{"id":3117,"date":"2023-07-19T03:04:16","date_gmt":"2023-07-19T03:04:16","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e5%88%97%e8%bb%8a%e3%81%ae%e8%a9%a6%e9%a8%93\/"},"modified":"2023-07-19T03:04:16","modified_gmt":"2023-07-19T03:04:16","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e5%88%97%e8%bb%8a%e3%81%ae%e8%a9%a6%e9%a8%93","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e5%88%97%e8%bb%8a%e3%81%ae%e8%a9%a6%e9%a8%93\/","title":{"rendered":"Pandas dataframe \u304b\u3089\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

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\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092<\/a>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5408\u3055\u305b\u308b\u3068\u304d\u3001\u591a\u304f\u306e\u5834\u5408\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092 2 \u3064\u306e\u30bb\u30c3\u30c8\u306b\u5206\u5272\u3057\u307e\u3059\u3002<\/span><\/p>\n

1. \u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8:<\/strong>\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 (\u5143\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e 70 \uff5e 80%)<\/span><\/p>\n

2. \u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8:<\/strong>\u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u306e\u4e0d\u504f\u63a8\u5b9a\u5024\u3092\u53d6\u5f97\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059 (\u5143\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e 20 \uff5e 30%)<\/span><\/p>\n

Python \u3067\u306f\u3001pandas DataFrame \u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u5206\u5272\u3059\u308b\u4e00\u822c\u7684\u306a\u65b9\u6cd5\u304c 2 \u3064\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n

\u65b9\u6cd5 1: sklearn \u306e train_test_split() \u3092\u4f7f\u7528\u3059\u308b<\/strong><\/span><\/p>\n

 from<\/span> sklearn. model_selection<\/span> import<\/span> train_test_split\n\ntrain, test = train_test_split(df, test_size= 0.2<\/span> , random_state= 0<\/span> )<\/strong><\/pre>\n
\u65b9\u6cd52: pandas\u306esample()\u3092\u4f7f\u7528\u3059\u308b<\/strong><\/span><\/p>\n
 train = df. sample<\/span> (frac= 0.8<\/span> , random_state= 0<\/span> )\ntest = df. drop<\/span> ( train.index<\/span> )<\/strong><\/pre>\n
\u6b21\u306e\u4f8b\u306f\u3001\u6b21\u306e pandas DataFrame \u3067\u5404\u30e1\u30bd\u30c3\u30c9\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#make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (1)\n\n#create DataFrame with 1,000 rows and 3 columns\n<\/span>df = pd. DataFrame<\/span> (<\/span> {' x1<\/span> ': np.random.randint<\/span> (30,size=1000),\n                   ' x2<\/span> ': np. random<\/span> . randint<\/span> (12, size=1000),\n                   ' y<\/span> ': np. random<\/span> . randint<\/span> (2, size=1000)})\n\n#view first few rows of DataFrame<\/span>\ndf. head<\/span> ()\n\n        x1 x2 y\n0 5 1 1\n1 11 8 0\n2 12 4 1\n3 8 7 0\n4 9 0 0\n<\/strong><\/pre>\n
\u4f8b 1: sklearn \u306e train_test_split() \u3092\u4f7f\u7528\u3059\u308b<\/strong><\/span><\/h3>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001 sklearn<\/strong>\u306etrain_test_split()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u5206\u5272\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/span><\/p>\n
 from<\/span> sklearn. model_selection<\/span> import<\/span> train_test_split\n\n#split original DataFrame into training and testing sets\n<\/span>train, test = train_test_split(df, test_size= 0.2<\/span> , random_state= 0<\/span> )\n\n#view first few rows of each set<\/span>\nprint<\/span> ( train.head<\/span> ())\n\n     x1 x2 y\n687 16 2 0\n500 18 2 1\n332 4 10 1\n979 2 8 1\n817 11 1 0\n\nprint<\/span> ( test.head<\/span> ())\n\n     x1 x2 y\n993 22 1 1\n859 27 6 0\n298 27 8 1\n553 20 6 0\n672 9 2 1\n\n#print size of each set<\/span>\nprint<\/span> (train. shape<\/span> , test. shape<\/span> )\n\n(800, 3) (200, 3)\n<\/strong><\/pre>\n
\u7d50\u679c\u304b\u3089\u30012 \u3064\u306e\u30bb\u30c3\u30c8\u304c\u4f5c\u6210\u3055\u308c\u305f\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n