使用:

df = df.groupby(df['values'].ne(df['values'].shift()).cumsum())['values'].value_counts()

或者:

df = df.groupby([df['values'].ne(df['values'].shift()).cumsum(), 'values']).size()

print (df)
values  values
1       10        2
2       23        2
3       9         3
4       10        4
5       12        1
Name: values, dtype: int64

最后删除第一级:

df = df.reset_index(level=0, drop=True)
print (df)
values
10    2
23    2
9     3
10    4
12    1
dtype: int64

说明:

通过 shift比较原始列代码>不等于ne 然后添加 cumsum for helper Series:

print (pd.concat([df['values'], a, b, c], 
                 keys=('orig','shifted', 'not_equal', 'cumsum'), axis=1))
    orig  shifted  not_equal  cumsum
0     10      NaN       True       1
1     10     10.0      False       1
2     23     10.0       True       2
3     23     23.0      False       2
4      9     23.0       True       3
5      9      9.0      False       3
6      9      9.0      False       3
7     10      9.0       True       4
8     10     10.0      False       4
9     10     10.0      False       4
10    10     10.0      False       4
11    12     10.0       True       5