Скачать или смотреть Merging DataFrames in Pandas: Multicolumn Conditions and Comparison

Learn how to effectively merge DataFrames in Pandas with multicolumn conditions and comparisons to keep track of statuses over time.
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Merging DataFrames in Pandas: Multicolumn Conditions and Comparison

When working with datasets in Python using Pandas, a common challenge arises: how do you effectively merge DataFrames based on multiple columns, especially when trying to track statuses over time? This guide will guide you through a specific example of merging DataFrames to compare email delivery and the corresponding actions taken by customers.

Understanding the Problem

Consider a situation where you have a DataFrame that records interactions with customers regarding emails sent to them. Each email may have different statuses such as "Delivered" or "Opened". For instance:

DateCustomer_IDSubjectStatus1-1-2020123How can I Help?Delivered1-1-2020123How can I Help?Opened1-2-2021100New OfferDelivered1-4-2021144Last chance, buy now!Delivered2-4-2021144Last chance, buy now!OpenedIn this scenario, you want to keep track of when each email was delivered and if it was opened by the customer. Your goal is to merge the data so that each delivery record harmoniously integrates with its corresponding action.

Step-by-Step Solution

The solution involves creating two separate DataFrames: one for "Delivered" emails and another for "Opened" emails. Here’s how you can do it:

1. Create Initial DataFrame

First, we need to create our initial DataFrame:

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2. Filtering for Delivered and Opened

Next, filter the DataFrame into two: one for delivered emails and another for opened emails:

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3. Adding a Pseudo 'Order' Column

To correctly align the emails that were delivered and opened, we will introduce a pseudo 'order' column. This will help in differentiating between multiple actions taken by the same customer on the same subject:

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4. Renaming Columns for Clarity

Next, let’s rename the columns in preparation for the merge:

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5. Merging DataFrames

Now, merge the two DataFrames based on Customer_ID, Subject, and the new order column to combine the information:

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6. Filling Missing Dates

Finally, if the last action date is NaN (not a number), you can fill it with the Date Delivered:

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Result

After executing these steps, your merged DataFrame will look like this:

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

Conclusion

Merging DataFrames with multiple conditions in Pandas can certainly enhance your data analysis by allowing for better tracking and comparisons over time. By using a structured approach to filter, rename, and merge your data carefully, you can provide clearer insights into your customer interactions.

With these steps, you should feel confident in tackling similar scenarios in your data analysis projects!