Скачать или смотреть How to Retrieve Row Numbers from Given Indices in Pandas DataFrames

Discover how to easily find row numbers from given indices in Pandas DataFrames using simple methods in Python.
---
This video is based on the question https://stackoverflow.com/q/63553184/ asked by the user 'Forklift17' ( https://stackoverflow.com/u/5162520/ ) and on the answer https://stackoverflow.com/a/63553199/ provided by the user 'Scott Boston' ( https://stackoverflow.com/u/6361531/ ) 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: Get the row number from given indeces in Pandas

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

Pandas is an immensely popular data manipulation library in Python that helps you work with structured data seamlessly. One common task while dealing with DataFrames is retrieving row numbers from specific index labels. This ability to convert index labels into their corresponding row numbers is essential for efficiently accessing data. In this guide, we'll explore how to achieve this transformation and address a specific scenario where you might need to perform this operation.

Problem Definition

Suppose you have a DataFrame with custom index labels, and you want to find out which row numbers correspond to specific index labels in that DataFrame. For example, given the index labels ['second', 'third'], how do you return their respective row numbers? Understanding how to efficiently extract these row numbers will streamline your data analysis tasks.

Sample DataFrame

First, let’s take a look at our sample DataFrame:

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

The DataFrame looks like this:

ABfirst14second25third36In this example, the indices are first, second, and third, and you want to convert the index labels ['second', 'third'] into their respective row numbers.

Solution: Using get_indexer

To find the row numbers corresponding to specific index labels, you can use the get_indexer method available in Pandas. Here’s how you can do it:

Step-by-Step Breakdown

Initialize your DataFrame: Make sure you have your DataFrame set up with the desired index labels.

Use get_indexer: This method will allow you to specify a list of index labels, and it will return their corresponding row numbers as an array.

Retrieve the row numbers: Call the get_indexer method on the DataFrame's index, passing in the list of index labels.

Here’s the code that accomplishes these steps:

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

Output Explained

When you run the above snippet, you will get the following output:

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

This output indicates that the index label second corresponds to row number 1 and third corresponds to row number 2. This simple array allows you to easily reference the row numbers for further data manipulation or analysis.

Conclusion

Retrieving row numbers from index labels in Pandas is a straightforward process using the get_indexer method. This technique not only simplifies the way data scientists and analysts refer to their data but also enhances data operation efficiency. By mastering such methods, you empower yourself to navigate DataFrames effortlessly.

Now that you have this knowledge at your disposal, you can efficiently manipulate your datasets in Pandas with ease! Don't hesitate to explore more functionalities the Pandas library offers to become more proficient in data analysis.