How to Convert Time Zone Date Column to 13-Digit Timestamp in Python Pandas

Learn how to efficiently transform date columns in Python Pandas into a 13-digit Unix timestamp format, accommodating time zone data.
---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Convert time zone date column to timestamp format

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Transforming Time Zone Dates into 13-Digit Timestamps with Python Pandas

If you handle date and time data in Python, especially with the Pandas library, you may encounter the need to convert date columns with time zone information into a specific timestamp format. More often than not, a common requirement is to convert these dates into a 13-digit Unix timestamp, which represents milliseconds since the epoch (January 1, 1970).

This guide will guide you through the process of converting a time zone date column to a 13-digit timestamp, addressing a common challenge faced by data analysts and developers alike.

Understanding the Problem

You may have a date in string format, richer in detail with time zone suffixes, such as:

[[See Video to Reveal this Text or Code Snippet]]

However, after attempting to convert the date using the following code:

[[See Video to Reveal this Text or Code Snippet]]

You notice that the outcome is a 10-digit timestamp instead of the desired 13-digit format. It’s crucial to comprehend that the output directly relates to how the timestamp is formatted and calculated.

The Solution

To obtain a 13-digit timestamp from your dates, you'll need to consider the following steps which involve using NumPy and Pandas. Below, I break down the process into easy-to-follow sections:

Step 1: Import Necessary Libraries

First, ensure that you have NumPy and Pandas imported into your script:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Convert String to Datetime

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Convert Datetime to Unix Timestamp in Milliseconds

Once you have your datetime object, you can transform it into a Unix timestamp. To achieve a 13-digit timestamp, you'll utilize the .view() method on the datetime object:

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Output Your Result

Finally, print your result to check the output timestamp:

[[See Video to Reveal this Text or Code Snippet]]

Example Code

Here’s the complete code snippet to convert your date column effectively:

[[See Video to Reveal this Text or Code Snippet]]

Important Notes

The standard int64 representation in Pandas outputs timestamps in nanoseconds; hence, division by 1e6 converts it to milliseconds.

If you require a 13-digit representation with microseconds, you would divide by 1e3 instead.

Conclusion

With these simple steps, you can efficiently convert a date string containing time zone information to a 13-digit Unix timestamp format using Python Pandas. This conversion is particularly useful in data analysis and processing tasks where accurate time representation is paramount.

If you have further questions or need assistance with more date-time manipulations, feel free to explore additional resources or ask in the comments below!