Ever wondered why the data industry chooses Python libraries for data science? It is because Python is the most widely used programming language in all industries. Again, why? It is because – Python is a high-performing, object-oriented open-source language that is easy-to-learn & easy to debug as well.
Also, as of now, there are 1,37,000 advanced- level Python libraries to create apps and models in a range of fields. Such fields include data science, machine learning, data visualization, data & image manipulation, & many more.
If you’re an aspiring data scientist, then this read will walk you through the 10 best Python libraries for data science that help you build an application or a data science project as you wish. Let’s see all the 10 Python libraries for data science in reverse rank orde
#10 Beautiful Soup
Imagine a situation where you’d be needing data from a website for building your application. Say, you’d need the data on Amazon best selling books, then you’d use data scraping or web scraping to import the data into a spreadsheet or local storage in your computer.
Beautiful Soup is one popular library of Python that helps collect data from HTML & XML files & arranges them in proper format. This library provides various ways to search, navigate, & modify the parse tree for obtaining the data you need even without a proper CSV or API.
Another point to be noted is that many web data extraction projects need a combination of web crawling & web scraping. Beautiful soup is quite good at doing the job.
When data scientists are also programmers, who are quite familiar with Python programming language. Then, Pytorch is the best compatible tool for processing large-scale image analysis, that includes object detection, classification, segmentation, & complex algorithms. Here’s a quick fact, just so you know – PyTorch is a deep learning & machine learning tool developed by Facebook’s Artificial Intelligence(AI) division.
Scrapy is also good for scraping data from websites. But here, Scrapy is not a library but a framework, which is best enough to build web scrapers more easily & maintaining them is no big deal.
But, when you compare Scrapy with beautiful soup for the job, Scrapy is for large or complex data projects. Data scientists find Scrapy as an awesome tool for proxies & data pipelines in their projects. While Beautiful Soup is for low-level complex or small projects.
The essential Machine Learning branch of data science can be handled by Scikit-learn package. It is specially built on NumPy, SciPy, and Matplotlib & contains bundles of handy algorithms that can be used to create various ML models. One can implement the ML models for regression, classification, clustering & such other actions. Further, Scikit-learn can be used to prepare, evaluate & create post-model data analysis.
The highly recommended deep learning API for Machine learning beginners is Keras. It is because of the fact that Keras provides a minimal approach to running deep learning models & neural networks. Keras focuses on reducing the cognitive load on humans (developers especially) by providing easily understandable & consistent methods such as straightforward error messages or feedback.
An ML library in python – ‘Pandas’ is the game changer for Data scientists & Analysts who seek something powerful than just a spreadsheet like MS Excel or Google sheet. Pandas makes it both easy & intuitive while working with relational or labeled data by using its fast, flexible & expressive data structures. Popular apps like Netflix & Spotify use the miracles of Pandas for its great recommendations that you usually get while using these apps.
Entering the field of data visualization in data science, Matplotlib is the leading package that offers various plots & figures for developers. The object-oriented API of Matplotlib makes it easy to embed these plots into applications.
Also, Matplotlib has the ability to deal with many operating systems & graphics backends. So, with this plotting library, you can work in any operating system as you wish & deal with any output format that you are in need of. The bonus benefit is its better runtime behavior with low memory consumption.
Numerical computations in Python come straight through NumPy. And, NumPy boosts the soul of mathematics in data science. With high-level arrays & matrices, Numpy adds the most powerful data structures to Python, further promising efficient calculations. Thus, this hugely addresses the slowness caused by numerical routines.
#2 Scientific Python(SciPy)
SciPy is a huge collection of mathematical algorithms & functions that are built on the NumPy extension. It significantly boosts the interactive Python session by offering the user advanced commands & classes to manipulate & visualize the data.
The pro-tool library for professionals to solve differential equations, linear algebra, Fourier transform, & optimize algorithms.
Do you know? A Data Scientist with TensorFlow developer skills earns a salary package of ₹14LPA.
There are more python libraries to explore other than the important ones mentioned above. Well, are you a beginner? Interested in mastering data science with IIT-M certified python? Hop into GUVI’s ZEN Career Program offering 100% Placement in Data Science & IIT-M Certified Professional Programming course. Become a Data Scientist in No Time!