Published Apr 20, 2023
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The .to_datetime() function returns a pandas datetime object for a given object, often an array or dictionary-like type such as a Series or DataFrame.


This function returns a value in datetime format. Various input arguments can be used as described below.

pandas.to_datetime(arg, format=None, errors='raise', dayfirst=False, yearfirst=False, utc=None, box=True, infer_datetime_format=False, origin='unix', cache=True)
Parameter Name Data Type Usage
arg int, float, str, datetime, list, tuple, 1-d array, Series, DateFrame/dict-like Converts given data into a datetime
errors ‘ignore’, ‘raise’, ‘coerce’ The given keyword determines the handling of errors
dayfirst bool (default False) Specifies that the str or list-like object begins with a day
yearfirst bool (default True) Specifies that the str or list-like object begins with a year
utc bool (default None) When True, output is converted to UTC time zone
format str (default None) Pass a strftime to specify the format of the datetime conversion
exact bool (default True) Determines how the format parameter is applied
unit str (default ‘ns’) Specifies the units of the passed object
infer_datetime_format bool (default False) When True, and no format has been passed, the datetime string will be based on the first non-NaN element within the object
origin scalar (default unix) Sets the reference date
cache bool (default True) Allows the use of a unique set of converted dates to apply the conversion (only applied when object contains at least 50 values)


The code below demonstrates the conversion of a string to a datetime object with the .to_datetime() function.

import pandas as pd
my_list = ['11/09/30']
xyz = pd.to_datetime(my_list, dayfirst=True)

This example results in the following output::

DatetimeIndex(['2030-11-09'], dtype='datetime64[ns]', freq=None)

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