Скачать или смотреть How to Drop Columns from a Pandas DataFrame

Learn how to effectively drop multiple columns from a Pandas DataFrame using lists and ensure smooth data manipulation.
---
This video is based on the question https://stackoverflow.com/q/67146616/ asked by the user 'silent_hunter' ( https://stackoverflow.com/u/10924836/ ) and on the answer https://stackoverflow.com/a/67147211/ provided by the user 'sophocles' ( https://stackoverflow.com/u/9167382/ ) 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: Dropping several columns in data frame from list selection

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.
---
Introduction

When working with a dataset in Python, data manipulation and preparation are key steps in the data analysis process. One common task involves separating numerical columns from categorical columns within a DataFrame. This task can become tricky, especially for beginners, leading to errors when trying to drop certain columns from the dataset.

In this guide, we'll explore how to properly drop multiple numerical columns from a DataFrame that stores their names in a list. We will use a dataset related to the Titanic for reference, but the approach can be applied to any DataFrame in Python using Pandas.

The Problem

In the scenario presented, the user is trying to:

Identify numerical and categorical columns in a Titanic dataset.

Drop all numerical columns, which are specified in a list, from the DataFrame.

Despite following what seemed like the right approach, the user encountered issues when attempting to drop the columns using the names stored in their list. This is a common challenge faced when manipulating data in Pandas.

Solution Breakdown

To resolve the issue, we will break down the solution into clear sections:

1. Identifying Numerical Columns

The first step is to identify all numerical columns in the DataFrame. In the provided code, the user does this successfully using the following code snippet:

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

This code correctly outputs the numerical column names.

2. Storing Column Names

After identifying the numerical columns, the user attempts to store these names in the columns_names variable for later use. However, the original code had an issue with creating this list. The corrected segment of code should be:

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

Key Change: Make sure to add .columns to retrieve the column names instead of the data.

3. Dropping Columns

Now that we have a list of column names, the next step is to drop these columns from the original DataFrame. The user originally attempted this with brackets around the column names, leading to errors. The correct approach is to simply pass the columns_names list directly to the drop() function.

Here's how it should look:

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

Important: There's no need for brackets around columns_names, as this would create an additional unnecessary layer.

4. Example Code

Putting it all together, here's the complete code to properly drop numerical columns from a DataFrame:

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

Conclusion

Dropping columns from a DataFrame in Pandas can be straightforward if you follow these simple steps. Identifying the column types, correctly storing their names, and accurately using the drop() method are essential to ensure your data stays clean and manageable.

With these adjustments, you can easily manipulate your datasets and focus on your analytical insights. If you follow the steps outlined in this guide, you'll be well on your way to mastering data manipulation in Python using Pandas!