{"id":3751,"date":"2023-07-15T18:16:37","date_gmt":"2023-07-15T18:16:37","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%92%e1%85%a1%e1%84%85%e1%85%ae%e1%84%8b%e1%85%a6-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8\/"},"modified":"2023-07-15T18:16:37","modified_gmt":"2023-07-15T18:16:37","slug":"%e1%84%92%e1%85%a1%e1%84%85%e1%85%ae%e1%84%8b%e1%85%a6-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%92%e1%85%a1%e1%84%85%e1%85%ae%e1%84%8b%e1%85%a6-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8\/","title":{"rendered":"Pandas dataframe\uc5d0\uc11c \ub0a0\uc9dc\ubcc4\ub85c \uadf8\ub8f9\ud654\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568)"},"content":{"rendered":"

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\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc5d0\uc11c \ud589\uc744 \uc77c\ubcc4\ub85c \uadf8\ub8f9\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 df. groupby<\/span> (df. your_date_column<\/span> . dt<\/span> . day<\/span> )[' values_column<\/span> ']. sum<\/span> ()\n<\/strong><\/pre>\n
 \uc774 \ud2b9\uc815 \uc218\uc2dd\uc740 your_date_column<\/strong> \uc758 \ub0a0\uc9dc\ubcc4\ub85c \ud589\uc744 \uadf8\ub8f9\ud654\ud558\uace0 DataFrame\uc758 value_column<\/strong> \uac12\uc758 \ud569\uacc4\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 dt.day()<\/strong> \ud568\uc218\ub294 pandas\uc758 \ub0a0\uc9dc \uc5f4\uc5d0\uc11c \ub0a0\uc9dc\ub97c \ucd94\ucd9c\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc774 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608: Pandas\uc5d0\uc11c \ub0a0\uc9dc\ubcc4\ub85c \uadf8\ub8f9\ud654\ud558\ub294 \ubc29\ubc95<\/strong><\/span><\/h2>\n
 \ub2e4\uc591\ud55c \ub0a0\uc9dc\uc5d0 \ud68c\uc0ac\uc758 \ub9e4\ucd9c\uc744 \ubcf4\uc5ec\uc8fc\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' date<\/span> ': pd. date_range<\/span> (start=' 1\/1\/2020<\/span> ', freq=' 8h<\/span> ', periods= 10<\/span> ),\n                   ' sales<\/span> ': [6, 8, 9, 11, 13, 8, 8, 15, 22, 9],\n                   ' returns<\/span> ': [0, 3, 2, 2, 1, 3, 2, 4, 1, 5]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n                 date sales returns\n0 2020-01-01 00:00:00 6 0\n1 2020-01-01 08:00:00 8 3\n2 2020-01-01 16:00:00 9 2\n3 2020-01-02 00:00:00 11 2\n4 2020-01-02 08:00:00 13 1\n5 2020-01-02 16:00:00 8 3\n6 2020-01-03 00:00:00 8 2\n7 2020-01-03 08:00:00 15 4\n8 2020-01-03 16:00:00 22 1\n9 2020-01-04 00:00:00 9 5<\/strong><\/span><\/pre>\n
 \uad00\ub828 \ud56d\ubaa9:<\/strong>Pandas\uc5d0\uc11c \ub0a0\uc9dc \ubc94\uc704\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a><\/span><\/p>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc77c\ubcc4\ub85c \uadf8\ub8f9\ud654\ub41c \ub9e4\ucd9c \ud569\uacc4\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #calculate sum of sales grouped by day\ndf. groupby<\/span> (df. date<\/span> . dt<\/span> . day<\/span> )[' sales<\/span> ']. sum<\/span> ()\n\ndate\n1 23\n2 32\n3 45\n4 9\nName: sales, dtype: int64<\/span><\/span><\/strong><\/pre>\n
 \uacb0\uacfc\ub97c \ud574\uc11d\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n