{"id":35098,"date":"2019-12-07T18:45:38","date_gmt":"2019-12-07T09:45:38","guid":{"rendered":"https:\/\/blog.jetbrains.com\/jp\/?p=2404"},"modified":"2019-12-10T09:42:46","modified_gmt":"2019-12-10T00:42:46","slug":"2404","status":"publish","type":"post","link":"https:\/\/blog.jetbrains.com\/ja\/2019\/12\/07\/2404\/","title":{"rendered":"\u306f\u3058\u3081\u3066\u306ePyCharm \uff0b Jupyter Notebook\uff08\u305d\u306e\uff12\uff09"},"content":{"rendered":"\u3053\u3093\u306b\u3061\u306f\u3002JetBrains\u5800\u5ca1\u3067\u3059\u3002\r\n\r\n\u300c\u306f\u3058\u3081\u3066\u306ePyCharm \uff0b Jupyter Notebook\uff08\u305d\u306e\uff11\uff09\u300d\u306e\u7d9a\u304d\u3067\u3059\u3002\u4eca\u56de\u3082\u00a0Maria Khalusova\u306b\u3088\u308bPyCharm \u82f1\u8a9e\u30d6\u30ed\u30b0\u300cJupyter, PyCharm and Pizza\u300d\u306e\u8a18\u4e8b\u3092\u30d9\u30fc\u30b9\u306b\u5c11\u3057\u9055\u3063\u305f\u89b3\u70b9\u3067\u3001PyCharm\u3068Jupyter Notebook\u306e\u9023\u643a\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\r\n\r\n\u305d\u306e\uff11\u3067\u306f\u74b0\u5883\u306e\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\u3068\u57fa\u672c\u7684\u306a\u6a5f\u80fd\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3057\u305f\u3002\u4eca\u56de\u306f\u3001\u82f1\u8a9e\u30d6\u30ed\u30b0\u306b\u51fa\u3066\u304f\u308b\u30c1\u30e3\u30fc\u30c8\u3092\u4e00\u901a\u308a\u8868\u793a\u3067\u304d\u308b\u74b0\u5883\u3092\u69cb\u7bc9\u3057\u3001PyCharm\u4e0a\u3067\u5206\u6790\u3092\u4f53\u9a13\u3059\u308b\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\r\n\r\n\r\n\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\r\n\u30c6\u30b9\u30c8\u74b0\u5883\u306f\u305d\u306e\uff11\u3068\u540c\u3058\u3067\u3059\u3002\r\nGithub\u304b\u3089\u306e\u65b0\u898f\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u4f5c\u6210\r\n\u4eca\u56de\u306f\u3001PyCharm \u82f1\u8a9e\u30d6\u30ed\u30b0\u300cJupyter, PyCharm and Pizza\u300d\u306b\u51fa\u3066\u304f\u308b\u30c1\u30e3\u30fc\u30c8\u304c\u8868\u793a\u3067\u304d\u308b\u74b0\u5883\u3092\u5225PyCharm\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3068\u3057\u3066\u4f5c\u308a\u307e\u3059\u3002\u30d6\u30ed\u30b0\u3067\u4f7f\u7528\u3055\u308c\u305fnotebook\u306fGitHub\u304b\u3089\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u53ef\u80fd\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u306e\u3067\u3001GitHub\u4e0a\u306e\u30b3\u30fc\u30c9\u304b\u3089\u65b0\u3057\u3044PyCharm\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3092\u4f5c\u3063\u3066\u8a66\u3057\u307e\u3059\u3002\r\n\r\nPyCharm\u306e\u30b9\u30bf\u30fc\u30c8\u753b\u9762\u3067\u300cGet from Version Control\u300d\u3092\u9078\u629e\u3057\u307e\u3059\u3002\r\n\r\n\r\n\r\nGet from Version Control\u306e\u753b\u9762\u3067\u4ee5\u4e0b\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002\r\n\r\n\tURL:\u00a0https:\/\/github.com\/MKhalusova\/pizza.git\r\n\tDirectory: pizza\u3060\u3068\u79c1\u306e\u74b0\u5883\u3067\u306f\u524d\u56de\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u304b\u3076\u308b\u306e\u3067\u300c_mk\u300d\u3092\u8ffd\u52a0\u3057\u307e\u3057\u305f\u3002\r\n\r\n\r\n\r\n\u4eca\u56de\u306fPyCharm\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u4f5c\u6210\u6642\u306bPython interpreter\u304c\u6307\u5b9a\u3055\u308c\u3066\u3044\u306a\u3044\u306e\u3067\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u8b66\u544a\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002\u3053\u3053\u3067\u3001Configure Python interpreter\u00a0\u3092\u30af\u30ea\u30c3\u30af\u3057\u307e\u3059\u3002\r\n\r\n\r\n\r\n\u524d\u56de\u540c\u69d8New environment\u3092\u9078\u629e\u3057\u3001[OK]\u3092\u30af\u30ea\u30c3\u30af\u3057\u3001Conda\u4eee\u60f3\u74b0\u5883\u3092\u65b0\u898f\u4f5c\u6210\u3057\u307e\u3059\u3002\r\n\r\n\r\nPython\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\r\n\u524d\u56de\u540c\u69d8\u3001Jupyter\u7b49\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u306f\u5f53\u7136\u5fc5\u8981\u306b\u306a\u308a\u307e\u3059\u304c\u3001\u8a66\u3057\u3066\u307f\u305f\u3068\u3053\u308d\u3001notebook\u3067\u51fa\u529b\u3059\u308b\u30c1\u30e3\u30fc\u30c8\u3092\u8868\u793a\u3059\u308b\u305f\u3081\u306b\u306f\u3001\u82f1\u8a9e\u30d6\u30ed\u30b0\u3067\u306f\u8a73\u3057\u304f\u8a00\u53ca\u3055\u308c\u3066\u3044\u306a\u3044\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u304c\u5fc5\u8981\u3067\u3057\u305f\u3002\u300c\u30d1\u30c3\u30b1\u30fc\u30b8\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb&\u30a8\u30e9\u30fc\u30e1\u30c3\u30bb\u30fc\u30b8\u306e\u78ba\u8a8d\u300d\u306e\u7e70\u308a\u8fd4\u3057\u3092\u4f53\u9a13\u3057\u305f\u3044\u65b9\u306f\u3001\u4ee5\u4e0b\u3092\u30b9\u30ad\u30c3\u30d7\u3057\u3066\u3001\u305d\u306e\u307e\u307e\u304a\u8a66\u3057\u3044\u305f\u3060\u304d\u305f\u3044\u306e\u3067\u3059\u304c\u3001\u697d\u3092\u3057\u305f\u3044\u65b9\u306f\u00a0PyCharm\u306e\u753b\u9762\u4e0b\u90e8\u306eTerminal\u3067conda\u30b3\u30de\u30f3\u30c9\u3067\u4ee5\u4e0b\u5fc5\u8981\u306a\u3082\u306e\u3092\u307e\u3068\u3081\u3066\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3057\u3087\u3046\u3002\r\nconda install jupyter matplotlib pandas plotly psutil requests\r\nconda install -c plotly plotly-orca\r\nNotebook\u306e\u5b9f\u884c\r\n\u30d1\u30c3\u30b1\u30fc\u30b8\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3001PyCharm\u306eindexing\u51e6\u7406\u304c\u7d42\u308f\u3063\u305f\u306e\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002\u305d\u306e\u5f8c\u3001\u7de8\u96c6\u753b\u9762\u5de6\u4e0a\u306e[\u9ec4\u8272\u96fb\u7403\u30a2\u30a4\u30b3\u30f3] - [Clear Outputs]\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002\r\n\r\n\u300c\r\n\r\n\u6b21\u306b\u3001\uff08\u9ec4\u8272\u96fb\u7403\u30a2\u30a4\u30b3\u30f3\u306e\u96a3\u306e\uff09Run All\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002\r\n\r\n\r\n\r\n\u30a8\u30e9\u30fc\u306a\u304f\u5b9f\u884c\u3067\u304d\u3066Preview\u753b\u9762\u3092\u30b9\u30af\u30ed\u30fc\u30eb\u3057\u3066\u3044\u3063\u3066\u30c1\u30e3\u30fc\u30c8\u304c\u8868\u793a\u3055\u308c\u3066\u3044\u308b\u306e\u3092\u78ba\u8a8d\u3067\u304d\u308c\u3070\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\u6210\u529f\u3067\u3059🎉\u3002\u8f9b\u62b1\u5f37\u304f\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u3084\u308a\u305f\u304f\u306a\u3044\u65b9\u306f\u3001\u3053\u308c\u3067\u6e80\u8db3\u3067\u304d\u305f\u306e\u3067\u306f\u306a\u3044\u3067\u3057\u3087\u3046\u304b😄\r\n\r\n\r\n\u82f1\u8a9e\u30d6\u30ed\u30b0\u3067\u7d39\u4ecb\u3055\u308c\u3066\u3044\u305fNotebook\u306e\u7406\u89e3\r\n\u82f1\u8a9e\u30d6\u30ed\u30b0\u306e\u4e2d\u3067\u306f\u3001\u5b9f\u73fe\u3057\u305f\u3044\u3053\u3068\u3068\u3001\u30b3\u30fc\u30c9\u306e\u65ad\u7247\u304c\u7d39\u4ecb\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u30d6\u30ed\u30b0\u3067\u7d39\u4ecb\u3055\u308c\u3066\u3044\u308b\u5185\u5bb9\u3092\u65e5\u672c\u8a9e\u3067\u3056\u3063\u304f\u308a\u7406\u89e3\u3067\u304d\u308b\u3088\u3046\u3001Notebook\u30d5\u30a1\u30a4\u30eb\u306e\u30bd\u30fc\u30b9\u306b\u65e5\u672c\u8a9e\u3067\u30b3\u30e1\u30f3\u30c8\u3092\u8ffd\u52a0\u3057\u305f\u3082\u306e\u3092\u4ee5\u4e0b\u306b\u6e96\u5099\u3057\u307e\u3057\u305f\u3002\r\n\r\nNotebook\u306e\u30b3\u30fc\u30c9\u3092\u8a66\u3057\u305f\u3044\u65b9\u306f\u3001\r\n\r\n\t[\u9ec4\u8272\u96fb\u7403\u30a2\u30a4\u30b3\u30f3] - [Clear outputs]\r\n\t\u4e00\u756a\u6700\u521d\u306e\u30bb\u30eb\u304b\u3089\u30bb\u30eb\u3054\u3068\u306b\u5b9f\u884c(Shift+Enter\uff09\u3057\u306a\u304c\u3089\u3001Variable\u30c4\u30fc\u30eb\u30a6\u30a3\u30f3\u30c9\u30a6\u3092\u78ba\u8a8d\u3059\u308b\u3001\u30b3\u30fc\u30c9\u3092\u5c11\u3057\u5909\u66f4\u3057\u3066\u307f\u305f\u308a\u3001\u5fc5\u8981\u306b\u5fdc\u3058\u3066PyCharm\u306e\u30c7\u30d0\u30c3\u30ac\u3092\u4f7f\u3044\u3001\u52d5\u4f5c\u3092\u78ba\u8a8d\u3059\u308b\u3068PyCharm\u306e\u4f7f\u3044\u52dd\u624b\u3092\u78ba\u8a8d\u3067\u304d\u308b\u3067\u3057\u3087\u3046\u3002\r\n\r\n#%%\r\n\r\n#\u304a\u307e\u3058\u306a\u3044\r\n%matplotlib inline\r\n%config InlineBackend.figure_format = 'retina' \r\n\r\n#%%\r\n\r\nimport pandas as pd\r\ndf = pd.read_csv(\"..\/data\/Datafiniti_Pizza_Restaurants_and_the_Pizza_They_Sell_May19.csv\")\r\n\r\n#%%\r\n\r\n#\u30c6\u30fc\u30d6\u30eb\u30b5\u30a4\u30ba\u78ba\u8a8d\r\ndf.shape\r\n\r\n#%%\r\n\r\n#\u5217\u540d\u78ba\u8a8d\r\ndf.columns\r\n\r\n#%%\r\n\r\n#\u6700\u521d\u306e5\u884c\u53d6\u5f97\r\ndf.head(5)\r\n\r\n#%%\r\n\r\n#country \u306b\u3069\u306e\u3088\u3046\u306a\u5024\u304c\u3042\u308b\u304b\u78ba\u8a8d\r\ndf['country'].value_counts()\r\n\r\n#%%\r\n\r\n#\u5229\u7528\u3057\u306a\u3044\u30ab\u30e9\u30e0\u306e\u524a\u9664\r\ndf.drop(['country', 'keys', 'menuPageURL','menus.currency', 'priceRangeCurrency'],axis = 1, inplace = True)\r\ndf.shape\r\n\r\n#%%\r\n\r\n#\u30ab\u30e9\u30e0\u540d\u306e\u5909\u66f4\r\ndf.rename(columns={\"province\": \"state\"}, inplace = True)\r\n#\u5dde\u306e\u8868\u8a18\u3092\u5909\u66f4\u3059\u308b\u305f\u3081\u306e\u30de\u30c3\u30d4\u30f3\u30b0\u4f5c\u6210\r\nstates = {\"AL\":\"Alabama\", \"AK\":\"Alaska\", \"AZ\": \"Arizona\", \"AR\": \"Arkansas\", \"CA\": \"California\", \"CO\":\"Colorado\", \r\n          \"CT\": \"Connecticut\", \"DE\": \"Delaware\", \"FL\": \"Florida\", \"GA\": \"Georgia\", \"HI\": \"Hawaii\", \"ID\": \"Idaho\", \r\n          \"IL\": \"Illinois\", \"IN\": \"Indiana\", \"IA\": \"Iowa\", \"KS\": \"Kansas\", \"KY\": \"Kentucky\", \"LA\": \"Louisiana\", \r\n          \"ME\": \"Maine\", \"MD\": \"Maryland\", \"MA\": \"Massachusetts\", \"MI\": \"Michigan\", \"MN\": \"Minnesota\", \r\n          \"MS\": \"Mississippi\", \"MO\": \"Missouri\", \"MT\": \"Montana\", \"NE\": \"Nebraska\", \"NV\": \"Nevada\", \r\n          \"NH\": \"New Hampshire\", \"NJ\": \"New Jersey\", \"NM\": \"New Mexico\", \"NY\": \"New York\", \"NC\": \"North Carolina\",\r\n          \"ND\": \"North Dakota\", \"OH\": \"Ohio\", \"OK\": \"Oklahoma\", \"OR\": \"Oregon\", \"PA\": \"Pennsylvania\", \r\n          \"RI\": \"Rhode Island\", \"SC\": \"South Carolina\", \"SD\": \"South Dakota\", \"TN\": \"Tennessee\", \"TX\": \"Texas\", \r\n          \"UT\": \"Utah\", \"VT\": \"Vermont\", \"VA\": \"Virginia\", \"WA\": \"Washington\", \"WV\": \"West Virginia\", \r\n          \"WI\": \"Wisconsin\", \"WY\": \"Wyoming\"}\r\ndf['state'] = df['state'].map(states)\r\n\r\n\r\n#%%\r\n\r\n#\u30e1\u30cb\u30e5\u30fc\u540d\u306etop 10\u3092\u5186\u30b0\u30e9\u30d5\u3067\u8868\u793a\r\nax = df['menus.name'].value_counts().nlargest(10).plot.pie().set(xlabel='', ylabel='')\r\n\r\n\r\n#%%\r\n\r\n#\u30ec\u30b9\u30c8\u30e9\u30f3ID\u3067\u30b0\u30eb\u30fc\u30d7\u5316\u3057\u3066\u305d\u308c\u305e\u308c\u306e\u6700\u521d\u306e\u884c->\u30ec\u30b9\u30c8\u30e9\u30f3\u4e00\u89a7\r\nunique_restaurants = df.groupby('id').first()\r\n\r\n    \r\n#%%\r\n\r\n#\u30ec\u30b9\u30c8\u30e9\u30f3\u4e00\u89a7\u3092\u30de\u30c3\u30d7\u4e0a\u306b\u8868\u793a\r\nimport plotly.express as px\r\nimport plotly.io as pio\r\npio.renderers.default = 'svg'\r\n#\u30ec\u30b9\u30c8\u30e9\u30f3\u4e00\u89a7\u306eDataframe\u3068\u7def\u5ea6\u7d4c\u5ea6\u306b\u5229\u7528\u3059\u308b\u30ab\u30e9\u30e0\u540d\u3092\u6307\u5b9a\u3057\u3001\u5730\u56f3\u4e0a\u306e\u6563\u5e03\u56f3\u3092\u4f5c\u6210\r\nfig = px.scatter_geo(unique_restaurants, lat = 'latitude', lon = 'longitude', scope = 'usa', template = 'seaborn', opacity = 0.5)\r\nfig.show()\r\n\r\n#%%\r\n\r\n#\u30a2\u30e1\u30ea\u30ab\u306e\u5dde\u3054\u3068\u306e\u4eba\u53e3\u30c7\u30fc\u30bf from https:\/\/www.census.gov\/\r\npop = pd.read_csv(\"..\/data\/SCPRC-EST2018-18+POP-RES.csv\", usecols = ['NAME','POPESTIMATE2018'])\r\n#[\u5dde\u540d:\u4eba\u53e3]\u306e\u8f9e\u66f8\u3092\u4f5c\u6210\r\npopulation = pop.iloc[1:52].set_index('NAME').to_dict()['POPESTIMATE2018']\r\n\r\n\r\n#%%\r\n\r\n#per_cap = 1\/(\u5dde\u306e\u4eba\u53e3\/100000) = 100000\/\u5dde\u306e\u4eba\u53e3 \u306e\u30ab\u30e9\u30e0\u3092\u8ffd\u52a0\r\nunique_restaurants['per_cap'] = unique_restaurants.apply(lambda row: 100000\/population[row['state']], axis=1)\r\n# unique_restaurants.groupby('state')['per_cap'].sum() = \u5dde\u3054\u3068\u306e\u30ec\u30b9\u30c8\u30e9\u30f3\u6570*100000\/\u5dde\u306e\u4eba\u53e3 = \u5dde\u306b\u304a\u3051\u308b100000\u4eba\u3042\u305f\u308a\u306e\u30ec\u30b9\u30c8\u30e9\u30f3\u6570 \r\nax = unique_restaurants.groupby('state')['per_cap'].sum().nlargest(10).sort_values().plot.barh()\\\r\n     .set(xlabel='Number of pizza restaurants per capita (100 000 residents)', ylabel='')\r\n\r\n\r\n\r\n\r\n\u307e\u3068\u3081\r\nPyCharm \u82f1\u8a9e\u30d6\u30ed\u30b0\u300cJupyter, PyCharm and Pizza\u300d\u3067\u5229\u7528\u3057\u3066\u308bJupyter Notebook\u30d5\u30a1\u30a4\u30eb\u3092GitHub\u4e0a\u304b\u3089\u53d6\u5f97\u3057\u3001PyCharm\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u4f5c\u6210\u3057Notebook\u3092\u5b9f\u884c\u3059\u308b\u624b\u9806\u3092\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002PyCharm Professional(\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u306f\u3053\u3061\u3089)\u3092\u4f7f\u3048\u3070\u3001\u30d6\u30e9\u30a6\u30b6\u306b\u3088\u308bJupyter Notebook\u3068\u6b86\u3069\u5909\u308f\u308a\u306a\u304f\u30bb\u30eb\u306e\u5b9f\u884c\u304c\u53ef\u80fd\u3067\u3059\u3002\u52a0\u3048\u3066\u3001IDE\u306e\u5f37\u307f\u3092\u751f\u304b\u3057\u3066Python\u30b3\u30fc\u30c9\u306e\u8aad\u307f\u66f8\u304d\u3092\u3088\u308a\u751f\u7523\u7684\u306b\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u308b\u3053\u3068\u3092\u4f53\u9a13\u3057\u3066\u3044\u305f\u3060\u3051\u307e\u3057\u305f\u3089\u5e78\u3044\u3067\u3059\u3002\r\n\r\n\u30b3\u30e1\u30f3\u30c8\u3084\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u304c\u3042\u308c\u3070\u662f\u975e\u304a\u77e5\u3089\u305b\u4e0b\u3055\u3044\u3002","protected":false},"excerpt":{"rendered":"\u3053\u3093\u306b\u3061\u306f\u3002JetBrains\u5800\u5ca1\u3067\u3059\u3002\u300c\u306f\u3058\u3081\u3066\u306ePyCharm \uff0b Jupyter Notebook\uff08\u305d\u306e\uff11\uff09\u300d\u306e\u7d9a\u304d\u3067\u3059\u3002\u4eca\u56de\u3082\u00a0Maria Khalusova\u306b\u3088\u308bPyCharm \u82f1\u8a9e\u30d6\u30ed\u30b0\u300cJupyter, PyCharm and Pizza\u300d\u306e\u8a18\u4e8b\u3092\u30d9\u30fc\u30b9\u306b\u5c11\u3057\u9055\u3063\u305f\u89b3\u70b9\u3067\u3001PyCharm\u3068Jupyter Notebook\u306e\u9023\u643a\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\u305d\u306e\uff11\u3067\u306f\u74b0\u5883\u306e\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7","protected":false},"author":{"name":"Masaru Horioka","link":"https:\/\/blog.jetbrains.com\/ja\/author\/mhoriokajb"},"featured_media":35141,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[4115],"tags":[],"cross-post-tag":[],"acf":[],"featured_image":"https:\/\/blog.jetbrains.com\/wp-content\/uploads\/2019\/11\/jp-PyCharmJupyterBanner1.png","_links":{"self":[{"href":"https:\/\/blog.jetbrains.com\/ja\/wp-json\/wp\/v2\/posts\/35098"}],"collection":[{"href":"https:\/\/blog.jetbrains.com\/ja\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.jetbrains.com\/ja\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.jetbrains.com\/ja\/wp-json\/wp\/v2\/users\/875"},{"embeddable":true,"href":"https:\/\/blog.jetbrains.com\/ja\/wp-json\/wp\/v2\/users\/875"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.jetbrains.com\/ja\/wp-json\/wp\/v2\/comments?post=35098"}],"version-history":[{"count":0,"href":"https:\/\/blog.jetbrains.com\/ja\/wp-json\/wp\/v2\/posts\/35098\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.jetbrains.com\/ja\/wp-json\/wp\/v2\/media\/35141"}],"wp:attachment":[{"href":"https:\/\/blog.jetbrains.com\/ja\/wp-json\/wp\/v2\/media?parent=35098"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.jetbrains.com\/ja\/wp-json\/wp\/v2\/categories?post=35098"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.jetbrains.com\/ja\/wp-json\/wp\/v2\/tags?post=35098"},{"taxonomy":"cross-post-tag","embeddable":true,"href":"https:\/\/blog.jetbrains.com\/ja\/wp-json\/wp\/v2\/cross-post-tag?post=35098"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}