Скачать или смотреть Optimize Your Python Code: Avoiding iterrows() with Pandas

Discover how to streamline your Pandas dataframe operations by avoiding `iterrows()`, and learn efficient techniques with `apply`, `applymap`, and `map` for better performance.
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This video is based on the question https://stackoverflow.com/q/68114631/ asked by the user 'Matt' ( https://stackoverflow.com/u/11389503/ ) and on the answer https://stackoverflow.com/a/68114983/ provided by the user 'Sawradip Saha' ( https://stackoverflow.com/u/13945920/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Taming the Dataframe: A Guide to Avoiding iterrows() in Pandas

When working with data in Python's Pandas library, many developers are aware that the use of iterrows() can lead to slower performance, especially with larger datasets. In this guide, we will explore a specific challenge regarding this practice and provide an optimized solution to enhance the speed and efficiency of your dataframe operations.

The Challenge

The challenge originates from a requirement to compare elements in a set against a column in a dataframe to compile a list. Specifically, you have:

A set of unique job numbers.

A transactional dataframe that contains job numbers and associated run numbers.

The task is to take a complete pass through the dataframe and generate a dictionary where:

The keys are the job numbers.

The values are lists of run numbers corresponding to each job.

Many newcomers tend to default to using iterrows() in such scenarios. However, this method can slow down the processing significantly.

The Current Solution

Below is an example of the current inefficient approach using iterrows() to achieve the desired result:

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

While this code may provide the required output, it indeed runs slowly because of the nested loop involving iterrows(), which iterates over each row for each job.

The Optimized Solution

To avoid the performance hit from iterrows(), we can leverage powerful Pandas functionalities. Here’s an enhanced version of your original problem using a more efficient method:

Step-by-Step Breakdown

Create a Dictionary: Instead of repeatedly iterating through the dataframe, we can create a filtered dataframe for each job directly.

Filtering Dataframes: By using Boolean indexing, we can filter the rows that correspond to each job without looping through every dataframe entry manually.

Using tolist(): Once we have the filtered dataframe, we can easily convert the list of run numbers to a straightforward list with the tolist() method.

Here’s how the optimized code looks:

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

Explanation of the Code

Filtering with Conditions: filt = (dict_val['JobNumber'] == job) creates a mask that can filter the main dataframe.

Creating a Subdataframe: new_df = dict_val[filt] generates a subdataframe only containing the rows for the current job number.

Extracting Values: temp_list = new_df['RunNumber'].tolist() efficiently collects all the run numbers for the current job.

Conclusion

By using the provided solutions, you optimize the data processing time significantly while avoiding the undesirable use of iterrows(). This approach aligns more with the best practices in Pandas programming, enhancing the overall performance of your code.

Remember, eliminating the requirement to iterate manually through rows in a dataframe not only saves time but also leads to cleaner, more readable code. Embrace these techniques to improve your Python data handling skills!