合并 pandas 不起作用，看起来像 concat

Question

I've been working with two dataframes (info_clients and metadata_clients) both have an user_id and id_wp column as associated key , respectively, and I loeaded info_clients into sql table and get the PK associated, then merge these dfs on user_id (by the left side) and id_wp (on the right).

info_clients: (72232, 1)

 user_id
0       0
1       1
2       4
3       5
4   39784

metadata_clients: (72232, 2)

        id  id_wp
0  1158426      0
1  1158427      1
2  1158428      4
3  1158429      5
4  1158430  39784

I used this:

merge = pd.merge( info_clients, metadata_clients, left_on=['user_id'], 
                            right_on=['id_wp'], how='left')

But it doesn't work as I expected, I had this result:

  user_id  cliente_fk  id_wp
0       0     1158426      0
1       1     1158427      1
2       4     1158428      4
3       5     1158429      5
4   39784     1158430  39784
Datamerge shape: (126680, 3)

When I save the info_clients data into sql table, I verify these data and I have 72232 clients saved. I don't have nulls or nan values, I cleaned the data and checked its dtypes, both keys are int64.

Answer 1

You have a situation where you have duplicates:

No, I don't have duplicates, I removed in a previoust step, using:
data.drop_duplicates(keep='first')

I don't know if data is your first ( info_clients ) or your second ( metadata_clients ) but if you drop duplicates without set a subset of columns, it's likely you have no duplicate on entire row. You should try:

data = data.drop_duplicates('user_id', keep='first')

# OR

data = data.drop_duplicates('wp_id', keep='first')

You should try to debug with value_counts :

data.value_counts('user_id')

# OR

data.value_counts('wp_id')