A Comprehensive NumPy Cheat Sheet Of 40 Most Used Methods
...that data scientists use 95% of the time.
NumPy documentation is pretty intimidating, in my opinion.
Anyone who wants to learn its method is quite likely to be intimidated by its API reference topics:
If you are in a similar situation, I once prepared this NumPy cheat sheet, which depicts the 40 most commonly used methods from NumPy:
Having used NumPy for over 4.5 years, I can confidently say that you will use these methods 95% of the time working with NumPy.
It is important to understand that whenever you are learning a new library, mastering/practicing each and every method is not necessary.
Instead, put Pareto’s principle to work :)
20% of your inputs contribute towards generating 80% of your outputs.
👉 Over to you: Have I missed any commonly used method?
👉 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.
Thanks for reading!
Latest full articles
If you’re not a full subscriber, here’s what you missed last month:
A Beginner-friendly and Comprehensive Deep Dive on Vector Databases.
You Are Probably Building Inconsistent Classification Models Without Even Realizing
Why Sklearn’s Logistic Regression Has no Learning Rate Hyperparameter?
PyTorch Models Are Not Deployment-Friendly! Supercharge Them With TorchScript.
How To (Immensely) Optimize Your Machine Learning Development and Operations with MLflow.
DBSCAN++: The Faster and Scalable Alternative to DBSCAN Clustering.
Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.
You Cannot Build Large Data Projects Until You Learn Data Version Control!
To receive all full articles and support the Daily Dose of Data Science, consider subscribing:
👉 Tell the world what makes this newsletter special for you by leaving a review here :)
👉 If you love reading this newsletter, feel free to share it with friends!
Fantastic!
Thanks a lot, seems to be very usefulf!