Pandas Data Series: Convert year-month string to dates adding a specified day of the month

Last update on December 21 2024 07:42:39 (UTC/GMT +8 hours)

Write a Pandas program to convert year-month string to dates adding a specified day of the month.

Sample Solution :

Python Code :

import pandas as pd
from dateutil.parser import parse
date_series = pd.Series(['Jan 2015', 'Feb 2016', 'Mar 2017', 'Apr 2018', 'May 2019'])
print("Original Series:")
print(date_series)
print("\nNew dates:")
result = date_series.map(lambda d: parse('11 ' + d))
print(result)

Sample Output:

Original Series:
0    Jan 2015
1    Feb 2016
2    Mar 2017
3    Apr 2018
4    May 2019
dtype: object

New dates:
0   2015-01-11
1   2016-02-11
2   2017-03-11
3   2018-04-11
4   2019-05-11
dtype: datetime64[ns]

Explanation:

In the above exercise -

date_series = pd.Series(['Jan 2015', 'Feb 2016', 'Mar 2017', 'Apr 2018', 'May 2019']): This line creates a Pandas Series object 'date_series' containing five strings representing months and years in the format "MMM YYYY", where MMM is the three-letter abbreviation for the month.

result = date_series.map(lambda d: parse('11 ' + d)): This code applies the map() method to the Pandas Series object 'date_series' and a lambda function to parse each string into a Pandas Timestamp object using the dateutil.parser.parse() method.

The lambda function prepends the string "11 " to each input string, effectively adding a day value of 11 to each month-year string. This is necessary because parse() expects a day value in the input string.

Python-Pandas Code Editor:

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