{"id":2025,"date":"2023-07-24T00:47:19","date_gmt":"2023-07-24T00:47:19","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%a3%e3%83%ab%e3%83%8a\/"},"modified":"2023-07-24T00:47:19","modified_gmt":"2023-07-24T00:47:19","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%83%95%e3%82%a3%e3%83%ab%e3%83%8a","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%a3%e3%83%ab%e3%83%8a\/","title":{"rendered":"Pandas fillna() \u3092\u4f7f\u7528\u3057\u3066 nan \u5024\u3092\u7f6e\u304d\u63db\u3048\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

fillna()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u5185\u306e NaN \u5024\u3092\u7f6e\u304d\u63db\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n

 #replace NaN values in one column<\/span>\ndf[' col1<\/span> '] = df[' col1<\/span> ']. fillna<\/span> (0)\n\n#replace NaN values in multiple columns\n<\/span>df[[' col1<\/span> ', ' col2<\/span> ']] = df[[' col1<\/span> ', ' col2<\/span> ']]. fillna<\/span> (0) \n\n#replace NaN values in all columns<\/span>\ndf = df. fillna<\/span> (0)<\/strong><\/pre>\n
\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u6b21\u306e pandas DataFrame \u3067\u3053\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\n\n#create DataFrame with some NaN values<\/span>\ndf = pd.DataFrame({'rating': [np.nan, 85, np.nan, 88, 94, 90, 76, 75, 87, 86],\n                   'points': [25, np.nan, 14, 16, 27, 20, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, np.nan, 5, 7, 6, 9, 9, 5],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n#view DataFrame<\/span>\ndf\n\n        rating points assists rebounds\n0 NaN 25.0 5.0 11\n1 85.0 NaN 7.0 8\n2 NaN 14.0 7.0 10\n3 88.0 16.0 NaN 6\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
\u4f8b 1: \u5217\u5185\u306e NaN \u5024\u3092\u7f6e\u63db\u3059\u308b<\/strong><\/span><\/h3>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u300cnote\u300d\u5217\u306e NaN \u5024\u3092\u30bc\u30ed\u306b\u7f6e\u304d\u63db\u3048\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #replace NaNs with zeros in 'rating' column<\/span>\ndf[' rating<\/span> '] = df[' rating<\/span> ']. fillna<\/span> (0)\n\n#view DataFrame<\/span>\ndf\n\n\trating points assists rebounds\n0 0.0 25.0 5.0 11\n1 85.0 NaN 7.0 8\n2 0.0 14.0 7.0 10\n3 88.0 16.0 NaN 6\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
\u4f8b 2: \u8907\u6570\u306e\u5217\u306e NaN \u5024\u3092\u7f6e\u63db\u3059\u308b<\/strong><\/h3>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u300cgrade\u300d\u5217\u3068\u300cpoints\u300d\u5217\u306e NaN \u5024\u3092\u30bc\u30ed\u306b\u7f6e\u304d\u63db\u3048\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #replace NaNs with zeros in 'rating' and 'points' columns<\/span>\ndf[[' rating<\/span> ', ' points<\/span> ']] = df[[' rating<\/span> ', ' points<\/span> ']]. fillna<\/span> (0)\n\n#view DataFrame\n<\/span>df\n\n\trating points assists rebounds\n0 0.0 25.0 5.0 11\n1 85.0 0.0 7.0 8\n2 0.0 14.0 7.0 10\n3 88.0 16.0 NaN 6\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
\u4f8b 3: \u3059\u3079\u3066\u306e\u5217\u306e NaN \u5024\u3092\u7f6e\u304d\u63db\u3048\u308b<\/strong><\/h3>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u5404\u5217\u306e NaN \u5024\u3092\u30bc\u30ed\u306b\u7f6e\u304d\u63db\u3048\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #replace NaNs with zeros in all columns<\/span> \ndf = df. fillna<\/span> (0)\n\n#view DataFrame\n<\/span>df\n\n        rating points assists rebounds\n0 0.0 25.0 5.0 11\n1 85.0 0.0 7.0 8\n2 0.0 14.0 7.0 10\n3 88.0 16.0 0.0 6\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
 fillna()<\/strong>\u95a2\u6570\u306e\u5b8c\u5168\u306a\u30aa\u30f3\u30e9\u30a4\u30f3 \u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306f\u3001\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><\/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
 \u30d1\u30f3\u30c0\u3067\u6b20\u640d\u5024\u3092\u6570\u3048\u308b\u65b9\u6cd5<\/a>
Pandas\u3067NaN\u5024\u3092\u542b\u3080\u884c\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5<\/a>
Pandas\u3067\u7279\u5b9a\u306e\u5024\u3092\u542b\u3080\u884c\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
fillna()\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u5185\u306e NaN \u5024\u3092\u7f6e\u304d\u63db\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002 \u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002 #replace NaN values in one column d […]<\/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-2025","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nPandas fillna() \u3092\u4f7f\u7528\u3057\u3066 NaN \u5024\u3092\u7f6e\u304d\u63db\u3048\u308b\u65b9\u6cd5 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas fillna() \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066 DataFrame \u5185\u306e NaN \u5024\u3092\u7f6e\u304d\u63db\u3048\u308b\u65b9\u6cd5\u3092\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\u30d5\u30a3\u30eb\u30ca\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas fillna() \u3092\u4f7f\u7528\u3057\u3066 NaN \u5024\u3092\u7f6e\u304d\u63db\u3048\u308b\u65b9\u6cd5 \u2013 Statorials\" \/>\n<meta property=\"og:description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas fillna() \u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066 DataFrame \u5185\u306e NaN \u5024\u3092\u7f6e\u304d\u63db\u3048\u308b\u65b9\u6cd5\u3092\u4f8b\u3068\u3068\u3082\u306b\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta 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