{"id":2026,"date":"2023-07-24T00:47:19","date_gmt":"2023-07-24T00:47:19","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%91%e1%85%b5%e1%86%af%e1%84%82%e1%85%a1\/"},"modified":"2023-07-24T00:47:19","modified_gmt":"2023-07-24T00:47:19","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%91%e1%85%b5%e1%86%af%e1%84%82%e1%85%a1","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%91%e1%85%b5%e1%86%af%e1%84%82%e1%85%a1\/","title":{"rendered":"Pandas fillna()\ub97c \uc0ac\uc6a9\ud558\uc5ec nan \uac12\uc744 \ubc14\uafb8\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

fillna()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 NaN \uac12\uc744 \ubc14\uafc0 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ud568\uc218\ub294 \ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/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
 \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub2e4\uc74c pandas DataFrame\uc5d0\uc11c \uc774 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/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
 \uc608\uc2dc 1: \uc5f4\uc758 NaN \uac12 \ubc14\uafb8\uae30<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 “note” \uc5f4\uc5d0\uc11c NaN \uac12\uc744 0\uc73c\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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
 \uc608\uc2dc 2: \uc5ec\ub7ec \uc5f4\uc758 NaN \uac12 \ubc14\uafb8\uae30<\/strong><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 “\ub4f1\uae09” \ubc0f “\uc810\uc218” \uc5f4\uc5d0\uc11c NaN \uac12\uc744 0\uc73c\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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
 \uc608\uc2dc 3: \ubaa8\ub4e0 \uc5f4\uc758 NaN \uac12 \ubc14\uafb8\uae30<\/strong><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \uac01 \uc5f4\uc758 NaN \uac12\uc744 0\uc73c\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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> \ud568\uc218\uc5d0 \ub300\ud55c \uc804\uccb4 \uc628\ub77c\uc778 \uc124\uba85\uc11c\ub294 \uc5ec\uae30\uc5d0\uc11c<\/a> \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ud32c\ub354\uc5d0\uc11c \ub204\ub77d\ub41c \uac12\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c NaN \uac12\uc774 \uc788\ub294 \ud589\uc744 \uc0ad\uc81c\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c \ud2b9\uc815 \uac12\uc774 \ud3ec\ud568\ub41c \ud589\uc744 \uc0ad\uc81c\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
fillna() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 NaN \uac12\uc744 \ubc14\uafc0 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 \ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. #replace NaN values in one column df[‘ col1 ‘] = df[‘ col1 ‘]. fillna (0) #replace NaN values in multiple columns df[[‘ col1 ‘, ‘ col2 ‘]] = df[[‘ col1 ‘, ‘ col2 ‘]]. fillna (0) #replace NaN values […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-2026","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas fillna()\ub97c \uc0ac\uc6a9\ud558\uc5ec NaN \uac12\uc744 \ubc14\uafb8\ub294 \ubc29\ubc95 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 pandas fillna() 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