Due to its exceptional abilities, Python is the most commonly used programming language in the field of Data Science these days. While Python provides a lot of functionality, the availability of various multi-purpose, ready-to-use libraries is what makes the language top choice for Data Scientists. Some of these libraries are well known and widely used, while others are not so common. In this article I have tried to compile a list of Python libraries and categorized them according to their functionality.
These libraries are a part of standard Python package and can just be imported if users want to make use of their functionality.
Short for Numerical Python, NumPy has been designed specifically for mathematical operations. It primarily supports multi-dimensional arrays and vectors for complex arithmetic operations. In addition to the data structures, the library has a rich set of functions to perform algebraic operations on the supported data types.
Another advantage of the library is its interoperability with other programming languages like C/C++, FORTRAN, and database management systems. Also, as the set of provided functions is precompiled, the computations are performed in an efficient manner.
Based on NumPy, the Scientific Python library extends its capabilities by offering advanced operations such as integration, regression and probability to name a few. In order to use SciPy, we must install NumPy first, as it makes use of the underlying modules. What makes SciPy one of the extensively used libraries is the hierarchy in which the sub-modules are organized, and the manuals do an excellent job of explaining the meaning and usability of the exported modules.
Python Data Analysis Library is an open source library that helps organize data across various parameters, depending upon requirements. The variety of built-in data types like series, frames, and panels make Pandas a favorite library among Data Scientists. The tabular format of frames allow database-like add/delete operations on the data which makes grouping an easy task.
In addition, Panda provides a three dimensional panel data structure which helps in better visualization of the data types. The flexibility of the library supports multiple data formats including missing data.
The StatsModels module allows users to perform statistical modelling on the data using the modelling and plotting support of the library. The models could be used for the purpose for forecasting across various domains. Model types supported include linear as well as regression models.
StatsModels also support time series analysis capabilities which are particularly poplar in the financial organizations to maintain the stock market information in a convenient format, for instance. Also, the models are fast enough to be used for the big data sets, making it an optimal choice for the same.
An essential function of any library would be the ability to represent the outcome of the complex operations performed on the data in an easy to understand format. The libraries enlisted in this section focus on that aspect of the process.
A part of the SciPy core package, Matplotlib is used for the graphical representation of the processed data as per the user's requirements. We can generate various types of graphs including histograms, pie-charts, or a simple bar chart. It provides an object oriented MATLAB-like interface for users to perform desired operations on the data. An important feature of the library is its ability to offer customization to almost every available feature which makes the usage very flexible to the users.
Primarily focused on 3D plotting, Plotly can be integrated flawlessly with web applications and provides a number of useful APIs for languages to import. It uses data driven documents at its core for real time data representation, and users can configure it to process the graphics at server-side and send the results to the client or otherwise. We can also share the data with others over the platform, if required. There is also inter-operability between Plotly and Matplotlib data formats.
Machine Learning has emerged as an essential field of computing in the last few years, and Data Scientists need tools to make the most of upcoming trends in the field. Listed here are few Python libraries that provide Machine Learning functionality.
For a more in-depth look in to the most popular machine learning articles, check out this article.
Licensed under BSD, Scikit-Learn is an open source Machine Learning toolkit built on top of NumPy and SciPy. It features commonly used ML algorithms for preprocessing, classification, regression as well as clustering. The algorithms include support vector machines, ridge regressions, grid search, k-means clustering, and many more.
Along with the algorithms, the kit also provides sample datasets to experiment with. The well documented APIs are easy to use for beginners, as well as advanced users. Due to its good performance across almost all platforms, it's popular for academic use as well as commercial purposes alike.
Implemented in C++, Shogun is an open source toolbox used for ML, providing a unified interface to multiple languages and platforms including Python. It focuses on scalable kernel methods to solve regression as well as classification problems.
The major focus during development was on bioinformatics, hence Shogun can scale to process over 10 million data samples while maintaining accuracy.
An advanced area in the field of Machine Learning, Deep Learning is opening plenty of unexplored avenues for researchers using supervised learning, neural networks, and natural language processing.
Primarily focused on neural networks, TensorFlow is a Deep Learning library developed by Google engineers. The library is very extensible and supports numerous platforms, also including GPU support for better visualization. The classes of algorithms include classification, estimation models and differentiation to name a few.
Its rich API support makes it top choice for training neural networks and speech recognition using natural language processing.
Theano is a combination of a library and a compiler targeted towards solving complex mathematical equations in the DL area. It uses a multi-dimensional matrix using NumPy to perform the operations. Keeping performance in mind, Theano is very tightly coupled with NumPy and is precompiled, hence is platform independent and makes use of GPU as well. Along with these features, it also provides a unit testing framework for error detection and mitigation.
Keras is a neural network library which is capable of execution on top of Google's TensorFlow or Microsoft's CNTK (Cognitive Toolkit). It is designed to be abstract in nature and acts more as a plugin for other deep learning libraries.
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Keras can support standard, convolutional, as well as recurrent neural networks and provides distributed interfaces to the models on GPU clusters. Its easy-to-use interface is ideal for quick prototypes and their deployment on the supported platforms.
Natural Language Processing
We have seen a sharp surge in speech recognition applications lately, thanks to the research in the field of Natural Language Processing. No wonder there are plenty of libraries in the field.
The Natural Language Toolkit supports the commonly needed features for English language processing such as classification, tokenization, parsing and semantic analysis. After breaking the words into tokens using syntactical analysis, the kit forms a tree like structure using the language semantics and stores the data in its models. Supported on all major platforms, NLTK is an open source community maintained project. The applications are wide reaching, such as sentiment analysis and anti-spam engines.
A scalable, robust and platform independent library for NLP, Gensim uses NumPy and SciPy packages underneath. Short for 'Generate Similar', it is designed keeping large amount of data in memory and therefore is performance centric. It differs from other packages in the implementation as it uses data in a cascading manner as opposed to grouping it together.
Due to its efficiency, it's used widely across the domains such as healthcare and financial institutions.
Another open source library targeted towards NLP, SpaCy encompasses neural network models for various languages viz. English, German, French, Italian, and Dutch, among 30 other languages. Unlike other NLP libraries used primarily for academic purposes, SpaCy is focused on commercial usage.
It also provides extensions for machine learning as well as deep learning APIs. Some popular tech companies, like Airbnb and Quora, use SpaCy as a part of their platforms. What makes it stand out from other libraries is its ability to process documents rather than process data as multiple tokens.
As the size of content being uploaded to the web increases multifold with each passing day, web scraping has gained lot of importance to solve problems related to indexing and crawling of the data. Due to the tedious nature of this job, automation is an ideal solution for the same. There are Python libraries available for scraping data through web pages in an efficient manner.
Standing true to its name, Scrapy is an open source framework aimed at scraping through the data on the worldwide web. Initially designed to extract the data using exported functions, it has evolved into a framework that is used for designing web crawlers to parse through the web pages and store their data in a structured format. Following Python's object oriented and reusability philosophy, Scrapy is structured around a base class named
Spider, and keeps adding layers of functionality, as required, around it.
Data mining is a stream of computing where we try to find the patterns in the huge amount of data for analytical purposes. Let's take a look at the popular Orange library often used in data mining.
Along with machine learning support, the Orange toolkit also features visual analytical platform for interactive data mining. It's an open source package released under General Public License and is designed using C++ with Python wrappers on top.
The Orange package includes a set of widgets for visualization, classification, regression and evaluation of the datasets. The fields where Orange is often used range from DNA research and pharmaceutical domain analysis.
Here is a librariy that doesn't fit into any of the earlier categories, but is worth a mention.
While not directly used for data science and analytics, SymPy is a symbolic computation Python library targeted towards algebraic computations. Many data scientists use the library for intermediate mathematical analysis of their data, later to be consumed by other libraries, such as plotting or Machine Learning.
Out of the numerous Python libraries available for Data Science research purpose, I have tried to enlist and categorize the most commonly used. I hope the article helps Data Science enthusiasts to deep dive in the field and make the most out of these libraries.