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Asked: 2026-06-15T13:32:45+00:00

I have a pandas DataFrame with 18 columns and about 10000 rows. My first

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I have a pandas DataFrame with 18 columns and about 10000 rows.

My first 3 columns have separate values for YEAR, MONTH, and DAY. I need to merge these three columns and have the entire date in one column for all the rows.

My code so far is:

df.merge('Year','/','Month')
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1 Answer

  1. Editorial Team

    You are looking for apply (merge is like a database join.):

    In [1]: from pandas import DataFrame

In [2]: df = DataFrame([[1,11,2012],[1,10,2012]], columns=['day','month','year'])

In [3]: df
Out[3]: 
   day month  year
0    1    11  2012
1    1    10  2012

In [4]: df.apply(lambda row: str(row['day'])+'/'+str(row['month'])+'/'+str(row['year']), axis=1)
Out[4]: 
0    1/11/2012
1    1/10/2012

    The axis=1 part means you are selecting columns rather than row.

    If you wanted to give a specific date you could use datetime:

    In [5]: import datetime

In [6]: df.apply(lambda row: datetime.datetime(row['year'],row['month'],row['day']), axis=1)
Out[6]: 
0    2012-11-01 00:00:00
1    2012-10-01 00:00:00

    You can add these as columns in you dataframe as follows:

    In [7]: df['new_date'] = df.apply(lambda row: str(row['day'])+'/'+str(row['month'])+'/'+str(row['year']), axis=1)

In [8]: df
Out[8]: 
   day month  year   new_date
0    1    11  2012  1/11/2012
1    1    10  2012  1/10/2012

    .

    It’s worth noting that pandas has an easy way to parse_dates when reading as a csv.

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