Скачать или смотреть How to Infer Date Typed Columns in Pandas with a Custom Format

Learn how to automatically identify and parse date columns in pandas using a custom format without manual specification.
---
This video is based on the question https://stackoverflow.com/q/72544705/ asked by the user 'Spok' ( https://stackoverflow.com/u/2118884/ ) and on the answer https://stackoverflow.com/a/72547693/ provided by the user 'Spok' ( https://stackoverflow.com/u/2118884/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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: In pandas, how to infer date typed columns with a custom format

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Automatically Inferring Date Typed Columns in Pandas with a Custom Format

When working with data in Python, particularly with time-series data, the ability to parse date columns correctly can be a game changer. If you've used pandas, you've likely encountered situations where dates are not automatically recognized due to a non-standard format. For example, a CSV file might contain date strings formatted as 22.01.2022, which means “day.month.year.” The good news is that there's a way to infer these date columns without manually specifying which columns contain dates, ensuring that your data parsing process is both efficient and adaptable.

The Problem: Dates Not Being Parsed Correctly

In the scenario we're addressing, someone attempted to read a CSV file using pandas' read_csv() method, but the date columns were not being parsed as expected due to their custom format. Here’s their initial approach:

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

While they included a custom date parser, it didn't yield the desired results. They discovered that specifying the columns to parse made it work correctly:

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

However, manually specifying date columns is not ideal especially when dealing with dynamic data sources where columns may change.

The Solution: Automating Date Parsing

To solve the problem and automate the parsing of date columns without having to spell out each column, we can take advantage of pandas' functionality along with some regex pattern matching. The idea is to load the dataframe and then check each column to see if it matches the defined date format. Here's how you can do it step by step:

Step 1: Load the Data

First, read your dataset into a DataFrame without specifying date columns:

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

Step 2: Identify Columns with Date Format

Next, use regex to identify which columns contain data that matches the day.month.year format:

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

This line goes through all the columns in the DataFrame, converting them to strings and checking if any of the entries in that column match the date format pattern.

Step 3: Convert the Matching Columns to Date

Finally, you can convert these identified columns to datetime format using pd.to_datetime:

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

At this point, all the columns that have date-like strings matching the pattern will be converted to proper datetime objects in the DataFrame.

Conclusion

By following this systematic approach, you can effectively let pandas determine which columns should be parsed as dates when working with custom formats. This not only saves time by eliminating the need for manual entry but also adapts seamlessly to changes in your data structure. Embracing pandas capabilities with regex can significantly streamline your data preprocessing tasks and enhance your data analysis experience.

Give it a try next time you're working with date data formats, and enjoy the benefits of a more automated workflow!