Скачать или смотреть Enhancing Your DataFrame: How to Add Columns Based on Existing Values in Pandas

Discover an efficient way to `add a new column` to your DataFrame by merging data from another DataFrame based on specific conditions using Pandas in Python.
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Enhancing Your DataFrame: How to Add Columns Based on Existing Values in Pandas

When working with data in Python, especially with the Pandas library, there are times when you'll need to combine information from multiple sources into a single DataFrame. A common scenario is when you want to add a new column to an existing DataFrame based on the values in another one. In this guide, we will illustrate how to achieve this through an engaging example.

The Problem Statement

Imagine you have two DataFrames:

DataFrame 1 consists of DNI and Email.

DataFrame 2 contains first name, last name, and num.

Here's a quick look at the structure of these DataFrames:

DataFrame 1:

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

DataFrame 2:

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

Your goal is to add a num column to DataFrame 1, ensuring that each email corresponds to the num from DataFrame 2. If an email doesn't match, you want to input 0.

The Solution

Here’s a step-by-step guide to implement this solution efficiently using Pandas:

Step 1: Create Email Column in DataFrame 2

To establish a clear connection between the two DataFrames, we first need to create an email column in DataFrame 2 that matches the format in DataFrame 1. We can do this by concatenating the first name and last name with the domain.com.

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

Step 2: Left Join Both DataFrames

Now that we have a common key (the email), we can merge both DataFrames. We will use a left join to ensure that all entries from DataFrame 1 remain intact, while additional information from DataFrame 2 is appended where matches are found.

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

Step 3: Handle Missing Values

After merging, the num column may contain NaN values where there were no corresponding matches. To maintain data consistency, we replace these NaN values with 0.

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

Finalizing Your DataFrame

At this point, you may want to remove any unnecessary columns generated during the merge or reorder your DataFrame as needed.

Conclusion

Working with Pandas can effectively streamline your data manipulation needs. By following the steps outlined above, you can efficiently add new columns based on existing values while handling missing data gracefully. The use of merge allows for a clean and efficient approach, avoiding the need for cumbersome loops.

Now you have all the tools you need to enhance your DataFrames dynamically!