{"id":4317,"date":"2023-07-12T01:20:23","date_gmt":"2023-07-12T01:20:23","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%aa%e1%84%92%e1%85%a1%e1%86%a8%e1%84%8c%e1%85%a5%e1%86%a8-%e1%84%91%e1%85%ad%e1%84%80%e1%85%b5%e1%84%87%e1%85%a5\/"},"modified":"2023-07-12T01:20:23","modified_gmt":"2023-07-12T01:20:23","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%aa%e1%84%92%e1%85%a1%e1%86%a8%e1%84%8c%e1%85%a5%e1%86%a8-%e1%84%91%e1%85%ad%e1%84%80%e1%85%b5%e1%84%87%e1%85%a5","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%aa%e1%84%92%e1%85%a1%e1%86%a8%e1%84%8c%e1%85%a5%e1%86%a8-%e1%84%91%e1%85%ad%e1%84%80%e1%85%b5%e1%84%87%e1%85%a5\/","title":{"rendered":"Pandas: explain()\uc744 \uc0ac\uc6a9\ud558\uace0 \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc744 \uc81c\uac70\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

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explain()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 \ubcc0\uc218\uc5d0 \ub300\ud55c \uc124\uba85 \ud1b5\uacc4\ub97c<\/a> \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

explain()<\/strong> \ud568\uc218\uc758 \ucd9c\ub825\uc5d0\uc11c \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc744 \uc81c\uac70\ud558\ub824\uba74 \ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: \uc5f4\uacfc \ud568\uaed8 explain()\uc744 \uc0ac\uc6a9\ud560 \ub54c \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc744 \uc81c\uac70\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n

 df[' my_column<\/span> ']. describe<\/span> (). apply<\/span> ( lambda<\/span> x: format<\/span> (x, ' f<\/span> '))<\/strong><\/span><\/pre>\n
 \ubc29\ubc95 2: \uc5ec\ub7ec \uc5f4\uc5d0 explain()\uc744 \uc0ac\uc6a9\ud560 \ub54c \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc744 \uc81c\uac70\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n
 df. describe<\/span> (). apply<\/span> ( lambda<\/span> x: x.apply<\/span> (' {0:.5f}<\/span> '. format<\/span> ))\n<\/strong><\/span><\/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> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' store<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' sales<\/span> ': [8450550, 406530, 53000, 6000, 2000, 4000, 5400, 6500],\n                   ' returns<\/span> ':[2212200, 145200, 300, 2500, 700, 600, 800, 1200]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  store sales returns\n0 A 8450550 2212200\n1 A 406530 145200\n2 A 53000 300\n3 A 6000 2500\n4 B 2000 700\n5 B 4000 600\n6 B 5400 800\n7 B 6500 1200<\/strong><\/span><\/pre>\n
 \uc608 1: \uc5f4\uacfc \ud568\uaed8 explain()\uc744 \uc0ac\uc6a9\ud560 \ub54c \uacfc\ud559 \ud45c\uae30\ubc95 \uc81c\uac70<\/strong><\/span><\/h2>\n
 explain()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec sales<\/strong> \uc5f4\uc5d0 \ub300\ud55c \uae30\uc220 \ud1b5\uacc4\ub97c \uacc4\uc0b0\ud558\uba74 \ucd9c\ub825 \uac12\uc774 \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc73c\ub85c \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 #calculate descriptive statistics for sales column\ndf[' sales<\/span> ']. describe<\/span> ()\n<\/span>\n<\/span>count 8.000000e+00\nmean 1.116748e+06\nstd 2.966552e+06\nmin 2.000000e+03\n25% 5.050000e+03\n50% 6.250000e+03\n75% 1.413825e+05\nmax 8.450550e+06\nName: sales, dtype: float64<\/strong><\/pre>\n
 \uac01 \ucd9c\ub825 \uac12\uc740 \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/span><\/p>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ucd9c\ub825\uc5d0\uc11c \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc744 \uc81c\uac70\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n
 #calculate descriptive statistics for sales column and suppress scientific notation<\/span>\ndf[' sales<\/span> ']. describe<\/span> (). apply<\/span> ( lambda<\/span> x: format<\/span> (x, ' f<\/span> '))\n\ncount 8.000000\nmean 1116747.500000\nstd 2966551.594104\nmin 2000.000000\n25% 5050.000000\n50% 6250.000000\n75% 141382.500000\nmax 8450550.000000\nName: sales, dtype: object\n<\/strong><\/span><\/pre>\n
 \uc774\uc81c \ucd9c\ub825 \uac12\uc774 \uacfc\ud559\uc801 \ud45c\uae30\ubc95 \uc5c6\uc774 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 2: \uc5ec\ub7ec \uc5f4\uc5d0 explain()\uc744 \uc0ac\uc6a9\ud560 \ub54c \uacfc\ud559 \ud45c\uae30\ubc95 \uc81c\uac70<\/strong><\/span><\/h2>\n
 explain()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac01 \uc22b\uc790 \uc5f4\uc5d0 \ub300\ud55c \uae30\uc220 \ud1b5\uacc4\ub97c \uacc4\uc0b0\ud558\uba74 \ucd9c\ub825 \uac12\uc774 \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc73c\ub85c \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 #calculate descriptive statistics for each numeric column\ndf. describe<\/span> ()\n<\/span><\/span>\n               sales returns\ncount 8.000000e+00 8.000000e+00\nmean 1.116748e+06 2.954375e+05\nstd 2.966552e+06 7.761309e+05\nmin 2.000000e+03 3.000000e+02\n25% 5.050000e+03 6.750000e+02\n50% 6.250000e+03 1.000000e+03\n75% 1.413825e+05 3.817500e+04\nmax 8.450550e+06 2.212200e+06<\/strong><\/pre>\n
 \uac01 \ucd9c\ub825 \uac12\uc740 \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ucd9c\ub825\uc5d0\uc11c \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc744 \uc81c\uac70\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #calculate descriptive statistics for numerical columns and suppress scientific notation<\/span>\ndf. describe<\/span> (). apply<\/span> ( lambda<\/span> x: x.apply<\/span> (' {0:.5f}<\/span> '. format<\/span> ))\n\n                sales returns\ncount 8.00000 8.00000\nmean 1116747.50000 295437.50000\nstd 2966551.59410 776130.93692\nmin 2000.00000 300.00000\n25% 5050.00000 675.00000\n50% 6250.00000 1000.00000\n75% 141382.50000 38175.00000\nmax 8450550.00000 2212200.00000\n<\/strong><\/span><\/pre>\n
 \uc774\uc81c \ucd9c\ub825 \uac12\uc774 \uacfc\ud559\uc801 \ud45c\uae30\ubc95 \uc5c6\uc774 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \uc608\uc5d0\uc11c\ub294 \ucd9c\ub825\uc5d0 \uc18c\uc218\uc810 \uc774\ud558 5<\/strong> \uc790\ub9ac\ub97c \ud45c\uc2dc\ud558\uae30 \uc704\ud574 0:.5f\ub97c<\/strong> \uc0ac\uc6a9\ud588\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e4\ub978 \uc18c\uc218 \uc790\ub9bf\uc218\ub97c \ud45c\uc2dc\ud558\ub824\uba74 5<\/strong> \ub97c \ub2e4\ub978 \uc22b\uc790\ub85c \ubc14\uafd4\ubcf4\uc138\uc694.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\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: \uadf8\ub8f9\ub2f9 \ub204\uc801 \ud569\uacc4\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas: \uadf8\ub8f9\ubcc4\ub85c \uace0\uc720\ud55c \uac12\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas: \uadf8\ub8f9\ubcc4 \uc0c1\uad00\uad00\uacc4\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
explain() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 \ubcc0\uc218\uc5d0 \ub300\ud55c \uc124\uba85 \ud1b5\uacc4\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. explain() \ud568\uc218\uc758 \ucd9c\ub825\uc5d0\uc11c \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc744 \uc81c\uac70\ud558\ub824\uba74 \ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: \uc5f4\uacfc \ud568\uaed8 explain()\uc744 \uc0ac\uc6a9\ud560 \ub54c \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc744 \uc81c\uac70\ud569\ub2c8\ub2e4. df[‘ my_column ‘]. describe (). apply ( lambda x: format (x, ‘ f ‘)) \ubc29\ubc95 2: \uc5ec\ub7ec \uc5f4\uc5d0 explain()\uc744 \uc0ac\uc6a9\ud560 \ub54c \uacfc\ud559\uc801 […]<\/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-4317","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas: explain()\uc744 \uc0ac\uc6a9\ud558\uace0 \uacfc\ud559\uc801 \ud45c\uae30\ubc95\uc744 \uc81c\uac70\ud558\ub294 \ubc29\ubc95 - 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