Pandas.to_datetime() – Examples

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  • Post last modified:June 19, 2025
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pandas.to_datetime() function in the Pandas library in Python used to convert arguments to DateTime. It’s quite handy for converting strings, timestamps, or mixed-type data into datetime objects. In this article, I will explain the pandas.to_datetime() function, its syntax, parameters, and usage of how to to_datetime() is a versatile function used extensively in data analysis and manipulation tasks, especially when dealing with time-series data.

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Key Points –

  • Pandas.to_datetime() converts input into datetime objects.
  • The function can infer datetime format or be explicitly provided with format specifications.
  • It supports handling of missing or erroneous data through optional parameters like errors and infer_datetime_format.
  • Pandas.to_datetime() is a versatile tool for data preprocessing, especially in time-series analysis and data cleaning tasks.
  • Supports options to handle errors gracefully, such as 'raise', 'ignore', and 'coerce'.
  • Converts various data types (strings, numbers, lists, etc.) into datetime objects.

Syntax of Pandas.to_datetime()

Following is the syntax of the Pandas.to_datetime() method.


# Pandas.to_datetime() syntax
Pandas.to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, 
     utc=None, format=None, exact=True, unit=None, 
     infer_datetime_format=False, origin='unix', cache=True)

Parameters of the Pandas.to_datetime()

Following are the parameters of the Pandas.to_datetime() function.

  • arg – An integer, string, float, list, or DataFrame/dict_like object to convert into a Datetime object.
  • errors – Take values raise, ignore or coerce. if ‘raise’ is used, raise a KeyError when a dict-like mapper, index, or column contains labels that are not present in the Index being transformed. Default set to ignore.
  • dayfirst – default set False, Boolean value places day first if True.
  • yearfirst – Boolean value places year first if True, the default set False.
  • utc – Boolean value, Returns the time in UTC DatetimeIndex if True.
  • format – String input to tell the position of the day, month, and year. default set None.
  • exact – Boolean value, If True, requires an exact format match. – If False, allow the format to match anywhere in the target string.
  • infer_datetime_formatbool – If True and no format is given, attempt to infer the format of the datetime strings based on the first non-NaN element. the default set False.

Usage of Pandas to_datetime() Method

The pandas.to_datetime() function is used to convert a wide variety of date and time formats to datetime64[ns] objects in Pandas. This is essential for performing time series analysis or date-based operations in data analysis workflows.

Now, let’s create a DataFrame with a few rows and columns, execute the above examples and validate results. Our DataFrame contains column names Courses, Fee, Duration, Discount and Inserted.


# Pandas.to_datetime() Syntax & Examples
import pandas as pd
from datetime import datetime, timedelta
from Pandas import DataFrame
df = DataFrame.from_dict(
    {'Courses':["Spark","Hadoop","Pandas"],
     'Fee' :[20000,25000,30000],
     'Duration':['30days','40days','35days'],
     'Discount':[1000,2500,1500],
     'Inserted': ["11/22/2021, 10:39:24","11/22/2021, 10:39:24","11/22/2021, 10:39:24"]},
     orient='index', 
     columns=['A','B','C']).T
print(df)

Yields below output. Note that Inserted column on the DataFrame has datetime in the format of "%m/%d/%Y, %H:%M:%S"


# Output:
  Courses    Fee Duration Discount              Inserted
A   Spark  20000   30days     1000  11/22/2021, 10:39:24
B  Hadoop  25000   40days     2500  11/22/2021, 10:39:24
C  Pandas  30000   35days     1500  11/22/2021, 10:39:24

Convert a Pandas String to DateTime

Converting a Pandas string to DateTime is a common operation when dealing with time-series data. You can use the pd.to_datetime() function


import pandas as pd

# Example string
date_string = '2024-05-07 10:30:00'

# Convert string to datetime
date_time = pd.to_datetime(date_string)
print(date_time)

# Output:
# 2024-05-07 10:30:00

In the above example, the date_string variable contains a string representing a date and time. The pd.to_datetime() function converts this string into a Pandas datetime object date_time. You can now use date_time for various datetime operations and analyses in Pandas.

FAQ on Pandas.to_datetime()

What is pandas.to_datetime() used for?

The pandas.to_datetime() function is used to convert an argument (string, list, dictionary, or a column in a DataFrame) into a datetime object, allowing for easier date manipulation and analysis in Pandas.

How do I use to_datetime() to convert a column of date strings to datetime objects?

To use pandas.to_datetime() to convert a column of date strings to datetime objects, you simply pass the column (which is typically a Series) to pd.to_datetime(). This will automatically convert the date strings to datetime64 type, which allows for easier date manipulation and analysis.

What if the date format is not standard? Can I specify the format?

You can specify the format parameter to tell to_datetime() the exact format of the input strings, which can speed up the conversion process and avoid errors.

How do I convert a Unix timestamp (seconds since epoch) to a datetime object?

You can pass a Unix timestamp (integer or float) to to_datetime(), and it will automatically be converted to a datetime object.

Can to_datetime() handle a column with mixed date formats?

to_datetime() can automatically infer date formats, even if the column has mixed formats, as long as they are recognizable by Pandas.

References

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