
" 127.0669578,\n",
" 126.9509748,\n",
" 126.8499269,\n",
" 127.1268224,\n",
" 127.0321577,\n",
" 126.886731,\n",
" 127.0052504,\n",
" 126.8656763,\n",
" 127.1271513,\n",
" 127.0717076,\n",
" 126.9831279,\n",
" 126.9287226,\n",
" 127.052682,\n",
" 127.0772119]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"station_lng"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.733311Z",
"start_time": "2020-05-28T01:11:22.702393Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>관서명</th>\n",
" <th>살인 발생</th>\n",
" <th>살인 검거</th>\n",
" <th>강도 발생</th>\n",
" <th>강도 검거</th>\n",
" <th>강간 발생</th>\n",
" <th>강간 검거</th>\n",
" <th>절도 발생</th>\n",
" <th>절도 검거</th>\n",
" <th>폭력 발생</th>\n",
" <th>폭력 검거</th>\n",
" <th>구별</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>중부서</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>105</td>\n",
" <td>65</td>\n",
" <td>1395</td>\n",
" <td>477</td>\n",
" <td>1355</td>\n",
" <td>1170</td>\n",
" <td>중구</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>종로서</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>115</td>\n",
" <td>98</td>\n",
" <td>1070</td>\n",
" <td>413</td>\n",
" <td>1278</td>\n",
" <td>1070</td>\n",
" <td>종로구</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>남대문서</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>65</td>\n",
" <td>46</td>\n",
" <td>1153</td>\n",
" <td>382</td>\n",
" <td>869</td>\n",
" <td>794</td>\n",
" <td>중구</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>서대문서</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>154</td>\n",
" <td>124</td>\n",
" <td>1812</td>\n",
" <td>738</td>\n",
" <td>2056</td>\n",
" <td>1711</td>\n",
" <td>서대문구</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>혜화서</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>96</td>\n",
" <td>63</td>\n",
" <td>1114</td>\n",
" <td>424</td>\n",
" <td>1015</td>\n",
" <td>861</td>\n",
" <td>종로구</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 관서명 살인 발생 살인 검거 강도 발생 강도 검거 강간 발생 강간 검거 절도 발생 절도 검거 폭력 발생 폭력 검거 \\\n",
"0 중부서 2 2 3 2 105 65 1395 477 1355 1170 \n",
"1 종로서 3 3 6 5 115 98 1070 413 1278 1070 \n",
"2 남대문서 1 0 6 4 65 46 1153 382 869 794 \n",
"3 서대문서 2 2 5 4 154 124 1812 738 2056 1711 \n",
"4 혜화서 3 2 5 4 96 63 1114 424 1015 861 \n",
"\n",
" 구별 \n",
"0 중구 \n",
"1 종로구 \n",
"2 중구 \n",
"3 서대문구 \n",
"4 종로구 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gu_name = []\n",
"\n",
"for name in station_address:\n",
" tmp = name.split() # 공백을 기준으로 분할\n",
" \n",
" # 두 번째 단어('구' 이름)를 선택\n",
" tmp_gu = [gu for gu in tmp if gu[-1] == '구'][0]\n",
" \n",
" gu_name.append(tmp_gu)\n",
" \n",
"crime_anal_police['구별'] = gu_name\n",
"crime_anal_police.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**교재 출판 당시에는 '서울금천경찰서'가 '관악구'에 위치해 있어서 '금천서'는 예외 처리를 해줘야만 했다**\n",
"\n",
"**그러나 2018년 '서울금천경찰서'가 '금천구' 관내로 이전하면서, 데이터 또한 update 되었다. 따라서 예외 처리 불필요!!**"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.756251Z",
"start_time": "2020-05-28T01:11:22.735315Z"
}
},
"outputs": [
{
"data": {
"text/html": [
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th>살인 발생</th>\n",
" <th>살인 검거</th>\n",
" <th>강도 발생</th>\n",
" <th>강도 검거</th>\n",
" <th>강간 발생</th>\n",
" <th>강간 검거</th>\n",
" <th>절도 발생</th>\n",
" <th>절도 검거</th>\n",
" <th>폭력 발생</th>\n",
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" <th>구별</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>금천서</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>151</td>\n",
" <td>122</td>\n",
" <td>1567</td>\n",
" <td>888</td>\n",
" <td>2054</td>\n",
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"text/plain": [
" 관서명 살인 발생 살인 검거 강도 발생 강도 검거 강간 발생 강간 검거 절도 발생 절도 검거 폭력 발생 폭력 검거 \\\n",
"15 금천서 3 4 6 6 151 122 1567 888 2054 1776 \n",
"\n",
" 구별 \n",
"15 금천구 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 실제로 데이터가 잘 update 되었는지 확인해보자\n",
"crime_anal_police[crime_anal_police['관서명'] == '금천서']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**위의 과정들을 거쳐 만들어진 crime_anal_police 데이터를, csv 파일로 내보내는 코드는 따로 실행하지 않겠다**\n",
"\n",
"**(이미 data 폴더에 해당 데이터가 존재하기 때문)**"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.762233Z",
"start_time": "2020-05-28T01:11:22.758244Z"
}
},
"outputs": [],
"source": [
"# crime_anal_police.to_csv(../data/02. crime_in_Seoul_include_gu_name.csv',\n",
"# sep = ',', encoding = 'utf-8')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**데이터 처리 작업을 '관서명'을 기준으로 수행해주었기 때문에, '구별' 컬럼에는 아래와 같이 중복 값이 존재!!**\n",
"\n",
"**즉, 같은 '구' 이름이 두 번 있을 수 있다는 말**"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.781184Z",
"start_time": "2020-05-28T01:11:22.765226Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
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" <th>살인 발생</th>\n",
" <th>살인 검거</th>\n",
" <th>강도 발생</th>\n",
" <th>강도 검거</th>\n",
" <th>강간 발생</th>\n",
" <th>강간 검거</th>\n",
" <th>절도 발생</th>\n",
" <th>절도 검거</th>\n",
" <th>폭력 발생</th>\n",
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" <th>구별</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>중부서</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>105</td>\n",
" <td>65</td>\n",
" <td>1395</td>\n",
" <td>477</td>\n",
" <td>1355</td>\n",
" <td>1170</td>\n",
" <td>중구</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>종로서</td>\n",
" <td>3</td>\n",
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" <td>5</td>\n",
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" <td>1278</td>\n",
" <td>1070</td>\n",
" <td>종로구</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>남대문서</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>65</td>\n",
" <td>46</td>\n",
" <td>1153</td>\n",
" <td>382</td>\n",
" <td>869</td>\n",
" <td>794</td>\n",
" <td>중구</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>서대문서</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>154</td>\n",
" <td>124</td>\n",
" <td>1812</td>\n",
" <td>738</td>\n",
" <td>2056</td>\n",
" <td>1711</td>\n",
" <td>서대문구</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>혜화서</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>96</td>\n",
" <td>63</td>\n",
" <td>1114</td>\n",
" <td>424</td>\n",
" <td>1015</td>\n",
" <td>861</td>\n",
" <td>종로구</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 관서명 살인 발생 살인 검거 강도 발생 강도 검거 강간 발생 강간 검거 절도 발생 절도 검거 폭력 발생 폭력 검거 \\\n",
"0 중부서 2 2 3 2 105 65 1395 477 1355 1170 \n",
"1 종로서 3 3 6 5 115 98 1070 413 1278 1070 \n",
"2 남대문서 1 0 6 4 65 46 1153 382 869 794 \n",
"3 서대문서 2 2 5 4 154 124 1812 738 2056 1711 \n",
"4 혜화서 3 2 5 4 96 63 1114 424 1015 861 \n",
"\n",
" 구별 \n",
"0 중구 \n",
"1 종로구 \n",
"2 중구 \n",
"3 서대문구 \n",
"4 종로구 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"crime_anal_police.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [참고] pandas의 pivot_table 익히기\n",
"- \"crime_in_Seoul_include_gu_name.csv\" 데이터를 정리해주기에 앞서, ```pandas```의 ```pivot_table```에 대해 간략하게 학습해본다\n",
"- 참고자료 출처: https://github.com/chris1610/pbpython/tree/master/data"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.788164Z",
"start_time": "2020-05-28T01:11:22.784175Z"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.849006Z",
"start_time": "2020-05-28T01:11:22.791156Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"\n",
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"</style>\n",
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" <th></th>\n",
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" <th>Name</th>\n",
" <th>Rep</th>\n",
" <th>Manager</th>\n",
" <th>Product</th>\n",
" <th>Quantity</th>\n",
" <th>Price</th>\n",
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" <th>0</th>\n",
" <td>714466</td>\n",
" <td>Trantow-Barrows</td>\n",
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" <td>Debra Henley</td>\n",
" <td>CPU</td>\n",
" <td>1</td>\n",
" <td>30000</td>\n",
" <td>presented</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>714466</td>\n",
" <td>Trantow-Barrows</td>\n",
" <td>Craig Booker</td>\n",
" <td>Debra Henley</td>\n",
" <td>Software</td>\n",
" <td>1</td>\n",
" <td>10000</td>\n",
" <td>presented</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>714466</td>\n",
" <td>Trantow-Barrows</td>\n",
" <td>Craig Booker</td>\n",
" <td>Debra Henley</td>\n",
" <td>Maintenance</td>\n",
" <td>2</td>\n",
" <td>5000</td>\n",
" <td>pending</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>737550</td>\n",
" <td>Fritsch, Russel and Anderson</td>\n",
" <td>Craig Booker</td>\n",
" <td>Debra Henley</td>\n",
" <td>CPU</td>\n",
" <td>1</td>\n",
" <td>35000</td>\n",
" <td>declined</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>146832</td>\n",
" <td>Kiehn-Spinka</td>\n",
" <td>Daniel Hilton</td>\n",
" <td>Debra Henley</td>\n",
" <td>CPU</td>\n",
" <td>2</td>\n",
" <td>65000</td>\n",
" <td>won</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Account Name Rep Manager \\\n",
"0 714466 Trantow-Barrows Craig Booker Debra Henley \n",
"1 714466 Trantow-Barrows Craig Booker Debra Henley \n",
"2 714466 Trantow-Barrows Craig Booker Debra Henley \n",
"3 737550 Fritsch, Russel and Anderson Craig Booker Debra Henley \n",
"4 146832 Kiehn-Spinka Daniel Hilton Debra Henley \n",
"\n",
" Product Quantity Price Status \n",
"0 CPU 1 30000 presented \n",
"1 Software 1 10000 presented \n",
"2 Maintenance 2 5000 pending \n",
"3 CPU 1 35000 declined \n",
"4 CPU 2 65000 won "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel(\"../data/02. sales-funnel.xlsx\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.862964Z",
"start_time": "2020-05-28T01:11:22.850996Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 17 entries, 0 to 16\n",
"Data columns (total 8 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Account 17 non-null int64 \n",
" 1 Name 17 non-null object\n",
" 2 Rep 17 non-null object\n",
" 3 Manager 17 non-null object\n",
" 4 Product 17 non-null object\n",
" 5 Quantity 17 non-null int64 \n",
" 6 Price 17 non-null int64 \n",
" 7 Status 17 non-null object\n",
"dtypes: int64(3), object(5)\n",
"memory usage: 1.2+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**위 데이터에서 'Name' 항목으로만 정렬**\n",
"- 'Name' 컬럼이 index가 되고, 특별히 다른 파라미터들을 지정하지 않았다면 '숫자형' 데이터 컬럼들(Account, Price, Quantity)만 출력된다\n",
"- 또한 중복된 'Name'의 항목은 하나로 합쳐지고 Account, Price, Quantity 각각의 값들은 '평균 값'을 갖는다"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.893882Z",
"start_time": "2020-05-28T01:11:22.866974Z"
},
"scrolled": false
},
"outputs": [
{
"data": {
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" </thead>\n",
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" <tr>\n",
" <th>Barton LLC</th>\n",
" <td>740150</td>\n",
" <td>35000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fritsch, Russel and Anderson</th>\n",
" <td>737550</td>\n",
" <td>35000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Herman LLC</th>\n",
" <td>141962</td>\n",
" <td>65000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jerde-Hilpert</th>\n",
" <td>412290</td>\n",
" <td>5000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kassulke, Ondricka and Metz</th>\n",
" <td>307599</td>\n",
" <td>7000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Keeling LLC</th>\n",
" <td>688981</td>\n",
" <td>100000</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kiehn-Spinka</th>\n",
" <td>146832</td>\n",
" <td>65000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Koepp Ltd</th>\n",
" <td>729833</td>\n",
" <td>35000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kulas Inc</th>\n",
" <td>218895</td>\n",
" <td>25000</td>\n",
" <td>1.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Purdy-Kunde</th>\n",
" <td>163416</td>\n",
" <td>30000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Stokes LLC</th>\n",
" <td>239344</td>\n",
" <td>7500</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Trantow-Barrows</th>\n",
" <td>714466</td>\n",
" <td>15000</td>\n",
" <td>1.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Account Price Quantity\n",
"Name \n",
"Barton LLC 740150 35000 1.000000\n",
"Fritsch, Russel and Anderson 737550 35000 1.000000\n",
"Herman LLC 141962 65000 2.000000\n",
"Jerde-Hilpert 412290 5000 2.000000\n",
"Kassulke, Ondricka and Metz 307599 7000 3.000000\n",
"Keeling LLC 688981 100000 5.000000\n",
"Kiehn-Spinka 146832 65000 2.000000\n",
"Koepp Ltd 729833 35000 2.000000\n",
"Kulas Inc 218895 25000 1.500000\n",
"Purdy-Kunde 163416 30000 1.000000\n",
"Stokes LLC 239344 7500 1.000000\n",
"Trantow-Barrows 714466 15000 1.333333"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.pivot_table(df, index = ['Name'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**```index```를 여러 개 지정('Name', 'Rep', 'Manager')**"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.933774Z",
"start_time": "2020-05-28T01:11:22.897883Z"
}
},
"outputs": [
{
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" <td>1.000000</td>\n",
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" <tr>\n",
" <th>Fritsch, Russel and Anderson</th>\n",
" <th>Craig Booker</th>\n",
" <th>Debra Henley</th>\n",
" <td>737550</td>\n",
" <td>35000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Herman LLC</th>\n",
" <th>Cedric Moss</th>\n",
" <th>Fred Anderson</th>\n",
" <td>141962</td>\n",
" <td>65000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jerde-Hilpert</th>\n",
" <th>John Smith</th>\n",
" <th>Debra Henley</th>\n",
" <td>412290</td>\n",
" <td>5000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kassulke, Ondricka and Metz</th>\n",
" <th>Wendy Yule</th>\n",
" <th>Fred Anderson</th>\n",
" <td>307599</td>\n",
" <td>7000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Keeling LLC</th>\n",
" <th>Wendy Yule</th>\n",
" <th>Fred Anderson</th>\n",
" <td>688981</td>\n",
" <td>100000</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kiehn-Spinka</th>\n",
" <th>Daniel Hilton</th>\n",
" <th>Debra Henley</th>\n",
" <td>146832</td>\n",
" <td>65000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Koepp Ltd</th>\n",
" <th>Wendy Yule</th>\n",
" <th>Fred Anderson</th>\n",
" <td>729833</td>\n",
" <td>35000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kulas Inc</th>\n",
" <th>Daniel Hilton</th>\n",
" <th>Debra Henley</th>\n",
" <td>218895</td>\n",
" <td>25000</td>\n",
" <td>1.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Purdy-Kunde</th>\n",
" <th>Cedric Moss</th>\n",
" <th>Fred Anderson</th>\n",
" <td>163416</td>\n",
" <td>30000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Stokes LLC</th>\n",
" <th>Cedric Moss</th>\n",
" <th>Fred Anderson</th>\n",
" <td>239344</td>\n",
" <td>7500</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Trantow-Barrows</th>\n",
" <th>Craig Booker</th>\n",
" <th>Debra Henley</th>\n",
" <td>714466</td>\n",
" <td>15000</td>\n",
" <td>1.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Account Price \\\n",
"Name Rep Manager \n",
"Barton LLC John Smith Debra Henley 740150 35000 \n",
"Fritsch, Russel and Anderson Craig Booker Debra Henley 737550 35000 \n",
"Herman LLC Cedric Moss Fred Anderson 141962 65000 \n",
"Jerde-Hilpert John Smith Debra Henley 412290 5000 \n",
"Kassulke, Ondricka and Metz Wendy Yule Fred Anderson 307599 7000 \n",
"Keeling LLC Wendy Yule Fred Anderson 688981 100000 \n",
"Kiehn-Spinka Daniel Hilton Debra Henley 146832 65000 \n",
"Koepp Ltd Wendy Yule Fred Anderson 729833 35000 \n",
"Kulas Inc Daniel Hilton Debra Henley 218895 25000 \n",
"Purdy-Kunde Cedric Moss Fred Anderson 163416 30000 \n",
"Stokes LLC Cedric Moss Fred Anderson 239344 7500 \n",
"Trantow-Barrows Craig Booker Debra Henley 714466 15000 \n",
"\n",
" Quantity \n",
"Name Rep Manager \n",
"Barton LLC John Smith Debra Henley 1.000000 \n",
"Fritsch, Russel and Anderson Craig Booker Debra Henley 1.000000 \n",
"Herman LLC Cedric Moss Fred Anderson 2.000000 \n",
"Jerde-Hilpert John Smith Debra Henley 2.000000 \n",
"Kassulke, Ondricka and Metz Wendy Yule Fred Anderson 3.000000 \n",
"Keeling LLC Wendy Yule Fred Anderson 5.000000 \n",
"Kiehn-Spinka Daniel Hilton Debra Henley 2.000000 \n",
"Koepp Ltd Wendy Yule Fred Anderson 2.000000 \n",
"Kulas Inc Daniel Hilton Debra Henley 1.500000 \n",
"Purdy-Kunde Cedric Moss Fred Anderson 1.000000 \n",
"Stokes LLC Cedric Moss Fred Anderson 1.000000 \n",
"Trantow-Barrows Craig Booker Debra Henley 1.333333 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.pivot_table(df, index = ['Name', 'Rep', 'Manager'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**특정 값(value)만 지정**\n",
"- value를 pivot_table로 합친 경우, '평균치'가 기본이 된다\n",
"- 특정 값(들)만 지정해주고 싶은 경우, ```values``` 옵션을 사용하면 된다\n",
"- ex) Price 값만 지정"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.965690Z",
"start_time": "2020-05-28T01:11:22.935771Z"
}
},
"outputs": [
{
"data": {
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"<div>\n",
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" <th>Manager</th>\n",
" <th>Rep</th>\n",
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" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Debra Henley</th>\n",
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" <tr>\n",
" <th>Daniel Hilton</th>\n",
" <td>38333.333333</td>\n",
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" <th>John Smith</th>\n",
" <td>20000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Fred Anderson</th>\n",
" <th>Cedric Moss</th>\n",
" <td>27500.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wendy Yule</th>\n",
" <td>44250.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Price\n",
"Manager Rep \n",
"Debra Henley Craig Booker 20000.000000\n",
" Daniel Hilton 38333.333333\n",
" John Smith 20000.000000\n",
"Fred Anderson Cedric Moss 27500.000000\n",
" Wendy Yule 44250.000000"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.pivot_table(df, index = ['Manager', 'Rep'], values = ['Price'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**value를 '평균치'가 아닌 '합계'를 사용하고자 한다면, ```aggfunc``` 옵션에 ```np.sum``` 을 사용!!**"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:22.993614Z",
"start_time": "2020-05-28T01:11:22.967684Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
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"<div>\n",
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" </tr>\n",
" <tr>\n",
" <th>Manager</th>\n",
" <th>Rep</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Debra Henley</th>\n",
" <th>Craig Booker</th>\n",
" <td>80000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Daniel Hilton</th>\n",
" <td>115000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>John Smith</th>\n",
" <td>40000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Fred Anderson</th>\n",
" <th>Cedric Moss</th>\n",
" <td>110000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wendy Yule</th>\n",
" <td>177000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Price\n",
"Manager Rep \n",
"Debra Henley Craig Booker 80000\n",
" Daniel Hilton 115000\n",
" John Smith 40000\n",
"Fred Anderson Cedric Moss 110000\n",
" Wendy Yule 177000"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.pivot_table(df, index = ['Manager', 'Rep'], values = ['Price'], aggfunc = np.sum)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**value로 '평균치'와 '데이터의 갯수'를 사용하고자 한다면, ```aggfunc``` 옵션에 ```np.mean``` 과 ```len``` 을 사용!!**"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:23.039492Z",
"start_time": "2020-05-28T01:11:22.999598Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Debra Henley</th>\n",
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" <td>20000.000000</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Daniel Hilton</th>\n",
" <td>38333.333333</td>\n",
" <td>3</td>\n",
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" <tr>\n",
" <th>John Smith</th>\n",
" <td>20000.000000</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Fred Anderson</th>\n",
" <th>Cedric Moss</th>\n",
" <td>27500.000000</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wendy Yule</th>\n",
" <td>44250.000000</td>\n",
" <td>4</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean len\n",
" Price Price\n",
"Manager Rep \n",
"Debra Henley Craig Booker 20000.000000 4\n",
" Daniel Hilton 38333.333333 3\n",
" John Smith 20000.000000 2\n",
"Fred Anderson Cedric Moss 27500.000000 4\n",
" Wendy Yule 44250.000000 4"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.pivot_table(df, index = ['Manager', 'Rep'], values = ['Price'], aggfunc = [np.mean, len])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**```columns``` 옵션을 사용해서, 열 인덱스로 들어갈 키 열 또는 키 열의 리스트를 넣어주기**"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:23.082376Z",
"start_time": "2020-05-28T01:11:23.043482Z"
}
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">Price</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
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" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Debra Henley</th>\n",
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" <td>65000.0</td>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>Daniel Hilton</th>\n",
" <td>105000.0</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>John Smith</th>\n",
" <td>35000.0</td>\n",
" <td>5000.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Fred Anderson</th>\n",
" <th>Cedric Moss</th>\n",
" <td>95000.0</td>\n",
" <td>5000.0</td>\n",
" <td>NaN</td>\n",
" <td>10000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wendy Yule</th>\n",
" <td>165000.0</td>\n",
" <td>7000.0</td>\n",
" <td>5000.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sum \n",
" Price \n",
"Product CPU Maintenance Monitor Software\n",
"Manager Rep \n",
"Debra Henley Craig Booker 65000.0 5000.0 NaN 10000.0\n",
" Daniel Hilton 105000.0 NaN NaN 10000.0\n",
" John Smith 35000.0 5000.0 NaN NaN\n",
"Fred Anderson Cedric Moss 95000.0 5000.0 NaN 10000.0\n",
" Wendy Yule 165000.0 7000.0 5000.0 NaN"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.pivot_table(df, index = ['Manager', 'Rep'], values = ['Price'],\n",
" columns = ['Product'], aggfunc = [np.sum])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 'Product' 컬럼에 어떠한 값들이 있는지 확인\n",
" - CPU, Maintenance, Monitor, Software 이렇게 4개의 범주로 나뉜다"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:23.093347Z",
"start_time": "2020-05-28T01:11:23.084384Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0 CPU\n",
"1 Software\n",
"2 Maintenance\n",
"3 CPU\n",
"4 CPU\n",
"5 CPU\n",
"6 Software\n",
"7 Maintenance\n",
"8 CPU\n",
"9 CPU\n",
"10 CPU\n",
"11 Maintenance\n",
"12 Software\n",
"13 Maintenance\n",
"14 CPU\n",
"15 CPU\n",
"16 Monitor\n",
"Name: Product, dtype: object"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Product']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**```fill_value``` 옵션을 사용해서 NaN 값들을 다른 값으로 채워주기**\n",
"- 여기서는 NaN 값들을 '0'으로 채워주도록 지정"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:23.144212Z",
"start_time": "2020-05-28T01:11:23.096340Z"
}
},
"outputs": [
{
"data": {
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" <td>10000</td>\n",
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" <th>Daniel Hilton</th>\n",
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" <td>35000</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Fred Anderson</th>\n",
" <th>Cedric Moss</th>\n",
" <td>95000</td>\n",
" <td>5000</td>\n",
" <td>0</td>\n",
" <td>10000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wendy Yule</th>\n",
" <td>165000</td>\n",
" <td>7000</td>\n",
" <td>5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sum \n",
" Price \n",
"Product CPU Maintenance Monitor Software\n",
"Manager Rep \n",
"Debra Henley Craig Booker 65000 5000 0 10000\n",
" Daniel Hilton 105000 0 0 10000\n",
" John Smith 35000 5000 0 0\n",
"Fred Anderson Cedric Moss 95000 5000 0 10000\n",
" Wendy Yule 165000 7000 5000 0"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.pivot_table(df,index = [\"Manager\", \"Rep\"], values = [\"Price\"],\n",
" columns = [\"Product\"], aggfunc = [np.sum], fill_value = 0)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:23.185102Z",
"start_time": "2020-05-28T01:11:23.147205Z"
}
},
"outputs": [
{
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" </thead>\n",
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" <tr>\n",
" <th rowspan=\"7\" valign=\"top\">Debra Henley</th>\n",
" <th rowspan=\"3\" valign=\"top\">Craig Booker</th>\n",
" <th>CPU</th>\n",
" <td>65000</td>\n",
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" <th>Maintenance</th>\n",
" <td>5000</td>\n",
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" <tr>\n",
" <th>Software</th>\n",
" <td>10000</td>\n",
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" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Daniel Hilton</th>\n",
" <th>CPU</th>\n",
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" <tr>\n",
" <th>Software</th>\n",
" <td>10000</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">John Smith</th>\n",
" <th>CPU</th>\n",
" <td>35000</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maintenance</th>\n",
" <td>5000</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">Fred Anderson</th>\n",
" <th rowspan=\"3\" valign=\"top\">Cedric Moss</th>\n",
" <th>CPU</th>\n",
" <td>95000</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maintenance</th>\n",
" <td>5000</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Software</th>\n",
" <td>10000</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Wendy Yule</th>\n",
" <th>CPU</th>\n",
" <td>165000</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maintenance</th>\n",
" <td>7000</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Monitor</th>\n",
" <td>5000</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sum \n",
" Price Quantity\n",
"Manager Rep Product \n",
"Debra Henley Craig Booker CPU 65000 2\n",
" Maintenance 5000 2\n",
" Software 10000 1\n",
" Daniel Hilton CPU 105000 4\n",
" Software 10000 1\n",
" John Smith CPU 35000 1\n",
" Maintenance 5000 2\n",
"Fred Anderson Cedric Moss CPU 95000 3\n",
" Maintenance 5000 1\n",
" Software 10000 1\n",
" Wendy Yule CPU 165000 7\n",
" Maintenance 7000 3\n",
" Monitor 5000 2"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.pivot_table(df,index = [\"Manager\", \"Rep\", \"Product\"],\n",
" values = [\"Price\", \"Quantity\"], aggfunc = [np.sum], fill_value = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**```margins = True``` 옵션을 사용해서 행과 열을 기준으로 합계(All, row sum, column sum)를 같이 제시해주기**"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:23.255912Z",
"start_time": "2020-05-28T01:11:23.188094Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
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" text-align: left;\n",
" }\n",
"\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">sum</th>\n",
" <th colspan=\"2\" halign=\"left\">mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>Price</th>\n",
" <th>Quantity</th>\n",
" <th>Price</th>\n",
" <th>Quantity</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Manager</th>\n",
" <th>Rep</th>\n",
" <th>Product</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"7\" valign=\"top\">Debra Henley</th>\n",
" <th rowspan=\"3\" valign=\"top\">Craig Booker</th>\n",
" <th>CPU</th>\n",
" <td>65000</td>\n",
" <td>2</td>\n",
" <td>32500.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maintenance</th>\n",
" <td>5000</td>\n",
" <td>2</td>\n",
" <td>5000.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Software</th>\n",
" <td>10000</td>\n",
" <td>1</td>\n",
" <td>10000.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Daniel Hilton</th>\n",
" <th>CPU</th>\n",
" <td>105000</td>\n",
" <td>4</td>\n",
" <td>52500.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Software</th>\n",
" <td>10000</td>\n",
" <td>1</td>\n",
" <td>10000.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">John Smith</th>\n",
" <th>CPU</th>\n",
" <td>35000</td>\n",
" <td>1</td>\n",
" <td>35000.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maintenance</th>\n",
" <td>5000</td>\n",
" <td>2</td>\n",
" <td>5000.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"6\" valign=\"top\">Fred Anderson</th>\n",
" <th rowspan=\"3\" valign=\"top\">Cedric Moss</th>\n",
" <th>CPU</th>\n",
" <td>95000</td>\n",
" <td>3</td>\n",
" <td>47500.000000</td>\n",
" <td>1.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maintenance</th>\n",
" <td>5000</td>\n",
" <td>1</td>\n",
" <td>5000.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Software</th>\n",
" <td>10000</td>\n",
" <td>1</td>\n",
" <td>10000.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">Wendy Yule</th>\n",
" <th>CPU</th>\n",
" <td>165000</td>\n",
" <td>7</td>\n",
" <td>82500.000000</td>\n",
" <td>3.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maintenance</th>\n",
" <td>7000</td>\n",
" <td>3</td>\n",
" <td>7000.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Monitor</th>\n",
" <td>5000</td>\n",
" <td>2</td>\n",
" <td>5000.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <th></th>\n",
" <th></th>\n",
" <td>522000</td>\n",
" <td>30</td>\n",
" <td>30705.882353</td>\n",
" <td>1.764706</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sum mean \\\n",
" Price Quantity Price \n",
"Manager Rep Product \n",
"Debra Henley Craig Booker CPU 65000 2 32500.000000 \n",
" Maintenance 5000 2 5000.000000 \n",
" Software 10000 1 10000.000000 \n",
" Daniel Hilton CPU 105000 4 52500.000000 \n",
" Software 10000 1 10000.000000 \n",
" John Smith CPU 35000 1 35000.000000 \n",
" Maintenance 5000 2 5000.000000 \n",
"Fred Anderson Cedric Moss CPU 95000 3 47500.000000 \n",
" Maintenance 5000 1 5000.000000 \n",
" Software 10000 1 10000.000000 \n",
" Wendy Yule CPU 165000 7 82500.000000 \n",
" Maintenance 7000 3 7000.000000 \n",
" Monitor 5000 2 5000.000000 \n",
"All 522000 30 30705.882353 \n",
"\n",
" \n",
" Quantity \n",
"Manager Rep Product \n",
"Debra Henley Craig Booker CPU 1.000000 \n",
" Maintenance 2.000000 \n",
" Software 1.000000 \n",
" Daniel Hilton CPU 2.000000 \n",
" Software 1.000000 \n",
" John Smith CPU 1.000000 \n",
" Maintenance 2.000000 \n",
"Fred Anderson Cedric Moss CPU 1.500000 \n",
" Maintenance 1.000000 \n",
" Software 1.000000 \n",
" Wendy Yule CPU 3.500000 \n",
" Maintenance 3.000000 \n",
" Monitor 2.000000 \n",
"All 1.764706 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.pivot_table(df,index = [\"Manager\", \"Rep\", \"Product\"],\n",
" values = [\"Price\", \"Quantity\"],\n",
" aggfunc = [np.sum, np.mean], fill_value = 0, margins = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**여기까지 해서 pandas의 pivot_table에 대한 학습은 마무으리~!!**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 범죄 데이터 '구별'로 정리하기"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:23.278852Z",
"start_time": "2020-05-28T01:11:23.257909Z"
}
},
"outputs": [
{
"data": {
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" <th>0</th>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>종로서</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>115</td>\n",
" <td>98</td>\n",
" <td>1070</td>\n",
" <td>413</td>\n",
" <td>1278</td>\n",
" <td>1070</td>\n",
" <td>종로구</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>남대문서</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>65</td>\n",
" <td>46</td>\n",
" <td>1153</td>\n",
" <td>382</td>\n",
" <td>869</td>\n",
" <td>794</td>\n",
" <td>중구</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>서대문서</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>154</td>\n",
" <td>124</td>\n",
" <td>1812</td>\n",
" <td>738</td>\n",
" <td>2056</td>\n",
" <td>1711</td>\n",
" <td>서대문구</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>혜화서</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>96</td>\n",
" <td>63</td>\n",
" <td>1114</td>\n",
" <td>424</td>\n",
" <td>1015</td>\n",
" <td>861</td>\n",
" <td>종로구</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 관서명 살인 발생 살인 검거 강도 발생 강도 검거 강간 발생 강간 검거 절도 발생 절도 검거 \\\n",
"0 0 중부서 2 2 3 2 105 65 1395 477 \n",
"1 1 종로서 3 3 6 5 115 98 1070 413 \n",
"2 2 남대문서 1 0 6 4 65 46 1153 382 \n",
"3 3 서대문서 2 2 5 4 154 124 1812 738 \n",
"4 4 혜화서 3 2 5 4 96 63 1114 424 \n",
"\n",
" 폭력 발생 폭력 검거 구별 \n",
"0 1355 1170 중구 \n",
"1 1278 1070 종로구 \n",
"2 869 794 중구 \n",
"3 2056 1711 서대문구 \n",
"4 1015 861 종로구 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"crime_anal_raw = pd.read_csv(\"../data/02. crime_in_Seoul_include_gu_name.csv\",\n",
" encoding = 'utf-8')\n",
"crime_anal_raw.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**```pivot_table```을 사용해서 원 데이터를 '관서별'에서 '구별'로 변경**\n",
"- ```aggfunc``` 옵션에 ```np.sum``` 을 사용해서 '평균치'가 아닌 '합계'를 출력하도록 지정\n",
"\n",
"<참고> '관서명' 변수를 index로 지정해주고 싶으면, ```index_col = 0```(위치)이나 ```index_col = '관서명'``` 처럼 직접 변수 이름을 지정"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:23.313759Z",
"start_time": "2020-05-28T01:11:23.282841Z"
}
},
"outputs": [
{
"data": {
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" <tr>\n",
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" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <td>2248</td>\n",
" <td>2712</td>\n",
" </tr>\n",
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" <td>13</td>\n",
" <td>14</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>618</td>\n",
" <td>1434</td>\n",
" <td>2348</td>\n",
" <td>2649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>관악구</th>\n",
" <td>221</td>\n",
" <td>320</td>\n",
" <td>14</td>\n",
" <td>12</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>827</td>\n",
" <td>2706</td>\n",
" <td>2642</td>\n",
" <td>3298</td>\n",
" </tr>\n",
" <tr>\n",
" <th>광진구</th>\n",
" <td>220</td>\n",
" <td>240</td>\n",
" <td>26</td>\n",
" <td>14</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1277</td>\n",
" <td>3026</td>\n",
" <td>2180</td>\n",
" <td>2625</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 강간 검거 강간 발생 강도 검거 강도 발생 살인 검거 살인 발생 절도 검거 절도 발생 폭력 검거 폭력 발생\n",
"구별 \n",
"강남구 349 449 18 21 10 13 1650 3850 3705 4284\n",
"강동구 123 156 8 6 3 4 789 2366 2248 2712\n",
"강북구 126 153 13 14 8 7 618 1434 2348 2649\n",
"관악구 221 320 14 12 8 9 827 2706 2642 3298\n",
"광진구 220 240 26 14 4 4 1277 3026 2180 2625"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"crime_anal_raw = pd.read_csv(\"../data/02. crime_in_Seoul_include_gu_name.csv\",\n",
" encoding = 'utf-8', index_col = 0)\n",
"\n",
"crime_anal = pd.pivot_table(crime_anal_raw, index = '구별', aggfunc = np.sum)\n",
"crime_anal.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**각 범죄별 검거율을 계산**\n",
"- 검거 건수는 검거율로 대체할 수 있기 때문에 삭제하겠다"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:23.353654Z",
"start_time": "2020-05-28T01:11:23.315753Z"
}
},
"outputs": [
{
"data": {
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" </tr>\n",
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" <td>449</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>강동구</th>\n",
" <td>156</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>2366</td>\n",
" <td>2712</td>\n",
" <td>78.846154</td>\n",
" <td>133.333333</td>\n",
" <td>75.000000</td>\n",
" <td>33.347422</td>\n",
" <td>82.890855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강북구</th>\n",
" <td>153</td>\n",
" <td>14</td>\n",
" <td>7</td>\n",
" <td>1434</td>\n",
" <td>2649</td>\n",
" <td>82.352941</td>\n",
" <td>92.857143</td>\n",
" <td>114.285714</td>\n",
" <td>43.096234</td>\n",
" <td>88.637222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>관악구</th>\n",
" <td>320</td>\n",
" <td>12</td>\n",
" <td>9</td>\n",
" <td>2706</td>\n",
" <td>3298</td>\n",
" <td>69.062500</td>\n",
" <td>116.666667</td>\n",
" <td>88.888889</td>\n",
" <td>30.561715</td>\n",
" <td>80.109157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>광진구</th>\n",
" <td>240</td>\n",
" <td>14</td>\n",
" <td>4</td>\n",
" <td>3026</td>\n",
" <td>2625</td>\n",
" <td>91.666667</td>\n",
" <td>185.714286</td>\n",
" <td>100.000000</td>\n",
" <td>42.200925</td>\n",
" <td>83.047619</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 강간 발생 강도 발생 살인 발생 절도 발생 폭력 발생 강간검거율 강도검거율 살인검거율 \\\n",
"구별 \n",
"강남구 449 21 13 3850 4284 77.728285 85.714286 76.923077 \n",
"강동구 156 6 4 2366 2712 78.846154 133.333333 75.000000 \n",
"강북구 153 14 7 1434 2649 82.352941 92.857143 114.285714 \n",
"관악구 320 12 9 2706 3298 69.062500 116.666667 88.888889 \n",
"광진구 240 14 4 3026 2625 91.666667 185.714286 100.000000 \n",
"\n",
" 절도검거율 폭력검거율 \n",
"구별 \n",
"강남구 42.857143 86.484594 \n",
"강동구 33.347422 82.890855 \n",
"강북구 43.096234 88.637222 \n",
"관악구 30.561715 80.109157 \n",
"광진구 42.200925 83.047619 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 각 범죄별 검거율 계산\n",
"crime_anal['강간검거율'] = crime_anal['강간 검거'] / crime_anal['강간 발생'] * 100\n",
"crime_anal['강도검거율'] = crime_anal['강도 검거'] / crime_anal['강도 발생'] * 100\n",
"crime_anal['살인검거율'] = crime_anal['살인 검거'] / crime_anal['살인 발생'] * 100\n",
"crime_anal['절도검거율'] = crime_anal['절도 검거'] / crime_anal['절도 발생'] * 100\n",
"crime_anal['폭력검거율'] = crime_anal['폭력 검거'] / crime_anal['폭력 발생'] * 100\n",
"\n",
"# 각 범죄별 검거 건수는 삭제\n",
"del crime_anal['강간 검거']\n",
"del crime_anal['강도 검거']\n",
"del crime_anal['살인 검거']\n",
"del crime_anal['절도 검거']\n",
"del crime_anal['폭력 검거']\n",
"\n",
"# 위의 작업들이 완료된 데이터\n",
"crime_anal.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**위의 데이터를 살펴보면, 검거율이 100이 넘는 값들이 있다!?**\n",
"- 아마 그 전년도 범죄 발생 건수에 대한 검거도 포함을 시켜서 그런 듯하다\n",
"- 여기서는 그냥 100이 넘는 숫자들은 모두 100으로 처리해주겠다"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:23.381576Z",
"start_time": "2020-05-28T01:11:23.355646Z"
}
},
"outputs": [
{
"data": {
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" <td>92.857143</td>\n",
" <td>100.000000</td>\n",
" <td>43.096234</td>\n",
" <td>88.637222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>관악구</th>\n",
" <td>320</td>\n",
" <td>12</td>\n",
" <td>9</td>\n",
" <td>2706</td>\n",
" <td>3298</td>\n",
" <td>69.062500</td>\n",
" <td>100.000000</td>\n",
" <td>88.888889</td>\n",
" <td>30.561715</td>\n",
" <td>80.109157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>광진구</th>\n",
" <td>240</td>\n",
" <td>14</td>\n",
" <td>4</td>\n",
" <td>3026</td>\n",
" <td>2625</td>\n",
" <td>91.666667</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>42.200925</td>\n",
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" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" 강간 발생 강도 발생 살인 발생 절도 발생 폭력 발생 강간검거율 강도검거율 살인검거율 \\\n",
"구별 \n",
"강남구 449 21 13 3850 4284 77.728285 85.714286 76.923077 \n",
"강동구 156 6 4 2366 2712 78.846154 100.000000 75.000000 \n",
"강북구 153 14 7 1434 2649 82.352941 92.857143 100.000000 \n",
"관악구 320 12 9 2706 3298 69.062500 100.000000 88.888889 \n",
"광진구 240 14 4 3026 2625 91.666667 100.000000 100.000000 \n",
"\n",
" 절도검거율 폭력검거율 \n",
"구별 \n",
"강남구 42.857143 86.484594 \n",
"강동구 33.347422 82.890855 \n",
"강북구 43.096234 88.637222 \n",
"관악구 30.561715 80.109157 \n",
"광진구 42.200925 83.047619 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"con_list = ['강간검거율', '강도검거율', '살인검거율', '절도검거율', '폭력검거율']\n",
"\n",
"for column in con_list:\n",
" # 검거율이 100이 넘는 숫자들은 모두 100으로 처리\n",
" crime_anal.loc[crime_anal[column] > 100, column] = 100\n",
" \n",
"crime_anal.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**'강간 발생', '강도 발생', ... , '폭력 발생'의 변수명을 변경**\n",
"- '강간', '강도', ..., '폭력'으로 변수명을 변경"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:23.400527Z",
"start_time": "2020-05-28T01:11:23.384571Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
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" <td>92.857143</td>\n",
" <td>100.000000</td>\n",
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" <td>88.637222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>관악구</th>\n",
" <td>320</td>\n",
" <td>12</td>\n",
" <td>9</td>\n",
" <td>2706</td>\n",
" <td>3298</td>\n",
" <td>69.062500</td>\n",
" <td>100.000000</td>\n",
" <td>88.888889</td>\n",
" <td>30.561715</td>\n",
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],
"text/plain": [
" 강간 강도 살인 절도 폭력 강간검거율 강도검거율 살인검거율 절도검거율 \\\n",
"구별 \n",
"강남구 449 21 13 3850 4284 77.728285 85.714286 76.923077 42.857143 \n",
"강동구 156 6 4 2366 2712 78.846154 100.000000 75.000000 33.347422 \n",
"강북구 153 14 7 1434 2649 82.352941 92.857143 100.000000 43.096234 \n",
"관악구 320 12 9 2706 3298 69.062500 100.000000 88.888889 30.561715 \n",
"광진구 240 14 4 3026 2625 91.666667 100.000000 100.000000 42.200925 \n",
"\n",
" 폭력검거율 \n",
"구별 \n",
"강남구 86.484594 \n",
"강동구 82.890855 \n",
"강북구 88.637222 \n",
"관악구 80.109157 \n",
"광진구 83.047619 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"crime_anal.rename(columns = {'강간 발생':'강간',\n",
" '강도 발생':'강도',\n",
" '살인 발생':'살인',\n",
" '절도 발생':'절도',\n",
" '폭력 발생':'폭력'}, inplace = True) # inplace = True 옵션으로, crime_anal 데이터에 변경 내용이 바로 적용됨\n",
"crime_anal.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**데이터 표현을 위해 다듬기 --> \"```정규화```\" 작업!!**\n",
"- '강도', '살인' 사건은 두 자릿수인데, '절도'와 '폭력'은 네 자릿수이다\n",
"- 각각을 비슷한 범위에 놓고 비교하는 것이 편리하기 때문에, 데이터를 좀 다듬어주자\n",
" - 각 항목의 최댓값을 '1'로 두면, 추후 범죄 발생 건수를 종합적으로 비교할 때 편리할 것이다!\n",
" - 즉, 강간, 강도, 살인, 절도, 폭력에 대해 각 컬럼 별로 '정규화' 처리를 수행하였다\n",
" - 사이킷런의 최솟값, 최댓값을 이용해서 정규화시키는 ```MinMaxScaler()``` 함수 사용\n",
" \n",
"==> '정규화'처리된 데이터를 살펴보면 '구별'로 '강간', '강도', '살인', '절도', '폭력' 변수의 값들이 ```0 ~ 1 사이의 값```으로 변경되었음을 확인할 수 있다!"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:24.044802Z",
"start_time": "2020-05-28T01:11:23.402521Z"
}
},
"outputs": [
{
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" <td>100.000000</td>\n",
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" <tr>\n",
" <th>관악구</th>\n",
" <td>0.628242</td>\n",
" <td>0.411765</td>\n",
" <td>0.583333</td>\n",
" <td>0.562094</td>\n",
" <td>0.428234</td>\n",
" <td>69.062500</td>\n",
" <td>100.000000</td>\n",
" <td>88.888889</td>\n",
" <td>30.561715</td>\n",
" <td>80.109157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>광진구</th>\n",
" <td>0.397695</td>\n",
" <td>0.529412</td>\n",
" <td>0.166667</td>\n",
" <td>0.671570</td>\n",
" <td>0.269094</td>\n",
" <td>91.666667</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>42.200925</td>\n",
" <td>83.047619</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 강간 강도 살인 절도 폭력 강간검거율 강도검거율 \\\n",
"구별 \n",
"강남구 1.000000 0.941176 0.916667 0.953472 0.661386 77.728285 85.714286 \n",
"강동구 0.155620 0.058824 0.166667 0.445775 0.289667 78.846154 100.000000 \n",
"강북구 0.146974 0.529412 0.416667 0.126924 0.274769 82.352941 92.857143 \n",
"관악구 0.628242 0.411765 0.583333 0.562094 0.428234 69.062500 100.000000 \n",
"광진구 0.397695 0.529412 0.166667 0.671570 0.269094 91.666667 100.000000 \n",
"\n",
" 살인검거율 절도검거율 폭력검거율 \n",
"구별 \n",
"강남구 76.923077 42.857143 86.484594 \n",
"강동구 75.000000 33.347422 82.890855 \n",
"강북구 100.000000 43.096234 88.637222 \n",
"관악구 88.888889 30.561715 80.109157 \n",
"광진구 100.000000 42.200925 83.047619 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import preprocessing\n",
"\n",
"col = ['강간', '강도', '살인', '절도', '폭력']\n",
"\n",
"x = crime_anal[col].values\n",
"min_max_scaler = preprocessing.MinMaxScaler()\n",
"\n",
"x_scaled = min_max_scaler.fit_transform(x.astype(float))\n",
"crime_anal_norm = pd.DataFrame(x_scaled, columns = col, index = crime_anal.index)\n",
"\n",
"col2 = ['강간검거율', '강도검거율', '살인검거율', '절도검거율', '폭력검거율']\n",
"crime_anal_norm[col2] = crime_anal[col2]\n",
"crime_anal_norm.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1장에서 만들어 놓은 \"01. CCTV_result.csv\" 파일에서 필요한 변수들만 추출**\n",
"- <Step 1> 위 데이터의 '인구수'와 '소계' 변수를 추출\n",
"- <Step 2> 추출된 변수들을 crime_anal_norm 데이터에 '인구수'와 'CCTV'라는 변수명으로 추가"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:24.078714Z",
"start_time": "2020-05-28T01:11:24.046798Z"
}
},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>강간</th>\n",
" <th>강도</th>\n",
" <th>살인</th>\n",
" <th>절도</th>\n",
" <th>폭력</th>\n",
" <th>강간검거율</th>\n",
" <th>강도검거율</th>\n",
" <th>살인검거율</th>\n",
" <th>절도검거율</th>\n",
" <th>폭력검거율</th>\n",
" <th>인구수</th>\n",
" <th>CCTV</th>\n",
" </tr>\n",
" <tr>\n",
" <th>구별</th>\n",
" <th></th>\n",
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" <th>강남구</th>\n",
" <td>1.000000</td>\n",
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" <td>570500.0</td>\n",
" <td>2780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강동구</th>\n",
" <td>0.155620</td>\n",
" <td>0.058824</td>\n",
" <td>0.166667</td>\n",
" <td>0.445775</td>\n",
" <td>0.289667</td>\n",
" <td>78.846154</td>\n",
" <td>100.000000</td>\n",
" <td>75.000000</td>\n",
" <td>33.347422</td>\n",
" <td>82.890855</td>\n",
" <td>453233.0</td>\n",
" <td>773</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강북구</th>\n",
" <td>0.146974</td>\n",
" <td>0.529412</td>\n",
" <td>0.416667</td>\n",
" <td>0.126924</td>\n",
" <td>0.274769</td>\n",
" <td>82.352941</td>\n",
" <td>92.857143</td>\n",
" <td>100.000000</td>\n",
" <td>43.096234</td>\n",
" <td>88.637222</td>\n",
" <td>330192.0</td>\n",
" <td>748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>관악구</th>\n",
" <td>0.628242</td>\n",
" <td>0.411765</td>\n",
" <td>0.583333</td>\n",
" <td>0.562094</td>\n",
" <td>0.428234</td>\n",
" <td>69.062500</td>\n",
" <td>100.000000</td>\n",
" <td>88.888889</td>\n",
" <td>30.561715</td>\n",
" <td>80.109157</td>\n",
" <td>525515.0</td>\n",
" <td>1496</td>\n",
" </tr>\n",
" <tr>\n",
" <th>광진구</th>\n",
" <td>0.397695</td>\n",
" <td>0.529412</td>\n",
" <td>0.166667</td>\n",
" <td>0.671570</td>\n",
" <td>0.269094</td>\n",
" <td>91.666667</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>42.200925</td>\n",
" <td>83.047619</td>\n",
" <td>372164.0</td>\n",
" <td>707</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 강간 강도 살인 절도 폭력 강간검거율 강도검거율 \\\n",
"구별 \n",
"강남구 1.000000 0.941176 0.916667 0.953472 0.661386 77.728285 85.714286 \n",
"강동구 0.155620 0.058824 0.166667 0.445775 0.289667 78.846154 100.000000 \n",
"강북구 0.146974 0.529412 0.416667 0.126924 0.274769 82.352941 92.857143 \n",
"관악구 0.628242 0.411765 0.583333 0.562094 0.428234 69.062500 100.000000 \n",
"광진구 0.397695 0.529412 0.166667 0.671570 0.269094 91.666667 100.000000 \n",
"\n",
" 살인검거율 절도검거율 폭력검거율 인구수 CCTV \n",
"구별 \n",
"강남구 76.923077 42.857143 86.484594 570500.0 2780 \n",
"강동구 75.000000 33.347422 82.890855 453233.0 773 \n",
"강북구 100.000000 43.096234 88.637222 330192.0 748 \n",
"관악구 88.888889 30.561715 80.109157 525515.0 1496 \n",
"광진구 100.000000 42.200925 83.047619 372164.0 707 "
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result_CCTV = pd.read_csv('../data/01. CCTV_result.csv', encoding = 'UTF-8', \n",
" index_col = '구별')\n",
"crime_anal_norm[['인구수', 'CCTV']] = result_CCTV[['인구수', '소계']]\n",
"crime_anal_norm.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**각 범죄 발생 건수의 합을 '범죄'라는 항목으로 통합!**\n",
"- 위에서 정규화 작업을 해주지 않았다면, 몇 천건의 절도에 수십 건의 살인의 비중이 애매했을 것이다"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:24.106637Z",
"start_time": "2020-05-28T01:11:24.080708Z"
}
},
"outputs": [
{
"data": {
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" </tr>\n",
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" <th>강북구</th>\n",
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" <td>0.416667</td>\n",
" <td>0.126924</td>\n",
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" <th>광진구</th>\n",
" <td>0.397695</td>\n",
" <td>0.529412</td>\n",
" <td>0.166667</td>\n",
" <td>0.671570</td>\n",
" <td>0.269094</td>\n",
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" <td>83.047619</td>\n",
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],
"text/plain": [
" 강간 강도 살인 절도 폭력 강간검거율 강도검거율 \\\n",
"구별 \n",
"강남구 1.000000 0.941176 0.916667 0.953472 0.661386 77.728285 85.714286 \n",
"강동구 0.155620 0.058824 0.166667 0.445775 0.289667 78.846154 100.000000 \n",
"강북구 0.146974 0.529412 0.416667 0.126924 0.274769 82.352941 92.857143 \n",
"관악구 0.628242 0.411765 0.583333 0.562094 0.428234 69.062500 100.000000 \n",
"광진구 0.397695 0.529412 0.166667 0.671570 0.269094 91.666667 100.000000 \n",
"\n",
" 살인검거율 절도검거율 폭력검거율 인구수 CCTV 범죄 \n",
"구별 \n",
"강남구 76.923077 42.857143 86.484594 570500.0 2780 4.472701 \n",
"강동구 75.000000 33.347422 82.890855 453233.0 773 1.116551 \n",
"강북구 100.000000 43.096234 88.637222 330192.0 748 1.494746 \n",
"관악구 88.888889 30.561715 80.109157 525515.0 1496 2.613667 \n",
"광진구 100.000000 42.200925 83.047619 372164.0 707 2.034438 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"col = ['강간', '강도', '살인', '절도', '폭력']\n",
"# axis = 1. 즉, 열을 기준으로 sum\n",
"crime_anal_norm['범죄'] = np.sum(crime_anal_norm[col], axis = 1)\n",
"crime_anal_norm.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**각 범죄별 검거율의 합을 '검거'라는 항목으로 통합!**"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:24.133565Z",
"start_time": "2020-05-28T01:11:24.109629Z"
}
},
"outputs": [
{
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" <td>100.000000</td>\n",
" <td>43.096234</td>\n",
" <td>88.637222</td>\n",
" <td>330192.0</td>\n",
" <td>748</td>\n",
" <td>1.494746</td>\n",
" <td>406.943540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>관악구</th>\n",
" <td>0.628242</td>\n",
" <td>0.411765</td>\n",
" <td>0.583333</td>\n",
" <td>0.562094</td>\n",
" <td>0.428234</td>\n",
" <td>69.062500</td>\n",
" <td>100.000000</td>\n",
" <td>88.888889</td>\n",
" <td>30.561715</td>\n",
" <td>80.109157</td>\n",
" <td>525515.0</td>\n",
" <td>1496</td>\n",
" <td>2.613667</td>\n",
" <td>368.622261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>광진구</th>\n",
" <td>0.397695</td>\n",
" <td>0.529412</td>\n",
" <td>0.166667</td>\n",
" <td>0.671570</td>\n",
" <td>0.269094</td>\n",
" <td>91.666667</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>42.200925</td>\n",
" <td>83.047619</td>\n",
" <td>372164.0</td>\n",
" <td>707</td>\n",
" <td>2.034438</td>\n",
" <td>416.915211</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 강간 강도 살인 절도 폭력 강간검거율 강도검거율 \\\n",
"구별 \n",
"강남구 1.000000 0.941176 0.916667 0.953472 0.661386 77.728285 85.714286 \n",
"강동구 0.155620 0.058824 0.166667 0.445775 0.289667 78.846154 100.000000 \n",
"강북구 0.146974 0.529412 0.416667 0.126924 0.274769 82.352941 92.857143 \n",
"관악구 0.628242 0.411765 0.583333 0.562094 0.428234 69.062500 100.000000 \n",
"광진구 0.397695 0.529412 0.166667 0.671570 0.269094 91.666667 100.000000 \n",
"\n",
" 살인검거율 절도검거율 폭력검거율 인구수 CCTV 범죄 검거 \n",
"구별 \n",
"강남구 76.923077 42.857143 86.484594 570500.0 2780 4.472701 369.707384 \n",
"강동구 75.000000 33.347422 82.890855 453233.0 773 1.116551 370.084431 \n",
"강북구 100.000000 43.096234 88.637222 330192.0 748 1.494746 406.943540 \n",
"관악구 88.888889 30.561715 80.109157 525515.0 1496 2.613667 368.622261 \n",
"광진구 100.000000 42.200925 83.047619 372164.0 707 2.034438 416.915211 "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"col = ['강간검거율', '강도검거율', '살인검거율', '절도검거율', '폭력검거율']\n",
"# axis = 1. 즉, 열을 기준으로 sum\n",
"crime_anal_norm['검거'] = np.sum(crime_anal_norm[col], axis = 1)\n",
"crime_anal_norm.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**만들어진 최종 데이터 셋**"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:24.165480Z",
"start_time": "2020-05-28T01:11:24.135560Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
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"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>강간</th>\n",
" <th>강도</th>\n",
" <th>살인</th>\n",
" <th>절도</th>\n",
" <th>폭력</th>\n",
" <th>강간검거율</th>\n",
" <th>강도검거율</th>\n",
" <th>살인검거율</th>\n",
" <th>절도검거율</th>\n",
" <th>폭력검거율</th>\n",
" <th>인구수</th>\n",
" <th>CCTV</th>\n",
" <th>범죄</th>\n",
" <th>검거</th>\n",
" </tr>\n",
" <tr>\n",
" <th>구별</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>강남구</th>\n",
" <td>1.000000</td>\n",
" <td>0.941176</td>\n",
" <td>0.916667</td>\n",
" <td>0.953472</td>\n",
" <td>0.661386</td>\n",
" <td>77.728285</td>\n",
" <td>85.714286</td>\n",
" <td>76.923077</td>\n",
" <td>42.857143</td>\n",
" <td>86.484594</td>\n",
" <td>570500.0</td>\n",
" <td>2780</td>\n",
" <td>4.472701</td>\n",
" <td>369.707384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강동구</th>\n",
" <td>0.155620</td>\n",
" <td>0.058824</td>\n",
" <td>0.166667</td>\n",
" <td>0.445775</td>\n",
" <td>0.289667</td>\n",
" <td>78.846154</td>\n",
" <td>100.000000</td>\n",
" <td>75.000000</td>\n",
" <td>33.347422</td>\n",
" <td>82.890855</td>\n",
" <td>453233.0</td>\n",
" <td>773</td>\n",
" <td>1.116551</td>\n",
" <td>370.084431</td>\n",
" </tr>\n",
" <tr>\n",
" <th>강북구</th>\n",
" <td>0.146974</td>\n",
" <td>0.529412</td>\n",
" <td>0.416667</td>\n",
" <td>0.126924</td>\n",
" <td>0.274769</td>\n",
" <td>82.352941</td>\n",
" <td>92.857143</td>\n",
" <td>100.000000</td>\n",
" <td>43.096234</td>\n",
" <td>88.637222</td>\n",
" <td>330192.0</td>\n",
" <td>748</td>\n",
" <td>1.494746</td>\n",
" <td>406.943540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>관악구</th>\n",
" <td>0.628242</td>\n",
" <td>0.411765</td>\n",
" <td>0.583333</td>\n",
" <td>0.562094</td>\n",
" <td>0.428234</td>\n",
" <td>69.062500</td>\n",
" <td>100.000000</td>\n",
" <td>88.888889</td>\n",
" <td>30.561715</td>\n",
" <td>80.109157</td>\n",
" <td>525515.0</td>\n",
" <td>1496</td>\n",
" <td>2.613667</td>\n",
" <td>368.622261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>광진구</th>\n",
" <td>0.397695</td>\n",
" <td>0.529412</td>\n",
" <td>0.166667</td>\n",
" <td>0.671570</td>\n",
" <td>0.269094</td>\n",
" <td>91.666667</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>42.200925</td>\n",
" <td>83.047619</td>\n",
" <td>372164.0</td>\n",
" <td>707</td>\n",
" <td>2.034438</td>\n",
" <td>416.915211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>구로구</th>\n",
" <td>0.515850</td>\n",
" <td>0.588235</td>\n",
" <td>0.500000</td>\n",
" <td>0.435169</td>\n",
" <td>0.359423</td>\n",
" <td>58.362989</td>\n",
" <td>73.333333</td>\n",
" <td>75.000000</td>\n",
" <td>38.072805</td>\n",
" <td>80.877951</td>\n",
" <td>447874.0</td>\n",
" <td>1561</td>\n",
" <td>2.398678</td>\n",
" <td>325.647079</td>\n",
" </tr>\n",
" <tr>\n",
" <th>금천구</th>\n",
" <td>0.141210</td>\n",
" <td>0.058824</td>\n",
" <td>0.083333</td>\n",
" <td>0.172426</td>\n",
" <td>0.134074</td>\n",
" <td>80.794702</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>56.668794</td>\n",
" <td>86.465433</td>\n",
" <td>255082.0</td>\n",
" <td>1015</td>\n",
" <td>0.589867</td>\n",
" <td>423.928929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>노원구</th>\n",
" <td>0.273775</td>\n",
" <td>0.117647</td>\n",
" <td>0.666667</td>\n",
" <td>0.386589</td>\n",
" <td>0.292268</td>\n",
" <td>61.421320</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>36.525308</td>\n",
" <td>85.530665</td>\n",
" <td>569384.0</td>\n",
" <td>1265</td>\n",
" <td>1.736946</td>\n",
" <td>383.477292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>도봉구</th>\n",
" <td>0.000000</td>\n",
" <td>0.235294</td>\n",
" <td>0.083333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>44.967074</td>\n",
" <td>87.626093</td>\n",
" <td>348646.0</td>\n",
" <td>485</td>\n",
" <td>0.318627</td>\n",
" <td>432.593167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>동대문구</th>\n",
" <td>0.204611</td>\n",
" <td>0.470588</td>\n",
" <td>0.250000</td>\n",
" <td>0.314061</td>\n",
" <td>0.250887</td>\n",
" <td>84.393064</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>41.090358</td>\n",
" <td>87.401884</td>\n",
" <td>369496.0</td>\n",
" <td>1294</td>\n",
" <td>1.490147</td>\n",
" <td>412.885306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>동작구</th>\n",
" <td>0.527378</td>\n",
" <td>0.235294</td>\n",
" <td>0.250000</td>\n",
" <td>0.274376</td>\n",
" <td>0.100024</td>\n",
" <td>48.771930</td>\n",
" <td>55.555556</td>\n",
" <td>100.000000</td>\n",
" <td>35.442359</td>\n",
" <td>83.089005</td>\n",
" <td>412520.0</td>\n",
" <td>1091</td>\n",
" <td>1.387071</td>\n",
" <td>322.858850</td>\n",
" </tr>\n",
" <tr>\n",
" <th>마포구</th>\n",
" <td>0.553314</td>\n",
" <td>0.529412</td>\n",
" <td>0.500000</td>\n",
" <td>0.510434</td>\n",
" <td>0.353748</td>\n",
" <td>84.013605</td>\n",
" <td>71.428571</td>\n",
" <td>100.000000</td>\n",
" <td>31.819961</td>\n",
" <td>84.445189</td>\n",
" <td>389649.0</td>\n",
" <td>574</td>\n",
" <td>2.446908</td>\n",
" <td>371.707327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>서대문구</th>\n",
" <td>0.149856</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.256244</td>\n",
" <td>0.134547</td>\n",
" <td>80.519481</td>\n",
" <td>80.000000</td>\n",
" <td>100.000000</td>\n",
" <td>40.728477</td>\n",
" <td>83.219844</td>\n",
" <td>327163.0</td>\n",
" <td>962</td>\n",
" <td>0.540647</td>\n",
" <td>384.467802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>서초구</th>\n",
" <td>0.838617</td>\n",
" <td>0.235294</td>\n",
" <td>0.500000</td>\n",
" <td>0.537804</td>\n",
" <td>0.215654</td>\n",
" <td>63.358779</td>\n",
" <td>66.666667</td>\n",
" <td>75.000000</td>\n",
" <td>41.404175</td>\n",
" <td>87.453105</td>\n",
" <td>450310.0</td>\n",
" <td>1930</td>\n",
" <td>2.327368</td>\n",
" <td>333.882725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>성동구</th>\n",
" <td>0.069164</td>\n",
" <td>0.235294</td>\n",
" <td>0.166667</td>\n",
" <td>0.186110</td>\n",
" <td>0.029558</td>\n",
" <td>94.444444</td>\n",
" <td>88.888889</td>\n",
" <td>100.000000</td>\n",
" <td>37.149969</td>\n",
" <td>86.538462</td>\n",
" <td>311244.0</td>\n",
" <td>1062</td>\n",
" <td>0.686793</td>\n",
" <td>407.021764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>성북구</th>\n",
" <td>0.138329</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.247007</td>\n",
" <td>0.170726</td>\n",
" <td>82.666667</td>\n",
" <td>80.000000</td>\n",
" <td>100.000000</td>\n",
" <td>41.512605</td>\n",
" <td>83.974649</td>\n",
" <td>461260.0</td>\n",
" <td>1464</td>\n",
" <td>0.806061</td>\n",
" <td>388.153921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>송파구</th>\n",
" <td>0.340058</td>\n",
" <td>0.470588</td>\n",
" <td>0.750000</td>\n",
" <td>0.744441</td>\n",
" <td>0.427524</td>\n",
" <td>80.909091</td>\n",
" <td>76.923077</td>\n",
" <td>90.909091</td>\n",
" <td>34.856437</td>\n",
" <td>84.552352</td>\n",
" <td>667483.0</td>\n",
" <td>618</td>\n",
" <td>2.732611</td>\n",
" <td>368.150048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>양천구</th>\n",
" <td>0.806916</td>\n",
" <td>0.823529</td>\n",
" <td>0.666667</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>77.486911</td>\n",
" <td>84.210526</td>\n",
" <td>100.000000</td>\n",
" <td>48.469644</td>\n",
" <td>83.065080</td>\n",
" <td>479978.0</td>\n",
" <td>2034</td>\n",
" <td>4.297113</td>\n",
" <td>393.232162</td>\n",
" </tr>\n",
" <tr>\n",
" <th>영등포구</th>\n",
" <td>0.556196</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.650359</td>\n",
" <td>0.493024</td>\n",
" <td>62.033898</td>\n",
" <td>90.909091</td>\n",
" <td>85.714286</td>\n",
" <td>32.995951</td>\n",
" <td>82.894737</td>\n",
" <td>402985.0</td>\n",
" <td>904</td>\n",
" <td>3.699580</td>\n",
" <td>354.547963</td>\n",
" </tr>\n",
" <tr>\n",
" <th>용산구</th>\n",
" <td>0.265130</td>\n",
" <td>0.529412</td>\n",
" <td>0.250000</td>\n",
" <td>0.169004</td>\n",
" <td>0.133128</td>\n",
" <td>89.175258</td>\n",
" <td>100.000000</td>\n",
" <td>100.000000</td>\n",
" <td>37.700706</td>\n",
" <td>83.121951</td>\n",
" <td>244203.0</td>\n",
" <td>1624</td>\n",
" <td>1.346674</td>\n",
" <td>409.997915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>은평구</th>\n",
" <td>0.184438</td>\n",
" <td>0.235294</td>\n",
" <td>0.083333</td>\n",
" <td>0.291139</td>\n",
" <td>0.275715</td>\n",
" <td>84.939759</td>\n",
" <td>66.666667</td>\n",
" <td>100.000000</td>\n",
" <td>37.147335</td>\n",
" <td>86.920467</td>\n",
" <td>494388.0</td>\n",
" <td>1873</td>\n",
" <td>1.069920</td>\n",
" <td>375.674229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>종로구</th>\n",
" <td>0.314121</td>\n",
" <td>0.352941</td>\n",
" <td>0.333333</td>\n",
" <td>0.383510</td>\n",
" <td>0.190589</td>\n",
" <td>76.303318</td>\n",
" <td>81.818182</td>\n",
" <td>83.333333</td>\n",
" <td>38.324176</td>\n",
" <td>84.212822</td>\n",
" <td>162820.0</td>\n",
" <td>1002</td>\n",
" <td>1.574494</td>\n",
" <td>363.991830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>중구</th>\n",
" <td>0.195965</td>\n",
" <td>0.235294</td>\n",
" <td>0.083333</td>\n",
" <td>0.508040</td>\n",
" <td>0.174273</td>\n",
" <td>65.294118</td>\n",
" <td>66.666667</td>\n",
" <td>66.666667</td>\n",
" <td>33.712716</td>\n",
" <td>88.309353</td>\n",
" <td>133240.0</td>\n",
" <td>671</td>\n",
" <td>1.196905</td>\n",
" <td>320.649519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>중랑구</th>\n",
" <td>0.244957</td>\n",
" <td>0.352941</td>\n",
" <td>0.916667</td>\n",
" <td>0.366746</td>\n",
" <td>0.321589</td>\n",
" <td>79.144385</td>\n",
" <td>81.818182</td>\n",
" <td>92.307692</td>\n",
" <td>38.829040</td>\n",
" <td>84.545135</td>\n",
" <td>414503.0</td>\n",
" <td>660</td>\n",
" <td>2.202900</td>\n",
" <td>376.644434</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 강간 강도 살인 절도 폭력 강간검거율 \\\n",
"구별 \n",
"강남구 1.000000 0.941176 0.916667 0.953472 0.661386 77.728285 \n",
"강동구 0.155620 0.058824 0.166667 0.445775 0.289667 78.846154 \n",
"강북구 0.146974 0.529412 0.416667 0.126924 0.274769 82.352941 \n",
"관악구 0.628242 0.411765 0.583333 0.562094 0.428234 69.062500 \n",
"광진구 0.397695 0.529412 0.166667 0.671570 0.269094 91.666667 \n",
"구로구 0.515850 0.588235 0.500000 0.435169 0.359423 58.362989 \n",
"금천구 0.141210 0.058824 0.083333 0.172426 0.134074 80.794702 \n",
"노원구 0.273775 0.117647 0.666667 0.386589 0.292268 61.421320 \n",
"도봉구 0.000000 0.235294 0.083333 0.000000 0.000000 100.000000 \n",
"동대문구 0.204611 0.470588 0.250000 0.314061 0.250887 84.393064 \n",
"동작구 0.527378 0.235294 0.250000 0.274376 0.100024 48.771930 \n",
"마포구 0.553314 0.529412 0.500000 0.510434 0.353748 84.013605 \n",
"서대문구 0.149856 0.000000 0.000000 0.256244 0.134547 80.519481 \n",
"서초구 0.838617 0.235294 0.500000 0.537804 0.215654 63.358779 \n",
"성동구 0.069164 0.235294 0.166667 0.186110 0.029558 94.444444 \n",
"성북구 0.138329 0.000000 0.250000 0.247007 0.170726 82.666667 \n",
"송파구 0.340058 0.470588 0.750000 0.744441 0.427524 80.909091 \n",
"양천구 0.806916 0.823529 0.666667 1.000000 1.000000 77.486911 \n",
"영등포구 0.556196 1.000000 1.000000 0.650359 0.493024 62.033898 \n",
"용산구 0.265130 0.529412 0.250000 0.169004 0.133128 89.175258 \n",
"은평구 0.184438 0.235294 0.083333 0.291139 0.275715 84.939759 \n",
"종로구 0.314121 0.352941 0.333333 0.383510 0.190589 76.303318 \n",
"중구 0.195965 0.235294 0.083333 0.508040 0.174273 65.294118 \n",
"중랑구 0.244957 0.352941 0.916667 0.366746 0.321589 79.144385 \n",
"\n",
" 강도검거율 살인검거율 절도검거율 폭력검거율 인구수 CCTV 범죄 \\\n",
"구별 \n",
"강남구 85.714286 76.923077 42.857143 86.484594 570500.0 2780 4.472701 \n",
"강동구 100.000000 75.000000 33.347422 82.890855 453233.0 773 1.116551 \n",
"강북구 92.857143 100.000000 43.096234 88.637222 330192.0 748 1.494746 \n",
"관악구 100.000000 88.888889 30.561715 80.109157 525515.0 1496 2.613667 \n",
"광진구 100.000000 100.000000 42.200925 83.047619 372164.0 707 2.034438 \n",
"구로구 73.333333 75.000000 38.072805 80.877951 447874.0 1561 2.398678 \n",
"금천구 100.000000 100.000000 56.668794 86.465433 255082.0 1015 0.589867 \n",
"노원구 100.000000 100.000000 36.525308 85.530665 569384.0 1265 1.736946 \n",
"도봉구 100.000000 100.000000 44.967074 87.626093 348646.0 485 0.318627 \n",
"동대문구 100.000000 100.000000 41.090358 87.401884 369496.0 1294 1.490147 \n",
"동작구 55.555556 100.000000 35.442359 83.089005 412520.0 1091 1.387071 \n",
"마포구 71.428571 100.000000 31.819961 84.445189 389649.0 574 2.446908 \n",
"서대문구 80.000000 100.000000 40.728477 83.219844 327163.0 962 0.540647 \n",
"서초구 66.666667 75.000000 41.404175 87.453105 450310.0 1930 2.327368 \n",
"성동구 88.888889 100.000000 37.149969 86.538462 311244.0 1062 0.686793 \n",
"성북구 80.000000 100.000000 41.512605 83.974649 461260.0 1464 0.806061 \n",
"송파구 76.923077 90.909091 34.856437 84.552352 667483.0 618 2.732611 \n",
"양천구 84.210526 100.000000 48.469644 83.065080 479978.0 2034 4.297113 \n",
"영등포구 90.909091 85.714286 32.995951 82.894737 402985.0 904 3.699580 \n",
"용산구 100.000000 100.000000 37.700706 83.121951 244203.0 1624 1.346674 \n",
"은평구 66.666667 100.000000 37.147335 86.920467 494388.0 1873 1.069920 \n",
"종로구 81.818182 83.333333 38.324176 84.212822 162820.0 1002 1.574494 \n",
"중구 66.666667 66.666667 33.712716 88.309353 133240.0 671 1.196905 \n",
"중랑구 81.818182 92.307692 38.829040 84.545135 414503.0 660 2.202900 \n",
"\n",
" 검거 \n",
"구별 \n",
"강남구 369.707384 \n",
"강동구 370.084431 \n",
"강북구 406.943540 \n",
"관악구 368.622261 \n",
"광진구 416.915211 \n",
"구로구 325.647079 \n",
"금천구 423.928929 \n",
"노원구 383.477292 \n",
"도봉구 432.593167 \n",
"동대문구 412.885306 \n",
"동작구 322.858850 \n",
"마포구 371.707327 \n",
"서대문구 384.467802 \n",
"서초구 333.882725 \n",
"성동구 407.021764 \n",
"성북구 388.153921 \n",
"송파구 368.150048 \n",
"양천구 393.232162 \n",
"영등포구 354.547963 \n",
"용산구 409.997915 \n",
"은평구 375.674229 \n",
"종로구 363.991830 \n",
"중구 320.649519 \n",
"중랑구 376.644434 "
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"crime_anal_norm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## [참고] Seaborn 기초\n",
"- Matplotlib과 함꼐 사용하는 시각화 도구\n",
" - <주의> seaborn을 import할 때는 matplotlib도 같이 import 되어 있어야 한다!\n",
"- cmd 창을 켜서 ```pip install seaborn```명령문을 입력하여 설치!!"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:24.861618Z",
"start_time": "2020-05-28T01:11:24.167475Z"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import seaborn as sns\n",
"\n",
"x = np.linspace(0, 14, 100)\n",
"y1 = np.sin(x)\n",
"y2 = 2 * np.sin(x + 0.5)\n",
"y3 = 3 * np.sin(x + 1.0)\n",
"y4 = 4 * np.sin(x + 1.5)\n",
"\n",
"plt.figure(figsize = (10, 6))\n",
"plt.plot(x, y1, label = 'y1')\n",
"plt.plot(x, y2, label = 'y2')\n",
"plt.plot(x, y3, label = 'y3')\n",
"plt.plot(x, y4, label = 'y4')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**```seaborn```은 ```set_style()```로 그래프의 배경 및 grid 색상을 지정해줄 수 있다**\n",
"- \"whitegrid\"는 일반적인 grid, \"darkgrid\"로 색감(?)을 줄 수도 ^^\n",
"- \"white\" 또는 \"dark\"로 설정하면, 선 없이 색상만 설정 가능!"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:25.201710Z",
"start_time": "2020-05-28T01:11:24.863613Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style('darkgrid')\n",
"\n",
"plt.figure(figsize = (10, 6))\n",
"plt.plot(x, y1, x, y2, x, y3, x, y4)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:25.475977Z",
"start_time": "2020-05-28T01:11:25.203703Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style('whitegrid')\n",
"\n",
"plt.figure(figsize = (10, 6))\n",
"plt.plot(x, y1, x, y2, x, y3, x, y4)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**```despine``` 함수를 사용해서 축을 살짝 벌린다(?)**\n",
"- 그래프를 (0, 0) 부분을 보면, 살짝 벌어져 있음을 알 수 있다"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:25.804131Z",
"start_time": "2020-05-28T01:11:25.477971Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 6))\n",
"plt.plot(x, y1, x, y2, x, y3, x, y4)\n",
"\n",
"sns.despine(offset = 10)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Tips 데이터 셋을 예제로 사용하여 ```seaborn``` 연습해보기**\n",
"- Tips 데이터 셋은 요일별 점심, 저녁, 흡연 여부와 식사 금액과 팁을 정리한 데이터"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:25.831058Z",
"start_time": "2020-05-28T01:11:25.806094Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>total_bill</th>\n",
" <th>tip</th>\n",
" <th>sex</th>\n",
" <th>smoker</th>\n",
" <th>day</th>\n",
" <th>time</th>\n",
" <th>size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>16.99</td>\n",
" <td>1.01</td>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10.34</td>\n",
" <td>1.66</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>21.01</td>\n",
" <td>3.50</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>23.68</td>\n",
" <td>3.31</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>24.59</td>\n",
" <td>3.61</td>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>Sun</td>\n",
" <td>Dinner</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill tip sex smoker day time size\n",
"0 16.99 1.01 Female No Sun Dinner 2\n",
"1 10.34 1.66 Male No Sun Dinner 3\n",
"2 21.01 3.50 Male No Sun Dinner 3\n",
"3 23.68 3.31 Male No Sun Dinner 2\n",
"4 24.59 3.61 Female No Sun Dinner 4"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"\n",
"sns.set_style(\"whitegrid\")\n",
"\n",
"tips = sns.load_dataset(\"tips\")\n",
"tips.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**<참고> Boxplot 코드에 대한 자세한 설명이 나와있는 사이트: https://rfriend.tistory.com/410**"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:26.081355Z",
"start_time": "2020-05-28T01:11:25.833022Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (8, 6))\n",
"sns.boxplot(x = tips[\"total_bill\"]) # y = tips[\"total_bill\"] 로 설정하면, 일반적인 '세로' 형태의 상자그림을 그릴 수 있다\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**x축을 '요일(day)', y축은 '전체 금액(total_bill)'로 설정하고 boxplot 그리기**"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:26.339665Z",
"start_time": "2020-05-28T01:11:26.083351Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (8, 6))\n",
"sns.boxplot(x = 'day', y = 'total_bill', data = tips)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**```hue``` 인수**\n",
"- 두 가지 카테고리 값에 의한 실수 값의 변화를 보기 위한 hue 인수를 제공\n",
"- hue 인수에 카테고리 값을 가지는 변수의 이름을 지정하면 카테고리 값에 따라 다르게 시각화"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:26.702693Z",
"start_time": "2020-05-28T01:11:26.341659Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (8, 6))\n",
"sns.boxplot(x = 'day', y = 'total_bill', hue = 'smoker', data = tips, palette = 'Set3')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- ```hue```를 점심/저녁으로 구분"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:26.945045Z",
"start_time": "2020-05-28T01:11:26.704689Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (8, 6))\n",
"sns.boxplot(x = 'time', y = 'tip', data = tips, order = ['Dinner', 'Lunch'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**```swarmplot()```으로 swarmplot 그려보기**"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:27.173434Z",
"start_time": "2020-05-28T01:11:26.947039Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (8, 6))\n",
"sns.swarmplot(x = 'day', y = 'total_bill', data = tips, color = '0.5')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- 아래와 같이 boxplot과 swarmplot을 동시에 그려줄 수도 있다"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:27.442713Z",
"start_time": "2020-05-28T01:11:27.175428Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (8, 6))\n",
"sns.boxplot(x = 'day', y = 'total_bill', data = tips)\n",
"sns.swarmplot(x = 'day', y = 'total_bill', data = tips, color = '0.25')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**```lmplot()```을 이용하여 lmplot 그려보기**\n",
"- 산점도 위에 선형 회귀직선을 같이 그려준다\n",
"- Linear Regression 파트에서 많이 사용할 듯(?) 싶다"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:28.071032Z",
"start_time": "2020-05-28T01:11:27.445705Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x504 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style('darkgrid')\n",
"sns.lmplot(x = 'total_bill', y = 'tip', data = tips, height = 7)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- ```hue``` 인수로 'smoker'를 설정해서 lmplot 그리기"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:29.210340Z",
"start_time": "2020-05-28T01:11:28.073027Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 557.25x504 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.lmplot(x = 'total_bill', y = 'tip', hue = 'smoker', data = tips, height = 7)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- ```palette``` 인수를 조정해서 그래프에 표시되는 dot 및 line의 색상을 변경해줄 수 있다"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:30.147832Z",
"start_time": "2020-05-28T01:11:29.212335Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 557.25x504 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.lmplot(x = \"total_bill\", y = \"tip\", hue = \"smoker\", data = tips, palette = \"Set1\", height = 7)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**<참고>**\n",
"- numpy의 np.random.randint, np.random.rand, np.random.randn에 대한 설명이 나와있는 사이트: https://nittaku.tistory.com/443"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:30.159799Z",
"start_time": "2020-05-28T01:11:30.150826Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.5004866 , 0.09317986, 0.42674523, 0.37042427, 0.85308551,\n",
" 0.60532779, 0.01805685, 0.95800075, 0.10125669, 0.46237944,\n",
" 0.65104743, 0.43213378],\n",
" [0.25471612, 0.91103641, 0.30265202, 0.2292839 , 0.05516711,\n",
" 0.70846954, 0.61859957, 0.2246315 , 0.31869484, 0.20824171,\n",
" 0.06032734, 0.16688901],\n",
" [0.49162909, 0.73626662, 0.37767827, 0.20829824, 0.81313332,\n",
" 0.09300668, 0.5977966 , 0.54592192, 0.70284839, 0.9921764 ,\n",
" 0.15003517, 0.63569026],\n",
" [0.13356767, 0.75966633, 0.04744922, 0.1815465 , 0.93951514,\n",
" 0.80631123, 0.09582963, 0.62975981, 0.7717638 , 0.81076558,\n",
" 0.4709916 , 0.19524524],\n",
" [0.0442873 , 0.77613234, 0.01613827, 0.87668502, 0.99394794,\n",
" 0.15413787, 0.08798884, 0.10921618, 0.12197744, 0.91241033,\n",
" 0.03248385, 0.83998824],\n",
" [0.33695541, 0.16145577, 0.02709829, 0.76792121, 0.66150465,\n",
" 0.71750024, 0.2922249 , 0.66479749, 0.88386943, 0.53377008,\n",
" 0.87447036, 0.84750388],\n",
" [0.71706747, 0.36953904, 0.83221278, 0.01282826, 0.05273001,\n",
" 0.45531048, 0.69807732, 0.60684268, 0.6193882 , 0.68092213,\n",
" 0.65377016, 0.40910283],\n",
" [0.16880819, 0.57247011, 0.89728224, 0.58271587, 0.60782948,\n",
" 0.83557564, 0.96382277, 0.49177998, 0.82143073, 0.24041263,\n",
" 0.0832159 , 0.39898524],\n",
" [0.33579185, 0.50077913, 0.89697246, 0.64444801, 0.18237001,\n",
" 0.99823309, 0.89557764, 0.24489221, 0.3691461 , 0.9874929 ,\n",
" 0.49887386, 0.87902097],\n",
" [0.99174336, 0.33787973, 0.5557974 , 0.48515564, 0.68271334,\n",
" 0.8564105 , 0.78541433, 0.02251763, 0.01201184, 0.92739424,\n",
" 0.29355211, 0.02100191]])"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# np.random.rand(): 0 부터 1 사이의 '균일 분포'에서 난수 matrix array 생성\n",
"uniform_data = np.random.rand(10, 12)\n",
"uniform_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**```heatmap()```으로 heatmap 그리기**\n",
"\n",
"<참고> heatmap()에 대한 자세한 설명이 나와있는 사이트: https://rfriend.tistory.com/419\n",
"\n",
"- x축과 y축에 2개의 범주형 자료의 class 별로 연속형 자료를 집계한 자료를 사용하여, 집계한 값에 비례하여 색깔을 다르게 해서 2차원으로 자료를 시각화"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:30.713323Z",
"start_time": "2020-05-28T01:11:30.162791Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(uniform_data)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:31.055405Z",
"start_time": "2020-05-28T01:11:30.715314Z"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# vmin과 vmax로 heatmap 오른쪽에 표시되는 색깔 봉의 범위를 지정\n",
"sns.heatmap(uniform_data, vmin = 0, vmax = 1)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**연도 및 월별 항공기 승객수를 기록한 데이터를 예시로 heatmap 그려보기**\n",
"- 데이터 명은 \"flights\"이다"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:31.075351Z",
"start_time": "2020-05-28T01:11:31.057400Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>passengers</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1949</td>\n",
" <td>January</td>\n",
" <td>112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1949</td>\n",
" <td>February</td>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1949</td>\n",
" <td>March</td>\n",
" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1949</td>\n",
" <td>April</td>\n",
" <td>129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1949</td>\n",
" <td>May</td>\n",
" <td>121</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month passengers\n",
"0 1949 January 112\n",
"1 1949 February 118\n",
"2 1949 March 132\n",
"3 1949 April 129\n",
"4 1949 May 121"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flights = sns.load_dataset(\"flights\")\n",
"flights.head()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:31.096329Z",
"start_time": "2020-05-28T01:11:31.077345Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>year</th>\n",
" <th>1949</th>\n",
" <th>1950</th>\n",
" <th>1951</th>\n",
" <th>1952</th>\n",
" <th>1953</th>\n",
" <th>1954</th>\n",
" <th>1955</th>\n",
" <th>1956</th>\n",
" <th>1957</th>\n",
" <th>1958</th>\n",
" <th>1959</th>\n",
" <th>1960</th>\n",
" </tr>\n",
" <tr>\n",
" <th>month</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>January</th>\n",
" <td>112</td>\n",
" <td>115</td>\n",
" <td>145</td>\n",
" <td>171</td>\n",
" <td>196</td>\n",
" <td>204</td>\n",
" <td>242</td>\n",
" <td>284</td>\n",
" <td>315</td>\n",
" <td>340</td>\n",
" <td>360</td>\n",
" <td>417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>February</th>\n",
" <td>118</td>\n",
" <td>126</td>\n",
" <td>150</td>\n",
" <td>180</td>\n",
" <td>196</td>\n",
" <td>188</td>\n",
" <td>233</td>\n",
" <td>277</td>\n",
" <td>301</td>\n",
" <td>318</td>\n",
" <td>342</td>\n",
" <td>391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>March</th>\n",
" <td>132</td>\n",
" <td>141</td>\n",
" <td>178</td>\n",
" <td>193</td>\n",
" <td>236</td>\n",
" <td>235</td>\n",
" <td>267</td>\n",
" <td>317</td>\n",
" <td>356</td>\n",
" <td>362</td>\n",
" <td>406</td>\n",
" <td>419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>April</th>\n",
" <td>129</td>\n",
" <td>135</td>\n",
" <td>163</td>\n",
" <td>181</td>\n",
" <td>235</td>\n",
" <td>227</td>\n",
" <td>269</td>\n",
" <td>313</td>\n",
" <td>348</td>\n",
" <td>348</td>\n",
" <td>396</td>\n",
" <td>461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>May</th>\n",
" <td>121</td>\n",
" <td>125</td>\n",
" <td>172</td>\n",
" <td>183</td>\n",
" <td>229</td>\n",
" <td>234</td>\n",
" <td>270</td>\n",
" <td>318</td>\n",
" <td>355</td>\n",
" <td>363</td>\n",
" <td>420</td>\n",
" <td>472</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"year 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 \\\n",
"month \n",
"January 112 115 145 171 196 204 242 284 315 340 360 \n",
"February 118 126 150 180 196 188 233 277 301 318 342 \n",
"March 132 141 178 193 236 235 267 317 356 362 406 \n",
"April 129 135 163 181 235 227 269 313 348 348 396 \n",
"May 121 125 172 183 229 234 270 318 355 363 420 \n",
"\n",
"year 1960 \n",
"month \n",
"January 417 \n",
"February 391 \n",
"March 419 \n",
"April 461 \n",
"May 472 "
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pandas의 pivot()을 이용해서, 여러 분류로 섞인 행 데이터를 열 데이터로 회전!\n",
"# 결과적으로 pivot()을 사용해서 간편하게 월별, 연도별로 구분이 가능해졌다\n",
"flights = flights.pivot(index = 'month', columns = 'year', values = 'passengers')\n",
"flights.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- ```annot = True```로 설정해서, 각 셀에 숫자를 입력\n",
"- ```fmt = 'd'```로 설정해서, '정수' 형태로 숫자를 입력하게끔 지정"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:31.819362Z",
"start_time": "2020-05-28T01:11:31.098289Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x576 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10, 8))\n",
"sns.heatmap(flights, annot = True, fmt = 'd')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**<heatmap 결과 해석>**\n",
"\n",
"**결과를 보면, 1959년과 1960년 7월과 8월에 항공기를 이용한 승객들이 많은 것을 알 수 있다**\n",
"\n",
"**역시 옛날에도(?) 여름 휴가철 기간인 7 ~ 8월 사이에 여행을 가는 사람이 많았구나 ㅎㅎ**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**------------------------------------------------(구분선)------------------------------------------------**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**iris 데이터를 예시로 사용하여 pairplot 과 lmplot 그려보기**\n",
"- 꽃잎, 꽃받침의 너비와 폭을 가지고 '종(species)'을 구분해보자!\n",
"\n",
"<잠깐!!>\n",
"\n",
"**pairplot이란?**\n",
"- 데이터 프레임을 인수로 받아서, grid 형태로 각 데이터 열의 조합에 대해 scatter plot을 그린다. 또한 대각선 영역에는 해당 데이터의 histogram을 그린다!"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:31.842299Z",
"start_time": "2020-05-28T01:11:31.821357Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5.4</td>\n",
" <td>3.9</td>\n",
" <td>1.7</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4.6</td>\n",
" <td>3.4</td>\n",
" <td>1.4</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>5.0</td>\n",
" <td>3.4</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4.4</td>\n",
" <td>2.9</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4.9</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.1</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
"1 4.9 3.0 1.4 0.2 setosa\n",
"2 4.7 3.2 1.3 0.2 setosa\n",
"3 4.6 3.1 1.5 0.2 setosa\n",
"4 5.0 3.6 1.4 0.2 setosa\n",
"5 5.4 3.9 1.7 0.4 setosa\n",
"6 4.6 3.4 1.4 0.3 setosa\n",
"7 5.0 3.4 1.5 0.2 setosa\n",
"8 4.4 2.9 1.4 0.2 setosa\n",
"9 4.9 3.1 1.5 0.1 setosa"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sns.set(style = 'ticks')\n",
"iris = sns.load_dataset('iris')\n",
"iris.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- ```hue``` 인수에 'species'를 지정해서, '종(species)'별로 시각화\n",
"- 추가적으로 ```markers```를 동그라미, 네모, 세모로 지정해서 쉽게 구분가도록 해주었다"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"ExecuteTime": {
"end_time": "2020-05-28T01:11:36.209651Z",
"start_time": "2020-05-28T01:11:31.844294Z"
}
},
"outputs": [
{
"data": {
"image/png": 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