Скачать или смотреть Sum Values of Two DataFrames with Conditions in Pandas

Learn how to efficiently sum values from two DataFrames based on specific conditions in `Pandas`. This essential guide simplifies merging data and calculating sums in Python.
---
This video is based on the question https://stackoverflow.com/q/74144843/ asked by the user 'M.A.Sedigh' ( https://stackoverflow.com/u/14765678/ ) and on the answer https://stackoverflow.com/a/74144927/ provided by the user 'Naveed' ( https://stackoverflow.com/u/3494754/ ) 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: Sum values of two Dataframe with Conditions

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.
---
Summing Values of Two DataFrames with Conditions in Pandas

When working with data in Python, especially using the Pandas library, it's common to encounter situations where you need to merge datasets and perform aggregations based on certain conditions. One typical scenario is when you're reading multiple CSV files and need to process them into a main DataFrame (DF). In this blog, we'll discuss a common problem: summing values from new DataFrames into a main DataFrame based on specific conditions.

The Problem

Imagine you have a main DataFrame containing energy consumption data and you continuously receive new data from various sources. If the new data for a particular building and day is already present in the main DataFrame, you want to update the existing entry by summing the previous value with the new one. Otherwise, you want to simply add the new DataFrame as it is.

Here's a sample structure of your DataFrames:

Main DataFrame (main_df)

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

New DataFrames

new_df_1

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

new_df_2

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

The goal is to determine whether to sum the existing values or to concatenate new data.

The Solution

To achieve this, we can use the concat and groupby functions available in Pandas. The process can be broken down into a few simple steps.

Step-by-Step Guide

Combine the DataFrames: We will initially concatenate all DataFrames together.

Group by Conditions: We'll group the combined DataFrame to aggregate the kw/h values based on the building and day.

Sum the Values: Finally, we will sum the kw/h for each unique combination of building and day.

Implementation in Python

Here’s how you can implement this:

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

Result

After executing the above code, the final DataFrame should look like this:

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

Conclusion

This approach ensures that we accurately sum the energy consumption values by building and day, effectively handling the merging of DataFrames in Pandas. By leveraging the concat and groupby functions, we can simplify what might seem like a complex problem into an easy-to-understand solution. Feel free to adapt the implemented code for your specific use-case or dataset. Happy coding!