There are so many Python libraries for data science out there today that you will get the simplest alternative of almost any library. As a data scientist, you need to keep improving your skills, so you need to keep learning new libraries if they are better than the ones you are already using. But there are some very important Python libraries for data science that will help you learn the basics of working with data for a specific problem. Such libraries will not only help to solve any kind of data science problem but also help to learn and understand new libraries. So, if you want to know about all the important Python libraries for data science, this article is for you. In this article, I’m going to introduce you to all the important Python libraries for data science that every data scientist should know.
Important Python Libraries for Data Science
NumPy
NumPy is the fundamental Python library for scientific computing. It offers comprehensive mathematical functions, random number generation, linear algebra functions, Fourier transforms, and many other numerical calculation tools. Some of the benefits of using NumPy for data science are:
- It provides powerful N-Dimensional arrays
- It provides a large variety of numerical computing tools
- It is interoperable
- Easy to use and learn
You can learn more about NumPy for data science from here.
Pandas
Pandas is the foundational building block for real-world data analysis with Python. It provides a very fast and efficient DataFrame object for data analysis and manipulation. From reading a dataset to preparing the data, the pandas library is used everywhere in a data science task. It is undoubtedly one of the most important Python libraries for data science. You can learn more about pandas for data science here.
Matplotlib
Another very important Python library for data science is Matplotlib. It is a data visualization library for creating static, animated, and interactive visualizations using Python. Some of the features provided by matplotlib are:
- used to generate publication-quality visualizations
- can create interactive figures
- customizable visual styles and layouts
- can export visualizations to many file formats
You can learn more about matplotlib for data science here.
Scikit-Learn
Scikit-learn is one of the most important data science Python libraries for implementing machine learning algorithms using Python. It provides simple and efficient tools for implementing machine learning algorithms for classification, regression, clustering, dimensionality reduction, model selection, preprocessing, etc. Spotify is one of the biggest names among companies using Scikit-learn. You can learn more about Scikit-learn for Data Science here.
TensorFlow
TensorFlow is an open-source framework for machine learning. Companies like Google, Spotify, Twitter, Airbnb, Intel and much more use TensorFlow in their applications. You can use TensorFlow for both machine learning and deep learning. It was developed by Google, and Google itself provides a lot of resources for learning TensorFlow using Python. You can learn more about TensorFlow here.
Summary
So these were the most important Python libraries that you should learn. These libraries will not only help in solving any kind of data science problem but will also help in learning and understanding new libraries. I hope you liked this article on the most important Python libraries for data science. Feel free to ask your valuable questions in the comments section below.
