How to Use to_datetime in pandas for Date Conversion

Use pandas.to_datetime() to convert strings, numbers, or lists into datetime objects in pandas. It automatically parses many date formats and can handle errors or specify formats for faster conversion.

Syntax

The basic syntax of pandas.to_datetime() is:

• arg: The data to convert (string, list, Series, or DataFrame column).
• format: Optional. Specify the date format to speed up parsing.
• errors: Optional. How to handle errors ('raise', 'coerce', 'ignore').
• utc: Optional. Convert to UTC time zone if True.
• dayfirst: Optional. Interpret the first value as day (True) or month (False).

python

pandas.to_datetime(arg, format=None, errors='raise', utc=False, dayfirst=False)

Example

This example shows how to convert a list of date strings into pandas datetime objects and how to handle errors by coercing invalid dates to NaT.

python

import pandas as pd

dates = ['2023-01-01', '2023/02/15', 'March 3, 2023', 'not a date']
dates_converted = pd.to_datetime(dates, errors='coerce')
print(dates_converted)

Output

[Timestamp('2023-01-01 00:00:00') Timestamp('2023-02-15 00:00:00') Timestamp('2023-03-03 00:00:00') NaT]

Common Pitfalls

Common mistakes include:

• Not handling errors, which causes the function to raise exceptions on invalid dates.
• Not specifying format when the date format is known, which slows down parsing.
• Confusing day and month order without setting dayfirst.

Example of wrong and right usage:

python

import pandas as pd

# Wrong: raises error on invalid date
try:
    pd.to_datetime(['2023-13-01'])
except Exception as e:
    print(f'Error: {e}')

# Right: coerce invalid dates to NaT
dates = pd.to_datetime(['2023-13-01'], errors='coerce')
print(dates)

Output

Error: month must be in 1..12
NaT

Quick Reference

Parameter	Description	Default
arg	Data to convert (string, list, Series)	Required
format	Date format string to speed parsing	None
errors	How to handle errors: 'raise', 'coerce', 'ignore'	'raise'
utc	Convert to UTC timezone if True	False
dayfirst	Parse day first if True	False

Key Takeaways

Use pandas.to_datetime() to convert various date formats into datetime objects easily.

Specify the format parameter when you know the date format to speed up conversion.

Use errors='coerce' to handle invalid dates without crashing your code.

Set dayfirst=True if your dates use day before month format.

The function returns pandas Timestamp or NaT for missing/invalid dates.