Python has become a big hit in the Data Science community over the last five years. So much so that it is slowly taking over R – the ‘lingua franca of statistics’ – as the preferred choice of tool for many, as this recent poll conducted by KDNuggets suggests. Python’s adoption rate has been staggering, but not really surprising. Its general-purpose nature, coupled with the efficiency and ease of use, make it easier for you to build your data science solutions without any hassle. You also have a rich suite of Python libraries available at your disposal for all your Data Science-related tasks – from basic web scraping to something as complex as training deep learning models.
In this article, we take a look at some of the most popular and widely used Python libraries and their application areas.
Web scraping is a popular information extraction technique from the web using the HTTP protocol, with the help of a web browser. The two most commonly used tools for web scraping are, unsurprisingly, Python-based.
Beautiful Soup is a popular Python library for extracting information out of the HTML and XML files. It provides a unique, easy way to navigate, search and modify the parsed data, potentially saving you hours of needless work. It works with both the versions of Python, i.e. 2.7 and 3.x and is very easy to use. Check out our latest tutorial on how to scrape web page using the Beautiful Soup.
Scrapy is a free, open source framework written in Python. Although developed for web scraping, it can also be used as a general web crawler and extract data using different APIs. Following the ‘Don’t Repeat Yourself’ philosophy of frameworks such as Django, Scrapy includes a set of self-contained crawlers, with each of them following specific instructions with a specific objective.
Scientific Computation and Data Analysis
Arguably the most common data science tasks, Python proves to be of great worth to data scientists by providing unique libraries for data manipulation and analysis, as well as mathematical computation.
NumPy is the most popular library for scientific computing in Python and is a part of the larger Python stack for scientific computation called SciPy (discussed below). Apart from its uses in linear algebra and other mathematical functions, it can also be used as a multi-dimensional container, or array, of generic data with arbitrary data types.
NumPy integrates seamlessly languages such as C/C++ and because of its support for multiple data types, it works well with a variety of databases as well.
SciPy is a Python-based framework containing open source libraries for mathematics, scientific computation and data analysis. The SciPy library is a collection of algorithms and tools for advanced mathematical computations, statistics and much more.
The SciPy stack consists of the following libraries:
- NumPy – Python package for numerical computation
- SciPy – One of the core packages of the SciPy stack for signal processing, optimization and advanced statistics
- matplotlib – Popular Python library for data visualization
- SymPy – Library for symbolic mathematics and algebra
- pandas – Python library for data manipulation and analysis
- iPython – Interactive console to run Python-based code
pandas is a widely used Python package providing data structures and tools for effective data manipulation and analysis. It is a popularly used tool for Quantitative Analysis and finds a lot of application in algorithmic trading and risk analysis.
With a large community of dedicated users, pandas is regularly updated to get new API changes, performance updates and bug fixes. This is one library you definitely need to work with to truly realize its power.
Machine Learning and Deep Learning
Python trumps all other languages when it comes to implementing efficient machine learning and deep learning models, simply by virtue of its diverse, effective and easy to use set of libraries. We see some of the most popular and commonly used Python libraries in this section:
scikit-learn is the most popular Python library for data mining, analysis and machine learning. It is built using the capabilities of NumPy, SciPy and matplotlib, and is commercially usable. You can implement a variety of machine learning techniques such as classification, regression, clustering and more, using scikit-learn. It is very easy to install and has a clean, slick documentation for anyone looking to get started with it.
Tensorflow is the popular machine learning library everyone seems to be talking about today. It is a Python-based framework for effective machine learning and deep learning using multiple CPUs or GPUs. Backed by Google, it was initially developed by the research team of Google Brain, and is the widely used framework in the world for machine intelligence. It enjoys the support of a large community of active users and is finding widespread application for advanced machine learning across a multitude of industrial domains – from manufacturing and retail to healthcare and smart cars. If you are interested to know more about Tensorflow, you can quickly check out the tutorial here.
Keras is a Python-based neural networks API, and offers a simplified interface to train and deploy your deep learning models with ease. It has support for a variety of deep learning frameworks such as Tensorflow, Deeplearning4j and CNTK.
Keras is very user-friendly, follows a modular approach and supports both CPU and GPU-based computations. If you want to make the deep learning process simpler and effective, this library is definitely worth checking out!
One of the more recent additions to Python deep learning family is PyTorch, a neural network modeling library with strong GPU support. Although still in a beta stage, this project is backed by bigwigs such as Facebook and Twitter. PyTorch builds on the architecture of Torch, another popular deep library, to enable more efficient tensor computation and implementation of dynamic neural networks.
Natural Language Processing
Natural Language Processing pertains to designing of systems that process, interpret and analyze human language, spoken or written. Python offers unique libraries for performing a variety of tasks such as working with structured and unstructured text, predictive analytics and much more.
NLTK is a popular Python library for language processing. It offers easy to use interfaces for a variety of NLP tasks such as text classification, tokenization, text parsing, semantic reasoning and much more. It is an open source, community-driven project, and has support for both Python 2 and Python 3.
SpaCy is another library for advanced natural language processing, based on Python and Cython. It has an extensive support for various deep learning libraries and frameworks such as Tensorflow and PyTorch. With SpaCy, you can build complex statistical models for NLP with relative ease.
SpaCy is easy to install and use, and proves to be of great help when it comes to large-scale extracting and analyzing of textual information.
Data visualization is a popularly used Data Science technique for visually analysing and communicating information and valuable business insights through graphs, charts, dashboards and reports. Python offers a lot of popular libraries for effective data storytelling. Some of them are listed below:
matplotlib is the most popular Python library for data visualization which allows for enterprise-grade 2D and 3D plotting. With matplotlib, you can build different kinds of visualizations such as histograms, bar charts, scatter plots and much more, with just a few lines of code. The popularity of matplotlib rivals that of R’s highly acclaimed ggplot2, and deciding which library is better has been a hot topic for debate, for many years now.
Matplotlib runs seamlessly on all Python consoles, including iPython and Jupyter notebooks, giving you all the necessary tools to create and share your data visualizations with others. Get to know about manipulating ticks in Matplotlib 2.0 in our recently published tutorial.
Seaborn is a Python-based data visualization library, which finds its roots in matplotlib. Apart from offering attractive and insightful data visualizations, seaborn also offers strong support for other Python libraries such as NumPy and pandas. Per the official seaborn page:
“If matplotlib “tries to make easy things easy and hard things possible”, seaborn tries to make a well-defined set of hard things easy too.”
Bokeh is an interactive data visualization library based on Python. It aims to provide D3.js style elegant graphics and visualizations, and runs primarily on modern web browsers. Apart from the ability to create a wide variety of visualizations, Bokeh also supports large-scale interactivity and visualizations of real-time datasets.
Plotly is a popularly used Python library which is used across the world for making publication-quality plots and graphs. With Plotly, you can build interactive dashboards, scatter plots, histograms, candlestick charts, heat maps, and a whole host of other data visualizations with ease. With superior interactivity, deployment and publication capabilities, Plotly is used across different domains, majorly finance and geospatial industries for effective data storytelling.
So there you have it! Python has an extensive suite of libraries for every data science related task, each equipped with unique features to make the task fast and hassle-free. While there are a lot more Python libraries out there, we cherry-picked these 15 libraries based on their popularity, usefulness and the the value they bring to the table. Also, the extensive community support for Python means you can get help for any kind of problem you might come across while using these tools.
Time now for you to go out there and crunch some data with some of these Python powered libraries!