Mastering Date Conversion: Handling Datetime Columns with Multiple Formats

Learn how to effectively manage and convert `datetime` columns with mixed formats into a unified structure using Python.
---

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: Datetime column with two different format

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Mastering Date Conversion: Handling Datetime Columns with Multiple Formats

Date and time data come in various formats, which can sometimes be a headache for data analysts and programmers alike. One common scenario arises when you have a column that mixes different datetime formats, creating confusion and potential errors in your analysis. In this guide, we will tackle a specific challenge: converting a datetime column that contains both a human-readable format and a UNIX timestamp into a single, consistent datetime format.

The Problem

Imagine you have a dataset with a column named datetime that contains dates and times in the following formats:

yyyy-mm-dd hh:mm:ss.s (e.g., 2018-05-07 04:28:45.970)

UNIX timestamps (e.g., 1527051855673000000)

This mixed-format column can make it difficult to carry out time-based analyses or visualizations. The key here is to segregate these date formats and convert them into one unified datetime column.

The Solution

We can tackle this problem efficiently using Python and its powerful pandas library. Below, we outline the steps you need to follow to achieve the desired transformation.

Step 1: Import Required Libraries

First, you will need to import the necessary libraries. Here, we will be using pandas for data manipulation and datetime for parsing the dates.

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

Step 2: Define the Conversion Function

Next, we will define a function that can differentiate between the two formats and convert them into a consistent format:

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

Explanation:

The function attempts to parse the string into a human-readable format first.

If it fails (throws an exception), it assumes the input is a UNIX timestamp and converts it accordingly.

Step 3: Read the Data

Assuming your data is stored in a CSV file, you will read the dataset as follows:

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

Step 4: Handling Nulls

To avoid errors while processing, we should drop any rows with null values in the datetime column:

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

Step 5: Apply the Conversion Function

Now, we can apply our conversion function to the datetime column and create a new column, say formatted, with the formatted datetime values:

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

This creates a new column that contains all of your dates in a consistent format.

Step 6: Review the Output

Finally, you can display the modified DataFrame to see the results:

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

Conclusion

By following these steps, you can easily manage and convert mixed-format datetime columns into a structured format. Utilizing the capabilities of Python's pandas library makes data manipulation not only efficient but also straightforward for users. Try applying this method to your datasets, and streamline your data analysis process significantly!

Now that you've learned how to handle datetime columns with mixed formats, your data cleaning process will become much easier and more efficient.