{"id":3128,"date":"2023-07-19T01:54:21","date_gmt":"2023-07-19T01:54:21","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%80%e1%85%a2%e1%86%a8%e1%84%8e%e1%85%a6%e1%84%85%e1%85%b3%e1%86%af-%e1%84%87%e1%85%ae%e1%84%83%e1%85%a9%e1%86%bc\/"},"modified":"2023-07-19T01:54:21","modified_gmt":"2023-07-19T01:54:21","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%80%e1%85%a2%e1%86%a8%e1%84%8e%e1%85%a6%e1%84%85%e1%85%b3%e1%86%af-%e1%84%87%e1%85%ae%e1%84%83%e1%85%a9%e1%86%bc","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%80%e1%85%a2%e1%86%a8%e1%84%8e%e1%85%a6%e1%84%85%e1%85%b3%e1%86%af-%e1%84%87%e1%85%ae%e1%84%83%e1%85%a9%e1%86%bc\/","title":{"rendered":"Pandas\uc5d0\uc11c \uac1d\uccb4\ub97c \ubd80\ub3d9 \uc18c\uc218\uc810\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568)"},"content":{"rendered":"

<\/p>\n

\n

\ub2e4\uc74c \ubc29\ubc95 \uc911 \ud558\ub098\ub97c \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 \uc5f4\uc744 \uac1d\uccb4\uc5d0\uc11c \ubd80\ub3d9 \uc18c\uc218\uc810\uc73c\ub85c \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: astype() \uc0ac\uc6a9<\/strong><\/span><\/p>\n

 df[' column_name<\/span> '] = df[' column_name<\/span> ']. astype<\/span> (float)<\/strong><\/pre>\n
 \ubc29\ubc95 2: to_numeric() \uc0ac\uc6a9<\/strong><\/span><\/p>\n
 df[' column_name<\/span> '] = pd. to_numeric<\/span> (df[' column_name<\/span> '])<\/strong><\/b><\/pre>\n
 \ub450 \ubc29\ubc95 \ubaa8\ub450 \ub3d9\uc77c\ud55c \uacb0\uacfc\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/span><\/p>\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\n<\/span><\/span>\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n                   ' points<\/span> ': ['18', '22.2', '19.1', '14', '14', '11.5', '20', '28'],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points assists\n0 to 18 5\n1 B 22.2 7\n2 C 19.1 7\n3 D 14 9\n4 E 14 12\n5 F 11.5 9\n6 G 20 9\n7:28 a.m. 4\n\n#check data type of each column\n<\/span>print<\/span> ( df.dtypes<\/span> )\n\nteam object\npoints object\nassists int64\ndtype:object\n<\/span><\/span><\/strong><\/pre>\n
 \ubc29\ubc95 1: astype()\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac1d\uccb4\ub97c \ubd80\ub3d9 \uc18c\uc218\uc810\uc73c\ub85c \ubcc0\ud658<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 astype()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac1d\uccb4 DataFrame\uc758 \ud3ec\uc778\ud2b8 \uc5f4\uc744 \ubd80\ub3d9 \uc18c\uc218\uc810\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #convert points column from object to float\ndf[' points<\/span> '] = df[' points<\/span> ']. astype<\/span> (float)\n<\/span>\n#view updated DataFrame\nprint<\/span> (df)\n\n  team points assists\n0 A 18.0 5\n1 B 22.2 7\n2 C 19.1 7\n3D 14.0 9\n4 E 14.0 12\n5 F 11.5 9\n6G 20.0 9\n7 A.M. 28.0 4\n<\/span>\n#view updated data types\nprint<\/span> ( df.dtypes<\/span> )\n\nteam object\nfloat64 points\nassists int64\ndtype:object<\/span><\/span><\/strong>\n<\/pre>\n
 \uc774\uc81c \ud3ec\uc778\ud2b8 \uc5f4\uc5d0\ub294 float64<\/strong> \ub370\uc774\ud130 \uc720\ud615\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n
 \ubc29\ubc95 2: to_numeric()\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac1d\uccb4\ub97c \ubd80\ub3d9 \uc18c\uc218\uc810\uc73c\ub85c \ubcc0\ud658<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 to_numeric()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac1d\uccb4 DataFrame\uc758 \ud3ec\uc778\ud2b8 \uc5f4\uc744 \ubd80\ub3d9 \uc18c\uc218\uc810\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #convert points column from object to float\ndf[' points<\/span> '] = pd. to_numeric<\/span> (df[' points<\/span> '], errors=' coerce<\/span> ')\n<\/span>\n#view updated DataFrame\nprint<\/span> (df)\n\n  team points assists\n0 A 18.0 5\n1 B 22.2 7\n2 C 19.1 7\n3D 14.0 9\n4 E 14.0 12\n5 F 11.5 9\n6G 20.0 9\n7 A.M. 28.0 4\n<\/span>\n#view updated data types\nprint<\/span> ( df.dtypes<\/span> )\n\nteam object\nfloat64 points\nassists int64\ndtype:object<\/span><\/span><\/strong>\n<\/pre>\n
 \uc774\uc81c \ud3ec\uc778\ud2b8 \uc5f4\uc5d0\ub294 float64<\/strong> \ub370\uc774\ud130 \uc720\ud615\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ub610\ud55c \uc774 \ubc29\ubc95\uc740 \uc774\uc804 \ubc29\ubc95\uacfc \uc815\ud655\ud788 \ub3d9\uc77c\ud55c \uacb0\uacfc\ub97c \uc0dd\uc131\ud569\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\uc5d0\uc11c \ubd80\uc6b8 \uac12\uc744 \uc815\uc218 \uac12\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c DateTime\uc744 \ubb38\uc790\uc5f4\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c \uc5f4\uc744 \uc815\uc218\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub2e4\uc74c \ubc29\ubc95 \uc911 \ud558\ub098\ub97c \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 \uc5f4\uc744 \uac1d\uccb4\uc5d0\uc11c \ubd80\ub3d9 \uc18c\uc218\uc810\uc73c\ub85c \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: astype() \uc0ac\uc6a9 df[‘ column_name ‘] = df[‘ column_name ‘]. astype (float) \ubc29\ubc95 2: to_numeric() \uc0ac\uc6a9 df[‘ column_name ‘] = pd. to_numeric (df[‘ column_name ‘]) \ub450 \ubc29\ubc95 \ubaa8\ub450 \ub3d9\uc77c\ud55c \uacb0\uacfc\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub2e4\uc74c Pandas DataFrame\uc5d0\uc11c \uac01 \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 […]<\/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-3128","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\n\ud32c\ub354\uc5d0\uc11c \uac1d\uccb4\ub97c \ubd80\ub3d9 \uc18c\uc218\uc810\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568) - 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