{"id":4160,"date":"2023-07-13T04:16:13","date_gmt":"2023-07-13T04:16:13","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%e8%a4%87%e6%95%b0%e3%81%ae%e5%88%97%e3%82%92%e5%89%8a%e9%99%a4%e3%81%97%e3%81%be%e3%81%99\/"},"modified":"2023-07-13T04:16:13","modified_gmt":"2023-07-13T04:16:13","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%e8%a4%87%e6%95%b0%e3%81%ae%e5%88%97%e3%82%92%e5%89%8a%e9%99%a4%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%e8%a4%87%e6%95%b0%e3%81%ae%e5%88%97%e3%82%92%e5%89%8a%e9%99%a4%e3%81%97%e3%81%be%e3%81%99\/","title":{"rendered":"Pandas \u3067\u8907\u6570\u306e\u5217\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5 (4 \u3064\u306e\u65b9\u6cd5)"},"content":{"rendered":"

<\/p>\n

\n

\u6b21\u306e\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u304b\u3089\u8907\u6570\u306e\u5217\u3092\u524a\u9664\u3067\u304d\u307e\u3059\u3002<\/span><\/span><\/p>\n

\u65b9\u6cd5 1: \u8907\u6570\u306e\u5217\u3092\u540d\u524d\u3067\u524a\u9664\u3059\u308b<\/strong><\/span><\/p>\n

 df. drop<\/span> (columns=[' col1<\/span> ', ' col2<\/span> ', ' col4<\/span> '], inplace= True<\/span> )\n<\/strong><\/pre>\n
\u65b9\u6cd5 2: \u7bc4\u56f2\u5185\u306e\u5217\u3092\u540d\u524d\u3067\u524a\u9664\u3059\u308b<\/strong><\/span><\/p>\n
 df. drop<\/span> (columns= df.loc<\/span> [:, ' col1<\/span> ':' col4<\/span> '], inplace= True<\/span> )<\/strong><\/pre>\n
\u65b9\u6cd5 3: \u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u3088\u3063\u3066\u8907\u6570\u306e\u5217\u3092\u524a\u9664\u3059\u308b<\/strong><\/span><\/p>\n
 df. drop<\/span> (columns=df. columns<\/span> [[0, 3, 4]], inplace= True<\/span> )\n<\/strong><\/pre>\n
\u65b9\u6cd5 4: \u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u3088\u3063\u3066\u7bc4\u56f2\u5185\u306e\u5217\u3092\u524a\u9664\u3059\u308b<\/strong><\/span><\/p>\n
 df. drop<\/span> (columns= df.columns<\/span> [1:4], inplace= True<\/span> )<\/strong><\/pre>\n
\u6ce8<\/strong>: inplace=True<\/strong>\u5f15\u6570\u306f\u3001DataFrame \u3092\u518d\u5272\u308a\u5f53\u3066\u305b\u305a\u306b\u30a4\u30f3\u30d7\u30ec\u30fc\u30b9\u5217\u3092\u524a\u9664\u3059\u308b\u3088\u3046\u306b\u30d1\u30f3\u30c0\u306b\u6307\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306e\u4f8b\u306f\u3001\u6b21\u306e pandas DataFrame \u3067\u5404\u30e1\u30bd\u30c3\u30c9\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd<\/span>\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n                   ' points<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n                   ' steals<\/span> ': [4, 5, 10, 12, 4, 8, 7, 2]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points assists rebounds steals\n0 A 18 5 11 4\n1 B 22 7 8 5\n2 C 19 7 10 10\n3 D 14 9 6 12\n4 E 14 12 6 4\n5 F 11 9 5 8\n6 G 20 9 9 7\n7:28 4 12 2<\/strong><\/pre>\n
\u4f8b 1: \u8907\u6570\u306e\u5217\u3092\u540d\u524d\u3067\u524a\u9664\u3059\u308b<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001 Points<\/strong> \u3001\u30ea\u30d0\u30a6\u30f3\u30c9<\/strong>\u3001\u304a\u3088\u3073Steels<\/strong>\u5217\u3092\u540d\u524d\u3067\u524a\u9664\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #drop multiple columns by name\n<\/span>df. drop<\/span> (columns=[' points<\/span> ', ' rebounds<\/span> ', ' steals<\/span> '], inplace= True<\/span> )\n\n#view updated Dataframe\n<\/span>print<\/span> (df)\n\n  team assists\n0 to 5\n1 B 7\n2 C 7\n3 D 9\n4 E 12\n5 F 9\n6 G 9\n7:04 a.m.<\/strong>\n<\/pre>\n
\u4f8b 2: \u7bc4\u56f2\u5185\u306e\u5217\u3092\u540d\u524d\u3067\u524a\u9664\u3059\u308b<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30dd\u30a4\u30f3\u30c8<\/strong>\u9593\u306e\u5404\u5217\u3092\u30c9\u30ed\u30c3\u30d7\u3057\u3001\u5217\u3092\u540d\u524d\u3067\u30d0\u30a6\u30f3\u30b9\u3059\u308b<\/strong>\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #drop columns in range by name\n<\/span>df. drop<\/span> (columns= df.loc<\/span> [:, ' points<\/span> ':' rebounds<\/span> '], inplace= True<\/span> )\n\n#view updated Dataframe\n<\/span>print<\/span> (df)\n\n  team steals\n0 to 4\n1 B 5\n2 C 10\n3 D 12\n4 E 4\n5 F 8\n6 G 7\n7 A.M. 2<\/strong><\/pre>\n
\u4f8b 3: \u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u3088\u3063\u3066\u8907\u6570\u306e\u5217\u3092\u524a\u9664\u3059\u308b<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001DataFrame \u304b\u3089\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u4f4d\u7f6e 0\u30013\u3001\u304a\u3088\u3073 4 \u306e\u5217\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #drop multiple columns by index\n<\/span>df. drop<\/span> (columns=df. columns<\/span> [[0, 3, 4]], inplace= True<\/span> )\n\n#view updated Dataframe\n<\/span>print<\/span> (df)\n\n   assist points\n0 18 5\n1 22 7\n2 19 7\n3 14 9\n4 14 12\n5 11 9\n6 20 9\n7 28 4<\/strong><\/pre>\n
\u4f8b 4: \u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u3088\u308b\u7bc4\u56f2\u5185\u306e\u5217\u306e\u524a\u9664<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001DataFrame \u304b\u3089\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u4f4d\u7f6e 0\u30013\u3001\u304a\u3088\u3073 4 \u306e\u5217\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #drop columns by index range\n<\/span>df. drop<\/span> (columns= df.columns<\/span> [1:4], inplace= True<\/span> )\n\n#view updated Dataframe\n<\/span>print<\/span> (df)\n\n  team steals\n0 to 4\n1 B 5\n2 C 10\n3 D 12\n4 E 4\n5 F 8\n6 G 7\n7 A.M. 2<\/strong><\/pre>\n
 df.columns[1:4]<\/strong>\u69cb\u6587\u306f\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u4f4d\u7f6e 1 \uff5e<\/em> 4 \u306e\u5217\u3092\u6307\u5b9a\u3059\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
\u3057\u305f\u304c\u3063\u3066\u3001\u3053\u306e\u69cb\u6587\u306f\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u4f4d\u7f6e 1\u30012\u3001\u304a\u3088\u3073 3 \u306b\u3042\u308b\u5217\u3092\u524a\u9664\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6ce8<\/strong>: pandas drop()<\/strong>\u95a2\u6570\u306e\u5b8c\u5168\u306a\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306f\u3053\u3053\u3067<\/a>\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30d1\u30f3\u30c0\u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u30bf\u30b9\u30af\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n
 Pandas: NaN \u5024\u3092\u542b\u3080\u5217\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5<\/a>
\u30d1\u30f3\u30c0: \u30ea\u30b9\u30c8\u306b\u306a\u3044\u5217\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5<\/a>
\u30d1\u30f3\u30c0: \u7279\u5b9a\u306e\u5217\u3092\u9664\u304f\u3059\u3079\u3066\u306e\u5217\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u6b21\u306e\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u304b\u3089\u8907\u6570\u306e\u5217\u3092\u524a\u9664\u3067\u304d\u307e\u3059\u3002 \u65b9\u6cd5 1: \u8907\u6570\u306e\u5217\u3092\u540d\u524d\u3067\u524a\u9664\u3059\u308b df. drop (columns=[‘ col1 ‘, ‘ col2 ‘, ‘ col4 ‘ […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-4160","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nPandas \u3067\u8907\u6570\u306e\u5217\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5 (4 \u3064\u306e\u65b9\u6cd5) \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame \u304b\u3089\u8907\u6570\u306e\u5217\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5\u3092\u3044\u304f\u3064\u304b\u306e\u4f8b\u3068\u3068\u3082\u306b\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306f\u8907\u6570\u306e\u5217\u3092\u524a\u9664\u3057\u307e\u3059\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas \u3067\u8907\u6570\u306e\u5217\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5 (4 \u3064\u306e\u65b9\u6cd5) \u2013 Statorials\" \/>\n<meta property=\"og:description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame \u304b\u3089\u8907\u6570\u306e\u5217\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5\u3092\u3044\u304f\u3064\u304b\u306e\u4f8b\u3068\u3068\u3082\u306b\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306f\u8907\u6570\u306e\u5217\u3092\u524a\u9664\u3057\u307e\u3059\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-13T04:16:13+00:00\" \/>\n<meta name=\"author\" content=\"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u57f7\u7b46\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u63a8\u5b9a\u8aad\u307f\u53d6\u308a\u6642\u9593\" 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Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/ja\/#website\"},\"datePublished\":\"2023-07-13T04:16:13+00:00\",\"dateModified\":\"2023-07-13T04:16:13+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/ja\/#\/schema\/person\/86b92d2dd87368b26360d19d9c6a5d83\"},\"description\":\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame 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