20 Python Libraries Every Data Scientist Needs to Know
Python is one of the most popular programming languages used across various tech disciplines, especially data science and its subfields. Because of Python’s popularity, the language has over 130,000 packages for different applications. This article is meant for data science novices or those who are curious about what they need to learn to write data science applications in Python. I’ll walk you through 20 packages you need to know as a data scientist to build any application you want.
20 Python Libraries for Data Scientists
- Beautiful Soup
At its core, data science is math and one of the most potent mathematical packages out there is NumPy. NumPy brings the power and simplicity of C and Fortran to Python. For data science in particular, NumPy is the foundation for many other packages that hold the data science ecosystem like Pandas, Matplotlib and Scikit-learn.
Keras is an API designed and developed to help people become proficient in machine learning. Keras’s primary goal is to reduce the developer's cognitive load by minimizing the number of required user actions by using straightforward error messages. Another great merit of Keras is how robust its documentation and tutorials are.
When you’re building a data science project, you will use the monster library Pandas to handle and analyze your data 100 percent of the time. Pandas offer developers fast, efficient and optimized objects for data manipulation in various academic and industrial fields. Pandas also has a welcoming community for beginners for both data science and open source communities.
Like many other tech fields, data science is constantly evolving, which means we’re seeing new research and developments every day. But sometimes, moving from research to practice is quite challenging. Luckily, PyTorch is a great package that helps developers move from theory and research to training and development with ease when it comes to machine learning research.
Many data science projects require different levels of optimization and integration. In addition, data science’s underlying mathematics, such as linear algebra equations, differential equations and statistics, need high-level solutions provided by SciPy. SciPy enables developers from all levels the ability to solve mathematical problems quickly and efficiently.
Machine learning is an essential branch of data science, specifically predictive data analysis. Scikit-learn, an open-source, accessible and reusable package built on NumPy, SciPy and Matplotlib. Scikit-learn offers a lot of functionality for various basic machine learning algorithms, like regression, classification and clustering.
So far, we’ve talked about packages to take machine learning algorithms from theory to practice, apply basic machine learning algorithms to your data or perform predictive analysis. But, if you have a machine learning model that you need to train and prepare for production, TensorFlow is the package to use.
Now that we’ve covered various core data science packages, let’s talk a little about visualizations. In data science, visualization plays a huge role in bringing your data to life and uncovering the story it's trying to tell. The core package used for data visualization is Matplotlib, a library that offers various plots and figures developers can use to create different visualizations.
Matplotlib is a basic data visualization library that offers fundamental charts. Seaborn was developed on top of Matplotlib to create more beautiful, interactive and captivating visualizations. Seaborn is the best high-level interface to create eye-catching, informative graphs and charts.
Data science mathematics can get very complicated, very quickly. Data scientists from various backgrounds may struggle to solve mathematical expressions involving multi-dimensional arrays. Here is where Theano comes to the rescue. This package offers functions to define, optimize and evaluate complex, multi-dimensional mathematical expressions.
Python data science packages can be divided into general-purpose ones that you can use in almost all your data science projects (like Pandas and NumPy) or application-specific ones like OpenCV. For example, OpenCV is a package designed to address real-time computer vision tools, software and hardware.
Another application-specific package is Mahotas, a computer vision library designed for image processing. Mahotas uses algorithms implemented in C++ while operating on top of NumPy for an easy-to-use, fast and clean Python interface. Mahotas provides various image processing functions like thresholding, convolution and Sobel edge detections.
A great open-source multi-dimensional image analysis package is SimpleITK. One of the most potent aspects of programming is using various programming languages to build the same application. Doing so means combining the advantages of the different languages while overcoming some of their disadvantages.
Our last image processing package on this list is Pillow. Pillow is a library that adds image processing capabilities to the Python interpreter by providing extensive file formats, internal representation and other image processing capabilities.
The heart of data science is (of course) data. We often collect information from the web and then use the data to train our machine learning model or to apply it to new data. One of the Python libraries that allows us to communicate directly with APIs to collect data is Requests.
16. Beautiful Soup
If you want to collect data from HTML and XML files, then Beautiful Soup is the library for you. Beautiful Soup provides various approaches allowing you to navigate, search and modify the parse tree to obtain the data you need in no time, which can save you days of work.
When working on web-based data science applications, you want a lot of the work to be automated for faster and more efficient data parsing and processing. A great package to do just that is Selenium, which allows you to automate tedious administrative tasks and testing on your web-based applications.
The last web scraping library on this list is ScraPy. ScraPy is an open-source web-crawling framework designed to extract data using APIs or a general-purpose, fast and powerful web crawler.
19. & 20. PyTest and PyUnit
Regardless of the tech branch you’re learning, considering or working in, testing and debugging is an essential step. A Python library that will help you tackle testing the code for your data science applications is PyTest and its successor PyUnit.