Create Date column from Year, Month and Day in Pandas

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# Create Date column from Year, Month and Day in Pandas

Use the pandas.to_datetime() method to create a date column from year, month and day.

The method can be passed a dictionary containing the year, month and day and will convert the supplied arguments to datetime.

main.py

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import pandas as pd

df = pd.DataFrame({
    'year': [2011, 2012, 2013, 2014],
    'month': [1, 2, 3, 4],
    'day': [10, 12, 14, 16],
    'champion': ['Austria', 'Belgium', 'Canada', 'Denmark']
})

print(df)

df['date'] = pd.to_datetime(
    dict(year=df.year, month=df.month, day=df.day)
)

print('-' * 50)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

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   year  month  day champion
0  2011      1   10  Austria
1  2012      2   12  Belgium
2  2013      3   14   Canada
3  2014      4   16  Denmark
--------------------------------------------------
   year  month  day champion       date
0  2011      1   10  Austria 2011-01-10
1  2012      2   12  Belgium 2012-02-12
2  2013      3   14   Canada 2013-03-14
3  2014      4   16  Denmark 2014-04-16

pandas create date column from year month day

The pandas.to_datetime method takes an argument and converts it to datetime.

We used the dict() class to construct a dictionary from the year, month and day column values.

main.py

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df['date'] = pd.to_datetime(
    dict(year=df.year, month=df.month, day=df.day)
)

Note that the names of the columns you pass to pd.to_datetime() have to be:

year
month
day
hour
minute
second

# Create Date column from Year, Month, Day, Hours, Minutes, Seconds in Pandas

You can also include the time components when creating the date column if necessary.

main.py

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import pandas as pd

df = pd.DataFrame({
    'year': [2011, 2012, 2013, 2014],
    'month': [1, 2, 3, 4],
    'day': [10, 12, 14, 16],
    'hour': [3, 1, 5, 10],
    'minute': [32, 11, 22, 14],
    'second': [5, 10, 15, 20],
    'champion': ['Austria', 'Belgium', 'Canada', 'Denmark']
})

print(df)

df['date'] = pd.to_datetime(
    dict(year=df.year, month=df.month, day=df.day,
         hour=df.hour, minute=df.minute, second=df.second)
)

print('-' * 50)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
   year  month  day  hour  minute  second champion
0  2011      1   10     3      32       5  Austria
1  2012      2   12     1      11      10  Belgium
2  2013      3   14     5      22      15   Canada
3  2014      4   16    10      14      20  Denmark
--------------------------------------------------
   year  month  day  ...  second  champion                date
0  2011      1   10  ...       5   Austria 2011-01-10 03:32:05
1  2012      2   12  ...      10   Belgium 2012-02-12 01:11:10
2  2013      3   14  ...      15    Canada 2013-03-14 05:22:15
3  2014      4   16  ...      20   Denmark 2014-04-16 10:14:20

create date column from year month day hours minutes seconds

Make sure the keyword arguments you pass to the dict() class when calling pd.to_datetime() are spelled correctly.

main.py

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df['date'] = pd.to_datetime(
    dict(year=df.year, month=df.month, day=df.day,
         hour=df.hour, minute=df.minute, second=df.second)
)

You can also only include the year, month, day, hour or year, month, day, hour and minute arguments, however, you must at least supply values for year, month and day.

# Create Date column from Year, Month and Day using a subset of the `DataFrame`

You can also call the pandas.to_datetime() method with a subset of the DataFrame that only contains the year, month and day columns.

main.py

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import pandas as pd

df = pd.DataFrame({
    'year': [2011, 2012, 2013, 2014],
    'month': [1, 2, 3, 4],
    'day': [10, 12, 14, 16],
    'champion': ['Austria', 'Belgium', 'Canada', 'Denmark']
})

print(df)

df['date'] = pd.to_datetime(
    df[['year', 'month', 'day']]
)

print('-' * 50)

print(df)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
   year  month  day champion
0  2011      1   10  Austria
1  2012      2   12  Belgium
2  2013      3   14   Canada
3  2014      4   16  Denmark
--------------------------------------------------
   year  month  day champion       date
0  2011      1   10  Austria 2011-01-10
1  2012      2   12  Belgium 2012-02-12
2  2013      3   14   Canada 2013-03-14
3  2014      4   16  Denmark 2014-04-16

create date column from year month day using subset of dataframe

This time, we used bracket notation to only select a subset of the DataFrame when calling pandas.to_datetime().

main.py

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df['date'] = pd.to_datetime(
    df[['year', 'month', 'day']]
)

Here is the output of the expression.

main.py

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import pandas as pd

df = pd.DataFrame({
    'year': [2011, 2012, 2013, 2014],
    'month': [1, 2, 3, 4],
    'day': [10, 12, 14, 16],
    'champion': ['Austria', 'Belgium', 'Canada', 'Denmark']
})

#    year  month  day
# 0  2011      1   10
# 1  2012      2   12
# 2  2013      3   14
# 3  2014      4   16
print(df[['year', 'month', 'day']])

get subset containing year month day columns only

# Create Date column from Year, Month and Day using `format`

You can also use the format argument of the pandas.to_datetime method to create a date column from year, month and day.

main.py

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import pandas as pd

df = pd.DataFrame({
    'year': [2011, 2012, 2013, 2014],
    'month': [1, 2, 3, 4],
    'day': [10, 12, 14, 16],
    'champion': ['Austria', 'Belgium', 'Canada', 'Denmark']
})

df['date'] = pd.to_datetime(
    df.year*10000+df.month*100+df.day,
    format='%Y%m%d'
)

#    year  month  day champion       date
# 0  2011      1   10  Austria 2011-01-10
# 1  2012      2   12  Belgium 2012-02-12
# 2  2013      3   14   Canada 2013-03-14
# 3  2014      4   16  Denmark 2014-04-16
print(df)

create date column using format argument

The code for this article is available on GitHub

We used the multiplication and addition operators to construct a number that we can format.

The format argument is a string and represents the strftime to parse a date.

You can view all of the available directives and their descriptions in this section of the docs.

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Create Date column from Year, Month and Day in Pandas

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