How to Join a DataFrame with a Dictionary in Python Pandas

Discover an easy way to join a DataFrame with a dictionary in Python’s Pandas library, ensuring your data is perfectly matched and organized!
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Joining a DataFrame with a Dictionary in Python

When working with data in Python, particularly using the Pandas library, you may encounter a common scenario: needing to enrich a DataFrame with additional data from a dictionary. This can be particularly useful when you want to map codes or identifiers in your DataFrame to meaningful labels in a dictionary.

In this post, we will walk through a practical example that illustrates this process step by step.

The Problem

Imagine you have a DataFrame, which we'll call df, that contains codes and their corresponding designations. Here’s what the DataFrame looks like:

CodeDesignation01Example 12FExample 340Example 10Additionally, you have a dictionary named matching, which maps certain codes to larger identifiers:

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

Your goal is to add a new column, Custom 1, to your DataFrame, where the values correspond to keys from the dictionary based on the Code in the DataFrame.

The desired output would be something like:

CodeDesignationCustom 101Example 1ABCD2FExample 3EFG40Example 10nullThe challenge here is to appropriately match the codes to their corresponding labels from the dictionary.

The Solution

To achieve this, we’ll implement a function that searches the dictionary for each code in the DataFrame's Code column. Let's outline the solution:

Step 1: Import Necessary Libraries

First, we need to ensure we have Pandas and NumPy imported:

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

Step 2: Define the Search Function

Next, we define a function called searchDict. This function will take a code and search through the dictionary to find which key contains the given code. If a match is found, it returns the corresponding key; if not, it returns NaN.

Here's how the function looks:

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

Step 3: Create the New Column

Now that we have our search function defined, we can create a new column in our DataFrame. This new column will be filled by applying the searchDict function to each value in the Code column.

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

Final Result

After executing the above code, our DataFrame, df, will be updated with an additional column Custom 1, reflecting the results of the mapping:

CodeDesignationCustom 101Example 1ABCD2FExample 3EFG40Example 10nullConclusion

By following these steps, you can successfully join a DataFrame with a dictionary in Python using Pandas. This approach allows you to easily enrich your datasets with external mappings, improving data analysis and interpretation.

Feel free to modify the dictionary and DataFrame content to fit your specific needs. Happy coding!