Number of common elements by index in two dataframes

Question

I have the three following dataframes:

df_A = pd.DataFrame( {'id_A': [1, 1, 1, 1, 2, 2, 3, 3], 
                    'Animal_A': ['cat','dog','fish','bird','cat','fish','bird','cat' ]})

df_B = pd.DataFrame( {'id_B': [1, 2, 2, 3, 4, 4, 5], 
                    'Animal_B': ['dog','cat','fish','dog','fish','cat','cat' ]})

df_P = pd.DataFrame( {'id_A': [1, 1, 2, 3], 
                      'id_B': [2, 3, 4, 5]})

df_A

    id_A    Animal_A
0   1       cat
1   1       dog
2   1       fish
3   1       bird
4   2       cat
5   2       fish
6   3       bird
7   3       cat

df_B

    id_B    Animal_B
0   1       dog
1   2       cat
2   2       fish
3   3       dog
4   4       fish
5   4       cat
6   5       cat

df_P

    id_A    id_B
0   1       2
1   1       3
2   2       4
3   3       5

And I would like to get an additional column to df_P that tells the number of Animals shared between id_A and id_B. What I'm doing is:

df_P["n_common"] = np.nan
for i in df_P.index.tolist():
    id_A = df_P["id_A"][i]
    id_B = df_P["id_B"][i]
    df_P.iloc[i,df_P.columns.get_loc('n_common')] = len(set(df_A['Animal_A'][df_A['id_A']==id_A]).intersection(df_B['Animal_B'][df_B['id_B']==id_B]))

The result being:

df_P

    id_A    id_B    n_common
0   1       2       2.0
1   1       3       1.0
2   2       4       2.0
3   3       5       1.0

Is there a faster, more pythonic, way to do this? Is there a way to avoid the for loop?

Answer 1

Not sure if it is faster or more pythonic, but it avoids the for loop :)

import pandas as pd

df_A = pd.DataFrame( {'id_A': [1, 1, 1, 1, 2, 2, 3, 3], 
                      'Animal_A': ['cat','dog','fish','bird','cat','fish','bird','cat' ]})

df_B = pd.DataFrame( {'id_B': [1, 2, 2, 3, 4, 4, 5], 
                      'Animal_B': ['dog','cat','fish','dog','fish','cat','cat' ]})

df_P = pd.DataFrame( {'id_A': [1, 1, 2, 3], 
                      'id_B': [2, 3, 4, 5]})


df = pd.merge(df_A, df_P, on='id_A')
df = pd.merge(df_B, df, on='id_B')
df = df[df['Animal_A'] == df['Animal_B']].groupby(['id_A', 'id_B'])['Animal_A'].count().reset_index()
df.rename({'Animal_A': 'n_common'},inplace=True,axis=1)

Answer 2

您可以尝试以下方法：

df_A.merge(df_B, left_on = ['Animal_A'], right_on = ['Animal_B'] ).groupby(['id_A' ,'id_B']).count().reset_index().merge(df_P).drop('Animal_B', axis = 1).rename(columns = {'Animal_A': 'count'})