Pandas Data Series: Convert a series of date strings to a timeseries

Last update on December 21 2024 07:42:38 (UTC/GMT +8 hours)

Write a Pandas program to convert a series of date strings to a timeseries.

Sample Solution :

Python Code :

import pandas as pd
date_series = pd.Series(['01 Jan 2015', '10-02-2016', '20180307', '2014/05/06', '2016-04-12', '2019-04-06T11:20'])
print("Original Series:")
print(date_series)
print("\nSeries of date strings to a timeseries:")
print(pd.to_datetime(date_series))

Sample Output:

Original Series:
0         01 Jan 2015
1          10-02-2016
2            20180307
3          2014/05/06
4          2016-04-12
5    2019-04-06T11:20
dtype: object

Series of date strings to a timeseries:
0   2015-01-01 00:00:00
1   2016-10-02 00:00:00
2   2018-03-07 00:00:00
3   2014-05-06 00:00:00
4   2016-04-12 00:00:00
5   2019-04-06 11:20:00
dtype: datetime64[ns]

Explanation:

date_series = pd.Series(['01 Jan 2015', '10-02-2016', '20180307', '2014/05/06', '2016-04-12', '2019-04-06T11:20']): This code creates a Pandas Series object 'date_series' containing six strings representing dates in different formats.

pd.to_datetime(date_series): The code then applies the pd.to_datetime() method to the Pandas Series object 'date_series'. This method attempts to convert the input strings into Pandas Timestamp objects, which represent specific points in time.

Python-Pandas Code Editor:

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