Python Data Analytics Libraries

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Python Data Analytics Libraries

What is Data Analytics?

Data is power. Insights acquired from data is the key to unlock the internet age. With the web expanding, the challenge is to use the data being captured to provide meaningful insights. This is what Data Analytics is all about.

In simple terms, data analytics is a collection of tools to analyze complex data sets to draw useful conclusions.

These conclusions aid organizations in taking informed business decisions. It also helps researchers and scientists to prove their scientific approach.

Altogether, data analytics improves operational functionality, revenue, and customer retention.

The goal of data analytics is to improve business performance. Data Analytics is the buzz word driving any business, be it financial analysis, eCommerce, advertising, healthcare, research, etc.

Python Data Analytics Libraries

There are numerous libraries in Python that give Data Analysts the necessary functionality for crunching data sets.

It is worth to spend time to familiarize with the basic usage of these libraries.

Below are the major Python libraries used in the field of Data Analytics.

We have discussed the core libraries supported by Python in the field of Data Science and Data Analytics.

Apart from them, let’s discuss a few more Python libraries that are extensively used in the field of Data Analytics.

1. OpenCV

OpenCV (Open source Computer Vision) is a Python library used extensively used for data analytics using Computer Vision.

Computer Vision (CV) is a top trending field that makes use of computers to gain deep understanding of images and videos, thereby enabling computers to identify images and process images like humans.

Initially launched by Intel, this library is cross-platform and free for use under the open-source BSD license.

The OpenCV library supports object identification, facial recognition, motion tracking, Human-computer interaction, mobile robotics and many more.

This library supports several algorithms that are used to analyze images and extract valuable information, automatically.

Many e-commerce sites use image analysis to do predictive analytics by forecasting their customer’s needs.

OpenCV is also used to improve the results of search engines by contextualizing images in searches, by tagging and identifying objects. Hence, OpenCV supports useful functions and modules to support image Data Analysis.

2. PyQT

As data analytics deals with huge volumes of data, data analysts prefer to use tools with user-friendly GUIs.

PyQt is a popular Python binding toolkit that is used for cross-platform GUI.

This toolkit is implemented as a plugin. PyQt plugin is free to use and licensed under the GNU General Public License.

PyQt supports enormous classes and functions to make a data analyst’s journey easier. This application supports classes and functions for accessing SQL databases, provides an easy to use an XML parser, supports widgets that are automatically populated from a database, SVG support, and many other cool features to reduce the burdens of Data Analysts.

PyQT supports features to generate Python code from GUI designs that are created using Qt Designer. These features makes PyQt useful as a rapid prototyping tool for applications that will be implemented in C++, as the user interface designs can be re-used without modification.

3. Pandas

PANDAS stands for Python Data Analysis Library. Pandas is an open-source library in Python. It provides ready to use high-performance data structures and data analysis tools.

Pandas module runs on top of NumPy and it is popularly used for data science and data analytics. NumPy is a low-level data structure that supports multi-dimensional arrays and a wide range of mathematical array operations.

Pandas have a higher-level interface. It also provides streamlined alignment of tabular data and powerful time-series functionality.

DataFrame is the key data structure in Pandas. It allows us to store and manipulate tabular data as a 2-D data structure. Pandas provide a rich feature-set on the DataFrame. Using DataFrame, we can store and manage data from tables by performing manipulation over rows and columns.

Pandas library provides functions to merge data, thereby providing high performance. The panel data structure provided by the Pandas library gives a better visualization of data due to its 3D data structure.

4. PyBrain

PyBrain is a powerful library available in Python used for Data Analytics. PyBrain stands for Python Based Reinforcement Learning, Artificial Intelligence, and Neural network Library.

PyBrain offers flexible modules and algorithms for Data Analytics and advanced research and supports a wide variety of predefined environments to test and compare your algorithms.

The best part is that PyBrain is open source and free to use under BSD Software Licence.

Data visualization Libraries

“A picture is worth a thousand words”. The key function of any library is its ability to represent the results of the complex operations on the data in an understandable format.

A Data Analyst uses data techniques to gather meaningful insights and help organizations to make better decisions. The libraries listed below are mainly used for data visualization and plotting.

1. StatsModels

The StatsModels library in Python allows data Analysts to perform statistical modeling on data sets by making use of the plotting and data modeling features of the library. The models (linear and regression) can be used for forecasting across a variety of domains.

StatsModels library provides functions for the estimation of a huge variety of statistical models. The module also provides useful classes for performing statistical tests and data exploration.

A list of result statistics is available, which is then tested against existing packages to verify that statistics are correct.

StatsModels library supports time-series functionalities that are popular in the financial domain to maintain sensitive information in an easy to use format. These models are efficient for big data sets.

2. Matplotlib

Matplotlib is a Python library for data visualisation. It creates 2D plots and graphs using Python scripts.

Matplotlib has features to control line styles, axes, etc. It also supports a wide range of graphs and plots like histograms, bar charts, error charts, histograms, contour plots, etc.

In addition, Matplotlib provides an effective environment alternative for MatLab, when used along with NumPy.

3. Pydot

Pydot is a python library for generating complex oriented and non-oriented graphs. Pydot is an interface to Graphviz, that is written in Python.

By using Pydot, it is possible to show the structure of the graph that is often needed to build and analyze complex neural networks.

4. Bokeh

The Bokeh library is a standalone Python library that enables data Analysts to plot their data through a web interface.

It uses JavaScript and is therefore independent of the Matplotlib library. An essential feature of the Bokeh library is that it allows users to represent data in different formats like graphs, labels, plots, etc.

Bokeh library has proved to deliver high-performance interactivity over large datasets. Bokeh can help data Analysts to easily create interactive plots and data applications with little effort.

Data mining and Analysis

Data mining is a process of extracting useful data from analyzing patterns in large sets of unorganized data that is used for data analysis.

Data analysis is used to test models on the dataset. Python provides many important libraries for data mining and data analysis. Listed are a few popular ones.

1. Scikit-learn

Scikit-learn Python library supports a number of useful features for data mining and data analysis. This makes it a preferred choice for data Analysts.

It is built on top of NumPy, SciPy, and Matplotlib libraries. It acts as a foundation for other Machine Learning implementations. It features classical algorithms for statistical data modeling that includes classification, clustering, regression and preprocessing.

Scikit-learn supports popularly used supervised learning algorithms, as well as unsupervised learning algorithms. The algorithms include support vector machines, grid search, gradient boosting, k-means clustering, DBSCAN and many more.

Along with these algorithms, the kit provides sample datasets for data modeling. The well documented APIs are easily accessible.

Hence, it is used for academic and commercial purposes. Scikit-learn is used to build models and it is not recommended to use it for reading, manipulating and summarizing data as there are better frameworks available for the purpose. It is open-source and released under the BSD license.

2. Orange

Orange is an open-source data mining library to provide visual and interactive data analysis workflows in a large toolbox. The package was released under General Public License. It is designed using C++ and has Python wrappers on top of it.

The Orange package features a set of widgets for visualization, regression, evaluation, and classification of datasets. The interactive data analysis provides rapid and qualitative analysis.

Its Graphic user interface allows Analysts to focus on data mining, instead of coding from scratch. As an added advantage, clever defaults support prototyping of the data analysis workflow rapidly.

Conclusion

There is a huge demand for Data Analysts in the current decade. Getting to know the popular Python libraries in a Data Analyst’s toolbox is extremely worthy. With the advent and rise of data analytics, regular advancements are made to Python data analytics libraries. As Python provides a lot of multi-purpose, ready-to-use libraries, it is the language top choice for Data Analysts.

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