Most Popular Python Libraries for Data Science
Being a Data Scientist is much more than doing math and crunching numbers on the computer. Data Scientists often need to learn new tools and gain skills every day to keep up with the ever – changing world of technology. In this article, we’re going to take a look at some of the most popular Python libraries for Data Science. If you’re interested to learn more about this ever-changing interdisciplinary field, check out our in-depth Data Science guide.
You’ve probably heard about Python the programming language, and how popular it is for Data Science. There are a handful of popular Python libraries that help Data Scientists worldwide do what they do best. Python’s accessibility, simple syntax, and a big ecosystem of libraries are the main reasons why beginners and experts alike use this programming language.
8 Most Popular Python Libraries for Data Science in 2022
The first library we’re talking about is probably the most popular in the Data Science community. It’s called NumPy, and it provides support for multi-dimensional arrays and matrices as well as a large collection of high-level mathematical functions.
Now, you may be thinking, why use an external library like NumPy when Python ships with a number of built-in collection data types. Lists, Tuples, Sets and Python dictionaries can be slow to work with, and if you’re dealing with a lot of data – like you normally would in the field of Data Science, this can impact performance and potentially become an issue.
The speed of NumPy arrays comes from the fact that they’re stored in the same continuous place, unlike lists scattered all over the computer’s memory. This makes the retrieval of data neater and quicker, and that’s very important for Data Scientists.
Pandas is another widely used library for Python, and it’s used for analyzing big data, data manipulation, and making conclusions from datasets. You’ve probably heard that data needs to be cleaned before it’s tested and put into a model. Well this is where Pandas comes in. Data Scientists use it all the time to clean data collected in datasets and make the information more relevant. The data is often put in DataFrames, which are two-dimensional arrays that act as a table with rows and columns–something like you would see in Microsoft Excel.
TensorFlow is an open-source, end-to-end machine learning platform used for building Machine Learning models. TensorFlow is designed to help you build models quickly through its APIs and it provides you with a rich collection of tools for Machine Learning that you can use to train and test those models with your data.
It’s also very scalable, which means it’s accessible to everyone. Even beginners on lower-end computers can build models and deploy them to higher-end machines when needed. So, hypothetically, your model could start off running on a single CPU and then be scaled up to an enterprise-level infrastructure.
TensorFlow’s tools offer the ability to train and test data in new modern ways that that could help a lot of people and businesses – like diagnosing conditions early on, assisting farms in identifying their plant’s diseases, predicting drastic weather changes, and everything else that people couldn’t possibly do by trying to analyze a large dataset on their own.
Let’s say you want to analyze public data from the Internet – something like Google search results, articles, health statistics, and so on. Well, the first thing you’d have to do to obtain that data is to do a thing called “Web Scraping”. Web Scraping is the act of creating a “crawler” and letting it crawl and scrape the data from the websites of your choosing. Logically, this “crawler” entity is nothing more than an algorithm that collects data with the parameters you’ve given it.
And this is where BeautifulSoup comes into play, a Python library that enables you to crawl and scrape the Web. It sits on top of a HTML/XML parser, and it can extract and dynamically scrape web content with a set of given parameters.
Matplotlib is a nifty library for creating still, animated, and even interactive data plots. You can plot your data and move it around, zoom in and out and update it in real-time. There are quite a few ways to customize the appearance, style, and layout of your created plots. Matplotlib is the foundation of many third-party libraries that are built on top of it.
Seaborn is a statistical data visuzalization tool that integrates a top-level interface to draw informative yet beautiful statistical graphics. As we mentioned before, Matplotlib has a lot of libraries that are built on top of it, and Seaborn is one of them! It allows the user to create powerful plots that visualize relationships between the data and their distribution, estimate certain statistical elements and create error bars, among other things. It also has valuable plotting capabilities for complex categorical data, not just numbers.
When we’re talking about the most popular Python libraries for Data Science, we have to mention Scikit-Learn. Scikit-Learn is a simple, efficient, and powerful tool for predicting and analyzing datasets. It is open-source and commercially usable, accessible to everyone, and built on SciPy, Numpy, and Matplotlib. Its tools include robust data prediction and manipulation, such as classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
All of these things can be used for many things, like spam detection, predicting stock prices, grouping experiment outcomes, visualization, improving accuracy through comparisons, transforming input data for Machine Learning algorithms, and many other things.
Pillow is a Python Imaging library adds image processing support to Python, making it useful for analyzing and processing images. The main goal in image processing is to extract data and information that helps all areas of our lives. For example, you could use a camera in a parking garage to determine which spots are empty and which ones are taken. And that’s one example of how much this library and concept are helpful for the real world use-cases.