- Know which are the top 13 data science libraries in python
- Find suitable resources to learn about these python libraries for data science
- By no means is this list exhaustive. Feel free to add more in the comments.
Python has rapidly become the go-to language in the data science space and is among the first things recruiters search for in a data scientist’s skill set, there’s no doubt about it. It has consistently ranked top in global data science surveys and its widespread popularity only keeps on increasing!
But what makes Python so special for data scientists?
Just like our human body consists of multiple organs for multiple tasks and a heart to keep them running, similarly, the core Python provides us with the easy easy-to-code, object-oriented, high-level language (the heart). We have different libraries for each type of job like Math, Data Mining, Data Exploration, and visualization(the organs).
Top 13 Python libraries that you must Know!Let us learn about the Top 13 Python libraries for data science that you must Learn!
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 (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 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 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.
Data Exploration and Visualization
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.
Matplotlib is the most popular library for exploration and data visualization in the Python ecosystem. Every other library is built upon this library.
Matplotlib offers endless charts and customizations from histograms to scatterplots, matplotlib lays down an array of colors, themes, palettes, and other options to customize and personalize our plots. matplotlib is useful whether you’re performing data exploration for a machine learning project or building a report for stakeholders, it is surely the handiest library!
Plotly is a free and open-source data visualization library. I personally love this library because of its high quality, publication-ready and interactive charts. Boxplot, heatmaps, bubble charts are a few examples of the types of available charts.
It is one of the finest data visualization tools available built on top of visualization library D3.js, HTML, and CSS. It is created using Python and the Django framework. So if you are looking to explore data or simply wanting to impress your stakeholders, plotly is the way to go!
Seaborn is a free and open-source data visualization library based on Matplotlib. Many data scientists prefer seaborn over matplotlib due to its high-level interface for drawing attractive and informative statistical graphics.
Seaborn provides easy functions that help you focus on the plot and now how to draw it. Seaborn is an essential library you must master. Here’s a great resource to checkout –
Sklearn is the Swiss Army Knife of data science libraries. It is an indispensable tool in your data science armory that will carve a path through seemingly unassailable hurdles. In simple words, it is used for making machine learning models.
Scikit-learn is probably the most useful library for machine learning in Python. The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.
Tired of writing endless lines of code to build your machine learning model? PyCaret is the way to go!
PyCaret is an open-source, machine learning library in Python that helps you from data preparation to model deployment. It helps you save tons of time by being a low-code library.
It is an easy to use machine learning library that will help you perform end-to-end machine learning experiments, whether that’s imputing missing values, encoding categorical data, feature engineering, hyperparameter tuning, or building ensemble models. Here’s an excellent resource for you to learn PyCaret from scratch –
Over the years, TensorFlow, developed by the Google Brain team has gained traction and become the cutting edge library when it comes to machine learning and deep learning. TensorFlow had its first public release back in 2015. At the time, the evolving deep learning landscape for developers & researchers was occupied by Caffe and Theano. In a short time, TensorFlow emerged as the most popular library for deep learning.
TensorFlow is an end-to-end machine learning library that includes tools, libraries, and resources for the research community to push the state of the art in deep learning and developers in the industry to build ML & DL powered applications.