pandas.to_datetime() converts argument(s) to datetime. This function is essential for working with date and time data, especially when parsing strings or timestamps into Python's datetime64 format used in Pandas.

For Example:

import pandas as pd
d = ['2025-06-21', '2025-06-22']
res = pd.to_datetime(d)
print(res)

DatetimeIndex(['2025-06-21', '2025-06-22'], dtype='datetime64[ns]', freq=None)

Explanation: date strings are in ISO format (YYYY-MM-DD), which pandas.to_datetime() parses by default, returning a DatetimeIndex.

Syntax

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

Parameters:

Returns: A datetime64 dtype object, which can be a single Timestamp, DatetimeIndex or Series, depending on the input.

Examples

Example 1: In this example, we convert date strings written in DD/MM/YYYY format to datetime objects using the format parameter.

import pandas as pd
d = ['21/06/2025', '22/06/2025']
res = pd.to_datetime(d, format='%d/%m/%Y')
print(res)

DatetimeIndex(['2025-06-21', '2025-06-22'], dtype='datetime64[ns]', freq=None)

Explanation: pandas.to_datetime() expects dates in ISO format (YYYY-MM-DD), so we use 'format='%d/%m/%Y' to correctly parse day-first strings. It converts them into datetime objects and returns a DatetimeIndex holding datetime64 values.

Example 2: In this example, we convert a list of date strings and handle invalid entries using errors='coerce', which replaces the invalid value with NaT.

import pandas as pd
d = ['2025-06-21', 'invalid']
res = pd.to_datetime(d, errors='coerce')
print(res)

DatetimeIndex(['2025-06-21', 'NaT'], dtype='datetime64[ns]', freq=None)

Explanation: pandas.to_datetime() attempts to parse each string, but since 'invalid' is not a valid date, using errors='coerce' replaces it with NaT instead of raising an error, ensuring safe and error-free conversion.

Example 3: In this example, we convert a column of string dates in a DataFrame to datetime format using pd.to_datetime() for further time-based operations.

import pandas as pd
df = pd.DataFrame({'date': ['2025-06-21', '2025-06-22']})
df['date'] = pd.to_datetime(df['date'])
print(df)

        date
0 2025-06-21
1 2025-06-22

Explanation: pd.to_datetime() is applied to the 'date' column to convert string values into datetime objects.

Example 4: In this example, we parse dates where the day comes before the month by setting dayfirst=True.

import pandas as pd
d = ['21/06/2025', '22/06/2025']
res = pd.to_datetime(d, dayfirst=True)
print(res)

DatetimeIndex(['2025-06-21', '2025-06-22'], dtype='datetime64[ns]', freq=None)

Explanation: By setting dayfirst=True, pandas.to_datetime() correctly interprets the first number as the day, not the month, ensuring accurate conversion to datetime objects.

Example 5: In this example, we convert a list of integers into dates by treating them as day offsets from the origin date '2025-06-01'.

import pandas as pd
d = [0, 1, 2]
res = pd.to_datetime(d, unit='D', origin='2025-06-01')
print(res)

DatetimeIndex(['2025-06-01', '2025-06-02', '2025-06-03'], dtype='datetime64[ns]', freq=None)

Explanation: By setting unit='D' and specifying the origin, pandas calculates and returns the corresponding datetime values as a DatetimeIndex.

Example 6: In this example, we convert a datetime string into UTC timezone-aware datetime using utc=True.

import pandas as pd
d = ['2025-06-21 12:00']
res = pd.to_datetime(d, utc=True)
print(res)

DatetimeIndex(['2025-06-21 12:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)

Explanation: By passing utc=True, pandas.to_datetime() converts the datetime into a timezone-aware datetime in UTC (Coordinated Universal Time).