{"id":2404,"date":"2023-07-22T10:10:15","date_gmt":"2023-07-22T10:10:15","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%e3%82%a4%e3%83%b3%e3%83%86%e3%82%99%e3%83%83%e3%82%af%e3%82%b9%e3%81%a6%e3%82%99%e5%88%97%e3%82%92%e9%81%b8%e6%8a%9e%e3%81%97%e3%81%be%e3%81%99\/"},"modified":"2023-07-22T10:10:15","modified_gmt":"2023-07-22T10:10:15","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%e3%82%a4%e3%83%b3%e3%83%86%e3%82%99%e3%83%83%e3%82%af%e3%82%b9%e3%81%a6%e3%82%99%e5%88%97%e3%82%92%e9%81%b8%e6%8a%9e%e3%81%97%e3%81%be%e3%81%99","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%e3%82%a4%e3%83%b3%e3%83%86%e3%82%99%e3%83%83%e3%82%af%e3%82%b9%e3%81%a6%e3%82%99%e5%88%97%e3%82%92%e9%81%b8%e6%8a%9e%e3%81%97%e3%81%be%e3%81%99\/","title":{"rendered":"Pandas dataframe \u3067\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u3088\u3063\u3066\u5217\u3092\u9078\u629e\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u591a\u304f\u306e\u5834\u5408\u3001\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u5024\u306b\u57fa\u3065\u3044\u3066 pandas DataFrame \u5185\u306e\u5217\u3092\u9078\u629e\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u6574\u6570\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u57fa\u3065\u3044\u3066\u5217\u3092\u9078\u629e\u3059\u308b\u5834\u5408\u306f\u3001 .iloc<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u30e9\u30d9\u30eb\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u57fa\u3065\u3044\u3066\u5217\u3092\u9078\u629e\u3059\u308b\u5834\u5408\u306f\u3001 .loc<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u3053\u308c\u3089\u306e\u5404\u95a2\u6570\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u306e\u4f8b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u4f8b 1: \u6574\u6570\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u57fa\u3065\u3044\u3066\u5217\u3092\u9078\u629e\u3059\u308b<\/strong><\/span><\/h3>\n

\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001pandas DataFrame \u3092\u4f5c\u6210\u3057\u3001 .iloc<\/strong>\u3092\u4f7f\u7528\u3057\u3066\u6574\u6570\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u50243<\/strong>\u3092\u6301\u3064\u5217\u3092\u9078\u629e\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B'],\n                   ' points<\/span> ': [11, 7, 8, 10, 13, 13],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5]})\n\n#view DataFrame\n<\/span>df\n\n\tteam points assists rebounds\n0 A 11 5 11\n1 To 7 7 8\n2 to 8 7 10\n3 B 10 9 6\n4 B 13 12 6\n5 B 13 9 5\n\n<\/span>#select column with index position 3\n<\/span>df. iloc<\/span> [:, 3]\n\n0 11\n1 8\n2 10\n3 6\n4 6\n5 5\nName: rebounds, dtype: int64<\/span><\/span><\/span><\/strong><\/pre>\n
\u540c\u69d8\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u8907\u6570\u306e\u5217\u3092\u9078\u629e\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #select columns with index positions 1 and 3\ndf. iloc<\/span> [:, [1, 3]]\n\n\n        rebound points\n0 11 11\n1 7 8\n2 8 10\n3 10 6\n4 13 6\n5 13 5\n<\/span><\/span><\/strong><\/pre>\n
\u307e\u305f\u306f\u3001\u7bc4\u56f2\u5185\u306e\u3059\u3079\u3066\u306e\u5217\u3092\u9078\u629e\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #select columns with index positions in range 0 through 3\ndf. iloc<\/span> [:, 0:3]\n\n        team points assists\n0 to 11 5\n1 To 7 7\n2 to 8 7\n3 B 10 9\n4 B 13 12\n5 B 13 9<\/span><\/span><\/strong><\/pre>\n
\u4f8b 2: \u30e9\u30d9\u30eb\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u4ed8\u3051\u306b\u57fa\u3065\u3044\u3066\u5217\u3092\u9078\u629e\u3059\u308b<\/strong><\/span><\/h3>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001pandas DataFrame \u3092\u4f5c\u6210\u3057\u3001 .loc<\/strong>\u3092\u4f7f\u7528\u3057\u3066“bounces”<\/strong>\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9 \u30e9\u30d9\u30eb\u3092\u6301\u3064\u5217\u3092\u9078\u629e\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B'],\n                   ' points<\/span> ': [11, 7, 8, 10, 13, 13],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5]})\n\n#view DataFrame\n<\/span>df\n\n\tteam points assists rebounds\n0 A 11 5 11\n1 To 7 7 8\n2 to 8 7 10\n3 B 10 9 6\n4 B 13 12 6\n5 B 13 9 5\n\n#select column with index label 'rebounds'\n<\/span>df. loc<\/span> [:, ' rebounds<\/span> ']\n\n0 11\n1 8\n2 10\n3 6\n4 6\n5 5\nName: rebounds, dtype: int64<\/span><\/span><\/strong><\/pre>\n
\u540c\u69d8\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u7570\u306a\u308b\u30a4\u30f3\u30c7\u30c3\u30af\u30b9 \u30e9\u30d9\u30eb\u3092\u6301\u3064\u8907\u6570\u306e\u5217\u3092\u9078\u629e\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #select the columns with index labels 'points' and 'rebounds'\ndf. loc<\/span> [:,[' points<\/span> ',' rebounds<\/span> ']]\n\n\trebound points\n0 11 11\n1 7 8\n2 8 10\n3 10 6\n4 13 6\n5 13 5<\/span><\/span><\/strong><\/pre>\n
\u307e\u305f\u306f\u3001\u7bc4\u56f2\u5185\u306e\u3059\u3079\u3066\u306e\u5217\u3092\u9078\u629e\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #select columns with index labels between 'team' and 'assists'\ndf. loc<\/span> [:, ' team<\/span> ':' assists<\/span> ']\n\n\tteam points assists\n0 to 11 5\n1 To 7 7\n2 to 8 7\n3 B 10 9\n4 B 13 12\n5 B 13 9<\/span><\/span><\/strong><\/pre>\n
\u95a2\u9023:<\/strong><\/span> Pandas loc \u3068 iloc: \u9055\u3044\u306f\u4f55\u3067\u3059\u304b?<\/a><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30d1\u30f3\u30c0\u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u64cd\u4f5c\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n
 Pandas DataFrame \u3067\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u3088\u3063\u3066\u30b0\u30eb\u30fc\u30d7\u5316\u3059\u308b\u65b9\u6cd5<\/a>
Pandas DataFrame \u3067\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u3088\u3063\u3066\u884c\u3092\u9078\u629e\u3059\u308b\u65b9\u6cd5<\/a>
Pandas DataFrame \u3067\u884c\u756a\u53f7\u3092\u53d6\u5f97\u3059\u308b\u65b9\u6cd5<\/a>
Pandas DataFrame \u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u5217\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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