How to Properly Convert Datetime to UNIX Timestamp in Python Using Pandas

A guide on converting datetime columns to `UNIX` timestamps in pandas, addressing common issues and providing clear examples.
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Understanding UNIX Time Conversion in Pandas

When working with dates and times in data analysis, especially using Python's Pandas library, one common task is converting datetime columns to UNIX timestamps. This can be particularly important for applications that require time data in a standardized format. In this guide, we will address a common issue related to this conversion process, demonstrating how to successfully convert datetime columns while avoiding pitfalls.

The Problem: Unexpected Results During Conversion

Imagine you have a .csv file containing two datetime columns: created_at and actual_delivery_time. You’ve successfully converted the created_at column to UNIX time, but when you attempt the same for actual_delivery_time, the results appear inaccurate.

For instance, you may perform the following operation:

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

This code should effectively convert the actual_delivery_time column to UNIX timestamps; however, the output may be returned in scientific notation or as a float, leading to confusion regarding its accuracy.

Possible Cause of the Issue

Upon investigation, you may find that one column outputs timestamps as integers, while the other results in floating-point values. This discrepancy often occurs due to:

Blank or NaN Values: If any entries in the actual_delivery_time column are missing or marked as NaN, Pandas will represent these values as float, which leads to the conversion yielding unexpected results.

To diagnose this issue, you can check the data types of your DataFrame columns by using:

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

If actual_delivery_time shows up as a float type, you can confirm that there are indeed NaN values causing the conversion error.

Solution: Handling NaN Values

To ensure a successful conversion, you will want to handle any missing values in the actual_delivery_time column before proceeding with the timestamp conversion. Here are several approaches you can take:

Approach 1: Fill NaN with a Default Value

You can replace NaN values with a specific date, such as the epoch start date (1970-01-01):

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

Approach 2: Drop Rows with NaN Values

Alternatively, you can remove any rows that contain NaN values in the actual_delivery_time column:

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

After handling NaN values, you can re-run your conversion:

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

This should result in actual_unix being accurate and formatted correctly as integers.

Conclusion

Converting datetime columns to UNIX timestamps using Pandas can sometimes lead to unexpected results, particularly when NaN values are present. By understanding the data types and properly handling missing values, you can ensure that your conversions are accurate and reliable.

Feel free to experiment with the examples provided, and soon you'll find that converting datetime to UNIX timestamps can be a straightforward task in your data processing workflow.