{"id":2662,"date":"2023-07-21T06:47:49","date_gmt":"2023-07-21T06:47:49","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%86%ab%e1%84%83%e1%85%a1-%e1%84%91%e1%85%b5%e1%86%af%e1%84%82%e1%85%a1pandas-fillna\/"},"modified":"2023-07-21T06:47:49","modified_gmt":"2023-07-21T06:47:49","slug":"%e1%84%91%e1%85%a1%e1%86%ab%e1%84%83%e1%85%a1-%e1%84%91%e1%85%b5%e1%86%af%e1%84%82%e1%85%a1pandas-fillna","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%86%ab%e1%84%83%e1%85%a1-%e1%84%91%e1%85%b5%e1%86%af%e1%84%82%e1%85%a1pandas-fillna\/","title":{"rendered":"Pandas: nan \uac12\uc744 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6b0\ub294 \ubc29\ubc95(\uc608\uc81c 3\uac1c)"},"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 \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\ub294 \uc138 \uac00\uc9c0 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: \uc5f4\uc758 NaN \uac12\uc744 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6b0\uae30<\/strong><\/span><\/p>\n

 df[' col1<\/span> '] = df[' col1<\/span> ']. fillna<\/span> (df[' col1<\/span> ']. mean<\/span> ())<\/strong><\/pre>\n
 \ubc29\ubc95 2: \uc5ec\ub7ec \uc5f4\uc758 NaN \uac12\uc744 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6b0\uae30<\/strong><\/span><\/p>\n
 df[[' col1<\/span> ', ' col2<\/span> ']] = df[[' col1<\/span> ', ' col2<\/span> ']]. fillna<\/span> (df[[' col1<\/span> ',' col2<\/span> ']]. mean<\/span> ())<\/strong><\/pre>\n
 \ubc29\ubc95 3: \ubaa8\ub4e0 \uc5f4\uc758 NaN \uac12\uc744 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6b0\uae30<\/strong><\/span><\/p>\n
 df = df. fillna<\/span> ( df.mean<\/span> ())<\/strong><\/pre>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub2e4\uc74c pandas DataFrame\uc5d0\uc11c \uc2e4\uc81c\ub85c \uac01 \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\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<\/span> ({' rating<\/span> ': [np.nan, 85, np.nan, 88, 94, 90, 76, 75, 87, 86],\n                   ' points<\/span> ': [25, np.nan, 14, 16, 27, 20, 12, 15, 14, 19],\n                   ' assists<\/span> ': [5, 7, 7, np.nan, 5, 7, 6, 9, 9, 5],\n                   ' rebounds<\/span> ': [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\uc744 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6b0\uae30<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ub4f1\uae09<\/strong> \uc5f4\uc758 NaN \uac12\uc744 \ub4f1\uae09<\/strong> \uc5f4\uc758 \ud3c9\uade0\uac12\uc73c\ub85c \ucc44\uc6b0\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #fill NaNs with column mean in 'rating' column<\/span>\ndf[' rating<\/span> '] = df[' rating<\/span> ']. fillna<\/span> (df[' rating<\/span> ']. mean<\/span> ())\n\n<\/span>#view updated DataFrame<\/span>\ndf\n\n\trating points assists rebounds\n0 85.125 25.0 5.0 11\n1 85,000 NaN 7.0 8\n2 85.125 14.0 7.0 10\n3 88,000 16.0 NaN 6\n4 94,000 27.0 5.0 6\n5 90,000 20.0 7.0 9\n6 76,000 12.0 6.0 6\n7 75,000 15.0 9.0 10\n8 87,000 14.0 9.0 10\n9 86,000 19.0 5.0 7\n<\/strong><\/pre>\n
 \ub4f1\uae09<\/strong> \uc5f4\uc758 \ud3c9\uade0 \uac12\uc740 85.125<\/strong> \uc774\ubbc0\ub85c \ub4f1\uae09<\/strong> \uc5f4\uc758 \uac01 NaN \uac12\uc740 \ud574\ub2f9 \uac12\uc73c\ub85c \ucc44\uc6cc\uc84c\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\uc2dc 2:<\/strong> \uc5ec\ub7ec \uc5f4\uc758 NaN \uac12\uc744 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6b0\uae30<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ud3c9\uc810<\/strong> \ubc0f \uc810\uc218<\/strong> \uc5f4\uc758 NaN \uac12\uc744 \ud574\ub2f9 \uc5f4 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6b0\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #fill NaNs with column means in 'rating' and 'points' columns<\/span>\ndf[[' rating<\/span> ', ' points<\/span> ']] = df[[' rating<\/span> ', ' points<\/span> ']]. fillna<\/span> (df[[' rating<\/span> ',' points<\/span> ']]. mean<\/span> ())\n\n#view updated DataFrame\n<\/span>df\n\n\trating points assists rebounds\n0 85.125 25.0 5.0 11\n1 85,000 18.0 7.0 8\n2 85.125 14.0 7.0 10\n3 88,000 16.0 NaN 6\n4 94,000 27.0 5.0 6\n5 90,000 20.0 7.0 9\n6 76,000 12.0 6.0 6\n7 75,000 15.0 9.0 10\n8 87,000 14.0 9.0 10\n9 86,000 19.0 5.0 7\n<\/strong><\/pre>\n
 \uc131\uc801<\/strong> \ubc0f \uc810\uc218<\/strong> \uc5f4\uc758 NaN \uac12\uc740 \ud574\ub2f9 \uc5f4 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6cc\uc84c\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\uc2dc 3: \ubaa8\ub4e0 \uc5f4\uc758 NaN \uac12\uc744 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6c1\ub2c8\ub2e4.<\/strong><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \uac01 \uc5f4\uc758 NaN \uac12\uc744 \uc5f4 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6b0\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #fill NaNs with column means in each column<\/span> \ndf = df. fillna<\/span> ( df.mean<\/span> ())\n\n#view updated DataFrame\n<\/span>df\n\n        rating points assists rebounds\n0 85.125 25.0 5.000000 11\n1 85,000 18.0 7,000000 8\n2 85.125 14.0 7.000000 10\n3 88,000 16.0 6.666667 6\n4 94,000 27.0 5,000000 6\n5 90,000 20.0 7,000000 9\n6 76,000 12.0 6,000000 6\n7 75,000 15.0 9,000000 10\n8 87,000 14.0 9,000000 10\n9 86,000 19.0 5,000000 7\n<\/strong><\/pre>\n
 \uac01 \uc5f4\uc758 NaN \uac12\uc740 \ud574\ub2f9 \uc5f4\uc758 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6cc\uc84c\uc2b5\ub2c8\ub2e4.<\/span><\/p>\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 \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\ub294 \uc138 \uac00\uc9c0 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: \uc5f4\uc758 NaN \uac12\uc744 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6b0\uae30 df[‘ col1 ‘] = df[‘ col1 ‘]. fillna (df[‘ col1 ‘]. mean ()) \ubc29\ubc95 2: \uc5ec\ub7ec \uc5f4\uc758 NaN \uac12\uc744 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6b0\uae30 df[[‘ col1 ‘, ‘ col2 ‘]] = df[[‘ […]<\/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-2662","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas: NaN \uac12\uc744 \ud3c9\uade0\uc73c\ub85c \ucc44\uc6b0\ub294 \ubc29\ubc95(\uc608\uc81c 3\uac1c) - 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