{"id":3578,"date":"2023-07-16T17:22:59","date_gmt":"2023-07-16T17:22:59","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%83%95%e3%82%9a%e3%83%ad%e3%83%83%e3%83%88%e3%81%ae%e5%88%97%e5%88%86%e5%b8%83\/"},"modified":"2023-07-16T17:22:59","modified_gmt":"2023-07-16T17:22:59","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%83%95%e3%82%9a%e3%83%ad%e3%83%83%e3%83%88%e3%81%ae%e5%88%97%e5%88%86%e5%b8%83","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%83%95%e3%82%9a%e3%83%ad%e3%83%83%e3%83%88%e3%81%ae%e5%88%97%e5%88%86%e5%b8%83\/","title":{"rendered":"Pandas\u3067\u5217\u5024\u306e\u5206\u5e03\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u6b21\u306e\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u306e\u5217\u5024\u306e\u5206\u5e03\u3092\u30d7\u30ed\u30c3\u30c8\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u65b9\u6cd5 1: \u5217\u5185\u306e\u5024\u306e\u5206\u5e03\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b<\/strong><\/span><\/p>\n

 df[' my_column<\/span> ']. plot<\/span> (kind=' kde<\/span> ')\n<\/strong><\/pre>\n
\u65b9\u6cd5 2: 1 \u3064\u306e\u5217\u306e\u5024\u306e\u5206\u5e03\u3092\u5225\u306e\u5217\u3067\u30b0\u30eb\u30fc\u30d7\u5316\u3057\u3066\u30d7\u30ed\u30c3\u30c8\u3059\u308b<\/strong><\/span><\/p>\n
 df. groupby<\/span> (' group_column<\/span> ')[' values_column<\/span> ']. plot<\/span> (kind=' kde<\/span> ')<\/strong><\/pre>\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\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A',\n                            'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B'],\n                   ' points<\/span> ': [3, 3, 4, 5, 4, 7, 7, 7, 10, 11, \n                              8, 7, 8, 9, 12, 12, 12, 14, 15, 17]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n   team points\n0 to 3\n1 to 3\n2 to 4\n3 to 5\n4 to 4\n5 TO 7\n6 to 7\n7 to 7\n8 to 10\n9 to 11\n10 B 8\n11 B 7\n12 B 8\n13 B 9\n14 B 12\n15 B 12\n16 B 12\n17 B 14\n18 B 15\n19 B 17<\/strong><\/pre>\n
\u4f8b 1: \u5217\u5185\u306e\u5024\u306e\u5206\u5e03\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30dd\u30a4\u30f3\u30c8<\/strong>\u5217\u306e\u5024\u306e\u5206\u5e03\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span> <\/p>\n
 #plot distribution of values in points column<\/span>\ndf[' points<\/span> ']. plot<\/span> (kind=' kde<\/span> ')\n<\/strong><\/pre>\n
\"\"<\/p>\n
 kind=’kde’ \u306f<\/strong>\u3001\u5909\u6570\u306e\u5024\u306e\u5206\u5e03\u3092\u8981\u7d04\u3059\u308b\u6ed1\u3089\u304b\u306a\u66f2\u7dda\u3092\u751f\u6210\u3059\u308b\u30ab\u30fc\u30cd\u30eb\u5bc6\u5ea6\u63a8\u5b9a<\/strong>\u3092\u4f7f\u7528\u3059\u308b\u3088\u3046\u306b pandas \u306b\u6307\u793a\u3059\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/span><\/p>\n
\u4ee3\u308f\u308a\u306b\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\u3092\u4f5c\u6210\u3057\u305f\u3044\u5834\u5408\u306f\u3001\u6b21\u306e\u3088\u3046\u306bkind=’hist’<\/strong>\u3092\u6307\u5b9a\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n
 #plot distribution of values in points column using histogram<\/span>\ndf[' points<\/span> ']. plot<\/span> (kind=' hist<\/span> ', edgecolor=' black<\/span> ')<\/strong> <\/pre>\n
\"\"<\/p>\n
\u3053\u306e\u65b9\u6cd5\u3067\u306f\u3001\u5206\u5e03\u306e\u5f62\u72b6\u3092\u8981\u7d04\u3059\u308b\u6ed1\u3089\u304b\u306a\u7dda\u3067\u306f\u306a\u304f\u3001\u68d2\u3092\u4f7f\u7528\u3057\u3066\u70b9\u306e<\/strong>\u5217\u5185\u306e\u5024\u306e\u983b\u5ea6\u3092\u8868\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u4f8b 2: 1 \u3064\u306e\u5217\u306e\u5024\u306e\u5206\u5e03\u3092\u5225\u306e\u5217\u3067\u30b0\u30eb\u30fc\u30d7\u5316\u3057\u3066\u30d7\u30ed\u30c3\u30c8\u3059\u308b<\/strong><\/span><\/h2>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30c1\u30fc\u30e0<\/strong>\u5217\u3054\u3068\u306b\u30b0\u30eb\u30fc\u30d7\u5316\u3055\u308c\u305f\u30dd\u30a4\u30f3\u30c8<\/strong>\u5217\u306e\u5024\u306e\u5206\u5e03\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span> <\/p>\n
 import<\/span> matplotlib.pyplot as<\/span> plt\n\n#plot distribution of points by team \n<\/span>df. groupby<\/span> (' team<\/span> ')[' points<\/span> ']. plot<\/span> (kind=' kde<\/span> ')\n\n#add legend\n<\/span>plt. legend<\/span> ([' A<\/span> ',' B<\/span> '], title=' Team<\/span> ')\n\n#add x-axis label<\/span>\nplt. xlabel<\/span> (' Points<\/span> ')\n<\/strong><\/pre>\n
\"\"<\/p>\n
\u9752\u3044\u7dda\u306f\u30c1\u30fc\u30e0 A \u306e\u9078\u624b\u306e\u30dd\u30a4\u30f3\u30c8\u5206\u5e03\u3092\u793a\u3057\u3001\u30aa\u30ec\u30f3\u30b8\u8272\u306e\u7dda\u306f\u30c1\u30fc\u30e0 B \u306e\u9078\u624b\u306e\u30dd\u30a4\u30f3\u30c8\u5206\u5e03\u3092\u793a\u3057\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 \u3067\u30d7\u30ed\u30c3\u30c8\u306b\u30bf\u30a4\u30c8\u30eb\u3092\u8ffd\u52a0\u3059\u308b\u65b9\u6cd5<\/a>
\u30d1\u30f3\u30c0\u30d7\u30ed\u30c3\u30c8\u306e\u56f3\u306e\u30b5\u30a4\u30ba\u3092\u8abf\u6574\u3059\u308b\u65b9\u6cd5<\/a>
\u8907\u6570\u306e Pandas DataFrame \u3092\u30b5\u30d6\u30d7\u30ed\u30c3\u30c8\u306b\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5<\/a>
Pandas \u3067\u30d7\u30ed\u30c3\u30c8\u51e1\u4f8b\u3092\u4f5c\u6210\u304a\u3088\u3073\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u6b21\u306e\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u306e\u5217\u5024\u306e\u5206\u5e03\u3092\u30d7\u30ed\u30c3\u30c8\u3067\u304d\u307e\u3059\u3002 \u65b9\u6cd5 1: \u5217\u5185\u306e\u5024\u306e\u5206\u5e03\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b df[‘ my_column ‘]. plot (kind=’ kde ‘) \u65b9\u6cd5 2 […]<\/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-3578","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nPandas \u3067\u5217\u5024\u306e\u5206\u5e03\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame \u306e\u5217\u5024\u306e\u5206\u5e03\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5\u3092\u4f8b\u3092\u542b\u3081\u3066\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\u30d5\u309a\u30ed\u30c3\u30c8\u306e\u5217\u5206\u5e03\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas \u3067\u5217\u5024\u306e\u5206\u5e03\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5 \u2013 Statorials\" \/>\n<meta property=\"og:description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame 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Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/ja\/#website\"},\"datePublished\":\"2023-07-16T17:22:59+00:00\",\"dateModified\":\"2023-07-16T17:22:59+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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