How to Convert Pandas Timestamp to Datetime in Python   

In this tutorial, we'll see an example of converting Pandas Timestamp to datetime objects in Python. Whether you're processing time series data, analyzing financial records, or working with event logs, converting between Pandas Timestamp and Python datetime is essential for compatibility with standard libraries and date operations.

Pandas Timestamp objects are powerful for time series but sometimes you need standard Python datetime for compatibility with other libraries, APIs, or legacy code.

1. Converting Single Timestamp to Datetime

The to_pydatetime() method converts a Pandas Timestamp to a Python datetime object, enabling use with standard datetime functions.

import pandas as pd
from datetime import datetime

order_time = pd.Timestamp('2026-02-14 15:30:00')
event_time = pd.Timestamp('2026-03-20 09:45:30')

order_dt = order_time.to_pydatetime()
event_dt = event_time.to_pydatetime()

print(f"Order timestamp type: {type(order_time)}")
print(f"Converted type: {type(order_dt)}")
print(f"Order datetime: {order_dt.strftime('%Y-%m-%d %H:%M:%S')}")
print(f"Event datetime: {event_dt.strftime('%B %d, %Y at %I:%M %p')}")

Output Result:

Order timestamp type: <class 'pandas._libs.tslibs.timestamps.Timestamp'>
Converted type: <class 'datetime.datetime'>
Order datetime: 2026-02-14 15:30:00
Event datetime: March 20, 2026 at 09:45 AM

How it works: The to_pydatetime() method creates a native Python datetime object, making it compatible with datetime library functions, JSON serialization, and third-party libraries that don't support Pandas types.

2. Converting DataFrame Column from Timestamp to Datetime

When working with DataFrames, you often need to convert an entire timestamp column to datetime objects for processing or export.

import pandas as pd

transactions = pd.DataFrame({
    'transaction_id': [1001, 1002, 1003],
    'customer': ['Amazon', 'Google', 'Microsoft'],
    'timestamp': pd.to_datetime(['2026-02-10 14:30', '2026-02-12 09:15', '2026-02-14 16:45'])
})

transactions['datetime'] = transactions['timestamp'].apply(lambda x: x.to_pydatetime())

print(transactions)
print(f"\nTimestamp column type: {type(transactions['timestamp'].iloc[0])}")
print(f"Datetime column type: {type(transactions['datetime'].iloc[0])}")

Output Result:

   transaction_id    customer           timestamp            datetime
0            1001      Amazon 2026-02-10 14:30:00 2026-02-10 14:30:00
1            1002      Google 2026-02-12 09:15:00 2026-02-12 09:15:00
2            1003   Microsoft 2026-02-14 16:45:00 2026-02-14 16:45:00

Timestamp column type: <class 'pandas._libs.tslibs.timestamps.Timestamp'>
Datetime column type: <class 'datetime.datetime'>

Real-world use: This is essential when exporting to JSON, interfacing with APIs, or using datetime-specific functions that don't accept Pandas Timestamp objects.

3. Batch Conversion with List Comprehension

For efficient batch processing of multiple timestamps, use list comprehension to convert arrays of Pandas Timestamps.

import pandas as pd

events = pd.date_range('2026-01-01', periods=5, freq='W')
datetime_list = [ts.to_pydatetime() for ts in events]

print(f"Original: {events[:3]}")
print(f"Converted: {datetime_list[:3]}")
print(f"First datetime formatted: {datetime_list[0].strftime('%A, %B %d, %Y')}")

Output Result:

Original: DatetimeIndex(['2026-01-04', '2026-01-11', '2026-01-18'], dtype='datetime64[ns]', freq='W-SUN')
Converted: [datetime.datetime(2026, 1, 4, 0, 0), datetime.datetime(2026, 1, 11, 0, 0), datetime.datetime(2026, 1, 18, 0, 0)]
First datetime formatted: Sunday, January 04, 2026

Common Use Cases

JSON Serialization: Convert timestamps for API responses that require datetime objects

Database Integration: Prepare data for databases that expect Python datetime

Legacy Code: Interface with older systems using standard datetime

Third-Party Libraries: Use with libraries like requests, json, pickle

Time Calculations: Perform operations with datetime-specific functions

Quick Reference