Скачать или смотреть How to Drop Columns in Pandas DataFrame Based on Row Values

Learn how to effectively drop columns from a Pandas DataFrame in Python based on values in a specific row.
---
This video is based on the question https://stackoverflow.com/q/69692899/ asked by the user 'sibillaromana' ( https://stackoverflow.com/u/17229931/ ) and on the answer https://stackoverflow.com/a/69692946/ provided by the user 'nikeros' ( https://stackoverflow.com/u/7694263/ ) 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: Drop columns based on row -index 0

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.
---
How to Drop Columns in Pandas DataFrame Based on Row Values

If you're new to Python and working with data manipulation, you might encounter situations where you need to clean up your data. One common task is dropping specific columns from a DataFrame based on the values in a particular row. In this post, we will guide you through this process, specifically focusing on how to drop columns where the value in row 0 equals "rejected".

Understanding Your DataFrame

Let's say you have a DataFrame that looks like this:

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

In this DataFrame, row 0 indicates the status of each column (either "accepted" or "rejected"). Your goal is to drop any columns that have a status of "rejected".

Step-by-Step Solution

Here’s how you can achieve this with just a couple of lines of code in Python using the Pandas library.

Step 1: Import Pandas

Make sure you have the Pandas library installed. If you haven't installed it yet, you can do so using pip:

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

Now, import Pandas in your Python script:

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

Step 2: Create Your DataFrame

For demonstration purposes, let’s assume you have already created your DataFrame. However, if you're starting from scratch, here’s how to define it:

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

Step 3: Dropping Columns

To drop the columns where row 0 is "rejected", you can use the following code:

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

Explanation of the Code:

df.iloc[0]: This selects all the values in the first row (row index 0).

df.iloc[0] == "accepted": This creates a boolean Series that checks which columns have "accepted" in the first row.

df.loc[:, ...]: Using this boolean Series as a locator filters the DataFrame to only include the columns where the condition is true.

Step 4: Final DataFrame

Now df_filtered will contain only the columns that had an "accepted" status in the first row:

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

This output will only show the columns you wanted to keep, based on the specified condition.

Conclusion

Dropping columns from a Pandas DataFrame based on row-specific values is a straightforward task once you know the right commands. By using the iloc method combined with loc, you can manipulate your DataFrame to ensure it contains only the data you need. This technique is particularly useful in data cleaning and preparation tasks.

Feel free to experiment with other conditions or row values to tailor this solution to your specific needs!