pandas groupby 统计列上的字符串出现次数
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pandas groupby count string occurrence over column
提问by beta
I want to count the occurrence of a string in a grouped pandas dataframe column.
我想计算一个字符串在分组的 Pandas 数据框列中的出现次数。
Assume I have the following Dataframe:
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
First, I want to group by catAand catB. And for each of these groups I want to count the occurrence of RETin the scorescolumn.
首先，我想按catA和分组catB。而对于每个组的我要算发生RET在scores列。
The result should look something like this:
结果应该是这样的：
catA catB RET
A X 1
A Y 1
B Z 2
The grouping by two columns is easy: grouped = df.groupby(['catA', 'catB'])
按两列分组很容易： grouped = df.groupby(['catA', 'catB'])
But what's next?
但是接下来呢？
回答by EdChum
Call applyon the 'scores' column on the groupbyobject and use the vectorise strmethod contains, use this to filter the groupand call count:
调用对象apply上的“分数”列groupby并使用 vectorisestr方法contains，使用它来过滤group和调用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
To assign as a column use transformso that the aggregation returns a series with it's index aligned to the original df:
要分配为列使用，transform以便聚合返回一个系列，它的索引与原始 df 对齐：
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
