When in doubt, go to the libraries

Python is one of the most popular and widely used languages in the field of Data Science today. One of the key reasons for that is the wide variety of libraries it offers developers, greatly simplifying development. Therefore it is important to learn about the best ones to use.

In this article, we will discuss the 10 most important Python libraries that will help you become a better and faster developer. The list is split into four categories, aligned with the stages of a typical Data Science project: data manipulation, visualisation, machine learning and deployment.

So, let’s dive deeper into the world of Python libraries.

Top Data Manipulation Libraries

1.    NumPy

NumPy, an acronym for Numeric Python is one of the most basic and important Python libraries. It provides tools to build multi-dimensional, high-performance arrays and matrices and perform complex mathematical operations on them. NumPy is equally important for performing machine learning tasks since it is a part of the foundation of high-end machine learning libraries like TensorFlow. In addition to this, it also provides useful tools for integration with other programming languages like C, C++, Fortran etc. Visit the official NumPy website to learn more about it.

2.    Pandas

For data scientists and analysts, Pandas is one of the most popular choices for performing high-level data analysis and manipulation. The most important feature of this library is its ability to perform complex operations on data by using very few commands. Pandas also offers a wide range of in-built functions for operations like concatenation, indexing, sorting, aggregation, filtering and visualisation. Visit the official Pandas website to learn more about it.

Top Data Visualization Libraries

1.    Matplotlib

Matplotlib is the most popular Python library and comes in handy particularly when a developer needs to visualise and analyse the patterns in data.

It is used for creating two-dimensional (2-D) plots and graphs like histograms, bar charts, pie charts, error charts, etc. An additional feature of Matplotlib is that it also allows the user to build and analyze multiple graphs simultaneously. One such graph is shown in the figure below. Visit the official Matplotlib website to learn more about it.

Bar charts showing price comparison of books across Flipkart and Amazon

2.    Seaborn

Seaborn is another popular open-source Python library designed for making statistical charts. This library was built on top of Matplotlib and provides options for visualising univariate as well as bivariate distributions and also, comparing them. Seaborn also has a variety of in-built styles which help developers customise the appearance of the charts. Visit the official Seaborn website to learn more about it.

Top Machine Learning Libraries

1.    TensorFlow

TensorFlow is a popular Python library developed by the Google Brain Team, which comes with an easy-to-learn architecture that allows developers build and deploy machine learning, artificial intelligence and deep learning applications by performing high-level numerical computations. With the help of TensorFlow, the machine learning models can also be easily moved to the cloud or any other device on-premises. TensorFlow is widely used in applications like Google Voice Search.

Visit the official TensorFlow website to learn more about it.

2.    Scikit-Learn

Scikit-Learn is another simple, yet useful as well as flexible machine learning library that can be used in harmony with other Python libraries like NumPy.

It has a neat API and includes handy documentation for beginners. Scikit-Learn supports a wide variety of machine learning algorithms for predictive analysis, classification and clustering like regression, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), K-Means, Random Forest, Gradient Boosting, etc., which makes it popular among developers. Visit the official Scikit-Learn website to learn more about it.

3.    Keras

Keras is a great choice for developers who want to use deep neural networks because of its user-friendly and modular structure. It is written completely in Python and is also capable of running on top of other libraries like TensorFlow, CNTR and Theano. Keras also provides a range of tools that help in working with images and text and can also help building models for smartphones and Java virtual machines. It is amongst the favourite libraries for deep learning researches and has also been adopted by leading scientific organisations, such as CERA and NASA. Visit the official Keras website to learn more about it.

4.    PyTorch

This open-source machine learning library in Python was introduced in 2017 and was primarily developed by Facebook’s Artificial Intelligence Research Group. PyTorch has gained a lot of attention in the past few years because of its performance optimisation capabilities. It is primarily used for applications like Natural Language Processing (NLP) and Computer Vision but can also be used for many other machine learning programs and for building deep learning models. Large tech companies, such as Facebook, Uber and Twitter use PyTorch extensively for rapid prototyping. Visit the official PyTorch website to learn more about it.

Top Python Deployment Libraries

1.    Flask

Flask is a very popular Python framework for deployment of data science models. It provides developers with tools and frameworks that allow them to easily build web applications like web pages, blogs, and even full commercial websites. Another benefit of Flask is that it is lightweight and thus helps deploy applications quickly.

Since Flask is a part of the category of micro-frameworks, it has very few dependencies on external libraries, but additional functionality, if required, can be added using plug-ins. Visit the official Flask website to learn more about it.

Schematic depicting the workings of Flask

2.    Scrapy

Extracting data for analysis from websites can be a real challenge, but, not with this popular, open-source Python library called Scrapy. It helps perform large scale web scraping while providing tools for data processing and storing in preferred structures and formats.

New functionalities can also be plugged into the Scrapy framework, thereby, allowing it to perform operations like automated testing and information processing. Visit the official Scrapy website to learn more about it.

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