Top 15 Python Libraries For Data Science & Best Tutorials To Learn Them | April 2021

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. Python has been built with extraordinary Python libraries for data science that are used by programmers every day in solving problems.

Here today, We have curated a list of best 15 Python libraries that helps in Data Science and its periphery, when to use them, their advantages and best tutorials to learn them.

For some Python Code you may follow this GitHub Repository

1.Pandas

Pandas 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?
Pandas is the best tool for data wrangling or munging. It is built for quick and easy data manipulation, reading, aggregation, and visualization. Pandas 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.

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.

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 equations, Due to its optimized algorithms, it can do linear algebra computations very robustly and efficiently.

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.

5. Keras

Keras is an open-source software library that provides a Python interface for artificial neural networks . Keras acts as an interface for the TensorFlow library. It was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System), and its primary author is François Chollet, a Google engineer.

Using Keras, you can determine percentage accuracy, compute loss function, create custom function layers, built-in data and image processing, write functions with repeating code blocks: 20, 50, 100 layers deep and much more.

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, regression, model selection, dimensionality reduction, and classification. Another advantage? It comes with quality documentation and offers high performance. 

7. Matplotlib 

This is a standard data science library that helps to generate data visualizations such as two-dimensional diagrams and graphs (histograms, scatterplots, non-Cartesian coordinates graphs). Matplotlib is one of those plotting libraries that are really useful in data science projects - it  provides an object-oriented API for embedding plots into applications. 

Matplotlib also facilitates labels, grids, legends, and some more formatting entities with this library. Basically, everything that can be drawn!

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 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!

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. It is also used to gather data from APIs and follows a ‘Don't Repeat Yourself’ principle in the design of its interface, influencing users to write universal codes that can be reused for building and scaling large crawlers.

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.

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 Sci-kit 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. Consider, for instance, their implementation of Seasonal-Trend decomposition, which can help data scientists better understand their data and which transformations and algorithms are better suited to it - this information is tremendously valuable.

 

12. SpaCy 

SpaCy is a natural language processing library with excellent examples, API documentation, and demo applications. The library is written in the Cython language which is C extension of Python. It supports almost 30 languages, provides easy deep learning integration and promises robustness and high accuracy. Another great feature of spaCy is an architecture designed for entire documents processing, without breaking the document into phrases.

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. It may sound repetitive of what this library can do but every lib in Python was written to address some efficiency.

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. It's also used for other tasks – for example, for creating dynamic computational graphs and calculating gradients automatically. PyTorch is based on Torch, which is an open-source deep learning library implemented in C, with a wrapper in Lua. 

15. Beautiful Soup

Beautiful Soup is yet another Python library for scraping Web content. It is generally accepted that it has a relatively shorter learning curve compare with Scrapy.

Also, Beautiful Soup will be a better choice for relatively smaller-scaled problems and/or just a one-time job. 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.

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 library for data science in mind, please share it with us. We will add it in the Bonus Libraries (Recommended by Data Science Enthusiast).