{"id":4070,"date":"2023-07-13T18:54:14","date_gmt":"2023-07-13T18:54:14","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e5%90%84%e8%a1%8c%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e6%a8%99%e6%ba%96%e5%81%8f%e5%b7%ae\/"},"modified":"2023-07-13T18:54:14","modified_gmt":"2023-07-13T18:54:14","slug":"%e5%90%84%e8%a1%8c%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e6%a8%99%e6%ba%96%e5%81%8f%e5%b7%ae","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e5%90%84%e8%a1%8c%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e6%a8%99%e6%ba%96%e5%81%8f%e5%b7%ae\/","title":{"rendered":"\u30d1\u30f3\u30c0: \u5404\u884c\u306e\u6a19\u6e96\u504f\u5dee\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

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\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u306e\u5404\u884c\u306e\u5024\u306e\u6a19\u6e96\u504f\u5dee\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

 df. std<\/span> (axis= 1<\/span> , numeric_only= True<\/span> )\n<\/strong><\/pre>\n
 axis=1<\/strong>\u5f15\u6570\u306f\u3001\u30d1\u30f3\u30c0\u306b (\u5404\u5217\u3067\u306f\u306a\u304f) \u5404\u884c\u306b\u5bfe\u3057\u3066\u8a08\u7b97\u3092\u5b9f\u884c\u3059\u308b\u3088\u3046\u306b\u6307\u793a\u3057\u3001 numeric_only=True \u306f\u3001<\/strong>\u8a08\u7b97\u3092\u5b9f\u884c\u3059\u308b\u3068\u304d\u306b\u6570\u5024\u5217\u306e\u307f\u3092\u8003\u616e\u3059\u308b\u3088\u3046\u306b\u30d1\u30f3\u30c0\u306b\u6307\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u69cb\u6587\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u4f8b: Pandas \u306e\u5404\u884c\u306e\u6a19\u6e96\u504f\u5dee\u3092\u8a08\u7b97\u3059\u308b<\/strong><\/span><\/h2>\n
4 \u3064\u306e\u7570\u306a\u308b\u8a66\u5408\u4e2d\u306b\u3055\u307e\u3056\u307e\u306a\u30d0\u30b9\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb\u9078\u624b\u304c\u7372\u5f97\u3057\u305f\u30dd\u30a4\u30f3\u30c8\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3080\u6b21\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' player<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n                   ' game1<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28],\n                   ' game2<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' game3<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n                   ' game4<\/span> ': [9, 8, 8, 9, 14, 15, 10, 11]})\n                   \n#view DataFrame\n<\/span>print<\/span> (df)\n\n  player game1 game2 game3 game4\n0 A 18 5 11 9\n1 B 22 7 8 8\n2 C 19 7 10 8\n3 D 14 9 6 9\n4 E 14 12 6 14\n5 F 11 9 5 15\n6 G 20 9 9 10\n7:28 4 12 11<\/strong><\/span><\/pre>\n
\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u5404\u30d7\u30ec\u30fc\u30e4\u30fc\u304c\u7372\u5f97\u3057\u305f\u30dd\u30a4\u30f3\u30c8\u306e\u6a19\u6e96\u504f\u5dee\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #calculate standard deviation for each row\n<\/span>df. std<\/span> (axis= 1<\/span> , numeric_only= True<\/span> )\n\n0 5.439056\n1 7.182154\n2 5.477226\n3 3.316625\n4 3.785939\n5 4.163332\n6 5.354126\n7 10.144785\ndtype:float64<\/span><\/span><\/strong><\/pre>\n
\u7d50\u679c\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n