{"id":2377,"date":"2023-07-22T12:59:53","date_gmt":"2023-07-22T12:59:53","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%82%e1%85%a1%e1%86%ab%e1%84%8b%e1%85%b3%e1%86%af-%e1%84%81%e1%85%b3%e1%86%ab-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%90\/"},"modified":"2023-07-22T12:59:53","modified_gmt":"2023-07-22T12:59:53","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%82%e1%85%a1%e1%86%ab%e1%84%8b%e1%85%b3%e1%86%af-%e1%84%81%e1%85%b3%e1%86%ab-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%90","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%82%e1%85%b3%e1%86%ab-%e1%84%82%e1%85%a1%e1%86%ab%e1%84%8b%e1%85%b3%e1%86%af-%e1%84%81%e1%85%b3%e1%86%ab-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%90\/","title":{"rendered":"Pandas: nan \uac12\uc744 \ubb38\uc790\uc5f4\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec NaN \uac12\uc744 Pandas DataFrame\uc758 \ubb38\uc790\uc5f4\ub85c \ubc14\uafc0 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: DataFrame \uc804\uccb4\uc5d0\uc11c NaN \uac12\uc744 \ubb38\uc790\uc5f4\ub85c \ub300\uccb4<\/strong><\/span><\/p>\n

 df. fillna<\/span> ('', inplace= True<\/span> )\n<\/strong><\/pre>\n
 \ubc29\ubc95 2: \ud2b9\uc815 \uc5f4\uc758 NaN \uac12\uc744 \ubb38\uc790\uc5f4\ub85c \ub300\uccb4<\/strong><\/span><\/p>\n
 df[[' col1<\/span> ', ' col2<\/span> ']] = df[[' col1<\/span> ', ' col2<\/span> ']]. fillna<\/span> ('')<\/strong><\/pre>\n
 \ubc29\ubc95 3: NaN \uac12\uc744 \uc5f4\uc758 \ubb38\uc790\uc5f4\ub85c \ubc14\uafb8\uae30<\/strong><\/span><\/p>\n
 df. col1<\/span> = df. col1<\/span> . fillna<\/span> ('')<\/strong><\/pre>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub2e4\uc74c Pandas DataFrame\uc5d0\uc11c \uac01 \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#create DataFrame with some NaN values\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' points<\/span> ': [np.nan, 11, 7, 7, 8, 6, 14, 15],\n                   ' assists<\/span> ': [5, np.nan, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, np.nan, 6, 5, 9, np.nan]})<\/span>\n\n#view DataFrame<\/span>\ndf\n\nteam points assists rebounds\n0 A NaN 5.0 11.0\n1 A 11.0 NaN 8.0\n2 A 7.0 7.0 10.0\n3 A 7.0 9.0 NaN\n4 B 8.0 12.0 6.0\n5 B 6.0 9.0 5.0\n6 B 14.0 9.0 9.0\n7 B 15.0 4.0 NaN\n<\/span><\/span><\/strong><\/pre>\n
 \ubc29\ubc95 1: DataFrame \uc804\uccb4\uc5d0\uc11c NaN \uac12\uc744 \ubb38\uc790\uc5f4\ub85c \ub300\uccb4<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \uc804\uccb4 DataFrame\uc758 \uac01 NaN \uac12\uc744 \ube48 \ubb38\uc790\uc5f4\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #replace NaN values in all columns with empty string\n<\/span>df. fillna<\/span> ('', inplace= True<\/span> )\n\n#view updated DataFrame\n<\/span>df\n\n\tteam points assists rebounds\n0 A 5.0 11.0\n1 A 11.0 8.0\n2 A 7.0 7.0 10.0\n3 A 7.0 9.0\t\n4 B 8.0 12.0 6.0\n5 B 6.0 9.0 5.0\n6 B 14.0 9.0 9.0\n7B 15.0 4.0\t\n<\/span><\/span><\/strong><\/pre>\n
 \uac01 \uc5f4\uc758 \uac01 NaN \uac12\uc740 \ube48 \ubb38\uc790\uc5f4\ub85c \ub300\uccb4\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ubc29\ubc95 2: \ud2b9\uc815 \uc5f4\uc758 NaN \uac12\uc744 \ubb38\uc790\uc5f4\ub85c \ub300\uccb4<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ud2b9\uc815 \uc5f4\uc758 NaN \uac12\uc744 \ud2b9\uc815 \ubb38\uc790\uc5f4\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #replace NaN values in 'points' and 'rebounds' columns with 'none'\n<\/span>df[[' points<\/span> ', ' rebounds<\/span> ']] = df[[' points<\/span> ', ' rebounds<\/span> ']]. fillna<\/span> (' none<\/span> ')\n\n#view updated DataFrame\n<\/span>df\n\n        team points assists rebounds\n0 A none 5.0 11.0\n1 A 11.0 NaN 8.0\n2 A 7.0 7.0 10.0\n3 A 7.0 9.0 none\n4 B 8.0 12.0 6.0\n5 B 6.0 9.0 5.0\n6 B 14.0 9.0 9.0\n7 B 15.0 4.0 none\t\n<\/span><\/span><\/strong><\/pre>\n
 “\ud3ec\uc778\ud2b8” \ubc0f “\ub9ac\ubc14\uc6b4\ub4dc” \uc5f4\uc758 NaN \uac12\uc740 “none” \ubb38\uc790\uc5f4\ub85c \ub300\uccb4\ub418\uc5c8\uc9c0\ub9cc “\uc5b4\uc2dc\uc2a4\ud2b8” \uc5f4\uc758 NaN \uac12\uc740 \ubcc0\uacbd\ub418\uc9c0 \uc54a\uc558\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ubc29\ubc95 3: NaN \uac12\uc744 \uc5f4\uc758 \ubb38\uc790\uc5f4\ub85c \ubc14\uafb8\uae30<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \uc5f4\uc758 NaN \uac12\uc744 \ud2b9\uc815 \ubb38\uc790\uc5f4\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #replace NaN values in 'points' column with 'zero'\n<\/span>df. points<\/span> = df. points<\/span> . fillna<\/span> (' zero<\/span> ')\n\n#view updated DataFrame\n<\/span>df\n\n\tteam points assists rebounds\n0 To zero 5.0 11.0\n1 A 11.0 NaN 8.0\n2 A 7.0 7.0 10.0\n3 A 7.0 9.0 NaN\n4 B 8.0 12.0 6.0\n5 B 6.0 9.0 5.0\n6 B 14.0 9.0 9.0\n7 B 15.0 4.0 NaN\t\n<\/span><\/span><\/strong><\/pre>\n
 “\ud3ec\uc778\ud2b8” \uc5f4\uc758 NaN \uac12\uc740 \ubb38\uc790\uc5f4 “0”\uc73c\ub85c \ub300\uccb4\ub418\uc5c8\uc9c0\ub9cc “\uc5b4\uc2dc\uc2a4\ud2b8” \ubc0f “\ub9ac\ubc14\uc6b4\ub4dc” \uc5f4\uc758 NaN \uac12\uc740 \ubcc0\uacbd\ub418\uc9c0 \uc54a\uc740 \ucc44 \uc720\uc9c0\ub418\uc5c8\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
 Pandas: \uc870\uac74\uc5d0 \ub530\ub77c \uc5f4\uc758 \uac12\uc744 \ubc14\uafb8\ub294 \ubc29\ubc95<\/a>
 Pandas: NaN \uac12\uc744 0\uc73c\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95<\/a>
 Pandas: DataFrame\uc5d0\uc11c \ub204\ub77d\ub41c \uac12\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec NaN \uac12\uc744 Pandas DataFrame\uc758 \ubb38\uc790\uc5f4\ub85c \ubc14\uafc0 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: DataFrame \uc804\uccb4\uc5d0\uc11c NaN \uac12\uc744 \ubb38\uc790\uc5f4\ub85c \ub300\uccb4 df. fillna (”, inplace= True ) \ubc29\ubc95 2: \ud2b9\uc815 \uc5f4\uc758 NaN \uac12\uc744 \ubb38\uc790\uc5f4\ub85c \ub300\uccb4 df[[‘ col1 ‘, ‘ col2 ‘]] = df[[‘ col1 ‘, ‘ col2 ‘]]. fillna (”) \ubc29\ubc95 3: NaN \uac12\uc744 \uc5f4\uc758 \ubb38\uc790\uc5f4\ub85c \ubc14\uafb8\uae30 […]<\/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-2377","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas: NaN \uac12\uc744 \ubb38\uc790\uc5f4\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas DataFrame\uc758 NaN \uac12\uc744 \ud2b9\uc815 \ubb38\uc790\uc5f4\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95\uc744 \uba87 \uac00\uc9c0 \uc608\uc640 \ud568\uaed8 \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\/\u1111\u1162\u11ab\u1103\u1165\u1102\u1173\u11ab-\u1102\u1161\u11ab\u110b\u1173\u11af-\u1101\u1173\u11ab-\u1111\u1162\u11ab\u1110\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: NaN \uac12\uc744 \ubb38\uc790\uc5f4\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95 - 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