Avoid Using Pandas' Apply() Method At All Times

Clearing a common misconception about a popular method.

The apply() method in Pandas is the most common approach to apply a function along an axis of a DataFrame/Series.

But contrary to common belief, Pandas' apply() method:

• is NOT vectorized

• instead, it's a glorified for-loop

Thus, it does not offer any inherent optimization and the code runs at native Python speed.

One solution is to eliminate the apply() method by using a vectorized approach.

But it is understandable that at times, coming up with a vectorized approach is difficult. (Here’s one of my previous guides on this: If You Are Not Able To Code A Vectorized Approach, Try This)

Another solution is to parallelize the apply() method by using external libraries.

The image above compares the run-time of alternatives that support parallelization.

It is evident that Pandas’ apply() is not the optimal way to apply a method.

Get started with these libraries here:

• Swifter: https://github.com/jmcarpenter2/swifter

• Pandarallel: https://github.com/nalepae/pandarallel

• Parallel Pandas: https://pypi.org/project/parallel-pandas/

• Mapply: https://pypi.org/project/mapply/

👉 Over to you: What are some other techniques you commonly use to optimize Pandas’ operations?

---

👉 Read what others are saying about this post on LinkedIn and Twitter.

👉 Tell the world what makes this newsletter special for you by leaving a review here :)

👉 If you liked this post, don’t forget to leave a like ❤️. It helps more people discover this newsletter on Substack and tells me that you appreciate reading these daily insights. The button is located towards the bottom of this email.

👉 If you love reading this newsletter, feel free to share it with friends!

👉 Sponsor the Daily Dose of Data Science Newsletter. More info here: Sponsorship details.

---

Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.