Скачать или смотреть How to Create a Nested List from Unique Values in Python

Learn how to organize gene mutation data into a well-structured nested list using Python's built-in features and libraries.
---
This video is based on the question https://stackoverflow.com/q/63870551/ asked by the user 'Sahira Mena' ( https://stackoverflow.com/u/11217595/ ) and on the answer https://stackoverflow.com/a/63870775/ provided by the user 'furas' ( https://stackoverflow.com/u/1832058/ ) 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: Create nested-list from unique values of first value

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 Create a Nested List from Unique Values in Python

If you're working with biological data, especially genetic mutations, you may encounter lists that have repeating elements that need organizing. For instance, you could have a list of gene names paired with their mutations, and you may want to consolidate these into a nested list format. This guide will help you achieve just that with Python, showcasing methods to group related data effectively.

The Problem

Consider you have the following gene names and their corresponding mutations:

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

Using this information, you create a combined list:

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

However, you'd like to reformat the data into a nested list where each gene is listed alongside all its associated mutations, like so:

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

The Solution

Using defaultdict

One efficient way to group the gene mutations is by using the defaultdict from the collections module. Here’s how you can do it:

Import defaultdict
You'll need to import the defaultdict class first.

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

Set Up Your Data
Define your input list as shown below:

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

Create the Dictionary
Initialize a defaultdict to store your data grouped by gene names.

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

Convert to Nested List
Finally, convert the dictionary back to a nested list:

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

This will give you the desired structured output:

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

Using a Standard Dictionary

If you prefer to use a regular dictionary instead of defaultdict, you can do the following:

Initialize a Standard Dictionary
Create a regular dictionary and check for existing keys:

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

Convert to Nested List
The subsequent conversion to nested list remains the same as the previous method.

Utilizing Pandas for Grouping

If you're comfortable with using libraries, pandas is an excellent tool to handle data manipulation:

Import Pandas
Start by importing the pandas library.

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

Create a DataFrame
Convert your list to a DataFrame:

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

Group the Data
Use the groupby() function to group by gene names:

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

Combine and Convert
Lastly, combine the two columns into a list format:

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

Conclusion

Each of these methods allows you to transform a list of genes and their mutations into an organized nested list. Whether you choose to use defaultdict, a standard dict, or pandas, you can efficiently manage your genetic data and make further analyses much simpler.

Ready to implement this in your own projects? Happy coding!