大 pandas groupby计数字符串出现在列上 [英] pandas groupby count string occurrence over column

问题描述

我想统计在已分组的pandas数据框列中字符串的出现.

I want to count the occurrence of a string in a grouped pandas dataframe column.

假设我具有以下数据框:

Assume I have the following Dataframe:

catA    catB    scores
A       X       6-4 RET
A       X       6-4 6-4
A       Y       6-3 RET
B       Z       6-0 RET
B       Z       6-1 RET

首先，我想按catA和catB分组.对于这些组中的每一个，我想计算scores列中RET的出现.

First, I want to group by catA and catB. And for each of these groups I want to count the occurrence of RET in the scores column.

结果应如下所示:

catA    catB    RET
A       X       1
A       Y       1
B       Z       2

按两列分组很容易:grouped = df.groupby(['catA', 'catB'])

但是接下来是什么?

推荐答案

调用 count :

Call apply on the 'scores' column on the groupby object and use the vectorise str method contains, use this to filter the group and call count:

In [34]:    
df.groupby(['catA', 'catB'])['scores'].apply(lambda x: x[x.str.contains('RET')].count())

Out[34]:
catA  catB
A     X       1
      Y       1
B     Z       2
Name: scores, dtype: int64

要分配为列，请使用 transform 聚合返回一个序列，其索引与原始df对齐:

To assign as a column use transform so that the aggregation returns a series with it's index aligned to the original df:

In [35]:
df['count'] = df.groupby(['catA', 'catB'])['scores'].transform(lambda x: x[x.str.contains('RET')].count())
df

Out[35]:
  catA catB   scores count
0    A    X  6-4 RET     1
1    A    X  6-4 6-4     1
2    A    Y  6-3 RET     1
3    B    Z  6-0 RET     2
4    B    Z  6-1 RET     2

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