通过过滤 Pandas 中的其他 2 列，在第 3 列中获取唯一值

Question

I have a dataframe i would like to filter. Consider the below input dataframe.

a  b  c  
1  1  1
1  0  1
2  2  1
2  2  2

There are 3 columns ( a, b, and c)

I would like to get the count of unique values for c, for each unique pair of a and b, for a new d column, which has the count of unique values in c for its a, b pair:

a  b  c  d
1  1  1  1
1  0  1  1
1  2  1  2 
1  2  2  2

rows 0, 1 have different a,b column pairs, and so the appended d values for both rows would be 1.

rows 2 and 3 have shared a, b columns and 2 unique values for that pair, their d values would be 2

Answer 1

I think you want to use groupby and nunique

import pandas as pd
data = pd.DataFrame({
    'a':[1,1,2,2],
    'b':[1,0,2,2],
    'c':[1,1,2,3]
})

unique_count = data.groupby(
    ['a','b']
).c.nunique()

data.set_index(['a','b']).assign(
     d = unique_count
).reset_index()

Output:

a   b   c   d
1   1   1   1
1   0   1   1
2   2   2   2
2   2   3   2

Answer 2

Let us try

df['cnt'] = df.groupby(['a','b'])['c'].transform('nunique')
df
Out[303]: 
   a  b  c  cnt
0  1  1  1    1
1  1  0  1    1
2  2  2  1    2
3  2  2  2    2