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Popular Python Libraries For Data Science And Best Tutorials

Python is the most widely used programming language today. When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. Python is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance language, and there are many more benefits to Python programming. Here today, We have curated a list of best Python libraries for Data Science and its periphery, when to use them, their advantages and best tutorials to learn them.

Python Libraries For Data Science

1. Pandas

It is an open-source Python package that provides high-performance, easy-to-use data structures and data analysis tools for the labeled data in Python programming language. Pandas stand for Python Data Analysis Library Who ever knew that?

It is one of the best tool for data wrangling or munging. It is built for quick and easy data manipulation, reading, aggregation, and visualization. This library take data in a CSV or TSV file or a SQL database and create a Python object with rows and columns called a data frame. The data frame is very similar to a table in statistical software, say Excel or SPSS.

Best Video Tutorials to learn this Library: Learn Pandas with Corey Schafer or Keith Galli

2. NumPy

NumPy (Numerical Python) is a perfect tool for scientific computing and performing basic and advanced array operations.

It enables a higher speed of computation as long as most of the operations work on arrays and matrices, along a large set of high-level mathematical functions to operate on these arrays.

Best Video Tutorials to learn NumPy: Learn NumPy with FreeCodeCamp or Darek Banas

3. SciPy

As the name suggests, SciPy is mainly used for its scientific functions and mathematical functions derived from NumPy. Some useful functions which this library provides are stats functions, optimization functions, and signal processing functions. To solve differential equations and provide optimization, it includes functions for computing integrals numerically

Some of the applications which make SciPy important are Multi-dimensional image processing, Ability to solve Fourier transforms, and differential equation. Due to its optimized algorithms, it can do linear algebra computations very robustly and efficiently.

Best Video Tutorials to learn SciPy: Learn SciPy with Edureka or SciPy Lectures

4. TensorFlow

TensorFlow is a free and open-source software library for machine learning. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. TensorFlow was developed by the Google Brain team for internal Google use.

One of the most developed websites amongst all libraries is of TensorFlow Giants like Google, Coca-Cola, Airbnb, Twitter, Intel, DeepMind, everyone uses TensorFlow! This library is quite efficient when it comes to classification, perception, understanding, discovering, predicting, and creating data. It’s one of the best Python libraries for Data Science and Machine Learning enthusiast.

Best Video Tutorials to learn TensorFlow: Learn TensorFlow with Daniel Bourke or Code Basics

5. Keras

It’s an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library.

Using this library, you can determine percentage accuracy, compute loss function, create custom function layers, built-in data and image processing, write functions with repeating code blocks and much more.

Best Video Tutorials to learn Keras: Learn this Library with FreeCodeCamp

6. Scikit-learn

This is an industry-standard for data science projects based in Python. Scikits is a group of packages in the SciPy Stack that were created for specific functionalities – for example, image processing Scikit-learn uses the math operations of SciPy to expose a concise interface to the most common machine learning algorithms

Data scientists use it for handling standard machine learning and data mining tasks such as clustering, dimensionality reduction, and classification. It comes with quality documentation and offers high performance

Best Video Tutorials to learn Scikit-learn: Learn Scikit-learn with FreeCodeCamp or Data School

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7. Matplotlib

This is a standard data science library that helps to generate data visualizations such as two-dimensional diagrams and graphs. It provides an object-oriented API for embedding plots into applications.

Best Video Tutorials to learn Matplotlib: Learn Matplotlib with Darek Banas

8. Plotly

Plotly is a free and open-source data visualization library. Data Scientist 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. So if you are looking to explore data or simply wanting to impress your stakeholders, plotly is the way to go!

Best Video Tutorials to learn Plotly: Learn Plotly with Data Science Tutorials

9. Scrapy

The next known python libraries for data science is Scrapy. This library is one of the most popular, fast, open-source web crawling frameworks written in Python. It is commonly used to extract the data from the web page with the help of selectors based on XPath.

Scrapy helps in building crawling programs (spider bots) that can retrieve structured data from the web.

Best Video Tutorials to learn Scrapy: Learn Scrapy with Traversy

10. Seaborn

This library is based on Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics. Putting it simply, seaborn is an extension of Matplotlib with advanced features Matplotlib is used for basic plotting; bars, pies, lines, scatter plots and stuff whereas, seaborn provides a variety of visualization patterns with less complex and fewer syntax.

Best Video Tutorials to learn Seaborn: Learn SeaBorn with Data Talks

11. Statsmodels

Although data scientists are generally hesitant to approach statistical modelling methods, Statsmodels is a must-know library. Besides offering important implementations of algorithms like ANOVA and ARIMA that standard machine learning libraries like Scikit-Learn do not have, perhaps what is most valuable about Statsmodels is the sheer level of detail and information it provides.

Beyond incredibly detailed statistical modelling, Statsmodels also offers a variety of helpful data features and metrics.

Best Video Tutorials to learn Statsmodels: Learn Statsmodels with Data Talks

12. SpaCy

SpaCy is a natural language processing library with excellent examples, API documentation, and demo applications. It supports almost languages, provides easy deep learning integration and promises robustness and high accuracy.

Best Video Tutorials to learn Spacy: Learn Spacy with Explosion

13. NLTK

NLTK (Natural Language Toolkit) mainly works with human language more than computer language to apply natural language processing (NLP). It contains text processing libraries with which you can perform tokenization, parsing, classification, stemming, tagging and semantic reasoning of data.

Best Video Tutorials to learn NLTK: Learn NLTK with Sentdex

14. PyTorch

PyTorch is a framework that is perfect for data scientists who want to perform deep learning tasks easily. The tool allows performing tensor computations with GPU acceleration. PyTorch is based on Torch, which is an open-source deep learning library implemented in C, with a wrapper in Lua.

Best Video Tutorials to learn PyTorch: Learn PyTorch with 4 Times Kaggle Grandmaster

15. Beautiful Soup

Beautiful Soup is yet another Python library for scraping Web content.

Unlike Scrapy that you have to develop your own “spider” and go back to command-line the run it, Beautiful Soup allows you to import its functions and use them in-line. Therefore, you could even use it in your Jupyter notebooks.

Best Video Tutorials to learn Beautiful Soup: Learn Beautiful Soup with Corey Schaffer

Of course, this is not the definitive list and there are many other libraries and frameworks that are also worthy and deserve proper attention for particular tasks. A great example is different packages of Scikit that focus on specific domains, like Scikit-Image for working with images.

So, if you have another essential and must use python libraries for data science in mind, please share it with us. We will add it in the Bonus Libraries (Recommended by Data Science Enthusiast).