Efficiently Add New Columns to Dataframes with Pandas in Python

Discover how to quickly add new columns to a `Pandas` dataframe using values from another dataframe, improving processing speed significantly!
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Efficiently Add New Columns to Dataframes in Python with Pandas

Working with data in Python often involves manipulating datasets using libraries such as Pandas. A common challenge arises when you need to link two dataframes and pull specific column values from one dataframe into another. In this guide, we’ll explore how to efficiently add new columns to a dataframe by using values from another dataframe and how to avoid slow processing times.

The Problem

Imagine you have two dataframes: df1 and kts_df. The df1 contains a list of administrative regions with various attributes, while kts_df provides corresponding codes based on these regions. The goal is to extract the KTS codes from kts_df and add them as a new column in df1 using an existing column that identifies the type of administrative division.

While your initial approach might be functional, using lambda functions and the apply method can be slow, especially when dealing with large datasets, such as df1, which contains around 200,000 rows. Let’s explore a more efficient solution.

Efficient Solution

Step 1: Create a Gmina Types Mapping

First, we need to establish a mapping for the types of administrative divisions (i.e., gmina types). This is accomplished using a simple dataframe that associates each type with an ID.

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

Step 2: Join Dataframes

Next, merge kts_df with gmina_types_df. This allows us to enrich kts_df with additional information that corresponds to the types of gminas.

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

Now, we join this enriched kts_df with the original df1. The key to this join is using the rodzaj gminy column from df1, which will match the id column from the newly joined kts_df.

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

Code Example

Here’s how the complete implementation might look:

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

Benefits of This Approach

Speed: By using joins instead of apply, the operation is vectorized, resulting in significant performance improvements.

Simplicity: The code is cleaner and easier to maintain since it avoids complex row-wise operations.

Scalability: This approach can handle larger datasets gracefully, reducing processing time from an hour to mere seconds in many cases.

Conclusion

In summary, adding new columns to a dataframe using values from another dataframe can be done efficiently by leveraging the power of joins in Pandas. This method not only simplifies the process but significantly speeds it up, making it an invaluable technique for data manipulation. Whether you're analyzing administrative regions or any other data, this method will help you handle large datasets effortlessly.

By using these techniques, you can ensure that your data processing tasks are executed efficiently without compromising on clarity and maintainability. Happy coding!