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'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