Retaining Latest Values When Combining Two DataFrames with pandas in Python

Learn how to effectively combine two DataFrames in Python using `pandas` while ensuring that the latest values are retained based on a datetime column. This guide provides a comprehensive solution to your DataFrame merging problems.
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Combining Two DataFrames and Retaining the Latest Values

As someone new to Python and pandas, you may find yourself in situations where you need to handle multiple DataFrames containing overlapping information. In particular, you might want to combine two DataFrames while ensuring that the most recent values are retained. In this post, we will explore how to do just that with a practical example and code solution.

The Problem

Let's consider two DataFrames: current_df and old_df. These DataFrames share the same structure, consisting of the following columns:

Name

Id

Salary

Work_hours

Location

Date_updated

When merging these DataFrames, you want the following outcomes based on the Date_updated column:

If current_df has the latest Date_updated, update the fields Salary, Work_hours, Location, and Date_updated accordingly.

If there are any new employee records in current_df, simply add them to the resultant DataFrame.

If there are no changes other than the Date_updated, only update that field.

Using the wrong method to merge DataFrames can lead to duplicate entries and unwanted results, as noted from your previous experience when using combine_first(). So let’s take a look at an effective approach to this issue.

The Solution

To achieve the desired behavior when merging these DataFrames, you can follow the method laid out below. The function get_updated_df takes care of all the logic we discussed.

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

Explanation of the Code

Concatenation: We start by concatenating the two DataFrames to form a single DataFrame, df. This combines the old and new records together.

Validation: Before proceeding, we validate that each employee has a unique Id and Name. If there is a discrepancy (like an Id change), an error is raised.

Set Index: Next, we set the index of both DataFrames to Id to facilitate merging based on employee identifiers.

Sorting and Grouping: Finally, by sorting the DataFrame by Date_updated, we ensure that the latest records take precedence. We then group by Name and Id, taking the last entry for each employee. This gives us the most recent information for each employee.

Index Reset: The index is reset to bring Id back as a column to complete the structure of the DataFrame.

Example Use Case

Here’s a practical example to clarify how the function works:

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

Conclusion

Combining DataFrames in Python while ensuring the retention of the latest values can be done effectively with the above approach using pandas. By following the structured method outlined, you can avoid complications like duplicative records or inconsistencies in your data. The get_updated_df function provides a clear and manageable answer to merging two DataFrames with overlapping information in a way that maintains data integrity.

Remember, proper data handling is crucial in any data analysis and manipulation task. Happy coding!