{"id":64370,"date":"2018-07-17T23:16:48","date_gmt":"2018-07-17T14:16:48","guid":{"rendered":"https:\/\/www.sejuku.net\/blog\/?p=64370"},"modified":"2024-05-06T11:50:03","modified_gmt":"2024-05-06T02:50:03","slug":"ph5","status":"publish","type":"post","link":"https:\/\/www.sejuku.net\/blog\/64370","title":{"rendered":"\u3010pandas\u5165\u9580\u3011pd.series\u306e\u4f5c\u308a\u65b9\u3068\u57fa\u672c\u64cd\u4f5c\u307e\u3068\u3081"},"content":{"rendered":"

Pandas\u306e\u4f7f\u3044\u65b9\u306b\u3064\u3044\u3066<\/strong><\/span>\u3001\u4f8d\u3067\u306f\u3044\u304f\u3064\u304b\u8a18\u4e8b\u3092\u516c\u958b\u3057\u3066\u304d\u307e\u3057\u305f\u3002<\/p>\n

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\u3010\u6a5f\u68b0\u5b66\u7fd2\u5165\u9580\u3011\u30c7\u30fc\u30bf\u89e3\u6790\u306b\u6700\u9069\uff01pandas\u3092\u4f7f\u3063\u3066\u307f\u3088\u3046\uff01<\/div>\n
\u66f4\u65b0\u65e5\uff1a2024\u5e745\u67086\u65e5<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n
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\u3010Python\u5165\u9580\u3011pandas\u3067\u30c7\u30fc\u30bf\u3092\u6271\u304a\u3046<\/div>\n
\u66f4\u65b0\u65e5\uff1a2024\u5e745\u67086\u65e5<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n
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\u3010pandas\u5165\u9580\u3011plot\u6a5f\u80fd\u3067\u30b7\u30fc\u30e0\u30ec\u30b9\u306a\u53ef\u8996\u5316\u306b\u30c1\u30e3\u30ec\u30f3\u30b8<\/div>\n
\u66f4\u65b0\u65e5\uff1a2024\u5e745\u67086\u65e5<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n
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\u3010pandas\u5165\u9580\u3011DataFrame\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u306e\u64cd\u4f5c\u3092\u899a\u3048\u3088\u3046<\/div>\n
\u66f4\u65b0\u65e5\uff1a2024\u5e745\u67086\u65e5<\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/p>\n

\u3053\u308c\u3089\u306e\u8a18\u4e8b\u3067\u306f\u3001\u4e3b\u306bDataFrame<\/span><\/strong>\u3092\u6271\u3063\u3066\u3044\u307e\u3057\u305f\u304c\u3001pandas\u306b\u306f\u3053\u306e\u4ed6\u306b\u3082\u30c7\u30fc\u30bf\u3092\u6271\u3046\u30af\u30e9\u30b9\u304c\u8907\u6570\u3042\u308a\u307e\u3059\u3002<\/p>\n

DataFrame\u4ee5\u5916\u3067\u3088\u304f\u76ee\u306b\u3059\u308b\u30af\u30e9\u30b9\u3068\u8a00\u3048\u3070\u3001Series\u578b<\/strong><\/span>\u3067\u3059\u3002<\/p>\n

\u3053\u306e\u8a18\u4e8b\u3067\u306f<\/p>\n

    \n
  • pd.Series\u578b\u3068\u306f\u4f55\u304b<\/span><\/strong><\/li>\n
  • pd.Series\u578b\u306e\u4f5c\u308a\u65b9<\/span><\/strong><\/li>\n
  • pd.Series\u578b\u306e\u57fa\u672c\u64cd\u4f5c<\/span><\/strong><\/li>\n<\/ul>\n

    \u306b\u6ce8\u76ee\u3057\u3066Series\u578b\u306e\u4f7f\u3044\u65b9\u306b\u3064\u3044\u3066\u307e\u3068\u3081\u307e\u3057\u305f\uff01<\/p>\n

    pandas\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u89e3\u6790\u3067\u306f\u3088\u304f\u304a\u4e16\u8a71\u306b\u306a\u308bpd.Series\u3001\u4f7f\u3044\u3053\u306a\u3057\u3066\u4e00\u6b69\u5148\u3092\u884c\u304f\u30a8\u30f3\u30b8\u30cb\u30a2\u306b\u306a\u308a\u307e\u3057\u3087\u3046\uff01<\/p>\n

    \u203b \u3053\u306e\u8a18\u4e8b\u306e\u30b3\u30fc\u30c9\u306fPython 3.7, Ubuntu 18.04\u3067\u52d5\u4f5c\u78ba\u8a8d\u3057\u307e\u3057\u305f\u3002<\/strong><\/span><\/p>\n

    Series\u3068\u306f<\/h2>\n

    pd.Series\u3068\u306f\u3001pandas\u306e\u30c7\u30fc\u30bf\u578b\u306e\u4e00\u3064\u3067\u3059\u3002<\/p>\n

    pd.DataFrame\u304c\uff12\u6b21\u5143\u914d\u5217\uff08\u884c\u5217\uff09<\/strong><\/span>\u3092\u6271\u3063\u3066\u3044\u305f\u306e\u306b\u5bfe\u3057\u3066\u3001pd.Series\u306f\u4e00\u6b21\u5143\u914d\u5217\uff08\u30d9\u30af\u30c8\u30eb\uff09<\/strong><\/span>\u3092\u6271\u3044\u307e\u3059\u3002<\/p>\n

    DataFrame\u3092\u30b9\u30e9\u30a4\u30b9\u3057\u3066\u4e00\u6b21\u5143\u914d\u5217\u306e\u5f62\u306b\u306a\u3063\u305f\u3089\u3001\u305d\u308c\u3082pd.Series<\/span><\/strong>\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n

    \u591a\u5206\u3053\u306e\u540d\u524d\u3092\u77e5\u3089\u306a\u3044\u4eba\u3067\u3082\u3001DataFrame\u3092\u4f7f\u3063\u3066\u3044\u305f\u3089\u304a\u4e16\u8a71\u306b\u306a\u3063\u3066\u3044\u308b\u3053\u3068\u3067\u3057\u3087\u3046\u3002<\/p>\n

    Series\u306e\u4f5c\u308a\u65b9<\/h2>\n

    \u3053\u3053\u304b\u3089\u306fJupyterlab\u3084Jupyter Notebook<\/strong><\/span>\u3092\u4f7f\u3063\u3066\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n

    \u3053\u3053\u3067\u306f\u7df4\u7fd2\u7528\u306e\u30c7\u30fc\u30bf\u3068\u3057\u3066 boston house-prices dataset \u3092\u4f7f\u3063\u3066\u8a66\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n

    boston house-prices dataset\u306f\u3001\u30a2\u30e1\u30ea\u30ab\u306e\u30dc\u30b9\u30c8\u30f3\u5e02\u90ca\u5916\u306e\u5730\u57df\u5225\u4f4f\u5b85\u4fa1\u683c\u306e\u307e\u3068\u3081\u3089\u308c\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u3059\u3002<\/p>\n

    \u4e3b\u306b\u56de\u5e30\u554f\u984c\u306b\u7528\u3044\u3089\u308c\u307e\u3059\u3002<\/p>\n

    \u307e\u305a\u306f\u30e9\u30a4\u30d6\u30e9\u30ea\u3092import<\/strong><\/span>\u3002<\/p>\n

    import numpy as np\r\nimport pandas as pd\r\nfrom sklearn import datasets<\/pre>\n
    pd.Series\u306b\u3057\u305f\u3044\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
    boston = datasets.load_boston()\r\n\r\nboston.target<\/pre>\n
    [\u51fa\u529b\u7d50\u679c]<\/p>\n
    array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\r\n       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\r\n       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\r\n       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\r\n       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\r\n       ...<\u4e2d\u7565>...\r\n       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\r\n       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\r\n       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\r\n       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\r\n       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])<\/pre>\n
    pd.Series\u306e\u4f5c\u308a\u65b9\u306f\u7c21\u5358\u3067\u3059\u3002<\/p>\n
    \u6700\u3082\u7c21\u5358\u306apd.Series\u306e\u4f5c\u308a\u65b9\u306f\u30d9\u30af\u30c8\u30eb\u3092\u5f15\u6570\u306b\u6e21\u3059\u3060\u3051<\/strong><\/span>\u3067OK\u3067\u3059\u3002<\/p>\n
    sr = pd.Series(boston.target)\r\nsr<\/pre>\n
    [\u51fa\u529b\u7d50\u679c]<\/p>\n
    0      24.0\r\n1      21.6\r\n2      34.7\r\n3      33.4\r\n4      36.2\r\n5      28.7\r\n6      22.9\r\n7      27.1\r\n8      16.5\r\n9      18.9\r\n10     15.0\r\n       ... \r\n497    18.3\r\n498    21.2\r\n499    17.5\r\n500    16.8\r\n501    22.4\r\n502    20.6\r\n503    23.9\r\n504    22.0\r\n505    11.9\r\nLength: 506, dtype: float64<\/pre>\n
    \u3053\u308c\u3067pd.Series\u578b\u304c\u4f5c\u308c\u307e\u3057\u305f\u3002<\/p>\n
    \u666e\u901a\u306enp.array\u3068\u306e\u9055\u3044\u306f\u3001\u5404\u8981\u7d20\u306bindex\u304c\u660e\u793a\u7684\u306b\u3064\u3044\u3066\u3044\u308b<\/strong><\/span>\u3053\u3068\u3067\u3057\u3087\u3046\u3002<\/p>\n
    \u3053\u306eindex\u306f\u3082\u3061\u308d\u3093\u5f8c\u304b\u3089\u5909\u66f4\u3082\u3067\u304d\u307e\u3059\u3057\u3001\u521d\u671f\u5316\u3059\u308b\u969b\u306b\u6307\u5b9a\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n
    pd.Series(data, index=[data\u3068\u540c\u3058\u9577\u3055\u306e\u914d\u5217])<\/pre>\n
    \u3053\u306e\u3042\u305f\u308a\u306fDataFrame\u3068\u540c\u3058\u3067\u3059\u3002<\/p>\n
    \u307e\u305f\u3001DataFrame\u304b\u3089\u4e00\u90e8\u3092\u5207\u308a\u53d6\u308b\u3053\u3068\u3067\u3082\u4f5c\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
    df= pd.DataFrame(boston.data)\r\ndf.columns = boston.feature_names\r\ndf[\"label\"] = sr\r\ndf.head()<\/pre>\n
    \"\"<\/p>\n
    \u3053\u308c\u306e\u4e00\u90e8\u3092\u5207\u308a\u53d6\u308b\u3068\u4ee5\u4e0b\u306e\u3088\u3046\u306bSeries\u306b\u306a\u3063\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n
    type(df[\"label\"])\r\n\r\n# out: pandas.core.series.Series<\/pre>\n
    \u57fa\u672c\u64cd\u4f5c<\/h2>\n
    series\u304b\u3089np.array\u3092\u53d6\u308a\u51fa\u3059<\/h3>\n
    DataFrame\u306e\u3068\u304d\u3068\u540c\u3058\u3088\u3046\u306b\u3001series\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9.values<\/strong><\/span>\u3067\u914d\u5217\u3092\u53d6\u308a\u51fa\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
    sr.values<\/pre>\n
    [\u51fa\u529b\u7d50\u679c]<\/p>\n
    array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\r\n       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\r\n       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\r\n       ...<\u4e2d\u7565>...\r\n       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\r\n       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\r\n       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])<\/pre>\n
    \u30a4\u30f3\u30c7\u30af\u30b7\u30f3\u30b0<\/h3>\n
    \u30a4\u30f3\u30c7\u30af\u30b7\u30f3\u30b0\uff08\u8981\u7d20\u3092\u53d6\u308a\u51fa\u3059\uff09<\/strong><\/span>\u306e\u65b9\u6cd5\u306fnp.arary\u3068\u540c\u69d8\u3067\u3059\u3002<\/p>\n
    sr[0]\r\n\r\n# out: 24.0\r\n\r\nsr[1:5]\r\n\r\n# out:\r\n1    21.6\r\n2    34.7\r\n3    33.4\r\n4    36.2\r\ndtype: float64\r\n<\/pre>\n
    \u307e\u305f\u3001index\u304c\u6570\u5b57\u4ee5\u5916\u306b\u306a\u3063\u3066\u3044\u305f\u5834\u5408\u306f\u3001\u5148\u982d\u306e\u8981\u7d20\u30920\u3068\u3057\u305f\u6570\u5b57\u3067\u306e\u30a4\u30f3\u30c7\u30af\u30b7\u30f3\u30b0\u3060\u3051\u3067\u306a\u304f\u3001\u8f9e\u66f8\u578b\u306e\u3088\u3046\u306a\u30a2\u30af\u30bb\u30b9\u3082\u53ef\u80fd<\/strong><\/span>\u3067\u3059\u3002<\/p>\n
    print(df.iloc[0,:])\r\nprint(type(df.iloc[0,:]))<\/pre>\n
    [\u51fa\u529b\u7d50\u679c]<\/p>\n
    CRIM         0.00632\r\nZN          18.00000\r\nINDUS        2.31000\r\nCHAS         0.00000\r\nNOX          0.53800\r\nRM           6.57500\r\nAGE         65.20000\r\nDIS          4.09000\r\nRAD          1.00000\r\nTAX        296.00000\r\nPTRATIO     15.30000\r\nB          396.90000\r\nLSTAT        4.98000\r\nlabel       24.00000\r\nName: 0, dtype: float64\r\n<class 'pandas.core.series.Series'>\r\n<\/pre>\n
    df.iloc[0,:]\u306fpd.Series\u3067\u3001\u5c1a\u4e14index\u304c\u6587\u5b57\u5217\u578b\u3067\u3059\u3002<\/p>\n
    \u8981\u7d20\u3078\u30a2\u30af\u30bb\u30b9\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
    df.iloc[0,:][\"CRIM\"], df.iloc[0,:][0]\r\n\r\n# out: (0.00632, 0.00632)<\/pre>\n
    \uff11\u3064\u76ee\u306e\u66f8\u304d\u65b9\u304c\u8f9e\u66f8\u30b9\u30bf\u30a4\u30eb<\/strong><\/span>\u3001\uff12\u3064\u76ee\u306e\u66f8\u304d\u65b9\u304c\u914d\u5217\u30b9\u30bf\u30a4\u30eb<\/strong><\/span>\u3067\u3059\u3002<\/p>\n
    \u3069\u3061\u3089\u3082\u540c\u3058\u8981\u7d20\u3092\u53d6\u308a\u51fa\u305b\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u306d\u3002<\/p>\n
    \u4ee3\u5165<\/h3>\n
    \u8981\u7d20\u306e\u4ee3\u5165<\/strong><\/span>\u3082\u914d\u5217\u540c\u69d8\u306b\u884c\u3048\u307e\u3059\u3002<\/p>\n
    sr[0] = 1\r\nsr<\/pre>\n
    [\u51fa\u529b\u7d50\u679c]<\/p>\n
    0       1.0\r\n1      21.6\r\n2      34.7\r\n3      33.4\r\n4      36.2\r\n5      28.7\r\n6      22.9\r\n7      27.1\r\n8      16.5\r\n9      18.9\r\n10     15.0\r\n\u4ee5\u4e0b\u7565<\/pre>\n
    \u30b9\u30e9\u30a4\u30b9\u8a18\u6cd5\u3092\u4f7f\u3063\u305f\u4ee3\u5165\u3082\u53ef\u80fd\u3067\u3059\u304c\u3001\u53f3\u8fba\u3068\u5de6\u8fba\u304c\u540c\u3058\u8981\u7d20\u6570\u3067\u306a\u3044\u3068\u30a8\u30e9\u30fc\u304c\u3067\u308b<\/strong><\/span>\u306e\u3067\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
    sr[0:3] = [0,1,2]\r\nsr<\/pre>\n
    [\u51fa\u529b\u7d50\u679c]<\/p>\n
    0       0.0\r\n1       1.0\r\n2       2.0\r\n3      33.4\r\n4      36.2\r\n5      28.7\r\n6      22.9\r\n7      27.1\r\n8      16.5\r\n9      18.9\r\n10     15.0\r\n\u4ee5\u4e0b\u7565<\/pre>\n
    \u56db\u5247\u6f14\u7b97<\/h3>\n
    \u56db\u5247\u6f14\u7b97\u3082\u53ef\u80fd\u3067\u3059\u3002<\/p>\n
    \u6f14\u7b97\u5b50\u3092\u4f7f\u3046\u3068\u8981\u7d20\u3054\u3068\u306e\u6f14\u7b97<\/strong><\/span>\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
    df[\"CRIM\"] + df[\"ZN\"]<\/pre>\n
    [\u51fa\u529b\u7d50\u679c]<\/p>\n
    0      18.00632\r\n1       0.02731\r\n2       0.02729\r\n3       0.03237\r\n4       0.06905\r\n5       0.02985\r\n6      12.58829\r\n7      12.64455\r\n8      12.71124\r\n9      12.67004\r\n10     12.72489\r\n\u4ee5\u4e0b\u7565<\/pre>\n
    \u307e\u3068\u3081<\/h2>\n
    \u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001pandas\u306e\u30c7\u30fc\u30bf\u578bSeries\u30af\u30e9\u30b9\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3057\u305f<\/strong><\/span>\u3002<\/p>\n
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