{"id":75477,"date":"2018-10-09T19:25:59","date_gmt":"2018-10-09T10:25:59","guid":{"rendered":"https:\/\/www.sejuku.net\/blog\/?p=75477"},"modified":"2024-05-06T11:52:44","modified_gmt":"2024-05-06T02:52:44","slug":"pandas-dataframe%e3%81%ae%e8%a6%81%e7%b4%a0%e3%82%92%e3%82%b0%e3%83%ab%e3%83%bc%e3%83%94%e3%83%b3%e3%82%b0%e3%81%99%e3%82%8bgroupby%e3%82%92%e4%b8%81%e5%af%a7%e3%81%ab%e8%a7%a3%e8%aa%ac%ef%bc%81","status":"publish","type":"post","link":"https:\/\/www.sejuku.net\/blog\/75477","title":{"rendered":"Pandas.DataFrame\u306e\u8981\u7d20\u3092\u30b0\u30eb\u30fc\u30d4\u30f3\u30b0\u3059\u308bGroupBy\u3092\u4e01\u5be7\u306b\u89e3\u8aac\uff01"},"content":{"rendered":"

\u3053\u3093\u306b\u3061\u306f\uff01\u30a4\u30f3\u30b9\u30c8\u30e9\u30af\u30bf\u30fc\u306e\u30d5\u30af\u30ed\u30a6<\/a>\u3067\u3059\uff01\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001pandas.DataFrame\u306e\u8981\u7d20\u3092\u30b0\u30eb\u30fc\u30d4\u30f3\u30b0\u3059\u308b\u4e8b\u304c\u3067\u304d\u308bGroupBy\u3092\u89e3\u8aac\u3057\u307e\u3059\u3002<\/p>\n

\u3042\u308bDataFrame\u3092\u3001\u4efb\u610f\u306e\u6761\u4ef6\u3067\u3044\u304f\u3064\u304b\u306e\u5c0f\u3055\u306aDataFrame\u306b\u5206\u5272\u3057\u3066\u305d\u308c\u305e\u308c\u306b\u5bfe\u3057\u3066\u6f14\u7b97\u3057\u305f\u3044\u3001\u3068\u3044\u3046\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u3093\u306a\u6642\u3001GroupBy\u3092\u4f7f\u3046\u3068\u7c21\u5358\u306b\u305d\u308c\u304c\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3088\uff01<\/p>\n

\u958b\u767a\u74b0\u5883<\/h2>\n

\u3053\u306e\u8a18\u4e8b\u3092\u66f8\u304f\u305f\u3081\u306b\u4f7f\u3063\u305f\u958b\u767a\u74b0\u5883\u306f\u4ee5\u4e0b\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/p>\n

\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u306a\u3069<\/p>\n
    \n
  • OS=”Ubuntu 18.04.1 LTS”<\/li>\n
  • Python 3.7.0<\/li>\n
  • Pandas 0.23.4<\/li>\n<\/ul>\n<\/div>\n

    OS\u304c\u7570\u306a\u3063\u3066\u3082\u57fa\u672c\u7684\u306b\u306f\u540c\u3058\u64cd\u4f5c<\/b>\u3067\u8a18\u4e8b\u4e2d\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u306f\u518d\u73fe\u3067\u304d\u308b\u306e\u3067\u3001\u8a66\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\uff01<\/p>\n

    groupby\u306e\u4f7f\u3044\u65b9<\/h2>\n
    \n
    \n
    \n

    [\u524d\u6e96\u5099]DataFrame\u306e\u4f5c\u6210<\/h3>\n

    \u307e\u305a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092import\u3057\u3066\u304a\u304d\u307e\u3057\u3087\u3046\u3002\u305d\u3057\u3066\u6a5f\u68b0\u5b66\u7fd2\u30e9\u30a4\u30d6\u30e9\u30easklearn\u304b\u3089iris datasets\u3092\u8aad\u307f\u8fbc\u3093\u3067\u3001DataFrame\u3092\u4f5c\u308a\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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    In [1]:<\/div>\n
    \n
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    \n
    import pandas as pd\nimport numpy as np\nfrom sklearn import datasets\n\n# sklearn\u304b\u3089\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u307f\u307e\u3059\niris = datasets.load_iris()\n\n# \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089DataFrame\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002\u3053\u3053\u3067\u306f\u5217\u304c\u7279\u5fb4\u306b\u306a\u3063\u3066\u3044\u3066\u3001\u30af\u30e9\u30b9\u3092label\u5217\u3068\u3057\u3066\u8ffd\u52a0\u3057\u3066\u304a\u304d\u307e\u3059\u3002\ndf = pd.DataFrame(iris.data, columns=iris.feature_names)\ndf[\"label\"] = [iris.target_names[i] for i in iris.target]\ndf.head()<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
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    Out[1]:<\/div>\n
    \n
    \n\n\n\n\n\n\n\n\n\n
    <\/th>\nsepal length (cm)<\/th>\nsepal width (cm)<\/th>\npetal length (cm)<\/th>\npetal width (cm)<\/th>\nlabel<\/th>\n<\/tr>\n<\/thead>\n
    0<\/th>\n5.1<\/td>\n3.5<\/td>\n1.4<\/td>\n0.2<\/td>\nsetosa<\/td>\n<\/tr>\n
    1<\/th>\n4.9<\/td>\n3.0<\/td>\n1.4<\/td>\n0.2<\/td>\nsetosa<\/td>\n<\/tr>\n
    2<\/th>\n4.7<\/td>\n3.2<\/td>\n1.3<\/td>\n0.2<\/td>\nsetosa<\/td>\n<\/tr>\n
    3<\/th>\n4.6<\/td>\n3.1<\/td>\n1.5<\/td>\n0.2<\/td>\nsetosa<\/td>\n<\/tr>\n
    4<\/th>\n5.0<\/td>\n3.6<\/td>\n1.4<\/td>\n0.2<\/td>\nsetosa<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
    <\/div>\n
    \n
    \n
    groupby\u30e1\u30bd\u30c3\u30c9\u306e\u4f7f\u3044\u65b9<\/h3>\n
    groupby\u306f\u7279\u5b9a\u306e\u5217\u306e\u5024\u3054\u3068\u306b\u30b0\u30eb\u30fc\u30d7\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306b\u7d71\u8a08\u91cf\u3092\u8a08\u7b97\u3059\u308b\u306a\u3069\u306e\u4f7f\u3044\u65b9\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \u4f8b\u3048\u3070\u3001label\u5217\u306e\u8981\u7d20\u306f\u5168\u90e8\u3067\uff13\u3064\u3001[setosa,versicolor,virginica]\u3067\u3059\u3002\u3053\u306e\uff13\u3064\u306e\u30b0\u30eb\u30fc\u30d7\u3092\u4e00\u3064\u306eDataFrame\u304b\u3089\u4f5c\u6210\u3057\u3066\u3001\u305d\u308c\u305e\u308c\u306b\u5bfe\u3057\u3066max\u3084std\u3001sum\u306a\u3069\u306e\u95a2\u6570\u3092\u9069\u7528\u3067\u304d\u307e\u3059\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n
    \n
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    In [2]:<\/div>\n
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    df.groupby(\"label\")<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
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    Out[2]:<\/div>\n
    \n
    <pandas.core.groupby.groupby.DataFrameGroupBy object at 0x7f2b142f00f0><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
    \n
    \n
    groupby\u3067\u4f5c\u3063\u305f\u30b0\u30eb\u30fc\u30d7\u306f\u3001pandas\u306e\u7279\u6b8a\u306a\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306b\u306a\u3063\u3066\u3044\u308b\u306e\u3067print\u3057\u3066\u4e2d\u8eab\u3092\u78ba\u8a8d\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u305b\u3093\u3002\u3067\u3059\u304c\u3061\u3083\u3093\u3068\u305d\u308c\u305e\u308c\u3092DataFrame\u3068\u3057\u3066\u6271\u3044\u3001\u8a08\u7b97\u304c\u3067\u304d\u308b\u306e\u3067\u78ba\u304b\u3081\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
    groupby\u3092\u4f7f\u3063\u305f\u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306e\u8a08\u7b97<\/h3>\n<\/div>\n<\/div>\n<\/div>\n
    \n
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    In [3]:<\/div>\n
    \n
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    # \u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306b\u5408\u8a08\u5024\u3092\u51fa\u3059 \ndf.groupby(\"label\").sum()<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
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    \n
    Out[3]:<\/div>\n
    \n
    \n\n\n\n\n\n\n\n\n
    <\/th>\nsepal length (cm)<\/th>\nsepal width (cm)<\/th>\npetal length (cm)<\/th>\npetal width (cm)<\/th>\n<\/tr>\n
    label<\/th>\n<\/th>\n<\/th>\n<\/th>\n<\/th>\n<\/tr>\n<\/thead>\n
    setosa<\/th>\n250.3<\/td>\n171.4<\/td>\n73.1<\/td>\n12.3<\/td>\n<\/tr>\n
    versicolor<\/th>\n296.8<\/td>\n138.5<\/td>\n213.0<\/td>\n66.3<\/td>\n<\/tr>\n
    virginica<\/th>\n329.4<\/td>\n148.7<\/td>\n277.6<\/td>\n101.3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
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    In [4]:<\/div>\n
    \n
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    # \u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306b\u5206\u6563\u3092\u51fa\u3059 \ndf.groupby(\"label\").var()<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
    \n
    \n
    Out[4]:<\/div>\n
    \n
    \n\n\n\n\n\n\n\n\n
    <\/th>\nsepal length (cm)<\/th>\nsepal width (cm)<\/th>\npetal length (cm)<\/th>\npetal width (cm)<\/th>\n<\/tr>\n
    label<\/th>\n<\/th>\n<\/th>\n<\/th>\n<\/th>\n<\/tr>\n<\/thead>\n
    setosa<\/th>\n0.124249<\/td>\n0.143690<\/td>\n0.030159<\/td>\n0.011106<\/td>\n<\/tr>\n
    versicolor<\/th>\n0.266433<\/td>\n0.098469<\/td>\n0.220816<\/td>\n0.039106<\/td>\n<\/tr>\n
    virginica<\/th>\n0.404343<\/td>\n0.104004<\/td>\n0.304588<\/td>\n0.075433<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
    <\/div>\n
    \n
    \n
    \u51fa\u529b\u3055\u308c\u308b\u7d50\u679c\u306f\u30b0\u30eb\u30fc\u30d7\u306e\u5206\u3060\u3051\u884c\u304c\u3042\u308a\u3001\u305d\u308c\u305e\u308c\u306e\u30b0\u30eb\u30fc\u30d7\u3067\u8a08\u7b97\u304c\u3067\u304d\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3055\u3089\u306b\u3001\u7d71\u8a08\u91cf\u306e\u8a08\u7b97\u306fdescribe\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u3048\u3070\u4e00\u767a<\/b>\u3067\u4e00\u89a7\u304c\u51fa\u305b\u307e\u3059\u3002\u3053\u308c\u3082groupby\u3067\u4f7f\u3048\u308b\u306e\u3067\u8a66\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n
    \n
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    In [5]:<\/div>\n
    \n
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    # \u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306b\u7d71\u8a08\u91cf\u4e00\u89a7\u3092\u51fa\u3059 \ndf.groupby(\"label\").describe().T<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
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    Out[5]:<\/div>\n
    \n
    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    <\/th>\nlabel<\/th>\nsetosa<\/th>\nversicolor<\/th>\nvirginica<\/th>\n<\/tr>\n<\/thead>\n
    petal length (cm)<\/th>\ncount<\/th>\n50.000000<\/td>\n50.000000<\/td>\n50.000000<\/td>\n<\/tr>\n
    mean<\/th>\n1.462000<\/td>\n4.260000<\/td>\n5.552000<\/td>\n<\/tr>\n
    std<\/th>\n0.173664<\/td>\n0.469911<\/td>\n0.551895<\/td>\n<\/tr>\n
    min<\/th>\n1.000000<\/td>\n3.000000<\/td>\n4.500000<\/td>\n<\/tr>\n
    25%<\/th>\n1.400000<\/td>\n4.000000<\/td>\n5.100000<\/td>\n<\/tr>\n
    50%<\/th>\n1.500000<\/td>\n4.350000<\/td>\n5.550000<\/td>\n<\/tr>\n
    75%<\/th>\n1.575000<\/td>\n4.600000<\/td>\n5.875000<\/td>\n<\/tr>\n
    max<\/th>\n1.900000<\/td>\n5.100000<\/td>\n6.900000<\/td>\n<\/tr>\n
    petal width (cm)<\/th>\ncount<\/th>\n50.000000<\/td>\n50.000000<\/td>\n50.000000<\/td>\n<\/tr>\n
    mean<\/th>\n0.246000<\/td>\n1.326000<\/td>\n2.026000<\/td>\n<\/tr>\n
    std<\/th>\n0.105386<\/td>\n0.197753<\/td>\n0.274650<\/td>\n<\/tr>\n
    min<\/th>\n0.100000<\/td>\n1.000000<\/td>\n1.400000<\/td>\n<\/tr>\n
    25%<\/th>\n0.200000<\/td>\n1.200000<\/td>\n1.800000<\/td>\n<\/tr>\n
    50%<\/th>\n0.200000<\/td>\n1.300000<\/td>\n2.000000<\/td>\n<\/tr>\n
    75%<\/th>\n0.300000<\/td>\n1.500000<\/td>\n2.300000<\/td>\n<\/tr>\n
    max<\/th>\n0.600000<\/td>\n1.800000<\/td>\n2.500000<\/td>\n<\/tr>\n
    sepal length (cm)<\/th>\ncount<\/th>\n50.000000<\/td>\n50.000000<\/td>\n50.000000<\/td>\n<\/tr>\n
    mean<\/th>\n5.006000<\/td>\n5.936000<\/td>\n6.588000<\/td>\n<\/tr>\n
    std<\/th>\n0.352490<\/td>\n0.516171<\/td>\n0.635880<\/td>\n<\/tr>\n
    min<\/th>\n4.300000<\/td>\n4.900000<\/td>\n4.900000<\/td>\n<\/tr>\n
    25%<\/th>\n4.800000<\/td>\n5.600000<\/td>\n6.225000<\/td>\n<\/tr>\n
    50%<\/th>\n5.000000<\/td>\n5.900000<\/td>\n6.500000<\/td>\n<\/tr>\n
    75%<\/th>\n5.200000<\/td>\n6.300000<\/td>\n6.900000<\/td>\n<\/tr>\n
    max<\/th>\n5.800000<\/td>\n7.000000<\/td>\n7.900000<\/td>\n<\/tr>\n
    sepal width (cm)<\/th>\ncount<\/th>\n50.000000<\/td>\n50.000000<\/td>\n50.000000<\/td>\n<\/tr>\n
    mean<\/th>\n3.428000<\/td>\n2.770000<\/td>\n2.974000<\/td>\n<\/tr>\n
    std<\/th>\n0.379064<\/td>\n0.313798<\/td>\n0.322497<\/td>\n<\/tr>\n
    min<\/th>\n2.300000<\/td>\n2.000000<\/td>\n2.200000<\/td>\n<\/tr>\n
    25%<\/th>\n3.200000<\/td>\n2.525000<\/td>\n2.800000<\/td>\n<\/tr>\n
    50%<\/th>\n3.400000<\/td>\n2.800000<\/td>\n3.000000<\/td>\n<\/tr>\n
    75%<\/th>\n3.675000<\/td>\n3.000000<\/td>\n3.175000<\/td>\n<\/tr>\n
    max<\/th>\n4.400000<\/td>\n3.400000<\/td>\n3.800000<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
    <\/div>\n
    \n
    \n
    groupby\u3092\u4f7f\u3063\u305f\u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306e\u53ef\u8996\u5316<\/h3>\n
    \u53ef\u8996\u5316\u3082\u3044\u3051\u307e\u3059\uff01plot\u30e1\u30bd\u30c3\u30c9\u3067\u500b\u5225\u306b\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<\/div>\n<\/div>\n<\/div>\n
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    In [7]:<\/div>\n
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    df.groupby(\"label\").plot(kind=\"scatter\", # \u6563\u5e03\u56f3\u3092\u4f5c\u6210 \n           x=0, y=1) # x\u8ef8\u3068y\u8ef8\u3092\u6307\u5b9a<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    Out[7]:<\/div>\n
    \n
    label\nsetosa        AxesSubplot(0.125,0.125;0.775x0.755)\nversicolor    AxesSubplot(0.125,0.125;0.775x0.755)\nvirginica     AxesSubplot(0.125,0.125;0.775x0.755)\ndtype: object<\/pre>\n<\/div>\n<\/div>\n
    \n
    <\/div>\n
    <\/div>\n<\/div>\n
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    <\/div>\n
    <\/div>\n<\/div>\n
    \n
    <\/div>\n
    <\/div>\n
    \u3053\u306e\u3088\u3046\u306bDataFrame\u306e\u30e1\u30bd\u30c3\u30c9\u3067\u3067\u304d\u308b\u3053\u3068\u306f\u5927\u4f53\u3067\u304d\u308b\u306e\u3067\u3001\u5fdc\u7528\u304c\u3044\u304f\u3089\u3067\u3082\u52b9\u304d\u305d\u3046\u306a\u6a5f\u80fd\u3067\u3059\u306d\u3002<\/div>\n
    \u307e\u3068\u3081<\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001DataFrame\u306e\u8981\u7d20\u3092\u5185\u90e8\u3067\u30b0\u30eb\u30fc\u30d4\u30f3\u30b0\u3059\u308b\u30e1\u30bd\u30c3\u30c9\u3001groupby\u3092\u7d39\u4ecb\u3057\u307e\u3057\u305f\u3002<\/p>\n
    groupby\u306e\u8981\u70b9<\/b>
    \n
    <\/p>\n
      \n
    • groupby\u306fDataFrame\u306e\u30e1\u30bd\u30c3\u30c9<\/b><\/li>\n
    • groupby\u3067DataFrame\u306e\u8981\u7d20\u3092\u5217\u306e\u5024\u3092\u4f7f\u3063\u3066\u30b0\u30eb\u30fc\u30d4\u30f3\u30b0\u3067\u304d\u308b<\/b><\/li>\n
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