Pandas Merge - How to avoid duplicating columns

Question

I am attempting a merge between two data frames. Each data frame has two index levels (date, cusip). In the columns, some columns match between the two (currency, adj date

Answer 1

I use the suffixes option in .merge():

dfNew = df.merge(df2, left_index=True, right_index=True,
                 how='outer', suffixes=('', '_y'))
dfNew.drop(dfNew.filter(regex='_y$').columns.tolist(),axis=1, inplace=True)

Thanks @ijoseph

Answer 2

You can work out the columns that are only in one DataFrame and use this to select a subset of columns in the merge.

cols_to_use = df2.columns.difference(df.columns)

Then perform the merge (note this is an index object but it has a handy tolist() method).

dfNew = merge(df, df2[cols_to_use], left_index=True, right_index=True, how='outer')

This will avoid any columns clashing in the merge.

Answer 3

Building on @rprog's answer, you can combine the various pieces of the suffix & filter step into one line using a negative regex:

dfNew = df.merge(df2, left_index=True, right_index=True,
             how='outer', suffixes=('', '_DROP')).filter(regex='^(?!.*_DROP)')

Or using df.join:

dfNew = df.join(df2, lsuffix="DROP").filter(regex="^(?!.*DROP)")

The regex here is keeping anything that does not end with the word "DROP", so just make sure to use a suffix that doesn't appear among the columns already.

Answer 4

This is a bit of going around the problem, but I have written a function that basically deals with the extra columns:

def merge_fix_cols(df_company,df_product,uniqueID):
    
    df_merged = pd.merge(df_company,
                         df_product,
                         how='left',left_on=uniqueID,right_on=uniqueID)    
    for col in df_merged:
        if col.endswith('_x'):
            df_merged.rename(columns = lambda col:col.rstrip('_x'),inplace=True)
        elif col.endswith('_y'):
            to_drop = [col for col in df_merged if col.endswith('_y')]
            df_merged.drop(to_drop,axis=1,inplace=True)
        else:
            pass
    return df_merged

Seems to work well with my merges!

Answer 5

I'm freshly new with Pandas but I wanted to achieve the same thing, automatically avoiding column names with _x or _y and removing duplicate data. I finally did it by using this answer and this one from Stackoverflow

sales.csv

    city;state;units
    Mendocino;CA;1
    Denver;CO;4
    Austin;TX;2

revenue.csv

    branch_id;city;revenue;state_id
    10;Austin;100;TX
    20;Austin;83;TX
    30;Austin;4;TX
    47;Austin;200;TX
    20;Denver;83;CO
    30;Springfield;4;I

merge.py import pandas

def drop_y(df):
    # list comprehension of the cols that end with '_y'
    to_drop = [x for x in df if x.endswith('_y')]
    df.drop(to_drop, axis=1, inplace=True)


sales = pandas.read_csv('data/sales.csv', delimiter=';')
revenue = pandas.read_csv('data/revenue.csv', delimiter=';')

result = pandas.merge(sales, revenue,  how='inner', left_on=['state'], right_on=['state_id'], suffixes=('', '_y'))
drop_y(result)
result.to_csv('results/output.csv', index=True, index_label='id', sep=';')

When executing the merge command I replace the _x suffix with an empty string and them I can remove columns ending with _y

output.csv

    id;city;state;units;branch_id;revenue;state_id
    0;Denver;CO;4;20;83;CO
    1;Austin;TX;2;10;100;TX
    2;Austin;TX;2;20;83;TX
    3;Austin;TX;2;30;4;TX
    4;Austin;TX;2;47;200;TX