Everyone has a doubt on which language does a data scientist use. The data scientists use many languages such as R, Python, Java, SAS, SQL, MATLAB, Julia, etc., Among these Python holds a special space. It is a programming language that has become popular and the most important language in a very short time and which is suggested to use by the developers in Data Science Consulting companies.
Huge rise in technologies like machine learning, artificial intelligence, and predictive analytics, the need for professionals with a thorough knowledge of Python skills are much in demand.
In data science, machine learning is one of the significant elements used to maximize value from data. Using Python as the data science tool, exploring the basics of machine learning becomes easy and effective. To be more precise, machine learning is more about statistics, mathematical optimization, and probability which has become the most preferred machine learning tool in the way it allows aspirants to ‘do the math’ easily.
Consider any math function, and you have a Python package meeting the requirement. We have Numpy for numerical linear algebra, CVXOPT for convex optimization, Scipy for general scientific computing, SymPy for symbolic algebra, PYMC3, and Stats model for statistical modeling. Before we get into the in-depth details about each package let us first know why should we go for Python.
Benefits of using Python are in huge numbers compared to other languages.
- It is highly scalable. Python has emerged as a scalable language compared to R and is faster to use than Matlab and Stata
- There are lot many libraries available with Python
- Presence of Third Party Modules
- Open Source and Community Development
- Learning Ease and Support Available
- User-friendly Data Structures
- Productivity and Speed, etc.
Well, the data scientists work on handling the big data. While the future is all about playing with the data, therefore, most of the companies acknowledge the integral role that data will be playing in driving business decisions and understanding people’s perceptions. So this handling of big data has become easy with the libraries available in Python. Python libraries have been playing a key role in big data. Python libraries can handle any type of data.
Now, these libraries are a collection of modules, which are a set of standardized parts or independent units that can be used to construct a more complex structure. In simple terms, the Python library is a collection of functions and methods that allow you to perform many actions without writing your code.
There are 15 Python libraries available for data science, from information extraction to deep learning models. Python uses its rich libraries, ease of use and efficient nature to beat over Rand become the data science solutions.
Python libraries:
- Beautiful Soup. Extract info from HTML and XML.
- Scientific computing.
- Extract data and web crawler.
- Signal processing, optimization and statistics.
- Data manipulation and analysis.
- Scikit-learn. machine learning and data mining.
- Machine learning and deep learning.
- Neural networks API. Supports deep learning.
- Neural network modeling library with GUI.
- Language processing.
- Large-scale extracting and analyzing of textual information. Support deep learning.
- For data visualization.
- Also for data visualization. Also, support for pandas and Numpy.
- It supports large-scale interactivity and visualizations of real-time data sets.
- For making publication-quality plots and graphs. Widely used in finance and geospatial industries.
Among these, the popular ones are listed below:
NumPy
It is a Python extension module. It is said to be the core library for scientific computing in Python. It provides a high-performance multidimensional array object and tools for working with these arrays. With more than 15 thousand commits and over 500 contributors on its Github’s repository, clearly depicts that how much this library is popular. It is regarded as the most fundamental packages for data science. NumPy stands for (Numerical Python). It provides an abundance of useful features for operations on n-arrays and matrices in Python. This library provides a vectorization of mathematical operations on the NumPy array type. It also contains other things like:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
- It also contains a useful linear algebra, Fourier transform, and random number capabilities
NumPy is licensed under the BSD license, which enables reuse with few restrictions.
Pandas
The name is derived from “Panel Data”. The Pandas is an open source tool that provides data analysis tools for Python programming. Having more than 15000 commits and over 700 contributors, this is also one of the most commonly used libraries for data science. The package is designed to do work with labeled, relational, simple, and complex data. This package can also be used to add data structures and tools designed for practical data analysis in multiple streams such as finance, statistics, social sciences, and engineering.
Because of its adaptability, it is a very useful library and it can work perfectly well with incomplete, unstructured, and uncategorized data at the same time provides tools for shaping, merging, reshaping, and slicing of datasets as well. Apart from these it also includes the ability to load and save data from multiple formats and easy conversion from NumPy and Python data structures to Pandas objects.
- The library is used for data manipulation and analysis.
- Works with labeled and relational data.
- The Pandas work well even with rough and unlabeled data.
- Pandas library is a free software, released under the three-clause BSD license.
SicPy
It is the next important library which is an engineering and science library. It is different form SicPy stack because SciPy contains modules for linear algebra, optimization, integration, and statistics. It contains about 17000 commits and around 500 contributors on the Github’s repository.
It is open-source software for mathematics, science, and engineering. This SciPy library depends on NumPy, which provides convenient and fast N-dimensional array manipulation. It is built to work with NumPy arrays and provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. Combined together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy have the ease of use, but powerful enough to be depended upon by some of the world’s leading scientists and engineers.
Matplotlib:
This is a python library used to create 2D graphs and plots by using python scripts. To use this efficiently, you must have a strong command over the available functions in this library.
A module named pyplot makes things easy for plotting by providing a feature to control line styles, font properties, formatting axes etc which supports a very wide variety of graphs and also plots namely – histogram, bar charts, power spectra, error charts etc. Along with NumPy, it is used to provide an environment that is an effective open source alternative for MatLab. This can also be used with graphics toolkits like PyQt and wxPython.
This library is capable of producing publication quality figures in the form of plots, histograms, power spectra, bar charts, error charts, scatterplots, etc. in a wide variety of hardcopy formats and interactive environments across platforms.
Pybrain:
It is another top Python Library for Data Science that focuses on flexible, easy-to-use algorithms for Machine Learning tasks and a variety of predefined environments to test and compare the algorithms. It has been popular because of the flexibility and algorithms for state-of-the-art research.
As we are researching new techniques every day and constantly developing faster algorithms, this library will be used in neural networks especially for the reinforcement learning faster and unsupervised learning. As most of the current problems deal with continuous state and action spaces, function approximators like neural networks must be used to cope with the large dimensionality. It is built around neural networks in the kernel and all the training methods accept a neural network as the to-be-trained instance which makes PyBrain a powerful tool for real-life tasks as well.
Bokeh:
This library distinguishes itself from other Python visualization libraries such as Matplotlib or Seaborn in the fact that it is an interactive visualization library that is ideal for anyone who wishes to create interactive plots, dashboards, and data applications quickly and easily.
It is also known for enabling high-performance visual presentation of large data sets in modern web browsers.
It provides interactive visualization, independent of Matplotlib and makes its presentation via modern browsers in the style of Data-Driven Documents i.e. d3.js.
Bokeh is the ideal tool to build statistical charts quickly and easily for the data scientists. Besides, there are also other advantages, such as the various output options and the fact that you can embed your visualizations in applications. And let’s not forget that the wide variety of visualization customization options makes this Python library an indispensable tool for your data science toolbox.
Scikit Learn:
This is a Python module for machine learning built on top of SciPy. This module provides a set of common machine learning algorithms to users through a consistent and smooth interface. It helps to quickly implement popular algorithms on datasets and it includes tools for many standard machine-learning tasks such as clustering, classification, regression, etc.
This has 21000 commits and 800 contributors that have made this library concise in terms of code and consistent has an interface to the common machine learning algorithms, making it simple to bring ML into production systems.
Toolkits
Python language has a huge number of GUI frameworks (or toolkits) available for it, from Tkinter (traditionally bundled with Python, using Tk) to a number of other cross-platform solutions, as well as bindings to platform-specific technologies.
PyGTK (Graphics interface Tool Kit)
PyGTK is free software licensed by the LGPL. It is a multi-platform toolkit which can be used to create graphical interfaces that offer a complete set of widgets suitable for projects ranging from one tool oriented to complete application collection.
Key Features
- It has important sub-libraries like:
- Glib: It is a low-level fundamental library which forms the building blocks of GTK to provide Data Structure handling for C
- Pango: Its a layout for the rendering of text, which concentrates on classification
- Cairo: It is a 2D graphics library which supports various output devices
- ATK: A library with a set of interfaces which provides access to tools such as screen readers, magnifiers etc.
Pyjamas-Desktop (Toolkit)
It is a part of Google web toolkit which enables the development of advanced media Ajax applications using Python with no requirement of a special browser plugin. Additionally, it also has a desktop widget set which runs as pure Python, with three available ports. The prototype makes use of web browser technology which effectively provides a secondary widget sets, such as PyQT4 and Py GTK2 and gives an advantage of providing full support for HTML, CSS, plugins and other web-related functions.
Key Features
- It has well-developed local and remote CSS stylesheet functionality by using API manipulation
- It provides an option of Javascript execution for complete application manipulation
- It has complete URL support for local and remote loading of HTML pages
- Availability of plugins for multimedia
- Complete access to XML, XSLT, AJAX
Toga (Toolkit)
This toolkit is a native Python OS cross-platform for GUI toolkit which consists of basic components with a shared interface.
Key Features
The issue with this toolkit is that a widget toolkit faces is to put widgets on the screen in the right manner. Different widgets toolkits follow different approaches, constraints, packing techniques, and grid-based prototypes to overcome this problem. Its pack style engines are inspired by an HTML approach that is very novel for the widget toolkits but proven to be effective: cascading style Sheets (CSS)
Tkinter (Toolkit)
Tkinter is a binding Python GUI toolkit. This is a modern technology which is implemented to completely envelop Python and Tcl into an embedded interpreter. The calls are converted into Tcl commands which are used for the embedded interpreter, which blends Python and Tcl into a single application. This toolkit can run on various platforms like Linux, Microsoft Windows, and Mac OS X.
wxPython
It is a binder for the cross-platform GUI toolkits and wxWidgets. It was initially written in C++ and was later implemented in the Python.
This is a complex code to maintain and does not synchronize with wxWidgets versions. Later the issue was addressed by launching a project called Phoenix which was launched in the year 2010. Then they made an effort to clean up the wxPython applications and its functionalities and made it compatible with Python. This project was focusing on accelerating speed, durability, and maintenance of the wxPython toolkit.
The applications which used wxPython are BitTorrent, Chandler, Editra,
Google Drive, Métamorphose, GRASS GIS, Phatch, PlayOnLinux, and PlayOnMac.
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