├── .gitignore
├── 01_초판
├── 1_First_Step
│ └── 1_4_Python_Basic.ipynb
├── 2_Start_DataAnalysis
│ ├── 2_1_Pandas.ipynb
│ ├── 2_2_Crawling.ipynb
│ └── files
│ │ ├── sample.xlsx
│ │ ├── sample_1.xlsx
│ │ ├── sample_2.xlsx
│ │ ├── sample_codemaster.xlsx
│ │ └── sample_index_false.xlsx
├── 3_Tourists_Event
│ ├── 3_2_Data_Preprocessing.ipynb
│ ├── 3_3_Visulaization.ipynb
│ └── files
│ │ ├── kto_201001.xlsx
│ │ ├── kto_201002.xlsx
│ │ ├── kto_201003.xlsx
│ │ ├── kto_201004.xlsx
│ │ ├── kto_201005.xlsx
│ │ ├── kto_201006.xlsx
│ │ ├── kto_201007.xlsx
│ │ ├── kto_201008.xlsx
│ │ ├── kto_201009.xlsx
│ │ ├── kto_201010.xlsx
│ │ ├── kto_201011.xlsx
│ │ ├── kto_201012.xlsx
│ │ ├── kto_201101.xlsx
│ │ ├── kto_201102.xlsx
│ │ ├── kto_201103.xlsx
│ │ ├── kto_201104.xlsx
│ │ ├── kto_201105.xlsx
│ │ ├── kto_201106.xlsx
│ │ ├── kto_201107.xlsx
│ │ ├── kto_201108.xlsx
│ │ ├── kto_201109.xlsx
│ │ ├── kto_201110.xlsx
│ │ ├── kto_201111.xlsx
│ │ ├── kto_201112.xlsx
│ │ ├── kto_201201.xlsx
│ │ ├── kto_201202.xlsx
│ │ ├── kto_201203.xlsx
│ │ ├── kto_201204.xlsx
│ │ ├── kto_201205.xlsx
│ │ ├── kto_201206.xlsx
│ │ ├── kto_201207.xlsx
│ │ ├── kto_201208.xlsx
│ │ ├── kto_201209.xlsx
│ │ ├── kto_201210.xlsx
│ │ ├── kto_201211.xlsx
│ │ ├── kto_201212.xlsx
│ │ ├── kto_201301.xlsx
│ │ ├── kto_201302.xlsx
│ │ ├── kto_201303.xlsx
│ │ ├── kto_201304.xlsx
│ │ ├── kto_201305.xlsx
│ │ ├── kto_201306.xlsx
│ │ ├── kto_201307.xlsx
│ │ ├── kto_201308.xlsx
│ │ ├── kto_201309.xlsx
│ │ ├── kto_201310.xlsx
│ │ ├── kto_201311.xlsx
│ │ ├── kto_201312.xlsx
│ │ ├── kto_201401.xlsx
│ │ ├── kto_201402.xlsx
│ │ ├── kto_201403.xlsx
│ │ ├── kto_201404.xlsx
│ │ ├── kto_201405.xlsx
│ │ ├── kto_201406.xlsx
│ │ ├── kto_201407.xlsx
│ │ ├── kto_201408.xlsx
│ │ ├── kto_201409.xlsx
│ │ ├── kto_201410.xlsx
│ │ ├── kto_201411.xlsx
│ │ ├── kto_201412.xlsx
│ │ ├── kto_201501.xlsx
│ │ ├── kto_201502.xlsx
│ │ ├── kto_201503.xlsx
│ │ ├── kto_201504.xlsx
│ │ ├── kto_201505.xlsx
│ │ ├── kto_201506.xlsx
│ │ ├── kto_201507.xlsx
│ │ ├── kto_201508.xlsx
│ │ ├── kto_201509.xlsx
│ │ ├── kto_201510.xlsx
│ │ ├── kto_201511.xlsx
│ │ ├── kto_201512.xlsx
│ │ ├── kto_201601.xlsx
│ │ ├── kto_201602.xlsx
│ │ ├── kto_201603.xlsx
│ │ ├── kto_201604.xlsx
│ │ ├── kto_201605.xlsx
│ │ ├── kto_201606.xlsx
│ │ ├── kto_201607.xlsx
│ │ ├── kto_201608.xlsx
│ │ ├── kto_201609.xlsx
│ │ ├── kto_201610.xlsx
│ │ ├── kto_201611.xlsx
│ │ ├── kto_201612.xlsx
│ │ ├── kto_201701.xlsx
│ │ ├── kto_201702.xlsx
│ │ ├── kto_201703.xlsx
│ │ ├── kto_201704.xlsx
│ │ ├── kto_201705.xlsx
│ │ ├── kto_201706.xlsx
│ │ ├── kto_201707.xlsx
│ │ ├── kto_201708.xlsx
│ │ ├── kto_201709.xlsx
│ │ ├── kto_201710.xlsx
│ │ ├── kto_201711.xlsx
│ │ ├── kto_201712.xlsx
│ │ ├── kto_201801.xlsx
│ │ ├── kto_201802.xlsx
│ │ ├── kto_201803.xlsx
│ │ ├── kto_201804.xlsx
│ │ ├── kto_201805.xlsx
│ │ ├── kto_201806.xlsx
│ │ ├── kto_201807.xlsx
│ │ ├── kto_201808.xlsx
│ │ ├── kto_201809.xlsx
│ │ ├── kto_201810.xlsx
│ │ ├── kto_201811.xlsx
│ │ ├── kto_201812.xlsx
│ │ ├── kto_201901.xlsx
│ │ ├── kto_201902.xlsx
│ │ ├── kto_201903.xlsx
│ │ ├── kto_201904.xlsx
│ │ ├── kto_201905.xlsx
│ │ ├── kto_201906.xlsx
│ │ ├── kto_201907.xlsx
│ │ └── kto_201908.xlsx
├── 4_Jeju_Hotplace
│ ├── 4_1_Instagram_Crawling.ipynb
│ ├── 4_2_WordCloud.ipynb
│ ├── 4_3_Map.ipynb
│ └── files
│ │ ├── 3_1_crawling_jejuMatJip.xlsx
│ │ ├── 3_1_crawling_jejuYeoHang.xlsx
│ │ ├── 3_1_crawling_jejudoGwanGwang.xlsx
│ │ ├── 3_1_crawling_jejudoMatJip.xlsx
│ │ ├── 3_1_crawling_raw.xlsx
│ │ ├── 3_2_tag-wordcloud.png
│ │ ├── 3_3_jeju.html
│ │ ├── 3_3_jeju_cluster.html
│ │ ├── 3_3_location_counts.xlsx
│ │ ├── 3_3_location_inform.xlsx
│ │ └── 3_3_locations.xlsx
├── 5_Starbucks_Location
│ ├── 5_1_1_Crawling_Starbucks_List.ipynb
│ ├── 5_1_2_OpenData_API.ipynb
│ ├── 5_2_1_Starbucks_Address.ipynb
│ ├── 5_2_2_Starbucks_Data.ipynb
│ ├── 5_3_1_Starbucks_Map.ipynb
│ ├── 5_3_2_Starbucks_Location_Visualization.ipynb
│ ├── 5_3_3&4_Starbucks_Locations_Analysis.ipynb
│ ├── files
│ │ ├── 4_1_seoul_starbucks_list.xlsx
│ │ ├── 4_2_seoul_sgg_list.xlsx
│ │ ├── 4_3_sgg_biz.xlsx
│ │ ├── 4_3_sgg_pop.xlsx
│ │ ├── 4_4_seoul_starbucks_list.xlsx
│ │ └── 4_5_seoul_sgg_stat.xlsx
│ └── maps
│ │ └── seoul_sgg.geojson
└── 6_Best_Product
│ ├── 6_1&2_Crawling.ipynb
│ ├── 6_3_preprocessing.ipynb
│ ├── 6_4_Product_Analysis.ipynb
│ └── files
│ ├── 3_1_danawa_crawling_result.xlsx
│ └── 3_2_danawa_data_final.xlsx
├── 02_개정판
├── 1_First_Step
│ └── 1_3_Python_Basic.ipynb
├── 2_Data_Analysis_Basic
│ ├── 2_1_Pandas.ipynb
│ ├── 2_2_Crawling.ipynb
│ └── files
│ │ ├── sample.xlsx
│ │ ├── sample_1.xlsx
│ │ ├── sample_2.xlsx
│ │ ├── sample_codemaster.xlsx
│ │ └── sample_index_false.xlsx
├── 3_Data_Analysis_Exercise
│ ├── Chapter_3_1
│ │ ├── 3_1_1_MelOn_Crawling.ipynb
│ │ ├── 3_1_2_Bugs_Crawling.ipynb
│ │ ├── 3_1_3_Genie_Crawling.ipynb
│ │ ├── 3_1_4_Excel_Merge.ipynb
│ │ └── files
│ │ │ ├── bugs.xlsx
│ │ │ ├── genie.xlsx
│ │ │ ├── melon.xlsx
│ │ │ └── total.xlsx
│ └── Chapter_3_2
│ │ ├── 3.2.1_유튜브.ipynb
│ │ └── files
│ │ └── youtube_rank.xlsx
├── 4_Tourists_Event
│ ├── 4_2_Data_Preprocessing.ipynb
│ ├── 4_3_Visulaization.ipynb
│ └── files
│ │ ├── [국적별 관광객 데이터] GCC.xlsx
│ │ ├── [국적별 관광객 데이터] 교포.xlsx
│ │ ├── [국적별 관광객 데이터] 구주 기타.xlsx
│ │ ├── [국적별 관광객 데이터] 국적미상.xlsx
│ │ ├── [국적별 관광객 데이터] 그리스.xlsx
│ │ ├── [국적별 관광객 데이터] 남아프리카공화국.xlsx
│ │ ├── [국적별 관광객 데이터] 네덜란드.xlsx
│ │ ├── [국적별 관광객 데이터] 노르웨이.xlsx
│ │ ├── [국적별 관광객 데이터] 뉴질랜드.xlsx
│ │ ├── [국적별 관광객 데이터] 대만.xlsx
│ │ ├── [국적별 관광객 데이터] 대양주 기타.xlsx
│ │ ├── [국적별 관광객 데이터] 덴마크.xlsx
│ │ ├── [국적별 관광객 데이터] 독일.xlsx
│ │ ├── [국적별 관광객 데이터] 러시아.xlsx
│ │ ├── [국적별 관광객 데이터] 루마니아.xlsx
│ │ ├── [국적별 관광객 데이터] 마카오.xlsx
│ │ ├── [국적별 관광객 데이터] 말레이시아.xlsx
│ │ ├── [국적별 관광객 데이터] 멕시코.xlsx
│ │ ├── [국적별 관광객 데이터] 몽골.xlsx
│ │ ├── [국적별 관광객 데이터] 미국.xlsx
│ │ ├── [국적별 관광객 데이터] 미얀마.xlsx
│ │ ├── [국적별 관광객 데이터] 미주 기타.xlsx
│ │ ├── [국적별 관광객 데이터] 방글라데시.xlsx
│ │ ├── [국적별 관광객 데이터] 베트남.xlsx
│ │ ├── [국적별 관광객 데이터] 벨기에.xlsx
│ │ ├── [국적별 관광객 데이터] 불가리아.xlsx
│ │ ├── [국적별 관광객 데이터] 브라질.xlsx
│ │ ├── [국적별 관광객 데이터] 스리랑카.xlsx
│ │ ├── [국적별 관광객 데이터] 스웨덴.xlsx
│ │ ├── [국적별 관광객 데이터] 스위스.xlsx
│ │ ├── [국적별 관광객 데이터] 스페인.xlsx
│ │ ├── [국적별 관광객 데이터] 싱가포르.xlsx
│ │ ├── [국적별 관광객 데이터] 아시아 기타.xlsx
│ │ ├── [국적별 관광객 데이터] 아일랜드.xlsx
│ │ ├── [국적별 관광객 데이터] 아프리카 기타.xlsx
│ │ ├── [국적별 관광객 데이터] 영국.xlsx
│ │ ├── [국적별 관광객 데이터] 오스트레일리아.xlsx
│ │ ├── [국적별 관광객 데이터] 오스트리아.xlsx
│ │ ├── [국적별 관광객 데이터] 우즈베키스탄.xlsx
│ │ ├── [국적별 관광객 데이터] 우크라이나.xlsx
│ │ ├── [국적별 관광객 데이터] 이란.xlsx
│ │ ├── [국적별 관광객 데이터] 이스라엘.xlsx
│ │ ├── [국적별 관광객 데이터] 이탈리아.xlsx
│ │ ├── [국적별 관광객 데이터] 인도.xlsx
│ │ ├── [국적별 관광객 데이터] 인도네시아.xlsx
│ │ ├── [국적별 관광객 데이터] 일본.xlsx
│ │ ├── [국적별 관광객 데이터] 중국.xlsx
│ │ ├── [국적별 관광객 데이터] 카자흐스탄.xlsx
│ │ ├── [국적별 관광객 데이터] 캄보디아.xlsx
│ │ ├── [국적별 관광객 데이터] 캐나다.xlsx
│ │ ├── [국적별 관광객 데이터] 크로아티아.xlsx
│ │ ├── [국적별 관광객 데이터] 태국.xlsx
│ │ ├── [국적별 관광객 데이터] 터키.xlsx
│ │ ├── [국적별 관광객 데이터] 파키스탄.xlsx
│ │ ├── [국적별 관광객 데이터] 포르투갈.xlsx
│ │ ├── [국적별 관광객 데이터] 폴란드.xlsx
│ │ ├── [국적별 관광객 데이터] 프랑스.xlsx
│ │ ├── [국적별 관광객 데이터] 핀란드.xlsx
│ │ ├── [국적별 관광객 데이터] 필리핀.xlsx
│ │ ├── [국적별 관광객 데이터] 홍콩.xlsx
│ │ ├── [국적별 외국인 관광객] GCC.xlsx
│ │ ├── [국적별 외국인 관광객] 교포.xlsx
│ │ ├── [국적별 외국인 관광객] 구주 기타.xlsx
│ │ ├── [국적별 외국인 관광객] 국적미상.xlsx
│ │ ├── [국적별 외국인 관광객] 그리스.xlsx
│ │ ├── [국적별 외국인 관광객] 남아프리카공화국.xlsx
│ │ ├── [국적별 외국인 관광객] 네덜란드.xlsx
│ │ ├── [국적별 외국인 관광객] 노르웨이.xlsx
│ │ ├── [국적별 외국인 관광객] 뉴질랜드.xlsx
│ │ ├── [국적별 외국인 관광객] 대만.xlsx
│ │ ├── [국적별 외국인 관광객] 대양주 기타.xlsx
│ │ ├── [국적별 외국인 관광객] 덴마크.xlsx
│ │ ├── [국적별 외국인 관광객] 독일.xlsx
│ │ ├── [국적별 외국인 관광객] 러시아.xlsx
│ │ ├── [국적별 외국인 관광객] 루마니아.xlsx
│ │ ├── [국적별 외국인 관광객] 마카오.xlsx
│ │ ├── [국적별 외국인 관광객] 말레이시아.xlsx
│ │ ├── [국적별 외국인 관광객] 멕시코.xlsx
│ │ ├── [국적별 외국인 관광객] 몽골.xlsx
│ │ ├── [국적별 외국인 관광객] 미국.xlsx
│ │ ├── [국적별 외국인 관광객] 미얀마.xlsx
│ │ ├── [국적별 외국인 관광객] 미주 기타.xlsx
│ │ ├── [국적별 외국인 관광객] 방글라데시.xlsx
│ │ ├── [국적별 외국인 관광객] 베트남.xlsx
│ │ ├── [국적별 외국인 관광객] 벨기에.xlsx
│ │ ├── [국적별 외국인 관광객] 불가리아.xlsx
│ │ ├── [국적별 외국인 관광객] 브라질.xlsx
│ │ ├── [국적별 외국인 관광객] 스리랑카.xlsx
│ │ ├── [국적별 외국인 관광객] 스웨덴.xlsx
│ │ ├── [국적별 외국인 관광객] 스위스.xlsx
│ │ ├── [국적별 외국인 관광객] 스페인.xlsx
│ │ ├── [국적별 외국인 관광객] 싱가포르.xlsx
│ │ ├── [국적별 외국인 관광객] 아시아 기타.xlsx
│ │ ├── [국적별 외국인 관광객] 아일랜드.xlsx
│ │ ├── [국적별 외국인 관광객] 아프리카 기타.xlsx
│ │ ├── [국적별 외국인 관광객] 영국.xlsx
│ │ ├── [국적별 외국인 관광객] 오스트레일리아.xlsx
│ │ ├── [국적별 외국인 관광객] 오스트리아.xlsx
│ │ ├── [국적별 외국인 관광객] 우즈베키스탄.xlsx
│ │ ├── [국적별 외국인 관광객] 우크라이나.xlsx
│ │ ├── [국적별 외국인 관광객] 이란.xlsx
│ │ ├── [국적별 외국인 관광객] 이스라엘.xlsx
│ │ ├── [국적별 외국인 관광객] 이탈리아.xlsx
│ │ ├── [국적별 외국인 관광객] 인도.xlsx
│ │ ├── [국적별 외국인 관광객] 인도네시아.xlsx
│ │ ├── [국적별 외국인 관광객] 일본.xlsx
│ │ ├── [국적별 외국인 관광객] 중국.xlsx
│ │ ├── [국적별 외국인 관광객] 카자흐스탄.xlsx
│ │ ├── [국적별 외국인 관광객] 캄보디아.xlsx
│ │ ├── [국적별 외국인 관광객] 캐나다.xlsx
│ │ ├── [국적별 외국인 관광객] 크로아티아.xlsx
│ │ ├── [국적별 외국인 관광객] 태국.xlsx
│ │ ├── [국적별 외국인 관광객] 터키.xlsx
│ │ ├── [국적별 외국인 관광객] 파키스탄.xlsx
│ │ ├── [국적별 외국인 관광객] 포르투갈.xlsx
│ │ ├── [국적별 외국인 관광객] 폴란드.xlsx
│ │ ├── [국적별 외국인 관광객] 프랑스.xlsx
│ │ ├── [국적별 외국인 관광객] 핀란드.xlsx
│ │ ├── [국적별 외국인 관광객] 필리핀.xlsx
│ │ ├── [국적별 외국인 관광객] 홍콩.xlsx
│ │ ├── kto_201001.xlsx
│ │ ├── kto_201002.xlsx
│ │ ├── kto_201003.xlsx
│ │ ├── kto_201004.xlsx
│ │ ├── kto_201005.xlsx
│ │ ├── kto_201006.xlsx
│ │ ├── kto_201007.xlsx
│ │ ├── kto_201008.xlsx
│ │ ├── kto_201009.xlsx
│ │ ├── kto_201010.xlsx
│ │ ├── kto_201011.xlsx
│ │ ├── kto_201012.xlsx
│ │ ├── kto_201101.xlsx
│ │ ├── kto_201102.xlsx
│ │ ├── kto_201103.xlsx
│ │ ├── kto_201104.xlsx
│ │ ├── kto_201105.xlsx
│ │ ├── kto_201106.xlsx
│ │ ├── kto_201107.xlsx
│ │ ├── kto_201108.xlsx
│ │ ├── kto_201109.xlsx
│ │ ├── kto_201110.xlsx
│ │ ├── kto_201111.xlsx
│ │ ├── kto_201112.xlsx
│ │ ├── kto_201201.xlsx
│ │ ├── kto_201202.xlsx
│ │ ├── kto_201203.xlsx
│ │ ├── kto_201204.xlsx
│ │ ├── kto_201205.xlsx
│ │ ├── kto_201206.xlsx
│ │ ├── kto_201207.xlsx
│ │ ├── kto_201208.xlsx
│ │ ├── kto_201209.xlsx
│ │ ├── kto_201210.xlsx
│ │ ├── kto_201211.xlsx
│ │ ├── kto_201212.xlsx
│ │ ├── kto_201301.xlsx
│ │ ├── kto_201302.xlsx
│ │ ├── kto_201303.xlsx
│ │ ├── kto_201304.xlsx
│ │ ├── kto_201305.xlsx
│ │ ├── kto_201306.xlsx
│ │ ├── kto_201307.xlsx
│ │ ├── kto_201308.xlsx
│ │ ├── kto_201309.xlsx
│ │ ├── kto_201310.xlsx
│ │ ├── kto_201311.xlsx
│ │ ├── kto_201312.xlsx
│ │ ├── kto_201401.xlsx
│ │ ├── kto_201402.xlsx
│ │ ├── kto_201403.xlsx
│ │ ├── kto_201404.xlsx
│ │ ├── kto_201405.xlsx
│ │ ├── kto_201406.xlsx
│ │ ├── kto_201407.xlsx
│ │ ├── kto_201408.xlsx
│ │ ├── kto_201409.xlsx
│ │ ├── kto_201410.xlsx
│ │ ├── kto_201411.xlsx
│ │ ├── kto_201412.xlsx
│ │ ├── kto_201501.xlsx
│ │ ├── kto_201502.xlsx
│ │ ├── kto_201503.xlsx
│ │ ├── kto_201504.xlsx
│ │ ├── kto_201505.xlsx
│ │ ├── kto_201506.xlsx
│ │ ├── kto_201507.xlsx
│ │ ├── kto_201508.xlsx
│ │ ├── kto_201509.xlsx
│ │ ├── kto_201510.xlsx
│ │ ├── kto_201511.xlsx
│ │ ├── kto_201512.xlsx
│ │ ├── kto_201601.xlsx
│ │ ├── kto_201602.xlsx
│ │ ├── kto_201603.xlsx
│ │ ├── kto_201604.xlsx
│ │ ├── kto_201605.xlsx
│ │ ├── kto_201606.xlsx
│ │ ├── kto_201607.xlsx
│ │ ├── kto_201608.xlsx
│ │ ├── kto_201609.xlsx
│ │ ├── kto_201610.xlsx
│ │ ├── kto_201611.xlsx
│ │ ├── kto_201612.xlsx
│ │ ├── kto_201701.xlsx
│ │ ├── kto_201702.xlsx
│ │ ├── kto_201703.xlsx
│ │ ├── kto_201704.xlsx
│ │ ├── kto_201705.xlsx
│ │ ├── kto_201706.xlsx
│ │ ├── kto_201707.xlsx
│ │ ├── kto_201708.xlsx
│ │ ├── kto_201709.xlsx
│ │ ├── kto_201710.xlsx
│ │ ├── kto_201711.xlsx
│ │ ├── kto_201712.xlsx
│ │ ├── kto_201801.xlsx
│ │ ├── kto_201802.xlsx
│ │ ├── kto_201803.xlsx
│ │ ├── kto_201804.xlsx
│ │ ├── kto_201805.xlsx
│ │ ├── kto_201806.xlsx
│ │ ├── kto_201807.xlsx
│ │ ├── kto_201808.xlsx
│ │ ├── kto_201809.xlsx
│ │ ├── kto_201810.xlsx
│ │ ├── kto_201811.xlsx
│ │ ├── kto_201812.xlsx
│ │ ├── kto_201901.xlsx
│ │ ├── kto_201902.xlsx
│ │ ├── kto_201903.xlsx
│ │ ├── kto_201904.xlsx
│ │ ├── kto_201905.xlsx
│ │ ├── kto_201906.xlsx
│ │ ├── kto_201907.xlsx
│ │ ├── kto_201908.xlsx
│ │ ├── kto_201909.xlsx
│ │ ├── kto_201910.xlsx
│ │ ├── kto_201911.xlsx
│ │ ├── kto_201912.xlsx
│ │ ├── kto_202001.xlsx
│ │ ├── kto_202002.xlsx
│ │ ├── kto_202003.xlsx
│ │ ├── kto_202004.xlsx
│ │ ├── kto_202005.xlsx
│ │ ├── kto_china.xlsx
│ │ ├── kto_total.xlsx
│ │ └── 실습데이터.zip
├── 5_Jeju_Hotplace
│ ├── 5_1_Instagram_Crawling.ipynb
│ ├── 5_2_WordCloud.ipynb
│ ├── 5_3_Map.ipynb
│ ├── 5_4_Find_words.ipynb
│ └── files
│ │ ├── 1_crawling_jejuMatJip.xlsx
│ │ ├── 1_crawling_jejuYeoHang.xlsx
│ │ ├── 1_crawling_jejudoGwanGwang.xlsx
│ │ ├── 1_crawling_jejudoMatJip.xlsx
│ │ ├── 1_crawling_raw.xlsx
│ │ ├── 2_tag-wordcloud.png
│ │ ├── 3_jeju.html
│ │ ├── 3_jeju_cluster.html
│ │ ├── 3_location_counts.xlsx
│ │ ├── 3_location_inform.xlsx
│ │ ├── 3_locations.xlsx
│ │ ├── 4_select_data_게스트하우스.xlsx
│ │ ├── 4_select_data_박물관.xlsx
│ │ ├── 4_select_data_섭지코지.xlsx
│ │ ├── 4_select_data_해돋이.xlsx
│ │ ├── 4_select_data_힐링.xlsx
│ │ ├── insta_login_1.png
│ │ └── insta_login_2.png
├── 6_Starbucks_Location
│ ├── 6_1_1_Crawling_Starbucks_List.ipynb
│ ├── 6_1_2_edit_OpenData_Download.ipynb
│ ├── 6_2_1_Starbucks_Address.ipynb
│ ├── 6_2_2_Starbucks_Data.ipynb
│ ├── 6_3_1_Starbucks_Map.ipynb
│ ├── 6_3_2_Starbucks_Location_Visualization.ipynb
│ ├── 6_3_3&4_Starbucks_Locations_Analysis.ipynb
│ ├── files
│ │ ├── report.txt
│ │ ├── report2.txt
│ │ ├── seoul_sgg_list.xlsx
│ │ ├── seoul_sgg_stat.xlsx
│ │ ├── seoul_starbucks_list.xlsx
│ │ ├── sgg_biz.xlsx
│ │ └── sgg_pop.xlsx
│ └── maps
│ │ └── seoul_sgg.geojson
└── 7_Best_Product
│ ├── 7_1&2_Crawling.ipynb
│ ├── 7_3_preprocessing.ipynb
│ ├── 7_4_Product_Analysis.ipynb
│ └── files
│ ├── 1_danawa_crawling_result.xlsx
│ └── 2_danawa_data_final.xlsx
└── README.md
/.gitignore:
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1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | *.egg-info/
24 | .installed.cfg
25 | *.egg
26 | MANIFEST
27 |
28 | # PyInstaller
29 | # Usually these files are written by a python script from a template
30 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
31 | *.manifest
32 | *.spec
33 |
34 | # Installer logs
35 | pip-log.txt
36 | pip-delete-this-directory.txt
37 |
38 | # Unit test / coverage reports
39 | htmlcov/
40 | .tox/
41 | .coverage
42 | .coverage.*
43 | .cache
44 | nosetests.xml
45 | coverage.xml
46 | *.cover
47 | .hypothesis/
48 | .pytest_cache/
49 |
50 | # Translations
51 | *.mo
52 | *.pot
53 |
54 | # Django stuff:
55 | *.log
56 | local_settings.py
57 | db.sqlite3
58 |
59 | # Flask stuff:
60 | instance/
61 | .webassets-cache
62 |
63 | # Scrapy stuff:
64 | .scrapy
65 |
66 | # Sphinx documentation
67 | docs/_build/
68 |
69 | # PyBuilder
70 | target/
71 |
72 | # Jupyter Notebook
73 | .ipynb_checkpoints
74 |
75 | # pyenv
76 | .python-version
77 |
78 | # celery beat schedule file
79 | celerybeat-schedule
80 |
81 | # SageMath parsed files
82 | *.sage.py
83 |
84 | # Environments
85 | .env
86 | .venv
87 | env/
88 | venv/
89 | ENV/
90 | env.bak/
91 | venv.bak/
92 |
93 | # Spyder project settings
94 | .spyderproject
95 | .spyproject
96 |
97 | # Rope project settings
98 | .ropeproject
99 |
100 | # mkdocs documentation
101 | /site
102 |
103 | # mypy
104 | .mypy_cache/
105 | .DS_Store
106 |
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/01_초판/4_Jeju_Hotplace/4_2_WordCloud.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# 4.2 워드클라우드"
8 | ]
9 | },
10 | {
11 | "cell_type": "markdown",
12 | "metadata": {},
13 | "source": [
14 | "### 4.2.1 워드 클라우드를 만드는 과정"
15 | ]
16 | },
17 | {
18 | "cell_type": "code",
19 | "execution_count": null,
20 | "metadata": {
21 | "scrolled": true
22 | },
23 | "outputs": [],
24 | "source": [
25 | "# 예제 4-10 크롤링 결과 중 해시태그 데이터 불러오기\n",
26 | "import pandas as pd\n",
27 | "raw_total = pd.read_excel('./files/3_1_crawling_raw.xlsx')\n",
28 | "raw_total['tags'] [:3]"
29 | ]
30 | },
31 | {
32 | "cell_type": "code",
33 | "execution_count": null,
34 | "metadata": {},
35 | "outputs": [],
36 | "source": [
37 | "# 예제 4-11 해시태그 통합 저장하기\n",
38 | "tags_total = []\n",
39 | "\n",
40 | "for tags in raw_total['tags']:\n",
41 | " tags_list = tags[2:-2].split(\"', '\")\n",
42 | " for tag in tags_list:\n",
43 | " tags_total.append(tag)"
44 | ]
45 | },
46 | {
47 | "cell_type": "markdown",
48 | "metadata": {},
49 | "source": [
50 | "### 4.2.3 해시태그 출현 빈도 집계"
51 | ]
52 | },
53 | {
54 | "cell_type": "code",
55 | "execution_count": null,
56 | "metadata": {},
57 | "outputs": [],
58 | "source": [
59 | "# 예제 4-12 빈도수 집계하기(Counter)\n",
60 | "from collections import Counter\n",
61 | "tag_counts = Counter(tags_total)"
62 | ]
63 | },
64 | {
65 | "cell_type": "code",
66 | "execution_count": null,
67 | "metadata": {
68 | "scrolled": true
69 | },
70 | "outputs": [],
71 | "source": [
72 | "# 예제 4-13 가장 많이 사용된 해시태그 살펴보기 \n",
73 | "tag_counts.most_common(50)"
74 | ]
75 | },
76 | {
77 | "cell_type": "code",
78 | "execution_count": null,
79 | "metadata": {},
80 | "outputs": [],
81 | "source": [
82 | "# 예제 4-14 데이터 정제하기\n",
83 | "STOPWORDS = ['#일상', '#선팔', '#제주도', '#jeju', '#반영구', '#제주자연눈썹',\n",
84 | "'#서귀포눈썹문신', '#제주눈썹문신', '#소통', '#맞팔']\n",
85 | "\n",
86 | "tag_total_selected = []\n",
87 | "for tag in tags_total:\n",
88 | " if tag not in STOPWORDS:\n",
89 | " tag_total_selected.append(tag)\n",
90 | " \n",
91 | "tag_counts_selected = Counter(tag_total_selected)\n",
92 | "tag_counts_selected.most_common(10)\n"
93 | ]
94 | },
95 | {
96 | "cell_type": "markdown",
97 | "metadata": {},
98 | "source": [
99 | "### 4.2.4 막대차트로 해시태그 살펴보기"
100 | ]
101 | },
102 | {
103 | "cell_type": "code",
104 | "execution_count": null,
105 | "metadata": {},
106 | "outputs": [],
107 | "source": [
108 | "# 예제 4-15 시각화 라이브러리 호출 및 환경 설정(한글 폰트)\n",
109 | "import matplotlib.pyplot as plt\n",
110 | "import seaborn as sns\n",
111 | "from matplotlib import font_manager, rc\n",
112 | "import sys\n",
113 | "\n",
114 | "if sys.platform in [\"win32\", \"win64\"]:\n",
115 | " font_name = \"malgun gothic\"\n",
116 | "elif sys.platform == \"darwin\":\n",
117 | " font_name = \"AppleGothic\"\n",
118 | "\n",
119 | "rc('font',family=font_name)\n"
120 | ]
121 | },
122 | {
123 | "cell_type": "code",
124 | "execution_count": null,
125 | "metadata": {},
126 | "outputs": [],
127 | "source": [
128 | "# 예제 4-16 데이터 준비하기\n",
129 | "tag_counts_df = pd.DataFrame(tag_counts_selected.most_common(30))\n",
130 | "tag_counts_df.columns = ['tags', 'counts']"
131 | ]
132 | },
133 | {
134 | "cell_type": "code",
135 | "execution_count": null,
136 | "metadata": {},
137 | "outputs": [],
138 | "source": [
139 | "# 예제 4-17 막대 차트 그리기\n",
140 | "plt.figure(figsize=(10,8)) \n",
141 | "sns.barplot(x='counts', y='tags', data = tag_counts_df)"
142 | ]
143 | },
144 | {
145 | "cell_type": "markdown",
146 | "metadata": {},
147 | "source": [
148 | "### 4.2.5 워드 클라우드 그리기"
149 | ]
150 | },
151 | {
152 | "cell_type": "code",
153 | "execution_count": null,
154 | "metadata": {},
155 | "outputs": [],
156 | "source": [
157 | "# 예제 4-18 워드클라우드 라이브러리 불러오기\n",
158 | "import matplotlib.pyplot as plt\n",
159 | "from wordcloud import WordCloud\n",
160 | "import platform\n",
161 | "\n",
162 | "if platform.system() == 'Windows': #윈도우의 경우\n",
163 | " font_path = \"c:/Windows/Fonts/malgun.ttf\"\n",
164 | "elif platform.system() == \"Darwin\": #Mac 의 경우\n",
165 | " font_path = \"/Users/$USER/Library/Fonts/AppleGothic.ttf\"\n"
166 | ]
167 | },
168 | {
169 | "cell_type": "code",
170 | "execution_count": null,
171 | "metadata": {},
172 | "outputs": [],
173 | "source": [
174 | "# 예제 4-19 워드클라우드 만들기\n",
175 | "wordcloud=WordCloud(font_path= font_path, \n",
176 | " background_color=\"white\",\n",
177 | " max_words=100,\n",
178 | " relative_scaling= 0.3,\n",
179 | " width = 800,\n",
180 | " height = 400\n",
181 | " ).generate_from_frequencies(tag_counts_selected) \n",
182 | "plt.figure(figsize=(15,10))\n",
183 | "plt.imshow(wordcloud)\n",
184 | "plt.axis('off')\n",
185 | "plt.savefig('./files/3_2_tag-wordcloud.png') "
186 | ]
187 | }
188 | ],
189 | "metadata": {
190 | "kernelspec": {
191 | "display_name": "Python 3",
192 | "language": "python",
193 | "name": "python3"
194 | },
195 | "language_info": {
196 | "codemirror_mode": {
197 | "name": "ipython",
198 | "version": 3
199 | },
200 | "file_extension": ".py",
201 | "mimetype": "text/x-python",
202 | "name": "python",
203 | "nbconvert_exporter": "python",
204 | "pygments_lexer": "ipython3",
205 | "version": "3.7.4"
206 | }
207 | },
208 | "nbformat": 4,
209 | "nbformat_minor": 2
210 | }
211 |
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/01_초판/5_Starbucks_Location/5_1_1_Crawling_Starbucks_List.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": null,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "# 예제 5-1 라이브러리 임포트\n",
10 | "from selenium import webdriver\n",
11 | "from bs4 import BeautifulSoup\n",
12 | "import pandas as pd"
13 | ]
14 | },
15 | {
16 | "cell_type": "code",
17 | "execution_count": null,
18 | "metadata": {},
19 | "outputs": [],
20 | "source": [
21 | "# 예제 5-2 webdriver 실행 후 스타벅스의 지역별 매장 검색 화면에 접속\n",
22 | "browser = webdriver.Chrome('c:/playwithdata/chromedriver.exe')\n",
23 | "url = 'https://www.istarbucks.co.kr/store/store_map.do?disp=locale'\n",
24 | "browser.get(url)"
25 | ]
26 | },
27 | {
28 | "cell_type": "code",
29 | "execution_count": null,
30 | "metadata": {},
31 | "outputs": [],
32 | "source": [
33 | "# 예제 5-3 webdriver로 ‘서울’ 버튼 요소를 찾아 클릭\n",
34 | "seoul_btn = '#container > div > form > fieldset > div > section > article.find_store_cont > article > article:nth-child(4) > div.loca_step1 > div.loca_step1_cont > ul > li:nth-child(1) > a'\n",
35 | "browser.find_element_by_css_selector(seoul_btn).click()"
36 | ]
37 | },
38 | {
39 | "cell_type": "code",
40 | "execution_count": null,
41 | "metadata": {},
42 | "outputs": [],
43 | "source": [
44 | "# 예제 5-4 webdriver로 ‘전체’ 버튼 요소를 찾아 클릭\n",
45 | "all_btn = '#mCSB_2_container > ul > li:nth-child(1) > a'\n",
46 | "browser.find_element_by_css_selector(all_btn).click()"
47 | ]
48 | },
49 | {
50 | "cell_type": "code",
51 | "execution_count": null,
52 | "metadata": {},
53 | "outputs": [],
54 | "source": [
55 | "# 예제 5-5 BeautifulSoup으로 HTML 파서 만들기\n",
56 | "html = browser.page_source\n",
57 | "soup = BeautifulSoup(html, 'html.parser')"
58 | ]
59 | },
60 | {
61 | "cell_type": "code",
62 | "execution_count": null,
63 | "metadata": {},
64 | "outputs": [],
65 | "source": [
66 | "# 예제 5-6 select()를 이용해 원하는 HTML 태그를 모두 찾아오기\n",
67 | "starbucks_soup_list = soup.select('li.quickResultLstCon')\n",
68 | "print(len(starbucks_soup_list))"
69 | ]
70 | },
71 | {
72 | "cell_type": "code",
73 | "execution_count": null,
74 | "metadata": {},
75 | "outputs": [],
76 | "source": [
77 | "# 예제 5-7 태그 문자열 살펴보기\n",
78 | "starbucks_soup_list[0]"
79 | ]
80 | },
81 | {
82 | "cell_type": "code",
83 | "execution_count": null,
84 | "metadata": {},
85 | "outputs": [],
86 | "source": [
87 | "# 예제 5-8 스타벅스 매장 정보 샘플 확인\n",
88 | "startbucks_store = starbucks_soup_list[0]\n",
89 | "name = startbucks_store.select('strong')[0].text.strip()\n",
90 | "lat = startbucks_store['data-lat'].strip()\n",
91 | "lng = startbucks_store['data-long'].strip()\n",
92 | "store_type = startbucks_store.select('i')[0]['class'][0][4:]\n",
93 | "address = str(startbucks_store.select('p.result_details')[0]).split('
')[0].split('>')[1]\n",
94 | "tel = str(startbucks_store.select('p.result_details')[0]).split('
')[1].split('<')[0]\n",
95 | "\n",
96 | "print(name) # 매장명\n",
97 | "print(lat) # 위도\n",
98 | "print(lng) # 경도\n",
99 | "print(store_type) # 매장 타입\n",
100 | "print(address) # 주소\n",
101 | "print(tel) # 전화번호"
102 | ]
103 | },
104 | {
105 | "cell_type": "code",
106 | "execution_count": null,
107 | "metadata": {},
108 | "outputs": [],
109 | "source": [
110 | "# 예제 5-9 서울시 스타벅스 매장 목록 데이터 만들기\n",
111 | "starbucks_list = []\n",
112 | "for item in starbucks_soup_list:\n",
113 | " name = item.select('strong')[0].text.strip();\n",
114 | " lat = item['data-lat'].strip()\n",
115 | " lng = item['data-long'].strip()\n",
116 | " store_type = item.select('i')[0]['class'][0][4:]\n",
117 | " address = str(item.select('p.result_details')[0]).split('
')[0].split('>')[1]\n",
118 | " tel = str(item.select('p.result_details')[0]).split('
')[1].split('<')[0]\n",
119 | " \n",
120 | " starbucks_list.append( [ name, lat, lng, store_type, address, tel])"
121 | ]
122 | },
123 | {
124 | "cell_type": "code",
125 | "execution_count": null,
126 | "metadata": {},
127 | "outputs": [],
128 | "source": [
129 | "# 예제 5-10 pandas의 데이터프레임 생성\n",
130 | "columns = ['매장명','위도','경도','매장타입', '주소','전화번호']\n",
131 | "seoul_starbucks_df = pd.DataFrame(starbucks_list, columns = columns)\n",
132 | "seoul_starbucks_df.head()"
133 | ]
134 | },
135 | {
136 | "cell_type": "code",
137 | "execution_count": null,
138 | "metadata": {},
139 | "outputs": [],
140 | "source": [
141 | "# 예제 5-11 데이터프레임의 요약 정보 확인\n",
142 | "seoul_starbucks_df.info()"
143 | ]
144 | },
145 | {
146 | "cell_type": "code",
147 | "execution_count": null,
148 | "metadata": {},
149 | "outputs": [],
150 | "source": [
151 | "# 예제 5-12 엑셀로 저장\n",
152 | "seoul_starbucks_df.to_excel('./files/4_1_seoul_starbucks_list.xlsx', index=False)"
153 | ]
154 | },
155 | {
156 | "cell_type": "code",
157 | "execution_count": null,
158 | "metadata": {},
159 | "outputs": [],
160 | "source": []
161 | }
162 | ],
163 | "metadata": {
164 | "kernelspec": {
165 | "display_name": "Python 3",
166 | "language": "python",
167 | "name": "python3"
168 | },
169 | "language_info": {
170 | "codemirror_mode": {
171 | "name": "ipython",
172 | "version": 3
173 | },
174 | "file_extension": ".py",
175 | "mimetype": "text/x-python",
176 | "name": "python",
177 | "nbconvert_exporter": "python",
178 | "pygments_lexer": "ipython3",
179 | "version": "3.6.7"
180 | }
181 | },
182 | "nbformat": 4,
183 | "nbformat_minor": 2
184 | }
185 |
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/01_초판/5_Starbucks_Location/5_2_1_Starbucks_Address.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": null,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "# 예제 5-39 서울시 스타벅스 매장 목록이 담긴 엑셀 파일 불러오기\n",
10 | "import pandas as pd\n",
11 | "seoul_starbucks = pd.read_excel('./files/4_1_seoul_starbucks_list.xlsx', header=0)\n",
12 | "seoul_starbucks.head()"
13 | ]
14 | },
15 | {
16 | "cell_type": "code",
17 | "execution_count": null,
18 | "metadata": {},
19 | "outputs": [],
20 | "source": [
21 | "# 예제 5-40 스타벅스 주소 정보에서 시군구명 추출\n",
22 | "sgg_names = []\n",
23 | "for address in seoul_starbucks['주소']:\n",
24 | " sgg = address.split()[1]\n",
25 | " sgg_names.append(sgg)\n",
26 | "seoul_starbucks['시군구명'] = sgg_names\n",
27 | "seoul_starbucks.head()"
28 | ]
29 | },
30 | {
31 | "cell_type": "code",
32 | "execution_count": null,
33 | "metadata": {},
34 | "outputs": [],
35 | "source": [
36 | "# 예제 5-41 엑셀로 저장\n",
37 | "seoul_starbucks.to_excel('./files/4_4_seoul_starbucks_list.xlsx', index=False)"
38 | ]
39 | }
40 | ],
41 | "metadata": {
42 | "kernelspec": {
43 | "display_name": "Python 3",
44 | "language": "python",
45 | "name": "python3"
46 | },
47 | "language_info": {
48 | "codemirror_mode": {
49 | "name": "ipython",
50 | "version": 3
51 | },
52 | "file_extension": ".py",
53 | "mimetype": "text/x-python",
54 | "name": "python",
55 | "nbconvert_exporter": "python",
56 | "pygments_lexer": "ipython3",
57 | "version": "3.6.7"
58 | }
59 | },
60 | "nbformat": 4,
61 | "nbformat_minor": 2
62 | }
63 |
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/01_초판/5_Starbucks_Location/5_2_2_Starbucks_Data.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": null,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "# 예제 5-42 라이브러리 임포트\n",
10 | "import pandas as pd"
11 | ]
12 | },
13 | {
14 | "cell_type": "code",
15 | "execution_count": null,
16 | "metadata": {},
17 | "outputs": [],
18 | "source": [
19 | "# 예제 5-43 시군구 목록 데이터 불러오기\n",
20 | "seoul_sgg = pd.read_excel('./files/4_2_seoul_sgg_list.xlsx')\n",
21 | "seoul_sgg.head()"
22 | ]
23 | },
24 | {
25 | "cell_type": "code",
26 | "execution_count": null,
27 | "metadata": {},
28 | "outputs": [],
29 | "source": [
30 | "# 예제 5-44 서울시 스타벅스 매장 목록 데이터 불러오기\n",
31 | "seoul_starbucks = pd.read_excel('./files/4_4_seoul_starbucks_list.xlsx')\n",
32 | "seoul_starbucks.head()"
33 | ]
34 | },
35 | {
36 | "cell_type": "code",
37 | "execution_count": null,
38 | "metadata": {},
39 | "outputs": [],
40 | "source": [
41 | "# 예제 5-45 시군구별 스타벅스 매장 수 세기\n",
42 | "starbucks_sgg_count = seoul_starbucks.pivot_table(\n",
43 | " index = '시군구명', \n",
44 | " values='매장명', \n",
45 | " aggfunc='count'\n",
46 | " ).rename(columns={'매장명':'스타벅스_매장수'})\n",
47 | "starbucks_sgg_count.head()"
48 | ]
49 | },
50 | {
51 | "cell_type": "code",
52 | "execution_count": null,
53 | "metadata": {},
54 | "outputs": [],
55 | "source": [
56 | "# 예제 5-46 서울시 시군구 목록 데이터에 스타벅스 매장 수 데이터를 병합\n",
57 | "seoul_sgg = pd.merge(seoul_sgg, starbucks_sgg_count, how='left', on='시군구명')\n",
58 | "seoul_sgg.head()"
59 | ]
60 | },
61 | {
62 | "cell_type": "code",
63 | "execution_count": null,
64 | "metadata": {},
65 | "outputs": [],
66 | "source": [
67 | "# 예제 5-47 서울시 시군구별 인구통계 데이터 불러오기\n",
68 | "seoul_sgg_pop = pd.read_excel('./files/4_3_sgg_pop.xlsx')\n",
69 | "seoul_sgg_pop.head()"
70 | ]
71 | },
72 | {
73 | "cell_type": "code",
74 | "execution_count": null,
75 | "metadata": {},
76 | "outputs": [],
77 | "source": [
78 | "# 예제 5-48 서울시 시군구 목록 데이터에 서울시 시군구별 인구통계 데이터를 병합\n",
79 | "seoul_sgg = pd.merge(seoul_sgg, seoul_sgg_pop, how='left', on='시군구명')\n",
80 | "seoul_sgg.head()"
81 | ]
82 | },
83 | {
84 | "cell_type": "code",
85 | "execution_count": null,
86 | "metadata": {},
87 | "outputs": [],
88 | "source": [
89 | "# 예제 5-49 서울시 시군구 목록 데이터에 서울시 시군구별 사업체 수 통계 데이터를 병합\n",
90 | "seoul_sgg_biz = pd.read_excel('./files/4_3_sgg_biz.xlsx')\n",
91 | "seoul_sgg = pd.merge(\n",
92 | " seoul_sgg, \n",
93 | " seoul_sgg_biz,\n",
94 | " how='left',\n",
95 | " on='시군구명'\n",
96 | ")\n",
97 | "seoul_sgg.head()"
98 | ]
99 | },
100 | {
101 | "cell_type": "code",
102 | "execution_count": null,
103 | "metadata": {},
104 | "outputs": [],
105 | "source": [
106 | "# 예제 5-50 병합 결과를 엑셀 파일로 저장\n",
107 | "seoul_sgg.to_excel('./files/4_5_seoul_sgg_stat.xlsx', index=False)"
108 | ]
109 | }
110 | ],
111 | "metadata": {
112 | "kernelspec": {
113 | "display_name": "Python 3",
114 | "language": "python",
115 | "name": "python3"
116 | },
117 | "language_info": {
118 | "codemirror_mode": {
119 | "name": "ipython",
120 | "version": 3
121 | },
122 | "file_extension": ".py",
123 | "mimetype": "text/x-python",
124 | "name": "python",
125 | "nbconvert_exporter": "python",
126 | "pygments_lexer": "ipython3",
127 | "version": "3.6.7"
128 | }
129 | },
130 | "nbformat": 4,
131 | "nbformat_minor": 2
132 | }
133 |
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/01_초판/5_Starbucks_Location/5_3_1_Starbucks_Map.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": null,
6 | "metadata": {},
7 | "outputs": [],
8 | "source": [
9 | "# 예제 5-51 라이브러리 임포트\n",
10 | "import pandas as pd\n",
11 | "import folium\n",
12 | "import json"
13 | ]
14 | },
15 | {
16 | "cell_type": "code",
17 | "execution_count": null,
18 | "metadata": {},
19 | "outputs": [],
20 | "source": [
21 | "# 예제 5-52 folium 설치\n",
22 | "! pip install folium"
23 | ]
24 | },
25 | {
26 | "cell_type": "code",
27 | "execution_count": null,
28 | "metadata": {},
29 | "outputs": [],
30 | "source": [
31 | "# 예제 5-53 서울시 스타벅스 매장 목록 데이터 불러오기\n",
32 | "seoul_starbucks = pd.read_excel('./files/4_4_seoul_starbucks_list.xlsx')\n",
33 | "seoul_starbucks.head()"
34 | ]
35 | },
36 | {
37 | "cell_type": "code",
38 | "execution_count": null,
39 | "metadata": {},
40 | "outputs": [],
41 | "source": [
42 | "# 예제 5-54 folium을 이용한 지도 생성\n",
43 | "starbucks_map = folium.Map(\n",
44 | " location=[37.573050, 126.979189],\n",
45 | " tiles='Stamen Terrain',\n",
46 | " zoom_start=11\n",
47 | ")\n",
48 | "starbucks_map"
49 | ]
50 | },
51 | {
52 | "cell_type": "code",
53 | "execution_count": null,
54 | "metadata": {},
55 | "outputs": [],
56 | "source": [
57 | "# 예제 5-55 지도에 스타벅스 매장 위치를 나타내는 서클 마커 그리기\n",
58 | "for idx in seoul_starbucks.index:\n",
59 | " lat = seoul_starbucks.loc[idx, '위도']\n",
60 | " lng = seoul_starbucks.loc[idx, '경도']\n",
61 | "\n",
62 | " folium.CircleMarker(\n",
63 | " location=[lat, lng],\n",
64 | " fill = True, \n",
65 | " fill_color='green', \n",
66 | " fill_opacity=1,\n",
67 | " color='yellow', \n",
68 | " weight=1,\n",
69 | " radius=3\n",
70 | " ).add_to(starbucks_map)\n",
71 | "\n",
72 | "starbucks_map"
73 | ]
74 | },
75 | {
76 | "cell_type": "code",
77 | "execution_count": null,
78 | "metadata": {},
79 | "outputs": [],
80 | "source": [
81 | "# 예제 5-56 스타벅스 매장 타입별 위치 서클 마커 그리기\n",
82 | "starbucks_map2 = folium.Map(\n",
83 | " location=[37.573050, 126.979189],\n",
84 | " tiles='Stamen Terrain',\n",
85 | " zoom_start=11\n",
86 | ")\n",
87 | "\n",
88 | "for idx in seoul_starbucks.index:\n",
89 | " lat = seoul_starbucks.loc[idx, '위도']\n",
90 | " lng = seoul_starbucks.loc[idx, '경도']\n",
91 | " store_type = seoul_starbucks.loc[idx, '매장타입']\n",
92 | " \n",
93 | " # 매장 타입별 색상 선택을 위한 조건문\n",
94 | " fillColor = ''\n",
95 | " if store_type == 'general':\n",
96 | " fillColor = 'gray'\n",
97 | " size = 1\n",
98 | " elif store_type == 'reserve':\n",
99 | " fillColor = 'blue'\n",
100 | " size = 5\n",
101 | " elif store_type == 'generalDT':\n",
102 | " fillColor = 'red'\n",
103 | " size = 5\n",
104 | "\n",
105 | " folium.CircleMarker(\n",
106 | " location=[lat, lng],\n",
107 | " color=fillColor,\n",
108 | " fill = True,\n",
109 | " fill_color = fillColor, \n",
110 | " fill_opacity = 1,\n",
111 | " weight = 1,\n",
112 | " radius = size\n",
113 | " ).add_to(starbucks_map2)\n",
114 | "\n",
115 | "starbucks_map2"
116 | ]
117 | }
118 | ],
119 | "metadata": {
120 | "kernelspec": {
121 | "display_name": "Python 3",
122 | "language": "python",
123 | "name": "python3"
124 | },
125 | "language_info": {
126 | "codemirror_mode": {
127 | "name": "ipython",
128 | "version": 3
129 | },
130 | "file_extension": ".py",
131 | "mimetype": "text/x-python",
132 | "name": "python",
133 | "nbconvert_exporter": "python",
134 | "pygments_lexer": "ipython3",
135 | "version": "3.6.7"
136 | }
137 | },
138 | "nbformat": 4,
139 | "nbformat_minor": 2
140 | }
141 |
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/01_초판/5_Starbucks_Location/5_3_3&4_Starbucks_Locations_Analysis.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "### 5.3.3 스타벅스 매장 수와 인구수 비교"
8 | ]
9 | },
10 | {
11 | "cell_type": "code",
12 | "execution_count": null,
13 | "metadata": {},
14 | "outputs": [],
15 | "source": [
16 | "# 예제 5-65 라이브러리 임포트\n",
17 | "import pandas as pd\n",
18 | "import json\n",
19 | "import folium"
20 | ]
21 | },
22 | {
23 | "cell_type": "code",
24 | "execution_count": null,
25 | "metadata": {},
26 | "outputs": [],
27 | "source": [
28 | "# 예제 5-66 서울시 시군구별 통계 데이터 불러오기\n",
29 | "seoul_sgg_stat = pd.read_excel('./files/4_5_seoul_sgg_stat.xlsx')\n",
30 | "seoul_sgg_stat.head()"
31 | ]
32 | },
33 | {
34 | "cell_type": "code",
35 | "execution_count": null,
36 | "metadata": {},
37 | "outputs": [],
38 | "source": [
39 | "# 예제 5-67 서울시 시군구 행정 경계 지도 파일 불러오기\n",
40 | "sgg_geojson_file_path = './maps/seoul_sgg.geojson'\n",
41 | "seoul_sgg_geo_2 = json.load(open(sgg_geojson_file_path, encoding='utf-8'))"
42 | ]
43 | },
44 | {
45 | "cell_type": "code",
46 | "execution_count": null,
47 | "metadata": {},
48 | "outputs": [],
49 | "source": [
50 | "# 예제 5-68 서울시 시군구별 주민등록인구수 단계구분도 지도 시각화\n",
51 | "starbucks_choropleth = folium.Map(\n",
52 | " location=[37.573050, 126.979189],\n",
53 | " tiles='CartoDB dark_matter',\n",
54 | " zoom_start=11\n",
55 | ")\n",
56 | "\n",
57 | "folium.Choropleth(\n",
58 | " geo_data=seoul_sgg_geo_2,\n",
59 | " data=seoul_sgg_stat,\n",
60 | " columns=['시군구명', '주민등록인구'],\n",
61 | " fill_color = 'YlGn',\n",
62 | " fill_opacity=0.7,\n",
63 | " line_opacity=0.5,\n",
64 | " key_on='properties.SIG_KOR_NM'\n",
65 | " ).add_to(starbucks_choropleth)\n",
66 | "\n",
67 | "starbucks_choropleth"
68 | ]
69 | },
70 | {
71 | "cell_type": "code",
72 | "execution_count": null,
73 | "metadata": {},
74 | "outputs": [],
75 | "source": [
76 | "# 예제 5-69 인구 만 명당 스타벅스 매장 수 칼럼 추가\n",
77 | "seoul_sgg_stat['만명당_매장수'] = seoul_sgg_stat['스타벅스_매장수']/(seoul_sgg_stat['주민등록인구']/10000)"
78 | ]
79 | },
80 | {
81 | "cell_type": "code",
82 | "execution_count": null,
83 | "metadata": {},
84 | "outputs": [],
85 | "source": [
86 | "# 예제 5-70 인구 만 명당 스타벅스 매장 수 지도 시각화\n",
87 | "SGG_GEOJSON_FILE_PATH = './maps/seoul_sgg.geojson'\n",
88 | "seoul_sgg_geo_1 = json.load(open(SGG_GEOJSON_FILE_PATH, encoding='utf-8'))\n",
89 | "\n",
90 | "viz_map_1 = folium.Map(\n",
91 | " location=[37.573050, 126.979189],\n",
92 | " tiles='CartoDB dark_matter',\n",
93 | " zoom_start=11\n",
94 | ")\n",
95 | "\n",
96 | "# 지도 스타일 지정 함수\n",
97 | "def style_function(feature):\n",
98 | " return {\n",
99 | " 'opacity': 0.7,\n",
100 | " 'weight': 1,\n",
101 | " 'fillOpacity':0,\n",
102 | " }\n",
103 | "\n",
104 | "folium.GeoJson(\n",
105 | " seoul_sgg_geo_2,\n",
106 | " style_function=style_function,\n",
107 | ").add_to(viz_map_1)\n",
108 | "# 만명당 매장수 기준 상위 10% 추출 값\n",
109 | "top = seoul_sgg_stat ['만명당_매장수'].quantile(0.9)\n",
110 | "for idx in seoul_sgg_stat.index:\n",
111 | " lat = seoul_sgg_stat.loc[idx, '위도']\n",
112 | " lng = seoul_sgg_stat.loc[idx, '경도']\n",
113 | " r = seoul_sgg_stat.loc[idx, '만명당_매장수']\n",
114 | " if r > top:\n",
115 | " fillColor = '#FF3300' # (Red)\n",
116 | " else:\n",
117 | " fillColor = '#CCFF33' # (Green)\n",
118 | " \n",
119 | " folium.CircleMarker(\n",
120 | " location=[lat, lng], \n",
121 | " color='#FFFF00', # (Yellow)\n",
122 | " fill_color=fillColor, \n",
123 | " fill_opacity=0.7,\n",
124 | " weight=1.5,\n",
125 | " radius= r * 10\n",
126 | " ).add_to(viz_map_1)\n",
127 | "\n",
128 | "viz_map_1"
129 | ]
130 | },
131 | {
132 | "cell_type": "markdown",
133 | "metadata": {},
134 | "source": [
135 | "### 5.3.4 스타벅스 매장 수와 사업체 수 비교"
136 | ]
137 | },
138 | {
139 | "cell_type": "code",
140 | "execution_count": null,
141 | "metadata": {},
142 | "outputs": [],
143 | "source": [
144 | "# 예제 5-71 신규 칼럼을 생성해 값 입력\n",
145 | "seoul_sgg_stat['종사자수_만명당_매장수'] = seoul_sgg_stat['스타벅스_매장수']/(seoul_sgg_stat['종사자수']/10000)\n",
146 | "seoul_sgg_stat.head()"
147 | ]
148 | },
149 | {
150 | "cell_type": "code",
151 | "execution_count": null,
152 | "metadata": {},
153 | "outputs": [],
154 | "source": [
155 | "# 예제 5-72 종사자 수 1만 명당 스타벅스 매장 수 시각화\n",
156 | "seoul_sgg_geo_1 = json.load(open(SGG_GEOJSON_FILE_PATH, encoding='utf-8'))\n",
157 | "\n",
158 | "viz_map_1 = folium.Map(\n",
159 | " location=[37.573050, 126.979189],\n",
160 | " tiles='CartoDB dark_matter',\n",
161 | " zoom_start=11\n",
162 | ")\n",
163 | "\n",
164 | "folium.GeoJson(\n",
165 | " seoul_sgg_geo_1,\n",
166 | " style_function=style_function,\n",
167 | ").add_to(viz_map_1)\n",
168 | "\n",
169 | "top = seoul_sgg_stat['종사자수_만명당_매장수'].quantile(0.9)\n",
170 | "for idx in seoul_sgg_stat.index:\n",
171 | " name = seoul_sgg_stat.at[idx, '시군구명']\n",
172 | " lat = seoul_sgg_stat.loc[idx, '위도']\n",
173 | " lng = seoul_sgg_stat.loc[idx, '경도']\n",
174 | " r = seoul_sgg_stat.loc[idx, '종사자수_만명당_매장수']\n",
175 | " \n",
176 | " if r > top:\n",
177 | " fillColor = '#FF3300'\n",
178 | " else:\n",
179 | " fillColor = '#CCFF33'\n",
180 | " \n",
181 | " folium.CircleMarker(\n",
182 | " location=[lat, lng], \n",
183 | " color='#FFFF00', \n",
184 | " fill_color=fillColor, \n",
185 | " fill_opacity=0.7,\n",
186 | " weight=1.5,\n",
187 | " radius= r * 10\n",
188 | " ).add_to(viz_map_1)\n",
189 | "\n",
190 | "viz_map_1"
191 | ]
192 | }
193 | ],
194 | "metadata": {
195 | "kernelspec": {
196 | "display_name": "Python 3",
197 | "language": "python",
198 | "name": "python3"
199 | },
200 | "language_info": {
201 | "codemirror_mode": {
202 | "name": "ipython",
203 | "version": 3
204 | },
205 | "file_extension": ".py",
206 | "mimetype": "text/x-python",
207 | "name": "python",
208 | "nbconvert_exporter": "python",
209 | "pygments_lexer": "ipython3",
210 | "version": "3.6.7"
211 | }
212 | },
213 | "nbformat": 4,
214 | "nbformat_minor": 2
215 | }
216 |
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/01_초판/5_Starbucks_Location/files/4_1_seoul_starbucks_list.xlsx:
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/02_개정판/3_Data_Analysis_Exercise/Chapter_3_1/3_1_1_MelOn_Crawling.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "## 3.1.1 멜론 크롤링 결과를 엑셀로 저장하기"
8 | ]
9 | },
10 | {
11 | "cell_type": "code",
12 | "execution_count": 1,
13 | "metadata": {},
14 | "outputs": [],
15 | "source": [
16 | "# 예제 3-1 멜론 사이트 접속하기 \n",
17 | "from selenium import webdriver\n",
18 | "from bs4 import BeautifulSoup\n",
19 | "\n",
20 | "driver = webdriver.Chrome('c:/playwithdata/chromedriver.exe')\n",
21 | "url = 'http://www.melon.com/chart/index.htm'\n",
22 | "driver.get(url) \n",
23 | "\n",
24 | "html = driver.page_source\n",
25 | "soup = BeautifulSoup(html, 'html.parser') "
26 | ]
27 | },
28 | {
29 | "cell_type": "code",
30 | "execution_count": 2,
31 | "metadata": {},
32 | "outputs": [],
33 | "source": [
34 | "# 예제 3-2 반복문을 이용해 곡과 가수명을 song_data에 저장하기 \n",
35 | "song_data = []\n",
36 | "rank = 1\n",
37 | "\n",
38 | "songs = soup.select('table > tbody > tr')\n",
39 | "for song in songs: \n",
40 | " title = song.select('div.rank01 > span > a')[0].text\n",
41 | " singer = song.select('div.rank02 > a')[0].text\n",
42 | " song_data.append(['Melon', rank, title, singer])\n",
43 | " rank = rank + 1"
44 | ]
45 | },
46 | {
47 | "cell_type": "code",
48 | "execution_count": 3,
49 | "metadata": {},
50 | "outputs": [
51 | {
52 | "data": {
53 | "text/html": [
54 | "\n",
55 | "\n",
68 | "
\n",
69 | " \n",
70 | " \n",
71 | " | \n",
72 | " 서비스 | \n",
73 | " 순위 | \n",
74 | " 타이틀 | \n",
75 | " 가수 | \n",
76 | "
\n",
77 | " \n",
78 | " \n",
79 | " \n",
80 | " | 0 | \n",
81 | " Melon | \n",
82 | " 1 | \n",
83 | " Dynamite | \n",
84 | " 방탄소년단 | \n",
85 | "
\n",
86 | " \n",
87 | " | 1 | \n",
88 | " Melon | \n",
89 | " 2 | \n",
90 | " DON'T TOUCH ME | \n",
91 | " 환불원정대 | \n",
92 | "
\n",
93 | " \n",
94 | " | 2 | \n",
95 | " Melon | \n",
96 | " 3 | \n",
97 | " Lovesick Girls | \n",
98 | " BLACKPINK | \n",
99 | "
\n",
100 | " \n",
101 | " | 3 | \n",
102 | " Melon | \n",
103 | " 4 | \n",
104 | " 힘든 건 사랑이 아니다 | \n",
105 | " 임창정 | \n",
106 | "
\n",
107 | " \n",
108 | " | 4 | \n",
109 | " Melon | \n",
110 | " 5 | \n",
111 | " 취기를 빌려 (취향저격 그녀 X 산들) | \n",
112 | " 산들 | \n",
113 | "
\n",
114 | " \n",
115 | "
\n",
116 | "
')[0].split('>')[1]\n",
110 | "tel = str(startbucks_store.select('p.result_details')[0]).split('
')[1].split('<')[0]\n",
111 | "\n",
112 | "print(name) # 매장명\n",
113 | "print(lat) # 위도\n",
114 | "print(lng) # 경도\n",
115 | "print(store_type) # 매장 타입\n",
116 | "print(address) # 주소\n",
117 | "print(tel) # 전화번호"
118 | ]
119 | },
120 | {
121 | "cell_type": "code",
122 | "execution_count": null,
123 | "metadata": {},
124 | "outputs": [],
125 | "source": [
126 | "# 예제 6-9 서울시 스타벅스 매장 목록 데이터 만들기\n",
127 | "starbucks_list = []\n",
128 | "for item in starbucks_soup_list:\n",
129 | " name = item.select('strong')[0].text.strip();\n",
130 | " lat = item['data-lat'].strip()\n",
131 | " lng = item['data-long'].strip()\n",
132 | " store_type = item.select('i')[0]['class'][0][4:]\n",
133 | " address = str(item.select('p.result_details')[0]).split('
')[0].split('>')[1]\n",
134 | " tel = str(item.select('p.result_details')[0]).split('
')[1].split('<')[0]\n",
135 | " \n",
136 | " starbucks_list.append( [ name, lat, lng, store_type, address, tel])"
137 | ]
138 | },
139 | {
140 | "cell_type": "code",
141 | "execution_count": null,
142 | "metadata": {},
143 | "outputs": [],
144 | "source": [
145 | "# 예제 6-10 pandas의 데이터프레임 생성\n",
146 | "columns = ['매장명','위도','경도','매장타입', '주소','전화번호']\n",
147 | "seoul_starbucks_df = pd.DataFrame(starbucks_list, columns = columns)\n",
148 | "seoul_starbucks_df.head()"
149 | ]
150 | },
151 | {
152 | "cell_type": "code",
153 | "execution_count": null,
154 | "metadata": {},
155 | "outputs": [],
156 | "source": [
157 | "# 예제 6-11 데이터프레임의 요약 정보 확인\n",
158 | "seoul_starbucks_df.info()"
159 | ]
160 | },
161 | {
162 | "cell_type": "code",
163 | "execution_count": null,
164 | "metadata": {},
165 | "outputs": [],
166 | "source": [
167 | "# 예제 6-12 엑셀로 저장\n",
168 | "seoul_starbucks_df.to_excel('./files/seoul_starbucks_list.xlsx', index=False)"
169 | ]
170 | }
171 | ],
172 | "metadata": {
173 | "kernelspec": {
174 | "display_name": "Python 3.8.3 ('base')",
175 | "language": "python",
176 | "name": "python3"
177 | },
178 | "language_info": {
179 | "codemirror_mode": {
180 | "name": "ipython",
181 | "version": 3
182 | },
183 | "file_extension": ".py",
184 | "mimetype": "text/x-python",
185 | "name": "python",
186 | "nbconvert_exporter": "python",
187 | "pygments_lexer": "ipython3",
188 | "version": "3.8.3"
189 | },
190 | "vscode": {
191 | "interpreter": {
192 | "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186"
193 | }
194 | }
195 | },
196 | "nbformat": 4,
197 | "nbformat_minor": 2
198 | }
199 |
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/02_개정판/6_Starbucks_Location/6_2_1_Starbucks_Address.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# 6.2 데이터 전처리\n",
8 | "## 6.2.1 서울시 스타벅스 매장 목록, 인구, 사업체 데이터에 시군구명, 시군구코드 추가"
9 | ]
10 | },
11 | {
12 | "cell_type": "code",
13 | "execution_count": null,
14 | "metadata": {},
15 | "outputs": [],
16 | "source": [
17 | "# 예제 6-39 서울시 스타벅스 매장 목록이 담긴 엑셀 파일 불러오기\n",
18 | "import pandas as pd\n",
19 | "seoul_starbucks = pd.read_excel('./files/seoul_starbucks_list.xlsx', header=0)\n",
20 | "seoul_starbucks.head()"
21 | ]
22 | },
23 | {
24 | "cell_type": "code",
25 | "execution_count": null,
26 | "metadata": {},
27 | "outputs": [],
28 | "source": [
29 | "# 예제 6-40 스타벅스 주소 정보에서 시군구명 추출\n",
30 | "sgg_names = []\n",
31 | "for address in seoul_starbucks['주소']:\n",
32 | " sgg = address.split()[1]\n",
33 | " sgg_names.append(sgg)\n",
34 | "seoul_starbucks['시군구명'] = sgg_names\n",
35 | "seoul_starbucks.head()"
36 | ]
37 | },
38 | {
39 | "cell_type": "code",
40 | "execution_count": null,
41 | "metadata": {},
42 | "outputs": [],
43 | "source": [
44 | "# 예제 6-41 엑셀로 저장\n",
45 | "seoul_starbucks.to_excel('./files/seoul_starbucks_list.xlsx', index=False)"
46 | ]
47 | },
48 | {
49 | "cell_type": "code",
50 | "execution_count": null,
51 | "metadata": {},
52 | "outputs": [],
53 | "source": []
54 | }
55 | ],
56 | "metadata": {
57 | "kernelspec": {
58 | "display_name": "Python 3",
59 | "language": "python",
60 | "name": "python3"
61 | },
62 | "language_info": {
63 | "codemirror_mode": {
64 | "name": "ipython",
65 | "version": 3
66 | },
67 | "file_extension": ".py",
68 | "mimetype": "text/x-python",
69 | "name": "python",
70 | "nbconvert_exporter": "python",
71 | "pygments_lexer": "ipython3",
72 | "version": "3.7.4"
73 | }
74 | },
75 | "nbformat": 4,
76 | "nbformat_minor": 2
77 | }
78 |
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/02_개정판/6_Starbucks_Location/6_2_2_Starbucks_Data.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "## 6.2.2 스타벅스 분석 데이터 만들기"
8 | ]
9 | },
10 | {
11 | "cell_type": "code",
12 | "execution_count": null,
13 | "metadata": {},
14 | "outputs": [],
15 | "source": [
16 | "# 예제 6-42 라이브러리 임포트\n",
17 | "import pandas as pd"
18 | ]
19 | },
20 | {
21 | "cell_type": "code",
22 | "execution_count": null,
23 | "metadata": {},
24 | "outputs": [],
25 | "source": [
26 | "# 예제 6-43 시군구 목록 데이터 불러오기\n",
27 | "seoul_sgg = pd.read_excel('./files/seoul_sgg_list.xlsx')\n",
28 | "seoul_sgg.head()"
29 | ]
30 | },
31 | {
32 | "cell_type": "code",
33 | "execution_count": null,
34 | "metadata": {},
35 | "outputs": [],
36 | "source": [
37 | "# 예제 6-44 서울시 스타벅스 매장 목록 데이터 불러오기\n",
38 | "seoul_starbucks = pd.read_excel('./files/seoul_starbucks_list.xlsx')\n",
39 | "seoul_starbucks.head()"
40 | ]
41 | },
42 | {
43 | "cell_type": "code",
44 | "execution_count": null,
45 | "metadata": {},
46 | "outputs": [],
47 | "source": [
48 | "# 예제 6-45 시군구별 스타벅스 매장 수 세기\n",
49 | "starbucks_sgg_count = seoul_starbucks.pivot_table(\n",
50 | " index = '시군구명', \n",
51 | " values='매장명', \n",
52 | " aggfunc='count'\n",
53 | " ).rename(columns={'매장명':'스타벅스_매장수'})\n",
54 | "starbucks_sgg_count.head()"
55 | ]
56 | },
57 | {
58 | "cell_type": "code",
59 | "execution_count": null,
60 | "metadata": {},
61 | "outputs": [],
62 | "source": [
63 | "# 예제 6-46 서울시 시군구 목록 데이터에 스타벅스 매장 수 데이터를 병합\n",
64 | "seoul_sgg = pd.merge(seoul_sgg, starbucks_sgg_count, how='left', on='시군구명')\n",
65 | "seoul_sgg.head()"
66 | ]
67 | },
68 | {
69 | "cell_type": "code",
70 | "execution_count": null,
71 | "metadata": {},
72 | "outputs": [],
73 | "source": [
74 | "# 예제 6-47 서울시 시군구별 인구통계 데이터 불러오기\n",
75 | "seoul_sgg_pop = pd.read_excel('./files/sgg_pop.xlsx')\n",
76 | "seoul_sgg_pop.head()"
77 | ]
78 | },
79 | {
80 | "cell_type": "code",
81 | "execution_count": null,
82 | "metadata": {},
83 | "outputs": [],
84 | "source": [
85 | "# 예제 6-48 서울시 시군구 목록 데이터에 서울시 시군구별 인구통계 데이터를 병합\n",
86 | "seoul_sgg = pd.merge(seoul_sgg, seoul_sgg_pop, how='left', on='시군구명')\n",
87 | "seoul_sgg.head()"
88 | ]
89 | },
90 | {
91 | "cell_type": "code",
92 | "execution_count": null,
93 | "metadata": {},
94 | "outputs": [],
95 | "source": [
96 | "# 예제 6-49 서울시 시군구 목록 데이터에 서울시 시군구별 사업체 수 통계 데이터를 병합\n",
97 | "seoul_sgg_biz = pd.read_excel('./files/sgg_biz.xlsx')\n",
98 | "seoul_sgg = pd.merge(\n",
99 | " seoul_sgg, \n",
100 | " seoul_sgg_biz,\n",
101 | " how='left',\n",
102 | " on='시군구명'\n",
103 | ")\n",
104 | "seoul_sgg.head()"
105 | ]
106 | },
107 | {
108 | "cell_type": "code",
109 | "execution_count": null,
110 | "metadata": {},
111 | "outputs": [],
112 | "source": [
113 | "# 예제 6-50 병합 결과를 엑셀 파일로 저장\n",
114 | "seoul_sgg.to_excel('./files/seoul_sgg_stat.xlsx', index=False)"
115 | ]
116 | }
117 | ],
118 | "metadata": {
119 | "kernelspec": {
120 | "display_name": "Python 3",
121 | "language": "python",
122 | "name": "python3"
123 | },
124 | "language_info": {
125 | "codemirror_mode": {
126 | "name": "ipython",
127 | "version": 3
128 | },
129 | "file_extension": ".py",
130 | "mimetype": "text/x-python",
131 | "name": "python",
132 | "nbconvert_exporter": "python",
133 | "pygments_lexer": "ipython3",
134 | "version": "3.7.4"
135 | }
136 | },
137 | "nbformat": 4,
138 | "nbformat_minor": 2
139 | }
140 |
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/02_개정판/6_Starbucks_Location/6_3_1_Starbucks_Map.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# 6.3 데이터 시각화\n",
8 | "## 6.3.1 스타벅스 매장분포 시각화"
9 | ]
10 | },
11 | {
12 | "cell_type": "code",
13 | "execution_count": null,
14 | "metadata": {},
15 | "outputs": [],
16 | "source": [
17 | "# 예제 6-51 라이브러리 임포트\n",
18 | "import pandas as pd\n",
19 | "import folium\n",
20 | "import json"
21 | ]
22 | },
23 | {
24 | "cell_type": "code",
25 | "execution_count": null,
26 | "metadata": {},
27 | "outputs": [],
28 | "source": [
29 | "# 예제 6-52 folium 설치\n",
30 | "! pip install folium"
31 | ]
32 | },
33 | {
34 | "cell_type": "code",
35 | "execution_count": null,
36 | "metadata": {},
37 | "outputs": [],
38 | "source": [
39 | "# 예제 6-53 서울시 스타벅스 매장 목록 데이터 불러오기\n",
40 | "seoul_starbucks = pd.read_excel('./files/seoul_starbucks_list.xlsx')\n",
41 | "seoul_starbucks.head()"
42 | ]
43 | },
44 | {
45 | "cell_type": "code",
46 | "execution_count": null,
47 | "metadata": {},
48 | "outputs": [],
49 | "source": [
50 | "# 예제 6-54 folium을 이용한 지도 생성\n",
51 | "starbucks_map = folium.Map(\n",
52 | " location=[37.573050, 126.979189],\n",
53 | " tiles='Stamen Terrain',\n",
54 | " zoom_start=11\n",
55 | ")\n",
56 | "starbucks_map"
57 | ]
58 | },
59 | {
60 | "cell_type": "code",
61 | "execution_count": null,
62 | "metadata": {},
63 | "outputs": [],
64 | "source": [
65 | "# 예제 6-55 지도에 스타벅스 매장 위치를 나타내는 서클 마커 그리기\n",
66 | "for idx in seoul_starbucks.index:\n",
67 | " lat = seoul_starbucks.loc[idx, '위도']\n",
68 | " lng = seoul_starbucks.loc[idx, '경도']\n",
69 | "\n",
70 | " folium.CircleMarker(\n",
71 | " location=[lat, lng],\n",
72 | " fill = True, \n",
73 | " fill_color='green', \n",
74 | " fill_opacity=1,\n",
75 | " color='yellow', \n",
76 | " weight=1,\n",
77 | " radius=3\n",
78 | " ).add_to(starbucks_map)\n",
79 | "\n",
80 | "starbucks_map"
81 | ]
82 | },
83 | {
84 | "cell_type": "code",
85 | "execution_count": null,
86 | "metadata": {},
87 | "outputs": [],
88 | "source": [
89 | "# 예제 6-56 스타벅스 매장 타입별 위치 서클 마커 그리기\n",
90 | "starbucks_map2 = folium.Map(\n",
91 | " location=[37.573050, 126.979189],\n",
92 | " tiles='Stamen Terrain',\n",
93 | " zoom_start=11\n",
94 | ")\n",
95 | "\n",
96 | "for idx in seoul_starbucks.index:\n",
97 | " lat = seoul_starbucks.loc[idx, '위도']\n",
98 | " lng = seoul_starbucks.loc[idx, '경도']\n",
99 | " store_type = seoul_starbucks.loc[idx, '매장타입']\n",
100 | " \n",
101 | " # 매장 타입별 색상 선택을 위한 조건문\n",
102 | " fillColor = ''\n",
103 | " if store_type == 'general':\n",
104 | " fillColor = 'gray'\n",
105 | " size = 1\n",
106 | " elif store_type == 'reserve':\n",
107 | " fillColor = 'blue'\n",
108 | " size = 5\n",
109 | " elif store_type == 'generalDT':\n",
110 | " fillColor = 'red'\n",
111 | " size = 5\n",
112 | "\n",
113 | " folium.CircleMarker(\n",
114 | " location=[lat, lng],\n",
115 | " color=fillColor,\n",
116 | " fill = True,\n",
117 | " fill_color = fillColor, \n",
118 | " fill_opacity = 1,\n",
119 | " weight = 1,\n",
120 | " radius = size\n",
121 | " ).add_to(starbucks_map2)\n",
122 | "\n",
123 | "starbucks_map2"
124 | ]
125 | }
126 | ],
127 | "metadata": {
128 | "kernelspec": {
129 | "display_name": "Python 3",
130 | "language": "python",
131 | "name": "python3"
132 | },
133 | "language_info": {
134 | "codemirror_mode": {
135 | "name": "ipython",
136 | "version": 3
137 | },
138 | "file_extension": ".py",
139 | "mimetype": "text/x-python",
140 | "name": "python",
141 | "nbconvert_exporter": "python",
142 | "pygments_lexer": "ipython3",
143 | "version": "3.7.4"
144 | }
145 | },
146 | "nbformat": 4,
147 | "nbformat_minor": 2
148 | }
149 |
--------------------------------------------------------------------------------
/02_개정판/6_Starbucks_Location/6_3_2_Starbucks_Location_Visualization.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "## 6.3.2 시군구별 스타벅스 매장 수 시각화"
8 | ]
9 | },
10 | {
11 | "cell_type": "code",
12 | "execution_count": null,
13 | "metadata": {},
14 | "outputs": [],
15 | "source": [
16 | "# 예제 6-57 라이브러리 임포트\n",
17 | "import pandas as pd\n",
18 | "import folium\n",
19 | "import json"
20 | ]
21 | },
22 | {
23 | "cell_type": "code",
24 | "execution_count": null,
25 | "metadata": {
26 | "scrolled": true
27 | },
28 | "outputs": [],
29 | "source": [
30 | "# 예제 6-58 서울시 시군구별 통계 데이터 불러오기\n",
31 | "seoul_sgg_stat = pd.read_excel('./files/seoul_sgg_stat.xlsx', thousands = ',')\n",
32 | "seoul_sgg_stat.head()"
33 | ]
34 | },
35 | {
36 | "cell_type": "code",
37 | "execution_count": null,
38 | "metadata": {},
39 | "outputs": [],
40 | "source": [
41 | "# 예제 6-59 서울시 시군구 행정 경계 지도 파일 불러오기\n",
42 | "sgg_geojson_file_path = './maps/seoul_sgg.geojson'\n",
43 | "seoul_sgg_geo = json.load(open(sgg_geojson_file_path, encoding='utf-8'))\n",
44 | "seoul_sgg_geo['features'][0]['properties']"
45 | ]
46 | },
47 | {
48 | "cell_type": "code",
49 | "execution_count": null,
50 | "metadata": {},
51 | "outputs": [],
52 | "source": [
53 | "# 예제 6-60 folium 지도 생성\n",
54 | "starbucks_bubble = folium.Map(\n",
55 | " location=[37.573050, 126.979189], tiles ='CartoDB positron' , zoom_start=11 )\n",
56 | "# tiles='CartoDB dark_matter',\n",
57 | "# tiles='CartoDB',\n",
58 | " \n",
59 | "# )"
60 | ]
61 | },
62 | {
63 | "cell_type": "code",
64 | "execution_count": null,
65 | "metadata": {},
66 | "outputs": [],
67 | "source": [
68 | "starbucks_bubble"
69 | ]
70 | },
71 | {
72 | "cell_type": "code",
73 | "execution_count": null,
74 | "metadata": {},
75 | "outputs": [],
76 | "source": [
77 | "# 예제 6-61 서울시 시군구 경계 지도 그리기\n",
78 | "def style_function(feature):\n",
79 | " return {\n",
80 | " 'opacity': 0.7,\n",
81 | " 'weight': 1,\n",
82 | " 'color': 'white',\n",
83 | " 'fillOpacity':0,\n",
84 | " 'dashArray': '5, 5',\n",
85 | " }\n",
86 | "\n",
87 | "folium.GeoJson(\n",
88 | " seoul_sgg_geo,\n",
89 | " style_function=style_function\n",
90 | ").add_to(starbucks_bubble)\n",
91 | "\n",
92 | "starbucks_bubble"
93 | ]
94 | },
95 | {
96 | "cell_type": "code",
97 | "execution_count": null,
98 | "metadata": {},
99 | "outputs": [],
100 | "source": [
101 | "# 예제 6-62 서울시 시군구별 스타벅스 평균 매장 수 계산\n",
102 | "starbucks_mean = seoul_sgg_stat['스타벅스_매장수'].mean()\n",
103 | "print(starbucks_mean)"
104 | ]
105 | },
106 | {
107 | "cell_type": "code",
108 | "execution_count": null,
109 | "metadata": {},
110 | "outputs": [],
111 | "source": [
112 | "# 예제 6-63 서울시 시군구별 스타벅스 매장 수를 버블 지도로 시각화\n",
113 | "for idx in seoul_sgg_stat.index:\n",
114 | " lat = seoul_sgg_stat.loc[idx, '위도']\n",
115 | " lng = seoul_sgg_stat.loc[idx, '경도']\n",
116 | " count = seoul_sgg_stat.loc[idx, '스타벅스_매장수']\n",
117 | "\n",
118 | " if count > starbucks_mean:\n",
119 | " fillColor = 'red'\n",
120 | " else:\n",
121 | " fillColor = 'blue'\n",
122 | " \n",
123 | " folium.CircleMarker(\n",
124 | " location=[lat, lng], \n",
125 | " color='#FFFF00',\n",
126 | " fill_color=fillColor, \n",
127 | " fill_opacity=0.7,\n",
128 | " weight=1.5,\n",
129 | " radius=count/2\n",
130 | " ).add_to(starbucks_bubble)\n",
131 | "\n",
132 | "starbucks_bubble"
133 | ]
134 | },
135 | {
136 | "cell_type": "code",
137 | "execution_count": null,
138 | "metadata": {
139 | "scrolled": false
140 | },
141 | "outputs": [],
142 | "source": [
143 | "# 예제 6-64 서울시 시군구별 스타벅스 매장 수를 단계구분도로 시각화\n",
144 | "sgg_geojson_file_path = './maps/seoul_sgg.geojson'\n",
145 | "seoul_sgg_geo_2 = json.load(open(sgg_geojson_file_path, encoding='utf-8'))\n",
146 | "starbucks_choropleth = folium.Map(\n",
147 | " location=[37.573050, 126.979189],\n",
148 | " tiles='CartoDB dark_matter',\n",
149 | " zoom_start=11\n",
150 | ")\n",
151 | "\n",
152 | "folium.Choropleth(\n",
153 | " geo_data=seoul_sgg_geo_2,\n",
154 | " data=seoul_sgg_stat,\n",
155 | " columns=['시군구명', '스타벅스_매장수'],\n",
156 | " fill_color = 'YlGn',\n",
157 | " fill_opacity=0.7,\n",
158 | " line_opacity=0.5,\n",
159 | " key_on='properties.SIG_KOR_NM'\n",
160 | " ).add_to(starbucks_choropleth)\n",
161 | "\n",
162 | "starbucks_choropleth"
163 | ]
164 | },
165 | {
166 | "cell_type": "code",
167 | "execution_count": null,
168 | "metadata": {},
169 | "outputs": [],
170 | "source": []
171 | }
172 | ],
173 | "metadata": {
174 | "kernelspec": {
175 | "display_name": "Python 3",
176 | "language": "python",
177 | "name": "python3"
178 | },
179 | "language_info": {
180 | "codemirror_mode": {
181 | "name": "ipython",
182 | "version": 3
183 | },
184 | "file_extension": ".py",
185 | "mimetype": "text/x-python",
186 | "name": "python",
187 | "nbconvert_exporter": "python",
188 | "pygments_lexer": "ipython3",
189 | "version": "3.7.4"
190 | }
191 | },
192 | "nbformat": 4,
193 | "nbformat_minor": 2
194 | }
195 |
--------------------------------------------------------------------------------
/02_개정판/6_Starbucks_Location/6_3_3&4_Starbucks_Locations_Analysis.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "## 6.3.3 스타벅스 매장 수와 인구수 비교"
8 | ]
9 | },
10 | {
11 | "cell_type": "code",
12 | "execution_count": null,
13 | "metadata": {},
14 | "outputs": [],
15 | "source": [
16 | "# 예제 5-65 라이브러리 임포트\n",
17 | "import pandas as pd\n",
18 | "import json\n",
19 | "import folium"
20 | ]
21 | },
22 | {
23 | "cell_type": "code",
24 | "execution_count": null,
25 | "metadata": {},
26 | "outputs": [],
27 | "source": [
28 | "# 예제 6-66 서울시 시군구별 통계 데이터 불러오기\n",
29 | "seoul_sgg_stat = pd.read_excel('./files/seoul_sgg_stat.xlsx', thousands = ',')\n",
30 | "seoul_sgg_stat.head()"
31 | ]
32 | },
33 | {
34 | "cell_type": "code",
35 | "execution_count": null,
36 | "metadata": {},
37 | "outputs": [],
38 | "source": [
39 | "# 예제 6-67 서울시 시군구 행정 경계 지도 파일 불러오기\n",
40 | "sgg_geojson_file_path = './maps/seoul_sgg.geojson'\n",
41 | "seoul_sgg_geo_2 = json.load(open(sgg_geojson_file_path, encoding='utf-8'))"
42 | ]
43 | },
44 | {
45 | "cell_type": "code",
46 | "execution_count": null,
47 | "metadata": {},
48 | "outputs": [],
49 | "source": [
50 | "# 예제 6-68 서울시 시군구별 주민등록인구수 단계구분도 지도 시각화\n",
51 | "starbucks_choropleth = folium.Map(\n",
52 | " location=[37.573050, 126.979189],\n",
53 | " tiles='CartoDB dark_matter',\n",
54 | " zoom_start=11\n",
55 | ")\n",
56 | "\n",
57 | "folium.Choropleth(\n",
58 | " geo_data=seoul_sgg_geo_2,\n",
59 | " data=seoul_sgg_stat,\n",
60 | " columns=['시군구명', '주민등록인구'],\n",
61 | " fill_color = 'YlGn',\n",
62 | " fill_opacity=0.7,\n",
63 | " line_opacity=0.5,\n",
64 | " key_on='properties.SIG_KOR_NM'\n",
65 | " ).add_to(starbucks_choropleth)\n",
66 | "\n",
67 | "starbucks_choropleth"
68 | ]
69 | },
70 | {
71 | "cell_type": "code",
72 | "execution_count": null,
73 | "metadata": {},
74 | "outputs": [],
75 | "source": [
76 | "# 예제 6-69 인구 만 명당 스타벅스 매장 수 칼럼 추가\n",
77 | "seoul_sgg_stat['만명당_매장수'] = seoul_sgg_stat['스타벅스_매장수']/(seoul_sgg_stat['주민등록인구']/10000)"
78 | ]
79 | },
80 | {
81 | "cell_type": "code",
82 | "execution_count": null,
83 | "metadata": {},
84 | "outputs": [],
85 | "source": [
86 | "# 예제 6-70 인구 만 명당 스타벅스 매장 수 지도 시각화\n",
87 | "SGG_GEOJSON_FILE_PATH = './maps/seoul_sgg.geojson'\n",
88 | "seoul_sgg_geo_1 = json.load(open(SGG_GEOJSON_FILE_PATH, encoding='utf-8'))\n",
89 | "\n",
90 | "viz_map_1 = folium.Map(\n",
91 | " location=[37.573050, 126.979189],\n",
92 | " tiles='CartoDB dark_matter',\n",
93 | " zoom_start=11\n",
94 | ")\n",
95 | "\n",
96 | "# 지도 스타일 지정 함수\n",
97 | "def style_function(feature):\n",
98 | " return {\n",
99 | " 'opacity': 0.7,\n",
100 | " 'weight': 1,\n",
101 | " 'fillOpacity':0,\n",
102 | " }\n",
103 | "\n",
104 | "folium.GeoJson(\n",
105 | " seoul_sgg_geo_2,\n",
106 | " style_function=style_function,\n",
107 | ").add_to(viz_map_1)\n",
108 | "# 만명당 매장수 기준 상위 10% 추출 값\n",
109 | "top = seoul_sgg_stat ['만명당_매장수'].quantile(0.9)\n",
110 | "for idx in seoul_sgg_stat.index:\n",
111 | " lat = seoul_sgg_stat.loc[idx, '위도']\n",
112 | " lng = seoul_sgg_stat.loc[idx, '경도']\n",
113 | " r = seoul_sgg_stat.loc[idx, '만명당_매장수']\n",
114 | " if r > top:\n",
115 | " fillColor = '#FF3300' # (Red)\n",
116 | " else:\n",
117 | " fillColor = '#CCFF33' # (Green)\n",
118 | " \n",
119 | " folium.CircleMarker(\n",
120 | " location=[lat, lng], \n",
121 | " color='#FFFF00', # (Yellow)\n",
122 | " fill_color=fillColor, \n",
123 | " fill_opacity=0.7,\n",
124 | " weight=1.5,\n",
125 | " radius= r * 10\n",
126 | " ).add_to(viz_map_1)\n",
127 | "\n",
128 | "viz_map_1"
129 | ]
130 | },
131 | {
132 | "cell_type": "markdown",
133 | "metadata": {},
134 | "source": [
135 | "## 6.3.4 스타벅스 매장 수와 사업체 수 비교"
136 | ]
137 | },
138 | {
139 | "cell_type": "code",
140 | "execution_count": null,
141 | "metadata": {},
142 | "outputs": [],
143 | "source": [
144 | "# 예제 6-71 신규 칼럼을 생성해 값 입력\n",
145 | "seoul_sgg_stat['종사자수_만명당_매장수'] = seoul_sgg_stat['스타벅스_매장수']/(seoul_sgg_stat['종사자수']/10000)\n",
146 | "seoul_sgg_stat.head()"
147 | ]
148 | },
149 | {
150 | "cell_type": "code",
151 | "execution_count": null,
152 | "metadata": {},
153 | "outputs": [],
154 | "source": [
155 | "# 예제 6-72 종사자 수 1만 명당 스타벅스 매장 수 시각화\n",
156 | "seoul_sgg_geo_1 = json.load(open(SGG_GEOJSON_FILE_PATH, encoding='utf-8'))\n",
157 | "\n",
158 | "viz_map_1 = folium.Map(\n",
159 | " location=[37.573050, 126.979189],\n",
160 | " tiles='CartoDB dark_matter',\n",
161 | " zoom_start=11\n",
162 | ")\n",
163 | "\n",
164 | "folium.GeoJson(\n",
165 | " seoul_sgg_geo_1,\n",
166 | " style_function=style_function,\n",
167 | ").add_to(viz_map_1)\n",
168 | "\n",
169 | "top = seoul_sgg_stat['종사자수_만명당_매장수'].quantile(0.9)\n",
170 | "for idx in seoul_sgg_stat.index:\n",
171 | " name = seoul_sgg_stat.at[idx, '시군구명']\n",
172 | " lat = seoul_sgg_stat.loc[idx, '위도']\n",
173 | " lng = seoul_sgg_stat.loc[idx, '경도']\n",
174 | " r = seoul_sgg_stat.loc[idx, '종사자수_만명당_매장수']\n",
175 | " \n",
176 | " if r > top:\n",
177 | " fillColor = '#FF3300'\n",
178 | " else:\n",
179 | " fillColor = '#CCFF33'\n",
180 | " \n",
181 | " folium.CircleMarker(\n",
182 | " location=[lat, lng], \n",
183 | " color='#FFFF00', \n",
184 | " fill_color=fillColor, \n",
185 | " fill_opacity=0.7,\n",
186 | " weight=1.5,\n",
187 | " radius= r * 10\n",
188 | " ).add_to(viz_map_1)\n",
189 | "\n",
190 | "viz_map_1"
191 | ]
192 | }
193 | ],
194 | "metadata": {
195 | "kernelspec": {
196 | "display_name": "Python 3",
197 | "language": "python",
198 | "name": "python3"
199 | },
200 | "language_info": {
201 | "codemirror_mode": {
202 | "name": "ipython",
203 | "version": 3
204 | },
205 | "file_extension": ".py",
206 | "mimetype": "text/x-python",
207 | "name": "python",
208 | "nbconvert_exporter": "python",
209 | "pygments_lexer": "ipython3",
210 | "version": "3.7.4"
211 | }
212 | },
213 | "nbformat": 4,
214 | "nbformat_minor": 2
215 | }
216 |
--------------------------------------------------------------------------------
/02_개정판/6_Starbucks_Location/files/report.txt:
--------------------------------------------------------------------------------
1 | 기간 자치구 세대 인구 인구 인구 인구 인구 인구 인구 인구 인구 세대당인구 65세이상고령자
2 | 기간 자치구 세대 합계 합계 합계 한국인 한국인 한국인 등록외국인 등록외국인 등록외국인 세대당인구 65세이상고령자
3 | 기간 자치구 세대 계 남자 여자 계 남자 여자 계 남자 여자 세대당인구 65세이상고령자
4 | 2020.3/4 합계 4,405,833 9,953,009 4,840,912 5,112,097 9,699,232 4,719,170 4,980,062 253,777 121,742 132,035 2.2 1,552,356
5 | 2020.3/4 종로구 74,861 159,842 77,391 82,451 149,952 73,024 76,928 9,890 4,367 5,523 2 28,396
6 | 2020.3/4 중구 63,594 135,321 66,193 69,128 125,800 61,526 64,274 9,521 4,667 4,854 1.98 24,265
7 | 2020.3/4 용산구 112,451 244,953 119,074 125,879 229,786 110,604 119,182 15,167 8,470 6,697 2.04 39,995
8 | 2020.3/4 성동구 136,096 302,695 147,582 155,113 295,591 144,444 151,147 7,104 3,138 3,966 2.17 45,372
9 | 2020.3/4 광진구 166,857 361,923 174,077 187,846 348,064 168,095 179,969 13,859 5,982 7,877 2.09 50,047
10 | 2020.3/4 동대문구 167,232 358,679 176,433 182,246 344,416 170,748 173,668 14,263 5,685 8,578 2.06 61,408
11 | 2020.3/4 중랑구 185,109 400,989 198,012 202,977 395,997 195,981 200,016 4,992 2,031 2,961 2.14 68,658
12 | 2020.3/4 성북구 195,379 449,871 216,155 233,716 439,719 212,151 227,568 10,152 4,004 6,148 2.25 73,370
13 | 2020.3/4 강북구 145,790 313,550 152,458 161,092 309,996 151,117 158,879 3,554 1,341 2,213 2.13 62,951
14 | 2020.3/4 도봉구 139,029 329,300 160,526 168,774 327,248 159,723 167,525 2,052 803 1,249 2.35 61,388
15 | 2020.3/4 노원구 218,002 529,532 255,618 273,914 525,486 253,797 271,689 4,046 1,821 2,225 2.41 84,612
16 | 2020.3/4 은평구 213,039 485,842 233,104 252,738 481,546 231,364 250,182 4,296 1,740 2,556 2.26 84,353
17 | 2020.3/4 서대문구 144,494 323,860 153,763 170,097 313,212 149,993 163,219 10,648 3,770 6,878 2.17 53,869
18 | 2020.3/4 마포구 178,495 383,494 179,899 203,595 373,508 176,011 197,497 9,986 3,888 6,098 2.09 54,053
19 | 2020.3/4 양천구 180,309 460,048 225,596 234,452 456,240 223,889 232,351 3,808 1,707 2,101 2.53 64,804
20 | 2020.3/4 강서구 266,366 589,536 284,747 304,789 583,544 281,951 301,593 5,992 2,796 3,196 2.19 88,052
21 | 2020.3/4 구로구 179,792 435,751 217,380 218,371 405,579 200,268 205,311 30,172 17,112 13,060 2.26 69,312
22 | 2020.3/4 금천구 113,887 249,641 127,292 122,349 232,157 117,489 114,668 17,484 9,803 7,681 2.04 39,361
23 | 2020.3/4 영등포구 182,087 405,982 202,624 203,358 375,630 185,800 189,830 30,352 16,824 13,528 2.06 60,826
24 | 2020.3/4 동작구 184,193 404,617 195,373 209,244 393,554 190,422 203,132 11,063 4,951 6,112 2.14 64,674
25 | 2020.3/4 관악구 275,248 514,555 257,638 256,917 498,574 250,084 248,490 15,981 7,554 8,427 1.81 78,206
26 | 2020.3/4 서초구 173,483 429,995 205,671 224,324 426,009 203,686 222,323 3,986 1,985 2,001 2.46 59,495
27 | 2020.3/4 강남구 234,021 544,085 260,358 283,727 539,235 257,961 281,274 4,850 2,397 2,453 2.3 73,942
28 | 2020.3/4 송파구 281,417 676,673 326,602 350,071 670,331 323,646 346,685 6,342 2,956 3,386 2.38 92,149
29 | 2020.3/4 강동구 194,602 462,275 227,346 234,929 458,058 225,396 232,662 4,217 1,950 2,267 2.35 68,798
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/README.md:
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1 | # 직장인을 위한 데이터 분석 실무 with 파이썬
2 | ## 마케팅, 영업, 기획 실무 담당자를 위한 데이터 분석의 기술
3 |
4 | 
5 | ‘데이터 분석은 좋은 질문에서 시작합니다’
6 |
7 | ### 책 보러 가기: https://wikibook.co.kr/pwdrev/
8 |
9 | 이 책에서는 누구나 궁금했던 그 질문에 대해 데이터로 답해 봅니다.
10 |
11 |
12 | 이 책은 파이썬을 처음 접하는 마케팅, 영업, 기획 실무 담당자들이 파이썬을 활용한 데이터 분석에 재미있게 빠져들 수 있도록 실제 업무에 활용할 수 있거나 흥미로운 예제로 구성돼 있습니다.
13 |
14 |
15 | 이 책을 마치고 나면 데이터를 기반으로 좋은 질문에 답할 수 있는 실력을 키울 수 있을 것입니다.
16 |
17 | ★ 이 책에서 다루는 예제 ★
18 |
19 | * 사드(THAAD) 배치의 영향으로 중국인 관광객이 얼마나 줄었을까?
20 | * 사회 이슈에 따른 외국인 관광객수 변화 분석
21 |
22 | * 가장 뜨는 제주도 핫플레이스는 어디일까?
23 | * 인스타그램으로 살펴보는 트렌드 분석
24 |
25 | * 왜 우리동네에는 스타벅스가 없을까?
26 | * 스타벅스 입지전략 분석
27 |
28 | * 어떤 무선청소기가 인기가 좋을까?
29 | * 다나와(가격비교 사이트)를 통한 무선청소기 브랜드별 제품 비교 분석
30 |
31 |
32 |
33 |
34 | ### `수정사항`
35 |
36 | - 2022.10.03
37 | - `추가 안내사항` : 크롤링 진행시 chromedriver.exe 파일을 매번 다운받기 귀찮으신 분들은 자동으로 버전 관리를 해주는 라이브러리 활용하시면 편하게 이용 가능합니다. [링크](https://blog.naver.com/kiddwannabe/222355619596)를 참고하세요 ( #chromedriver_autoinstaller )
38 |
39 |
40 | - 2022.10.02
41 | - 크롤링 selenium 명령어 변경에 따른 코드 변경[참고](https://blog.naver.com/kiddwannabe/222559420797)
42 | - 변경전 : find_elements_by_css_selector('태그정보')
43 | - `변경후` : find_elements( 'css selector', '태그정보' )
44 | - 인스타그램 사이트 개편에 따른 크롤링 코드 변경
45 | - tqdm 상태진행바 명령어 변경에 따른 코드 변경
46 | - 변경전 : from tqdm import tqdm_notebook
47 | - `변경후` : from tqdm.notebook import tqdm
48 |
49 | - 2022.05.19
50 | - 다나와 크롤링&데이터 전처리 수정
51 | - 수정부분 : 7-2 크롤링 부분, 7-3 데이터 전처리 부분
52 |
53 |
54 | - 2021.06.11
55 | - 인스타그램 로그인 페이지 오류 발생시 해결 방법 [링크 추가](https://www.notion.so/playwithdata/c02e510507504b42ae6073c1fbb46f29)
56 |
57 |
58 | - 2021.01.25
59 | - 브라우저 변수 명 변경 : browser → driver
60 | - 수정부분: 6-1 스타벅스 매장정보 크롤링 부분
61 |
62 | - 2021.01.07
63 | - 서울열린광장 인구데이터 API 서비스 종료에 따른 실습코드 변경
64 | - 수정부분: 6-1 인구데이터 자료 다운/정리 부분
65 | - 데이터 다운 및 정리 방법 정리 안내 페이지: http://bit.ly/pwd_seouldata_guide
66 |
67 |
68 | - 2020.11.6
69 | - 개정판 코드 업로드
70 |
71 | - 2020.08.20
72 | - 인스타그램 한글 자음/모음 분리현상이 생겨, 통합하는 코드 추가하였습니다.
73 | - 4-1 인스타그램 크롤링 부분
74 |
75 |
76 | - 2020.07.13
77 | - 멜론 사이트 개편에 따른 크롤링 코드 수정 진행하였습니다.
78 | - 수정부분: 2-2 멜론 크롤링부분
79 |
80 |
81 |
82 | - 2020.05.15
83 | - 인스타그램 크롤링 부분 코드 변경 및 안내사항 추가하였습니다. (다음 게시물 화살표 코드 변경, 오류시 대기 코드 추가, 2차인증 관련 안내사항 추가 등)
84 | - 수정부분 : 4-1 인스타그램 크롤링부분
85 |
86 |
87 |
88 | - 2020.05.07
89 | - 인스타그램이 로그인을 해야만 게시물을 볼 수 있도록 운영 정책이 변경되었습니다(2020.04.27)
90 |
91 | - 4장 인스타그램 크롤링 과정 중에 인스타그램 로그인 하는 부분을 추가하였습니다.
92 |
93 | - 수정부분 : 4-1 인스타그램 크롤링 부분
94 |
95 |
96 | - 2020.02.17
97 |
98 | - 네이버 지도 API 서비스 종료에 따라, 카카오 검색 API 활용으로 변경하였습니다.
99 |
100 | - 수정부분: 4-3 인스타 지도 시각화 부분
101 |
102 | - 카카오 API 활용 안내 가이드: http://bit.ly/pwd_kakaoAPI_guide
103 |
104 |
105 |
106 |
107 | ### PlaywithData 안내 ###
108 |
109 | - 워크샵/세미나 안내 페이지 : https://bit.ly/pwd_announce
110 | - 페이스북 그룹 : http://bit.ly/forPlaywithData
111 |
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