{"id":3225,"date":"2023-07-18T14:10:40","date_gmt":"2023-07-18T14:10:40","guid":{"rendered":"https:\/\/statorials.org\/ko\/pandas-fillna-%e1%84%90%e1%85%b3%e1%86%a8%e1%84%8c%e1%85%a5%e1%86%bc-%e1%84%8b%e1%85%a7%e1%86%af\/"},"modified":"2023-07-18T14:10:40","modified_gmt":"2023-07-18T14:10:40","slug":"pandas-fillna-%e1%84%90%e1%85%b3%e1%86%a8%e1%84%8c%e1%85%a5%e1%86%bc-%e1%84%8b%e1%85%a7%e1%86%af","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/pandas-fillna-%e1%84%90%e1%85%b3%e1%86%a8%e1%84%8c%e1%85%a5%e1%86%bc-%e1%84%8b%e1%85%a7%e1%86%af\/","title":{"rendered":"Pandas: \ud2b9\uc815 \uc5f4\uc5d0 fillna()\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

fillna()<\/strong> \uc640 \ud568\uaed8 \ub2e4\uc74c \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 \ud2b9\uc815 \uc5f4\uc5d0 \uc788\ub294 NaN \uac12\uc744 \ubc14\uafc0 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: \ud2b9\uc815 \uc5f4\uc5d0 fillna() \uc0ac\uc6a9<\/strong><\/span><\/p>\n

 df[' col1<\/span> '] = df[' col1<\/span> ']. fillna<\/span> (0)\n<\/strong><\/pre>\n
 \ubc29\ubc95 2: \uc5ec\ub7ec \ud2b9\uc815 \uc5f4\uc5d0 fillna() \uc0ac\uc6a9<\/strong><\/span><\/p>\n
 df[[' col1<\/span> ', ' col2<\/span> ']] = df[[' col1<\/span> ', ' col2<\/span> ']]. 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<\/span> ({'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\uc81c 1: \ud2b9\uc815 \uc5f4\uc5d0 fillna() \uc0ac\uc6a9<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 fillna()\ub97c<\/strong> \uc0ac\uc6a9\ud558\uc5ec “note” \uc5f4\uc5d0\uc11c\ub9cc 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
 NaN \uac12\uc740 “note” \uc5f4\uc5d0\uc11c\ub9cc \ub300\uccb4\ub418\uc5c8\uc73c\uba70 \ub2e4\ub978 \ubaa8\ub4e0 \uc5f4\uc740 \uadf8\ub300\ub85c \uc720\uc9c0\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 2:<\/strong> \uc5ec\ub7ec \ud2b9\uc815 \uc5f4\uc5d0 fillna()<\/strong><\/span> \uc0ac\uc6a9<\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 fillna()\ub97c<\/strong> \uc0ac\uc6a9\ud558\uc5ec “grade” \ubc0f “points” \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
 NaN \uac12\uc740 “\ub4f1\uae09” \ubc0f “\uc810\uc218” \uc5f4\uc5d0\uc11c \ub300\uccb4\ub418\uc5c8\uc9c0\ub9cc \ub2e4\ub978 \uc5f4\uc740 \uadf8\ub300\ub85c \uc720\uc9c0\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \ucc38\uace0<\/strong> : \uc5ec\uae30\uc5d0\uc11c<\/a> pandas fillna()<\/strong> \ud568\uc218\uc5d0 \ub300\ud55c \uc804\uccb4 \ubb38\uc11c\ub97c \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() \uc640 \ud568\uaed8 \ub2e4\uc74c \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 \ud2b9\uc815 \uc5f4\uc5d0 \uc788\ub294 NaN \uac12\uc744 \ubc14\uafc0 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: \ud2b9\uc815 \uc5f4\uc5d0 fillna() \uc0ac\uc6a9 df[‘ col1 ‘] = df[‘ col1 ‘]. fillna (0) \ubc29\ubc95 2: \uc5ec\ub7ec \ud2b9\uc815 \uc5f4\uc5d0 fillna() \uc0ac\uc6a9 df[[‘ col1 ‘, ‘ col2 ‘]] = df[[‘ col1 ‘, ‘ col2 ‘]]. fillna (0) \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 […]<\/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-3225","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas: \ud2b9\uc815 \uc5f4\uc5d0 fillna()\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas\uc758 fillna() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud2b9\uc815 \uc5f4\uc758 \uac12\uc744 \ubc14\uafb8\ub294 \ubc29\ubc95\uc744 \uc608\uc81c\ub97c \ud3ec\ud568\ud558\uc5ec \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\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\/ko\/pandas-fillna-\u1110\u1173\u11a8\u110c\u1165\u11bc-\u110b\u1167\u11af\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: \ud2b9\uc815 \uc5f4\uc5d0 fillna()\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95 \u2013 Statorials\" \/>\n<meta property=\"og:description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas\uc758 fillna() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud2b9\uc815 \uc5f4\uc758 \uac12\uc744 \ubc14\uafb8\ub294 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