汇总Pandas DataFrame中的行

本文介绍了汇总Pandas DataFrame中的行的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我有以下几行:

ColumnID MenuID QuestionID ResponseCount RowID SourceColumnID SourceRowID SourceVariationID 22 -2 -2 319276487 28 3049400354 3049400356 3049400365 3049400365 23 -2 -2 319276487 31 3049400354 3049400356 3049400365 3049400365 24 -2 -2 319276487 37 3049400354 3049400356 3049400365 3049400365 25 -2 -2 319276487 28 3049400353 3049400357 3049400365 3049400365 26 -2 -2 319276487 45 3049400353 3049400357 3049400365 3049400365 27 -2 -2 319276487 46 3049400353 3049400357 3049400365 3049400365 28 -2 -2 319276487 26 3049400353 3049400358 3049400365 3049400365 29 -2 -2 319276487 33 3049400353 3049400358 3049400365 3049400365 30 -2 -2 319276487 39 3049400353 3049400358 3049400365 3049400365 31 -2 -2 319276487 26 3049400353 3049400359 3049400365 3049400365

我想压缩此数据帧，以便它通过RowID和SourceVariationID汇总ResponseCount中的总数.

And I want to squash this dataframe so that it sums up the total in ResponseCount by RowID and SourceVariationID.

例如:

ColumnID MenuID QuestionID ResponseCount RowID SourceColumnID SourceRowID SourceVariationID 22 -2 -2 319276487 96 3049400354 3049400356 3049400365 3049400365 23 -2 -2 319276487 243 3049400353 3049400356 3049400365

这是我到目前为止提出的:

This is what I've come up with so far:

(Pdb) new_df = df.groupby(['RowID', 'SourceVariationID', 'SourceRowID']).sum() (Pdb) new_df['ColumnID'] = -2 (Pdb) new_df['MenuID'] = -2 (Pdb) pp new_df ColumnID MenuID QuestionID ResponseCount SourceColumnID RowID SourceVariationID SourceRowID 3031434948 3031434943 3031434943 -2 -2 3805083612 141 36377219262 3031434945 3031434945 -2 -2 4439264214 237 42440089136 [2 rows x 5 columns]

推荐答案

您可以执行以下操作:

print df ColumnID MenuID QuestionID ResponseCount RowID SourceVariationID 0 -2 -2 319276487 28 3049400354 3049400365 1 -2 -2 319276487 31 3049400354 3049400365 2 -2 -2 319276487 37 3049400354 3049400365 3 -2 -2 319276487 28 3049400353 3049400365 4 -2 -2 319276487 45 3049400353 3049400365 5 -2 -2 319276487 46 3049400353 3049400365 6 -2 -2 319276487 26 3049400353 3049400365 7 -2 -2 319276487 33 3049400353 3049400365 8 -2 -2 319276487 39 3049400353 3049400365 9 -2 -2 319276487 26 3049400353 3049400365 def squash(group): x = group.iloc[1,:].drop(['RowID','SourceVariationID']) x['ResponseCount'] = group['ResponseCount'].sum() return x print df.groupby(['RowID','SourceVariationID']).apply(squash) ColumnID MenuID QuestionID ResponseCount RowID SourceVariationID 3049400353 3049400365 -2 -2 319276487 243 3049400354 3049400365 -2 -2 319276487 96