Data Science
10 Useful Python Libraries Every Data Scientist Should Be Using
Boost your data science workflow with these open-source packages
Python has become an essential tool for data scientists across the world.
To help you boost your efficiency doing data science, we’ve put together a list of the 10 most useful Python libraries for data scientists.
From speeding up your workflow with distributed computing to helping you perform feature engineering, these libraries will help streamline your workflow and turn you into an efficient data scientist.
Let’s dive in.
Here’s one for Machine Learning we recently published 👇
1. Fugue
Forget learning Spark or go to the documentation for Ray and Dask; with Fugue, you can just set engine = "Spark|Ray|Dask" and get access to distributed computing. With Fugue, you can port your code in Python, Pandas, and SQL to Spark, Dask, and Ray, minimizing the amount of code you have to write while making your code run efficiently.
Resources
2. SweetViz
With just two lines of code, you can generate an HTML page with rich visualizations to kickstart Exploratory Data Analysis (EDA). It’s similar to pandas-profiling, but it has a sweeter interface. With SweetViz, you can quickly visualize your data and write less boilerplate code.
Like the charts by SweetViz? You can create beautiful Python charts too.
Resources
3. imbalanced-learn
Most classification models only perform well with new data when data is balanced. However, data in the real world is often imbalanced. Part of scikit-learn compatible projects, Imbalanced learn provides tools for over- and under-sampling, helping you build more robust classification models.
Resources
4. Pandaral·lel
With just a one-line code change, your Pandas code will take advantage of the multiple cores on your computer. It’s a simple and efficient tool to parallelize Pandas operations. It also has a nice progress bar available in Jupyter notebooks and the terminal, so you don’t have to guess how long it’ll take.
Speaking of Pandas, here are 40 useful Pandas snippets.
Resources
5. Missingno
Missing data is everywhere in the real world. With Missingno, you have a toolkit to easily visualize missing data and get a quick visual summary of the missingness of your dataset. Aside from the matrix visual above, you can plot a bar chart, a heatmap, and a dendrogram.
Resources
6. Featuretools
“One of the holy grails of machine learning is to automate more and more of the feature engineering process.” ― Pedro Domingos, A Few Useful Things to Know about Machine Learning.
Featuretools is a package that automatically creates features from temporal and relational datasets. It uses Deep Feature Synthesis (DFS) to automate feature engineering, has APIs to ensure precise handling of time and prevent leakage issues, and you can create your custom primitives to reuse on other datasets.
Want a refresher on feature engineering? Read 👉 Feature Engineering 101
Resources
7. Category Encoders
Do you only use OneHotEncoders? It’s time to up your game! Category Encoderes is a set of scikit-learn-style transformers for encoding categorical variables into numeric. It comes with common encoders (i.e., ordinal, one-hot, and hashing) that scikit-learn provides out-of-the-box but has some extra useful properties, such as explicitly configuring which columns in the data are encoded and portability.
Resources
8. mlxtend
Written by Sebastian Raschka, a machine learning and AI researcher, Mlxtend (machine learning extensions) is a library full of useful tools for day-to-day data science tasks such as loading data, extracting and selecting features, preprocessing, plotting, dealing with images and text, math utilities, etc.
Resources
9. PyCaret
PyCaret is an awesome low-code machine-learning library in Python that automates ML workflows. It replaces hundreds of lines of code with a few lines, makes experimentation exponentially faster, and helps you focus on what matters. The image above shows how easy it is to set up and train 20+ models with just a few lines of code.
Read how we used PyCaret to predict NFT prices.
Resources
10. SHAP
SHAP (SHapley Additive exPlanations) is an approach to understanding the decisions made by machine learning models using game theory. With SHAP, you can understand not only the overall contribution of each feature but also how each feature interacts with other features. In today’s world, understanding why an ML model has made a particular prediction is becoming increasingly important to reduce bias and increase transparency.
Read our article on Making Machine Learning Models Interpretable
Read about the common misconception of SHAP
Resources
That’s all for this article. Am I missing other libraries that data scientists should use to boost their efficiency and productivity?
Comment down below!
Be sure to follow the bitgrit Data Science Publication to keep updated!
Want to discuss the latest developments in Data Science and AI with other data scientists? Join our discord server!
Follow Bitgrit below to stay updated on workshops and upcoming competitions!
Discord | Website | Twitter | LinkedIn | Instagram | Facebook | YouTube
Like my writing? Join Medium with my referral link for the price of ☕.
You’ll be supporting me directly 🤗
