Скачать или смотреть How to Create a New Column in Pandas DataFrame by Extracting Macros from Strings Efficiently

Learn how to create a new column in a Pandas DataFrame by extracting macro genres from a string column using Python. This guide includes tips and comparisons on different methods for improved performance.
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Create a New Column in Pandas DataFrame by Extracting Macros from Strings Efficiently

As a beginner in Python, you might find yourself facing various challenges when working with data, particularly when using libraries like Pandas. One common task is to process data within a DataFrame to extract specific portions of a string based on certain conditions. In this guide, we will tackle a scenario where we want to create a new column in a DataFrame containing only the "macro genres" from a column filled with genres.

Problem Overview

Let’s start by defining the problem: You have a DataFrame with a column named Genres, which includes strings that may contain multiple genres separated by ;. Your goal is to create a new column called macro_genres that captures the main genre from this list.

Example DataFrame

Here is a sample DataFrame that we will work with:

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

The output of this DataFrame looks like this:

GenresLast UpdatedFinanceMarch 10, 2018ArcadeMay 24, 2018BusinessApril 11, 2018PhotographyNovember 6, 2014Entertainment;Brain GamesMarch 9, 2018MedicalMay 17, 2018ToolsJune 3, 2016Casual;Brain GamesApril 10, 2016MedicalJuly 16, 2018EntertainmentMay 17, 2017The Solution

To achieve our goal of creating the macro_genres column, we can make use of the map() function and a lambda expression in Pandas.

Step-by-Step Breakdown

Utilize the map() function: This function allows us to apply a function to each element in the Series; in this case, each genre string.

Define the lambda function: The lambda function will split the genre strings by the ; delimiter and select the first part as the macro genre.

Assign the result to a new column: Finally, we will assign the output of the mapping process to the new column called macro_genres.

Here is how you can implement this:

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

Output

After executing the above code, your DataFrame will look like this:

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

Performance Comparison

When working with large DataFrames, it’s crucial to be aware of the performance of various methods. Here's a brief comparison of the runtime for different methods for a DataFrame of varying sizes:

Using apply() method: Slightly slower due to the overhead of function calls.

Using str.split() method: The slowest option when extracting values directly.

Using map() method: Among the fastest and most efficient way to perform this operation.

Timing Results

When executed with 1,000 entries:

apply(): 535 µs

str.split(): 1.36 ms

map(): 527 µs

This performance comparison makes the map() approach a preferred choice for large datasets.

Conclusion

Creating a new column based on specific conditions in string data can be efficiently handled in Pandas using the map() function combined with a lambda expression. This method allows you to streamline your data processing tasks while ensuring optimal performance. If you're just starting with Pandas, practicing these techniques will give you a solid foundation in data manipulation.

If you have any questions or would like to see further examples, feel free to ask in the comments!