{"id":3763,"date":"2023-07-15T16:32:02","date_gmt":"2023-07-15T16:32:02","guid":{"rendered":"https:\/\/statorials.org\/ko\/pandas-group%e1%84%87%e1%85%a7%e1%86%af-%e1%84%8e%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8c%e1%85%a5%e1%86%b7\/"},"modified":"2023-07-15T16:32:02","modified_gmt":"2023-07-15T16:32:02","slug":"pandas-group%e1%84%87%e1%85%a7%e1%86%af-%e1%84%8e%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8c%e1%85%a5%e1%86%b7","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/pandas-group%e1%84%87%e1%85%a7%e1%86%af-%e1%84%8e%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8c%e1%85%a5%e1%86%b7\/","title":{"rendered":"Pandas: diff\uc640 \ud568\uaed8 groupby\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

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\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec pandas\uc5d0\uc11c diff(<\/strong> ) \ud568\uc218\uc640 \ud568\uaed8 groupby()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 df = df. sort_values<\/span> (by=[' group_var1<\/span> ', ' group_var2<\/span> '])\n\ndf[' diff<\/span> '] = df. groupby<\/span> ([' group_var1<\/span> '])[' values_var<\/span> ']. diff<\/span> (). fillna<\/span> ( 0<\/span> )\n<\/strong><\/pre>\n
 \uc774 \ud2b9\uc815 \uc608\uc5d0\uc11c\ub294 \ub450 \uac1c\uc758 \ud2b9\uc815 \ubcc0\uc218\ub97c \uae30\uc900\uc73c\ub85c DataFrame\uc758 \ud589\uc744 \uc815\ub82c\ud55c \ub2e4\uc74c group_var1<\/strong> \uc744 \uae30\uc900\uc73c\ub85c \uadf8\ub8f9\ud654\ud558\uace0 value_var<\/strong> \uc5f4\uc758 \ud589 \uac04\uc758 \ucc28\uc774\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 fillna(0)<\/strong> \ub294 \uadf8\ub8f9 \ubcc0\uc218\uc758 \uac12\uc774 DataFrame\uc758 \uc5f0\uc18d \ub77c\uc778 \uc0ac\uc774\uc5d0\uc11c \ubcc0\uacbd\ub420 \ub54c\ub9c8\ub2e4 \ud32c\ub354\uc5d0\uac8c 0\uc744 \uc0bd\uc785\ud558\ub3c4\ub85d \uc9c0\uc2dc\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 diff\uc640 \ud568\uaed8 groupby\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95<\/strong><\/span><\/h2>\n
 \uc11c\ub85c \ub2e4\ub978 \ub0a0\uc9dc\uc5d0 \uc11c\ub85c \ub2e4\ub978 \ub450 \ub9e4\uc7a5\uc758 \ucd1d \ub9e4\ucd9c\uc744 \ud3ec\ud568\ud558\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\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                   ' date<\/span> ': pd. to_datetime<\/span> (['2022-01-01', '2022-01-02',\n                                           '2022-01-03', '2022-01-04',\n                                           '2022-01-01', '2022-01-02',\n                                           '2022-01-03', '2022-01-04']),\n                   ' sales<\/span> ': [12, 15, 24, 24, 14, 19, 12, 38]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  blind date sales\n0 A 2022-01-01 12\n1 A 2022-01-02 15\n2 A 2022-01-03 24\n3 A 2022-01-04 24\n4 B 2022-01-01 14\n5 B 2022-01-02 19\n6 B 2022-01-03 12\n7 B 2022-01-04 38<\/b><\/pre>\n
 \uc774\uc81c \ub9e4\uc7a5\ubcc4\ub85c \uadf8\ub8f9\ud654\ub41c \uc5f0\uc18d \ub0a0\uc9dc \uac04\uc758 \ub9e4\ucd9c \uac12 \ucc28\uc774\ub97c \ud3ec\ud568\ud558\ub294 sales_diff<\/strong> \ub77c\ub294 \uc0c8 \uc5f4\uc744 \uc0dd\uc131\ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774\ub97c \uc704\ud574 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #sort DataFrame by store and date\n<\/span>df = df. sort_values<\/span> (by=[' store<\/span> ', ' date<\/span> '])\n\n#create new column that contains difference between sales grouped by store\n<\/span>df[' sales_diff<\/span> '] = df. groupby<\/span> ([' store<\/span> '])[' sales<\/span> ']. diff<\/span> (). fillna<\/span> ( 0<\/span> )\n\n#view update DataFrame\n<\/span>print<\/span> (df)\n\n  store date sales sales_diff\n0 A 2022-01-01 12 0.0\n1 A 2022-01-02 15 3.0\n2 A 2022-01-03 24 9.0\n3 A 2022-01-04 24 0.0\n4 B 2022-01-01 14 0.0\n5 B 2022-01-02 19 5.0\n6 B 2022-01-03 12 -7.0\n7 B 2022-01-04 38 26.0\n<\/span><\/span><\/strong><\/pre>\n
 \uc0c8\ub85c\uc6b4 sales_diff<\/strong> \uc5f4\uc5d0\ub294 \ub9e4\uc7a5\ubcc4\ub85c \uadf8\ub8f9\ud654\ub41c \uc5f0\uc18d \ub0a0\uc9dc \uac04\uc758 \ub9e4\ucd9c \uac12 \ucc28\uc774\uac00 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4 \ub2e4\uc74c\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n