python - 使用 `Dataframe.value_counts()`时不指定列名如何实现 `Series.value_counts`

Question

比如我得到的dataframe如下:

    PassengerId   Survived    Pclass    
0   1             0           3 
1   2             1           1 
2   3             1           3 

并且在调用df.value_counts()之后，我可以得到所有列的value_counts()，而无需每次都指定一列，这可能是这样的:

1      1
2      1
3      1
Name: PassengerId, dtype: int64

0      1
1      2
Name: Survived, dtype: int64

3      2
1      1
Name: Survived, dtype: int64

我想知道如何实现它。
谁能帮帮我？
提前致谢。

Answer 1

有 2 个解决方案 DataFrame.apply用于按列应用函数，但索引按其交集对齐，因此添加了 NaN:

df1 = df.apply(pd.value_counts)
print (df1)
   PassengerId  Survived  Pclass
0          NaN       1.0     NaN
1          1.0       2.0     1.0
2          1.0       NaN     NaN
3          1.0       NaN     2.0

df1 = df.apply(pd.Series.value_counts)
print (df1)
   PassengerId  Survived  Pclass
0          NaN       1.0     NaN
1          1.0       2.0     1.0
2          1.0       NaN     NaN
3          1.0       NaN     2.0

为避免可能使用 SeriesGroupBy.value_counts :

df1 = df.stack().groupby(level=1).value_counts().rename_axis(('a','b')).reset_index(name='c')
print (df1)
             a  b  c
0  PassengerId  1  1
1  PassengerId  2  1
2  PassengerId  3  1
3       Pclass  3  2
4       Pclass  1  1
5     Survived  1  2
6     Survived  0  1

或原始解决方案 DataFrame.stack :

df1 = (df.apply(pd.Series.value_counts)
         .stack()
         .astype(int)
         .rename_axis(('a','b'))
         .reset_index(name='c')
print (df1)
   a            b  c
0  0     Survived  1
1  1  PassengerId  1
2  1     Survived  2
3  1       Pclass  1
4  2  PassengerId  1
5  3  PassengerId  1
6  3       Pclass  2