{"id":4293,"date":"2023-07-12T04:58:12","date_gmt":"2023-07-12T04:58:12","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%a6%e3%83%8b%e3%83%bc%e3%82%af%e3%81%aa%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%e3%83%8a%e3%83%b3%e3%82%92%e7%84%a1%e8%a6%96%e3%81%97%e3%81%be%e3%81%99\/"},"modified":"2023-07-12T04:58:12","modified_gmt":"2023-07-12T04:58:12","slug":"%e3%83%a6%e3%83%8b%e3%83%bc%e3%82%af%e3%81%aa%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%e3%83%8a%e3%83%b3%e3%82%92%e7%84%a1%e8%a6%96%e3%81%97%e3%81%be%e3%81%99","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%83%a6%e3%83%8b%e3%83%bc%e3%82%af%e3%81%aa%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%e3%83%8a%e3%83%b3%e3%82%92%e7%84%a1%e8%a6%96%e3%81%97%e3%81%be%e3%81%99\/","title":{"rendered":"\u30d1\u30f3\u30c0: \u4e00\u610f\u306e\u5024\u3092\u898b\u3064\u3051\u3066 nan \u3092\u7121\u8996\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u6b21\u306e\u30ab\u30b9\u30bf\u30e0\u95a2\u6570\u3092\u5b9a\u7fa9\u3057\u3066\u3001\u30d1\u30f3\u30c0\u3067\u4e00\u610f\u306e\u5024\u3092\u691c\u7d22\u3057\u3001NaN \u5024\u3092\u7121\u8996\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

 def<\/span> unique_no_nan(x):\n    return<\/span> x. dropna<\/span> (). single<\/span> ()\n<\/strong><\/pre>\n
\u3053\u306e\u95a2\u6570\u306f\u3001NaN \u5024\u3092\u9664\u304f\u3059\u3079\u3066\u306e\u4e00\u610f\u306e\u5024\u3092\u542b\u3080 pandas \u30b7\u30ea\u30fc\u30ba\u3092\u8fd4\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306e\u4f8b\u306f\u3001\u6b21\u306e pandas DataFrame \u3092\u4f7f\u7528\u3057\u305f\u3055\u307e\u3056\u307e\u306a\u30b7\u30ca\u30ea\u30aa\u3067\u3053\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Mavs', 'Mavs', 'Celtics', 'Celtics', 'Celtics'],\n                   ' points<\/span> ': [95, 95, 100, 113, 100, np.nan]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n      team points\n0 Mavs 95.0\n1 Mavs 95.0\n2 Mavs 100.0\n3 Celtics 113.0\n4 Celtics 100.0\n5 Celtics NaN\n<\/strong><\/pre>\n
\u4f8b 1: Pandas \u5217\u3067\u4e00\u610f\u306e\u5024\u3092\u691c\u7d22\u3057\u3001NaN \u5024\u3092\u7121\u8996\u3059\u308b<\/strong><\/span><\/h2>\n
pandas unique()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001DataFrame \u306e\u30dd\u30a4\u30f3\u30c8<\/strong>\u5217\u306b\u3059\u3079\u3066\u306e\u4e00\u610f\u306e\u5024\u3092\u8868\u793a\u3059\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #display unique values in 'points' column\n<\/span>df[' points<\/span> ']. single<\/span> ()\n\narray([ 95., 100., 113., no])\n<\/strong><\/pre>\n
 unique()<\/strong>\u95a2\u6570\u306e\u7d50\u679c\u306b\u306f\u30c7\u30d5\u30a9\u30eb\u30c8\u3067nan<\/strong>\u304c\u542b\u307e\u308c\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
\u305f\u3060\u3057\u3001\u4ee3\u308f\u308a\u306b\u30ab\u30b9\u30bf\u30e0unique_no_nan()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30dd\u30a4\u30f3\u30c8<\/strong>\u5217\u306b\u4e00\u610f\u306e\u5024\u3092\u8868\u793a\u3059\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #display unique values in 'points' column and ignore NaN\n<\/span>unique_no_nan(df[' points<\/span> '])\n\narray([ 95., 100., 113.])\n<\/strong><\/pre>\n
\u3053\u306e\u95a2\u6570\u306f\u3001NaN \u3092\u9664\u304f\u3001\u30dd\u30a4\u30f3\u30c8<\/strong>\u5217\u5185\u306e\u3059\u3079\u3066\u306e\u4e00\u610f\u306e\u5024\u3092\u8fd4\u3057\u307e\u3059\u3002<\/span><\/span><\/p>\n
\u4f8b 2: Pandas Groupby \u3067\u4e00\u610f\u306e\u5024\u3092\u691c\u7d22\u3057\u3001NaN \u5024\u3092\u7121\u8996\u3059\u308b<\/strong><\/span><\/h2>\n
pandas \u306egroupby() \u95a2\u6570<\/strong>\u3068agg()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c1\u30fc\u30e0<\/strong>\u5217\u3054\u3068\u306b\u30b0\u30eb\u30fc\u30d7\u5316\u3055\u308c\u305f\u30dd\u30a4\u30f3\u30c8<\/strong>\u5217\u306e\u3059\u3079\u3066\u306e\u4e00\u610f\u306e\u5024\u3092\u8868\u793a\u3059\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #display unique values in 'points' column grouped by team\n<\/span>df. groupby<\/span> (' team<\/span> ')[' points<\/span> ']. agg<\/span> ([' single<\/span> '])\n\n\tunique\nteam\t\nCeltics [113.0, 100.0, nah]\nMavs [95.0, 100.0]\n<\/strong><\/pre>\n
 unique()<\/strong>\u95a2\u6570\u306e\u7d50\u679c\u306b\u306f\u30c7\u30d5\u30a9\u30eb\u30c8\u3067nan<\/strong>\u304c\u542b\u307e\u308c\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
\u305f\u3060\u3057\u3001\u4ee3\u308f\u308a\u306b\u30ab\u30b9\u30bf\u30e0unique_no_nan()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c1\u30fc\u30e0<\/strong>\u5217\u3054\u3068\u306b\u30b0\u30eb\u30fc\u30d7\u5316\u3055\u308c\u305f\u30dd\u30a4\u30f3\u30c8<\/strong>\u5217\u306e\u4e00\u610f\u306e\u5024\u3092\u8868\u793a\u3059\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #display unique values in 'points' column grouped by team and ignore NaN\n<\/span>df. groupby<\/span> (' team<\/span> ')[' points<\/span> ']. apply<\/span> ( lambda<\/span> x: unique_no_nan(x))\n\nteam\nCeltics [113.0, 100.0]\nMavs [95.0, 100.0]\nName: points, dtype: object<\/strong><\/pre>\n
\u3053\u306e\u95a2\u6570\u306f\u3001NaN \u5024\u3092\u30ab\u30a6\u30f3\u30c8\u305b\u305a\u306b\u3001\u5404\u30c1\u30fc\u30e0<\/strong>\u306e\u30dd\u30a4\u30f3\u30c8<\/strong>\u5217\u306e\u3059\u3079\u3066\u306e\u4e00\u610f\u306e\u5024\u3092\u8fd4\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30d1\u30f3\u30c0\u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u6a5f\u80fd\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u30d1\u30f3\u30c0: DataFrame \u3067\u5358\u4e00\u884c\u3092\u9078\u629e\u3059\u308b\u65b9\u6cd5<\/a>
Pandas: \u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u5217\u304b\u3089\u4e00\u610f\u306e\u5024\u3092\u53d6\u5f97\u3059\u308b\u65b9\u6cd5<\/a>
\u30d1\u30f3\u30c0: 2 \u3064\u306e\u5217\u306e\u4e00\u610f\u306e\u7d44\u307f\u5408\u308f\u305b\u3092\u6570\u3048\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u6b21\u306e\u30ab\u30b9\u30bf\u30e0\u95a2\u6570\u3092\u5b9a\u7fa9\u3057\u3066\u3001\u30d1\u30f3\u30c0\u3067\u4e00\u610f\u306e\u5024\u3092\u691c\u7d22\u3057\u3001NaN \u5024\u3092\u7121\u8996\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002 def unique_no_nan(x): return x. dropna (). single () \u3053\u306e\u95a2\u6570\u306f\u3001NaN \u5024 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-4293","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\n\u30d1\u30f3\u30c0: \u4e00\u610f\u306e\u5024\u3092\u898b\u3064\u3051\u3066 NaN \u3092\u7121\u8996\u3059\u308b\u65b9\u6cd5 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30d1\u30f3\u30c0\u3067\u4e00\u610f\u306e\u5024\u3092\u691c\u7d22\u3057\u3001NaN \u5024\u3092\u7121\u8996\u3059\u308b\u65b9\u6cd5\u3092\u3044\u304f\u3064\u304b\u306e\u4f8b\u3068\u3068\u3082\u306b\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/ja\/\u30e6\u30cb\u30fc\u30af\u306a\u30cf\u309a\u30f3\u30bf\u3099\u306f\u30ca\u30f3\u3092\u7121\u8996\u3057\u307e\u3059\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u30d1\u30f3\u30c0: \u4e00\u610f\u306e\u5024\u3092\u898b\u3064\u3051\u3066 NaN \u3092\u7121\u8996\u3059\u308b\u65b9\u6cd5 \u2013 Statorials\" \/>\n<meta property=\"og:description\" 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