{"id":1241,"date":"2023-07-27T04:09:41","date_gmt":"2023-07-27T04:09:41","guid":{"rendered":"https:\/\/statorials.org\/ja\/numpy%e9%85%8d%e5%88%97%e3%82%92pandas%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%83%95%e3%83%ac%e3%83%bc%e3%83%a0%e3%81%ab%e8%bf%bd%e5%8a%a0%e3%81%97%e3%81%be%e3%81%99\/"},"modified":"2023-07-27T04:09:41","modified_gmt":"2023-07-27T04:09:41","slug":"numpy%e9%85%8d%e5%88%97%e3%82%92pandas%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%83%95%e3%83%ac%e3%83%bc%e3%83%a0%e3%81%ab%e8%bf%bd%e5%8a%a0%e3%81%97%e3%81%be%e3%81%99","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/numpy%e9%85%8d%e5%88%97%e3%82%92pandas%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%83%95%e3%83%ac%e3%83%bc%e3%83%a0%e3%81%ab%e8%bf%bd%e5%8a%a0%e3%81%97%e3%81%be%e3%81%99\/","title":{"rendered":"Numpy \u914d\u5217\u3092 pandas dataframe \u306b\u8ffd\u52a0\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

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NumPy \u914d\u5217\u3092\u65b0\u3057\u3044\u5217\u3068\u3057\u3066 pandas DataFrame \u306b\u8ffd\u52a0\u3057\u305f\u3044\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n

\u5e78\u3044\u306a\u3053\u3068\u306b\u3001\u3053\u308c\u306f\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u7c21\u5358\u306b\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

 df[' new_column<\/span> '] = array_name. tolist<\/span> ()\n<\/strong><\/pre>\n
\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u3053\u306e\u69cb\u6587\u306e\u5b9f\u969b\u306e\u4f7f\u7528\u4f8b\u3092\u3044\u304f\u3064\u304b\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u4f8b 1: NumPy \u914d\u5217\u3092 DataFrame \u306e\u65b0\u3057\u3044\u5217\u3068\u3057\u3066\u8ffd\u52a0\u3057\u307e\u3059<\/strong><\/h3>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30d0\u30b9\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb\u9078\u624b\u306e\u7d71\u8a08\u3092\u4fdd\u6301\u3059\u308b\u30d1\u30f3\u30c0 DataFrame \u3092\u4f5c\u6210\u3057\u3001\u300cblocks\u300d\u3068\u3044\u3046\u30e9\u30d9\u30eb\u306e\u4ed8\u3044\u305f\u65b0\u3057\u3044\u5217\u3068\u3057\u3066 NumPy \u914d\u5217\u3092\u8ffd\u52a0\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd<\/span>\n\n#create pandas DataFrame\n<\/span>df = pd.DataFrame({' points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#create NumPy array for 'blocks'\n<\/span>blocks = np. array<\/span> ([2, 3, 1, 0, 2, 7, 8, 2])\n\n#add 'blocks' array as new column in DataFrame\n<\/span>df[' blocks<\/span> '] = blocks. tolist<\/span> ()\n\n#display the DataFrame\n<\/span>print(df)\n\n   points assists rebounds blocks\n0 25 5 11 2\n1 12 7 8 3\n2 15 7 10 1\n3 14 9 6 0\n4 19 12 6 2\n5 23 9 5 7\n6 25 9 9 8\n7 29 4 12 2\n<\/strong><\/pre>\n
\u65b0\u3057\u3044 DataFrame \u306b\u306f\u3001 \u300c Blocks \u300d<\/em>\u3068\u3044\u3046\u30bf\u30a4\u30c8\u30eb\u306e\u8ffd\u52a0\u306e\u5217\u304c\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
\u4f8b 2: NumPy \u884c\u5217\u3092 DataFrame \u306e\u65b0\u3057\u3044\u5217\u3068\u3057\u3066\u8ffd\u52a0\u3059\u308b<\/strong><\/h3>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30d0\u30b9\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb\u9078\u624b\u306e\u7d71\u8a08\u3092\u4fdd\u6301\u3059\u308b\u30d1\u30f3\u30c0 DataFrame \u3092\u4f5c\u6210\u3057\u3001\u300cblocks\u300d\u3068\u3044\u3046\u30e9\u30d9\u30eb\u306e\u4ed8\u3044\u305f\u65b0\u3057\u3044\u5217\u3068\u3057\u3066 NumPy \u914d\u5217\u3092\u8ffd\u52a0\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd<\/span>\n\n#create pandas DataFrame\n<\/span>df = pd.DataFrame({' points<\/span> ': [25, 12, 15, 14, 19, 23\n\n#create NumPy matrix\n<\/span>mat = np.matrix([[2, 3],\n                 [1, 0],\n                 [2, 7],\n                 [8, 2],\n                 [3, 4],\n                 [7, 7],\n                 [7, 5],\n                 [6, 3]])\n\n#add NumPy matrix as new columns in DataFrame\n<\/span>df_new = pd. concat<\/span> ([df, pd.DataFrame(mat)], axis= 1<\/span> )\n\n#display new DataFrame\n<\/span>print(df_new)\n\n   points assists rebounds 0 1\n0 25 5 11 2 3\n1 12 7 8 1 0\n2 15 7 10 2 7\n3 14 9 6 8 2\n4 19 12 6 3 4\n5 23 9 5 7 7\n6 25 9 9 7 5\n7 29 4 12 6 3\n<\/strong><\/pre>\n
 DataFrame \u306b\u8ffd\u52a0\u3057\u305f\u884c\u5217\u306e\u5217\u540d\u306b\u306f\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u5217\u540d0<\/strong>\u30681<\/strong>\u304c\u4e0e\u3048\u3089\u308c\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
 df.columns<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u3053\u308c\u3089\u306e\u5217\u306e\u540d\u524d\u3092\u7c21\u5358\u306b\u5909\u66f4\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #rename columns<\/span>\ndf_new. columns<\/span> = ['pts', 'ast', 'rebs', 'new1', 'new2']<\/span>\n\n#display DataFrame<\/span>\nprint(df_new)\n<\/span>\npts ast rebs new1 new2\n0 25 5 11 2 3\n1 12 7 8 1 0\n2 15 7 10 2 7\n3 14 9 6 8 2\n4 19 12 6 3 4\n5 23 9 5 7 7\n6 25 9 9 7 5\n7 29 4 12 6 3\n<\/span><\/span><\/span><\/strong><\/pre>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
\u8907\u6570\u306e Pandas DataFrame \u3092\u30b9\u30bf\u30c3\u30af\u3059\u308b\u65b9\u6cd5<\/a>
\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u4e0a\u306e 2 \u3064\u306e Pandas DataFrame \u3092\u30de\u30fc\u30b8\u3059\u308b\u65b9\u6cd5
Pandas DataFrame \u3092 NumPy \u914d\u5217\u306b\u5909\u63db\u3059\u308b\u65b9\u6cd5<\/a>
Pandas \u3067\u5217\u306e\u540d\u524d\u3092\u5909\u66f4\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
NumPy \u914d\u5217\u3092\u65b0\u3057\u3044\u5217\u3068\u3057\u3066 pandas DataFrame \u306b\u8ffd\u52a0\u3057\u305f\u3044\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002 \u5e78\u3044\u306a\u3053\u3068\u306b\u3001\u3053\u308c\u306f\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u7c21\u5358\u306b\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002 df[‘ new_column ‘] = array_n […]<\/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-1241","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nNumpy \u914d\u5217\u3092 Pandas DataFrame \u306b\u8ffd\u52a0\u3059\u308b\u65b9\u6cd5 - Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u30011 \u3064\u4ee5\u4e0a\u306e NumPy \u914d\u5217\u3092 pandas DataFrame \u306b\u8ffd\u52a0\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\/numpy\u914d\u5217\u3092pandas\u30c6\u3099\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u8ffd\u52a0\u3057\u307e\u3059\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Numpy \u914d\u5217\u3092 Pandas DataFrame \u306b\u8ffd\u52a0\u3059\u308b\u65b9\u6cd5 - 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