Find source of Key Error in pandas dataframe merge-CodePudding

I am getting a KeyError: 'Cust_id_2' when I try to merge the following dataframes.

df =

Cust_id year    is_sub
0   4   1516    is_sub
1   4   1920    is_sub
2   4   1819    is_sub
3   4   1718    is_sub
4   4   1617    is_sub

df2 =

    Cust_id_2   year_freq_score
0   4   9.0
1   5   6.0
2   7   10.0
3   8   2.0
4   10  1.0

Most recently I have tried this:

result = pd.merge(
    df, 
    df2[['year_freq_score']],
    how='left',
    left_on='Cust_id',
    right_on='Cust_id_2'
)

df has 14,000 rows. df2 has 3,000 rows. df2 is a pivot table derived from df.

My first version had the Cust_id as the index of df2 and i tried to use 'right_index=True' which gave a KeyError.

I then reset the index and used 'on' columns having the same name (on='Cust_id) which gave KeyError: 'Cust_id'.

I then and changed df2 to ''Cust_id_2'' to isolate where the error was coming from and now receive 'KeyError: 'Cust_id_2''.

I've read through multiple posts on 'KeyError' but have not found (or understood) the solution to this issue.

Any help or pointers in the right direction greatly appreciated.

CodePudding user response：

You slice df2 to only keep year_freq_score, so there is no more Cust_id_2 column for the merge.

Do instead:

result = pd.merge(
    df, 
    df2,
    how='left',
    left_on='Cust_id',
    right_on='Cust_id_2'
)