Скачать или смотреть How to Merge Pandas DataFrames While Filling Empty Columns with Corresponding Values

Discover how to effectively `merge Pandas DataFrames` by filling missing values from one DataFrame into another, ensuring data consistency and integrity.
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Introduction: Merging DataFrames in Pandas

Working with data in Python often means dealing with DataFrames from the Pandas library. One common task is merging two DataFrames — especially when one DataFrame has additional columns that need to be filled with information from the other. In this post, we will explore how to merge two DataFrames, even when they contain a different number of columns.

The Problem

Imagine you have two DataFrames:

df1 with 5 rows and 3 columns that includes First_Name, Purchased, and Last_Name.

df2 with 5 rows but only 2 columns: First_Name and Purchased.

Here's a closer look at each DataFrame:

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

Expected Output

The goal is to append the rows from df2 to df1, populating the Last_Name column in the resulting DataFrame according to the names in df1. The expected output would look like this:

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

The Solution: Merging DataFrames Effectively

To accomplish this merge and fill operation, we can take advantage of the pd.concat() function, along with a few additional methods. Below are the steps you need to follow.

Step 1: Concatenating the DataFrames

First, we need to concatenate df1 and df2 in such a way that they are appended together:

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

This line of code combines both DataFrames and resets the index, allowing for a cleaner output.

Step 2: Sorting by First_Name

Next, we want to sort the resulting DataFrame by the First_Name to keep it organized:

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

Using inplace=True modifies the DataFrame in place without creating a new object.

Step 3: Filling Missing Values

Now comes the important part: filling in the missing values in Last_Name. For instance, when df2 has names not in df1, we want to fill these cases with the corresponding Last_Name when applicable. We can achieve this using fillna(method='ffill'), which fills the NaN values forward (taking the last valid observation):

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

Complete Code

Here’s how the complete process looks in one block of code:

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

Conclusion

Merging DataFrames in Pandas is straightforward once you understand how to use concat, sorting, and filling missing values effectively. By following these steps, you can ensure your data remains complete, organized, and ready for analysis. Give it a try next time you are handling datasets with differing structures!