Pandas to_datetime mixed format

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pandas to_datetime mixed format

The pandas.to_datetime() function in pandas library allows you to convert a string or a series of strings into a datetime object in pandas. It can handle a variety of date and time string formats, including mixed formats.

To convert strings with mixed formats into datetime objects, you need to provide a format argument to the pandas.to_datetime() function. The format argument is a string that specifies the expected format of the datetime string.

When dealing with mixed formats, you can use a combination of different format codes. Some commonly used format codes are:

  • %Y – 4 digit year
  • %m – 2 digit month (01 to 12)
  • %d – 2 digit day (01 to 31)
  • %H – 2 digit hour in 24-hour format (00 to 23)
  • %M – 2 digit minute (00 to 59)
  • %S – 2 digit second (00 to 59)
  • %f – microsecond (000000 to 999999)

Examples:

Let’s consider some examples to understand how pandas.to_datetime() handles mixed formats:

# Importing pandas library
import pandas as pd

# Creating a list of strings with mixed date formats
date_strings = ['2021-01-01', '2021/01/02', '01-03-2021', '20210304']

# Converting the list of strings to datetime objects
datetime_objects = pd.to_datetime(date_strings, format='%Y-%m-%d')

# Printing the datetime objects
print(datetime_objects)

In this example, we have a list of date strings with different formats. We provide the format='%Y-%m-%d' argument to pandas.to_datetime() function to specify the expected format. The function then converts the strings into datetime objects and returns a pandas Series of datetime objects. The output will be:

0   2021-01-01
1   2021-01-02
2   2021-01-03
3   2021-03-04
dtype: datetime64[ns]

The datetime objects are successfully created with the specified format.

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