How to Reference a DataFrame Using a Variable in Python's pandas

Описание: Learn how to dynamically access DataFrame values in Python's `pandas` with a variable reference. This guide walks you through creating a mapping system to retrieve data easily.
---
This video is based on the question https://stackoverflow.com/q/65461404/ asked by the user 'AndrewGraham' ( https://stackoverflow.com/u/14802243/ ) and on the answer https://stackoverflow.com/a/65461681/ provided by the user 'Ghifari Dwiki Ramadhan' ( https://stackoverflow.com/u/6907730/ ) 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: How do I reference a dataframe using a variable that holds the dataframe name?

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.
---
Accessing DataFrames Dynamically in Python's pandas

In the world of data manipulation, especially with Python's pandas, you might often find yourself in situations where you need to reference a DataFrame using a variable that holds the name of that DataFrame. This can be particularly useful when dealing with collections of items, such as inventories, where different variables represent various data components. In this guide, we will explore how to achieve this with a combination of DataFrames and dictionaries.

The Problem

Imagine you have several DataFrames, each representing different types of equipment in a game: packs, armors, swords, and bows. Each DataFrame has two columns: id (which serves as the index) and name. For example, your DataFrame for packs might look like this:

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

Your inventory consists of tuples that specify the type of equipment and its ID, as shown below:

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

Your goal is to create a function that outputs the name of each item in the inventory, such as:

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

However, you may find it challenging to access the appropriate DataFrame using the inventory keywords. This is where our solution comes in.

The Solution

To dynamically access the DataFrames based on the names stored in your inventory, you can create a dictionary that maps each equipment type to its corresponding DataFrame. This allows you to programmatically reference the DataFrames and retrieve item names using their IDs.

Step-by-Step Implementation

Define Your Inventory:
Keep your inventory as a collection of tuples, where the first element is the equipment type, and the second is the ID.

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

Create a Mapping Dictionary:
Make a dictionary that maps each type of item to its respective DataFrame.

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

Accessing DataFrames Using the Dictionary:
In your function, iterate through the inventory, using the dictionary to access the DataFrame and retrieve the name based on the corresponding ID.

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

Example Output

When you run the command_inventory() function, you should get an output like this (assuming the DataFrames have been populated accordingly):

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

Conclusion

By creating a mapping dictionary of your DataFrames, you can easily reference and retrieve data dynamically using variables in Python. This method not only simplifies your code but also enhances its readability and maintainability. Whether you are managing inventories in a game or processing various datasets, this approach can save you time and effort.



This method is a vital skill for any data scientist or developer working with data in Python, especially in complex applications. Now, you can tackle similar tasks with confidence!