pandas groupby 统计列上的字符串出现次数 [英] pandas groupby count string occurrence over column

问题描述

我想计算一个字符串在分组的 Pandas 数据框列中出现的次数.

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

假设我有以下数据框:

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 进行分组.对于这些组中的每一个，我想计算 scores 列中 RET 的出现次数.

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.

结果应该是这样的:

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

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

接下来呢?

推荐答案

调用 apply 在 groupby 对象并使用矢量化str 方法 contains，使用它来过滤group 并调用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

要指定为列，请使用 transform 以便聚合返回一个索引与原始 df 对齐的系列:

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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