Pandas Merge – How to avoid duplicating columns

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, axis=1, inplace=True)

Thanks @ijoseph

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

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.

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!

can’t you just subset the columns in either df first?

[i for i in df.columns if i not in df2.columns]

dfNew = merge(df **[i for i in df.columns if i not in df2.columns]**, df2, left_index=True, right_index=True, how='outer')

When the amount of columns you want to avoid is lower than the columns you want to keep… you could use this kind of filtering:

df.loc[:, ~df.columns.isin(['currency', 'adj_date'])]

This will filter all columns in the dataframe except the ‘currency’ and ‘adj_date’ columns, you have to write the merge something like this:

    dfNew = merge(df, 
                  df2.loc[:, ~df.columns.isin(['currency', 'adj_date'])], 
                  left_index=True,
                  right_index=True,
                  how='outer')

Note the "~", it means "not".

You can include duplicate columns in the key to merge on to ensure only a single copy appears in the result.

# Generate some dummy data.
shared = pd.DataFrame({'key': range(5), 'name': list('abcde')})
a = shared.copy()
a['value_a'] = np.random.normal(0, 1, 5)
b = shared.copy()
b['value_b'] = np.random.normal(0, 1, 5)

# Standard merge.
merged = pd.merge(a, b, on='key')
print(merged.columns)  # Index(['key', 'name_x', 'value_a', 'name_y', 'value_b'], dtype='object')

# Merge with both keys.
merged = pd.merge(a, b, on=['key', 'name'])
print(merged.columns)  # Index(['key', 'name', 'value_a', 'value_b'], dtype='object')

This method also ensures that values in columns that appear in both data frames are consistent (e.g. that the currency in both columns is the same). If they are not, the corresponding row will be dropped (if how = 'inner') or occur with missing values (if how = 'outer').

You can remove the duplicate y columns you don’t want after the join:

# Join df and df2
dfNew = merge(df, df2, left_index=True, right_index=True, how='inner')

Output: currency_x | adj_date_x | data_col1 | ... | currency_y | adj_date_y | data_col2

# Remove the y columns by selecting the columns you want to keep
dfNew = dfNew.loc[:, ("currency_x", "adj_date_x", "data_col1", "data_col2")]

Output: currency_x | adj_date_x | data_col1 | data_col2

If you’re merging on arbitrary columns and don’t want to keep the right key this will do the trick:

mrg = pd.merge(a, b, how="left", left_on="A_KEY", right_on="B_KEY")
mrg.drop(columns=b.columns.difference(cols_to_use))

If the indexes are the same (big if true!) you can do:

df = df1.copy()
df[df2.columns] = df2

this similar to merge

pd.merge(df1, df2, index_left=True, index_right=True)

but with no duplicate columns