{"id":2372,"date":"2023-07-22T13:25:44","date_gmt":"2023-07-22T13:25:44","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%87%e1%85%a5%e1%86%b7%e1%84%8c%e1%85%ae%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%87%e1%85%a7%e1%86%ab%e1%84%89%e1%85%ae%e1%84%85%e1%85%b3%e1%86%af-%e1%84%83%e1%85%b5%e1%84%8c%e1%85%b5%e1%84%90%e1%85%a5\/"},"modified":"2023-07-22T13:25:44","modified_gmt":"2023-07-22T13:25:44","slug":"%e1%84%87%e1%85%a5%e1%86%b7%e1%84%8c%e1%85%ae%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%87%e1%85%a7%e1%86%ab%e1%84%89%e1%85%ae%e1%84%85%e1%85%b3%e1%86%af-%e1%84%83%e1%85%b5%e1%84%8c%e1%85%b5%e1%84%90%e1%85%a5","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%87%e1%85%a5%e1%86%b7%e1%84%8c%e1%85%ae%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%87%e1%85%a7%e1%86%ab%e1%84%89%e1%85%ae%e1%84%85%e1%85%b3%e1%86%af-%e1%84%83%e1%85%b5%e1%84%8c%e1%85%b5%e1%84%90%e1%85%a5\/","title":{"rendered":"Pandas\uc5d0\uc11c \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uc22b\uc790\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc5d0\uc11c \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uc22b\uc790 \ubcc0\uc218\ub85c \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">column_name<\/span> '] = pd. <span style=\"color: #3366ff;\">factorize<\/span> (df[' <span style=\"color: #ff0000;\">column_name<\/span> '])[0]\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec DataFrame\uc758 \uac01 \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uc22b\uc790 \ubcc0\uc218\ub85c \ubcc0\ud658\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <b><span style=\"color: #008080;\">#identify all categorical variables<\/span>\ncat_columns = df. <span style=\"color: #3366ff;\">select_dtypes<\/span> ([' <span style=\"color: #ff0000;\">object<\/span> ']). <span style=\"color: #3366ff;\">columns<\/span>\n\n<span style=\"color: #008080;\">#convert all categorical variables to numeric<\/span>\ndf[cat_columns] = df[cat_columns]. <span style=\"color: #3366ff;\">apply<\/span> ( <span style=\"color: #008000;\">lambda<\/span> x: <span style=\"color: #3366ff;\">pd.factorize<\/span> (x)[ <span style=\"color: #008000;\">0<\/span> ])\n<\/b><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc774 \uad6c\ubb38\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\uc608 1: \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uc22b\uc790\ub85c \ubcc0\ud658<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<span style=\"color: #000000;\">df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],\n                   ' <span style=\"color: #ff0000;\">position<\/span> ': ['G', 'G', 'F', 'G', 'F', 'C', 'G', 'F', 'C'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4, 13],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12, 10]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<span style=\"color: #000000;\">df\n\n<\/span><span style=\"color: #000000;\">team position<\/span> <span style=\"color: #000000;\">points rebounds\n0 A G<\/span> <span style=\"color: #000000;\">5 11\n1 A G<\/span> <span style=\"color: #000000;\">7 8\n2 A F<\/span> <span style=\"color: #000000;\">7 10\n3 B G<\/span> <span style=\"color: #000000;\">9 6\n4 B F<\/span> <span style=\"color: #000000;\">12 6\n5 B C<\/span> <span style=\"color: #000000;\">9 5\n6 C G<\/span> <span style=\"color: #000000;\">9 9\n7 C F<\/span> <span style=\"color: #000000;\">4 12\n8 C C<\/span> <span style=\"color: #000000;\">13 10\n<\/span><\/span><\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec &#8220;\ud300&#8221; \uc5f4\uc744 \uc22b\uc790\ub85c \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#convert 'team' column to numeric\n<\/span>df[' <span style=\"color: #ff0000;\">team<\/span> '] = pd. <span style=\"color: #3366ff;\">factorize<\/span> (df[' <span style=\"color: #ff0000;\">team<\/span> '])[ <span style=\"color: #008000;\">0<\/span> ]<\/span>\n\n#view updated DataFrame\n<span style=\"color: #000000;\">df<\/span>\n\n<span style=\"color: #000000;\">team position<\/span> <span style=\"color: #000000;\">points rebounds\n0 0 G<\/span> <span style=\"color: #000000;\">5 11\n1 0 G<\/span> <span style=\"color: #000000;\">7 8\n2 0 F<\/span> <span style=\"color: #000000;\">7 10\n3 1 G<\/span> <span style=\"color: #000000;\">9 6\n4 1 F<\/span> <span style=\"color: #000000;\">12 6\n5 1 C<\/span> <span style=\"color: #000000;\">9 5\n6 2 G<\/span> <span style=\"color: #000000;\">9 9\n7 2 F<\/span> <span style=\"color: #000000;\">4 12\n8 2 C<\/span> <span style=\"color: #000000;\">13 10\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ubcc0\ud658\uc774 \uc9c4\ud589\ub41c \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uac12\uc774 &#8221; <strong>A<\/strong> &#8220;\uc778 \uac01 \ud300\uc740 <strong>0<\/strong> \uc73c\ub85c \ubcc0\ud658\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uac12\uc774 &#8221; <strong>B<\/strong> &#8220;\uc778 \uac01 \ud300\uc740 <strong>1<\/strong> \ub85c \ubcc0\ud658\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">&#8221; <strong>C<\/strong> &#8221; \uac12\uc744 \uac00\uc9c4 \uac01 \ud300\uc740 <strong>2<\/strong> \ub85c \ubcc0\ud658\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>\uc608 2: \uc5ec\ub7ec \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uc22b\uc790 \uac12\uc73c\ub85c \ubcc0\ud658<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc774 \uc788\ub2e4\uace0 \ub2e4\uc2dc \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<span style=\"color: #000000;\">df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],\n                   ' <span style=\"color: #ff0000;\">position<\/span> ': ['G', 'G', 'F', 'G', 'F', 'C', 'G', 'F', 'C'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4, 13],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12, 10]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>df\n\n        team position points rebounds\n0 A G 5 11\n1 A G 7 8\n2 A F 7 10\n3 B G 9 6\n4 B F 12 6\n5 B C 9 5\n6 C G 9 9\n7 C F 4 12\n8 C C 13 10\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec DataFrame\uc758 \uac01 \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uc22b\uc790 \ubcc0\uc218\ub85c \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#get all categorical columns\n<\/span>cat_columns = df. <span style=\"color: #3366ff;\">select_dtypes<\/span> ([' <span style=\"color: #ff0000;\">object<\/span> ']). <span style=\"color: #3366ff;\">columns<\/span>\n\n<span style=\"color: #008080;\">#convert all categorical columns to numeric\n<\/span>df[cat_columns] = df[cat_columns]. <span style=\"color: #3366ff;\">apply<\/span> ( <span style=\"color: #008000;\">lambda<\/span> x: <span style=\"color: #3366ff;\">pd.factorize<\/span> (x)[ <span style=\"color: #008000;\">0<\/span> ])\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span>df\n\n\tteam position points rebounds\n0 0 0 5 11\n1 0 0 7 8\n2 0 1 7 10\n3 1 0 9 6\n4 1 1 12 6\n5 1 2 9 5\n6 2 0 9 9\n7 2 1 4 12\n8 2 2 13 10\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\ub450 \uac1c\uc758 \ubc94\uc8fc\ud615 \uc5f4(\ud300 \ubc0f \uc704\uce58)\uc740 \ubaa8\ub450 \uc22b\uc790\ub85c \ubcc0\ud658\ub418\uc5c8\uc9c0\ub9cc \ud3ec\uc778\ud2b8 \ubc0f \ub9ac\ubc14\uc6b4\ub4dc \uc5f4\uc740 \ub3d9\uc77c\ud558\uac8c \uc720\uc9c0\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\ucc38\uace0<\/strong> : Pandas <strong>Factorize()<\/strong> \ud568\uc218\uc5d0 \ub300\ud55c \uc804\uccb4 \ubb38\uc11c\ub294 <a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.factorize.html\" target=\"_blank\" rel=\"noopener\">\uc5ec\uae30\uc5d0\uc11c<\/a> \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\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<p> <a href=\"https:\/\/statorials.org\/ko\/\u1101\u1173\u11ab-\u110b\u1171\u110b\u1174-\u1111\u1161\u11ab\u1103\u1161\/\" target=\"_blank\" rel=\"noopener\">Pandas DataFrame \uc5f4\uc744 \ubb38\uc790\uc5f4\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165\u1102\u1173\u11ab-\u110b\u1167\u11af\u110b\u1173\u11af-int\u1105\u1169-\u1107\u1167\u11ab\u1112\u116a\u11ab\u1112\u1161\u11b8\u1102\u1175\u1103\u1161.\/\" target=\"_blank\" rel=\"noopener\">Pandas DataFrame \uc5f4\uc744 \uc815\uc218\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1106\u116e\u11ab\u110c\u1161\u110b\u1167\u11af\u110b\u1173\u11af-\u1107\u116e\u1103\u1169\u11bc-\u1111\u1162\u11ab\u1103\u1165\u1105\u1169-\u1107\u1167\u11ab\u1112\u116a\u11ab\/\" target=\"_blank\" rel=\"noopener\">Pandas DataFrame\uc5d0\uc11c \ubb38\uc790\uc5f4\uc744 \ubd80\ub3d9 \uc18c\uc218\uc810\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc5d0\uc11c \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uc22b\uc790 \ubcc0\uc218\ub85c \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. df[&#8216; column_name &#8216;] = pd. factorize (df[&#8216; column_name &#8216;])[0] \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec DataFrame\uc758 \uac01 \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uc22b\uc790 \ubcc0\uc218\ub85c \ubcc0\ud658\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. #identify all categorical variables cat_columns = df. select_dtypes ([&#8216; object &#8216;]). columns #convert all categorical variables to numeric df[cat_columns] = df[cat_columns]. 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