Скачать или смотреть How to Assign Values to Columns Using Lookup Tables with Pandas

Learn how to efficiently assign values to a DataFrame column based on a lookup table in Pandas. This post provides a clear explanation and step-by-step guide.
---
This video is based on the question https://stackoverflow.com/q/62847023/ asked by the user 'Chaudhry Talha' ( https://stackoverflow.com/u/4337240/ ) and on the answer https://stackoverflow.com/a/62847145/ provided by the user 'this be Shiva' ( https://stackoverflow.com/u/4909087/ ) 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: Assign value to column based on lookup table using 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.
---
How to Assign Values to Columns Using Lookup Tables with Pandas

Pandas is a powerful library in Python that makes data manipulation and analysis easy. One common task that data analysts frequently encounter is the need to look up values in a dataset and append or modify columns based on those lookups. In this guide, we will be covering how to assign values to a column based on a lookup table using Pandas. We will break down the steps involved and provide a clear, organized explanation to help you understand the process.

The Problem

Imagine you have a matrix that represents working days for various destinations, and you want to assign specific values to another DataFrame (other_df) based on this matrix. Here's what the scenario looks like:

Your destinations are:

DC

NY

SF

AL

You have a working days matrix that outlines how many working days are allocated for pairs of destinations:

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

You have another DataFrame (other_df) with pairs of destinations, looking something like this:

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

Your goal is to create a new column in other_df that retrieves values based on the working days matrix. For example, if dest1 is NY and dest2 is AL, the value should be 7 since that’s the corresponding value from your matrix.

The Solution

To solve this problem, we can utilize the lookup method provided by the Pandas library. The lookup method allows us to efficiently access values based on row and column labels.

Steps to Follow

Ensure You Have Your DataFrames Ready: Make sure you've created your working_days_df and other_df.

Use the lookup Method: Here is the code you will need to add a new column to your other_df:

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

other_df['dest1'] provides the row labels (to look up), and other_df['dest2'] provides the column labels.

The lookup function will then return the corresponding values based on your matrix.

Review Your DataFrame: After running the above code, your other_df will now have a new column populated with the desired values.

Example Code

Here’s a complete example, putting it all together:

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

Conclusion

Using the lookup method in Pandas makes it straightforward to assign values from one DataFrame based on another's content. This powerful feature can streamline your data processing tasks significantly. Now, whenever you need to access values based on predetermined matrices, you can confidently use this method to enhance your workflows.

Remember, effective data manipulation is crucial in data analysis, and understanding techniques like this can save you time and effort in your projects.