Data Scientist Must Know Bout The Python Libraries
Data science is very hard. You’ll have to learn a handful of libraries as a beginner or a professional , even to solve the most fundamental tasks. Adding insult to injury, the libraries change and get updated constantly, and there’s almost always a better tool for the job.
The problem of not knowing which tool to use is simple to understand — it results in failing completely or not doing a task optimally. What’s also dangerous is not knowing libraries well enough. You end up implementing algorithms from scratch, completely unaware there’s already a function for that. Both cost you time, nerves, and potentially money.
If you find yourself overwhelmed by data science libraries, you’re in the right place. This article will show you 10 essential ones to kick-start your data science journey.
It’s crucial to understand that learning data science takes time. You can’t do it overnight. Reading books and watching videos is a good start, but solving problems you care about is the only long-term way.
NumPy
NumPy is one of the most essential Python Libraries for scientific computing and it is used heavily for the applications of Machine Learning and Deep Learning. NumPy stands for NUMerical PYthon. Machine learning algorithms are computationally complex and require multidimensional array operations. NumPy provides support for large multidimensional array objects and various tools to work with them.
Various other libraries which we are going to discuss further like Pandas, Matplotlib and Scikit-learn are built on top of this amazing library! I have just the right resource for you to get started with NumPy.
SciPy
SciPy (Scientific Python) is the go-to library when it comes to scientific computing used heavily in the fields of mathematics, science, and engineering. It is equivalent to using Matlab which is a paid tool.
SciPy as the Documentation says is — “provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.” It is built upon the NumPy library.
BeautifulSoup
BeautifulSoup is an amazing parsing library in Python that enables web scraping from HTML and XML documents.
BeautifulSoup automatically detects encodings and gracefully handles HTML documents even with special characters. We can navigate a parsed document and find what we need which makes it quick and painless to extract the data from the webpages. In this article, we will learn how to build web scrapers using Beautiful Soup in detail.
Scrapy
Scrapy is a Python framework for large scale web scraping. It gives you all the tools you need to efficiently extract data from websites, process them as you want, and store them in your preferred structure and format.
You can learn all about Web scraping and data mining in this article –
Pandas
From Data Exploration to visualization to analysis — Pandas is the almighty library you must master!
Pandas is an open-source package. It helps you to perform data analysis and data manipulation in Python language. Additionally, it provides us with fast and flexible data structures that make it easy to work with Relational and structured data.
If you are new to Pandas, you should definitely check out this free course –