Скачать или смотреть How to Merge DataFrame on Every Item in a List Using Python Pandas

Learn how to effectively merge two Pandas DataFrames in Python, especially when dealing with lists. This blog will guide you through a complete example of merging sales with products, resulting in a succinct table of corresponding values.
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Merging DataFrames in Python Pandas: A Step-By-Step Guide

Working with DataFrames in Python’s Pandas library can sometimes raise questions, especially when trying to merge data based on more complex datasets. One such example involves merging a sales DataFrame with a product DataFrame where one of the entries contains lists. In this post, we will guide you through the process of accomplishing this task with ease.

The Problem

Imagine you have the following two tables:

Sales Table

ProductIdSales[1]$199[2]$299[3, 4, 5]$399[6, 7, 8]$499Product Table

IDProduct1A2B3C4D5E6F7G8HThe challenge here is to create a new table called sales_product that merges these two tables in such a way that each product ID from the sales table is converted to its corresponding product name from the product table. In cases where asingle entry in ProductId contains multiple IDs, we want to return them as a comma-separated string.

Desired Output

The desired output should look like this:

ProductSalesA$199B$299C,D,E$399F,G,H$499The Solution

To achieve this merge, we can take advantage of Python’s ability to manipulate dictionaries alongside the Pandas library. Here’s a breakdown of the steps you need to follow to arrive at the desired DataFrame.

Step 1: Create a Lookup Dictionary

First, we need to create a lookup dictionary that maps the ID from the product DataFrame to the corresponding Product. This can be accomplished using the set_index method followed by to_dict().

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

Step 2: Map Product IDs to Product Names

With the lookup dictionary in hand, the next step is to apply a function that will transform the ProductId lists in the sales DataFrame to their corresponding product names. For this, we can use the apply method alongside a lambda function.

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

Here's a quick explanation of how this works:

The apply method iterates over each row in the ProductId column.

The lambda function splits each list element, maps them to their corresponding product names using the lookup dictionary, and finally joins those names with a comma.

Step 3: Review the Final Output

After executing the above steps, the sales DataFrame should now have a new column Product that features the desired merged product names.

You will end up with the following DataFrame:

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

Conclusion

Merging DataFrames effectively in Python, especially with list entries, can seem daunting at first. However, by creating a lookup dictionary and utilizing the power of the apply method, you can simplify this task significantly. This approach not only enhances the clarity of your data but also prepares it for further analysis without losing essential connections between entities.

Now you are equipped with the knowledge to handle similar challenges in your data manipulation tasks. Happy coding!