{"id":3236,"date":"2023-07-18T12:40:18","date_gmt":"2023-07-18T12:40:18","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%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%83%95%e3%83%ac%e3%83%bc%e3%83%a0%e3%82%92%e4%bf%9d%e5%ad%98%e3%81%97%e3%81%be%e3%81%99\/"},"modified":"2023-07-18T12:40:18","modified_gmt":"2023-07-18T12:40:18","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%af%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%82%92%e4%bf%9d%e5%ad%98%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%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%83%95%e3%83%ac%e3%83%bc%e3%83%a0%e3%82%92%e4%bf%9d%e5%ad%98%e3%81%97%e3%81%be%e3%81%99\/","title":{"rendered":"\u5f8c\u3067\u4f7f\u7528\u3067\u304d\u308b\u3088\u3046\u306b pandas dataframe \u3092\u4fdd\u5b58\u3059\u308b\u65b9\u6cd5 (\u4f8b\u3042\u308a)"},"content":{"rendered":"

<\/p>\n

\n

\u591a\u304f\u306e\u5834\u5408\u3001CSV \u30d5\u30a1\u30a4\u30eb\u304b\u3089\u30c7\u30fc\u30bf\u3092\u518d\u30a4\u30f3\u30dd\u30fc\u30c8\u305b\u305a\u306b\u3001\u5f8c\u3067\u4f7f\u7528\u3067\u304d\u308b\u3088\u3046\u306b\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4fdd\u5b58\u3057\u305f\u3044\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u308c\u3092\u884c\u3046\u6700\u3082\u7c21\u5358\u306a\u65b9\u6cd5\u306f\u3001 to_pickle()<\/a>\u3092\u4f7f\u7528\u3057\u3066 DataFrame \u3092 pickle \u30d5\u30a1\u30a4\u30eb\u3068\u3057\u3066\u4fdd\u5b58\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n

 df. to_pickle<\/span> (\" my_data.pkl<\/span> \")<\/strong><\/pre>\n
\u3053\u308c\u306b\u3088\u308a\u3001\u73fe\u5728\u306e\u4f5c\u696d\u74b0\u5883\u306b DataFrame \u304c\u4fdd\u5b58\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306b\u3001 read_pickle()<\/a>\u3092\u4f7f\u7528\u3057\u3066\u3001pickle \u30d5\u30a1\u30a4\u30eb\u304b\u3089 DataFrame \u3092\u3059\u3070\u3084\u304f\u8aad\u307f\u53d6\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 df = pd. read_pickle<\/span> (\" my_data.pkl<\/span> \")\n<\/strong><\/pre>\n
\u6b21\u306e\u4f8b\u306f\u3001\u3053\u308c\u3089\u306e\u95a2\u6570\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u4f8b: Pandas DataFrame \u3092\u4fdd\u5b58\u3057\u3066\u30ed\u30fc\u30c9\u3059\u308b<\/strong><\/span><\/h2>\n
\u3055\u307e\u3056\u307e\u306a\u30d0\u30b9\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb \u30c1\u30fc\u30e0\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3080\u6b21\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4f5c\u6210\u3059\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = 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\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points assists rebounds\n0 A 18 5 11\n1 B 22 7 8\n2 C 19 7 10\n3 D 14 9 6\n4 E 14 12 6\n5 F 11 9 5\n6 G 20 9 9\n7:28 4 12<\/strong><\/span><\/pre>\n
 df.info()<\/strong>\u3092\u4f7f\u7528\u3057\u3066\u3001DataFrame \u5185\u306e\u5404\u5909\u6570\u306e\u30c7\u30fc\u30bf\u578b\u3092\u8868\u793a\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #view DataFrame info\n<\/span>print<\/span> ( df.info<\/span> ())\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 8 entries, 0 to 7\nData columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 team 8 non-null object\n 1 point 8 non-null int64 \n 2 assists 8 non-null int64 \n 3 rebounds 8 non-null int64 \ndtypes: int64(3), object(1)\nmemory usage: 292.0+ bytes\nNone\n<\/strong><\/span><\/pre>\n
 to_pickle()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u306e DataFrame \u3092.pkl<\/strong>\u62e1\u5f35\u5b50\u306e\u4ed8\u3044\u305f pickle \u30d5\u30a1\u30a4\u30eb\u306b\u4fdd\u5b58\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #save DataFrame to pickle file\n<\/span>df. to_pickle<\/span> (\" my_data.pkl<\/span> \")\n<\/strong><\/span><\/pre>\n
 DataFrame \u306f\u3001\u73fe\u5728\u306e\u4f5c\u696d\u74b0\u5883\u3067 pickle \u30d5\u30a1\u30a4\u30eb\u3068\u3057\u3066\u4fdd\u5b58\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306b\u3001 read_pickle()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066 DataFrame \u3092\u3059\u3070\u3084\u304f\u8aad\u307f\u53d6\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #read DataFrame from pickle file\n<\/span>df=pd. read_pickle<\/span> (\" my_data.pkl<\/span> \")\n\n#view DataFrame<\/span>\nprint<\/span> (df)\n\nteam points assists rebounds\n0 A 18 5 11\n1 B 22 7 8\n2 C 19 7 10\n3 D 14 9 6\n4 E 14 12 6\n5 F 11 9 5\n6 G 20 9 9\n7:28 4 12\n<\/strong><\/span><\/pre>\n
\u518d\u5ea6df.info()<\/strong>\u3092\u4f7f\u7528\u3057\u3066\u3001\u5404\u5217\u306e\u30c7\u30fc\u30bf\u578b\u304c\u524d\u3068\u540c\u3058\u3067\u3042\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #view DataFrame info\n<\/span>print<\/span> ( df.info<\/span> ())\n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 8 entries, 0 to 7\nData columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 team 8 non-null object\n 1 point 8 non-null int64 \n 2 assists 8 non-null int64 \n 3 rebounds 8 non-null int64 \ndtypes: int64(3), object(1)\nmemory usage: 292.0+ bytes\nNone<\/strong><\/span><\/pre>\n
 pickle \u30d5\u30a1\u30a4\u30eb\u3092\u4f7f\u7528\u3059\u308b\u5229\u70b9\u306f\u3001DataFrame \u3092\u4fdd\u5b58\u304a\u3088\u3073\u30ed\u30fc\u30c9\u3059\u308b\u3068\u304d\u306b\u5404\u5217\u306e\u30c7\u30fc\u30bf\u578b\u304c\u4fdd\u6301\u3055\u308c\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u306b\u306f\u3001pickle \u30d5\u30a1\u30a4\u30eb\u304c DataFrame \u306e\u5143\u306e\u72b6\u614b\u3092\u4fdd\u6301\u3059\u308b\u305f\u3081\u3001DataFrame \u3067\u5909\u63db\u3092\u5b9f\u884c\u3059\u308b\u5fc5\u8981\u304c\u306a\u3044\u305f\u3081\u3001CSV \u30d5\u30a1\u30a4\u30eb\u306e\u4fdd\u5b58\u304a\u3088\u3073\u30ed\u30fc\u30c9\u3088\u308a\u3082\u5229\u70b9\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u306e\u4ed6\u306e\u4e00\u822c\u7684\u306a\u30a8\u30e9\u30fc\u3092\u4fee\u6b63\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n
 \u30d1\u30f3\u30c0\u306e KeyError \u3092\u4fee\u6b63\u3059\u308b\u65b9\u6cd5<\/a>
\u4fee\u6b63\u65b9\u6cd5: ValueError: float NaN \u3092 int \u306b\u5909\u63db\u3067\u304d\u307e\u305b\u3093<\/a>
\u4fee\u6b63\u65b9\u6cd5: ValueError: \u30aa\u30da\u30e9\u30f3\u30c9\u3092\u30b7\u30a7\u30a4\u30d7\u3067\u30d6\u30ed\u30fc\u30c9\u30ad\u30e3\u30b9\u30c8\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u591a\u304f\u306e\u5834\u5408\u3001CSV \u30d5\u30a1\u30a4\u30eb\u304b\u3089\u30c7\u30fc\u30bf\u3092\u518d\u30a4\u30f3\u30dd\u30fc\u30c8\u305b\u305a\u306b\u3001\u5f8c\u3067\u4f7f\u7528\u3067\u304d\u308b\u3088\u3046\u306b\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4fdd\u5b58\u3057\u305f\u3044\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002 \u3053\u308c\u3092\u884c\u3046\u6700\u3082\u7c21\u5358\u306a\u65b9\u6cd5\u306f\u3001 to_pickle()\u3092\u4f7f\u7528\u3057\u3066 DataFrame \u3092  […]<\/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-3236","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\n\u5f8c\u3067\u4f7f\u7528\u3067\u304d\u308b\u3088\u3046\u306b Pandas DataFrame \u3092\u4fdd\u5b58\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - \u7d71\u8a08<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame \u3092\u4fdd\u5b58\u3057\u3066\u5f8c\u3067\u4f7f\u7528\u3067\u304d\u308b\u3088\u3046\u306b\u3059\u308b\u65b9\u6cd5\u3092\u4f8b\u3092\u793a\u3057\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\u306f\u30c6\u3099\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4fdd\u5b58\u3057\u307e\u3059\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5f8c\u3067\u4f7f\u7528\u3067\u304d\u308b\u3088\u3046\u306b Pandas DataFrame \u3092\u4fdd\u5b58\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - 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