What are the best Python 2.7 modules for data mining?

In this article, we will learn the best Python 2.7 modules for data mining.

The following are some of the best Python 2.7 modules for data mining −

  • NLTK

  • Beautiful Soup

  • Matplotlib

  • mrjob

  • NumPy

  • pybrain

  • mlpy

  • Scrapy


Natural Language Processing (NLP) is the process of using software or a machine to manipulate or understand text or speech. Humans interact and understand each other's points of view and then respond appropriately. This interaction, understanding, and response are made by a machine rather than a human in NLP.

NLTK(Natural Language Toolkit) is a standard Python library that includes prebuilt functions and tools to make it easier to use and implement. It is a popular library for Natural Language Processing(NLP) and computational linguistics.

Beautiful Soup

The Beautiful Soup is a Python module named after a poem of the same name by Lewis Carroll in "Alice's Adventures in Wonderland." Beautiful Soup is a Python program that, as the name implies, parses undesirable material and helps to organize and format jumbled web data by repairing incorrect HTML and presenting it to us in an easily navigable XML structure.

Extracting information from HTML and XML files is a snap with the help of the Python module Beautiful Soup.


Matploitlib is a plotting−specific Python library. It provides object−oriented APIs for plotting application integration. It is compatible with Python scripts, shells, web application servers, and GUI toolkits.

It's a great Python module for making 2D array charts and visualizations. Based on NumPy arrays and intended to work with the rest of the SciPy stack, Matplotlib is a cross−platform data visualization program. Author John Hunter first used it in 2002.

The ability to access large amounts of data in visually appealing and easily understood formats is one of the greatest benefits of visualization. Among the many available plots in Matplotlib are line, bar, scatter, histogram, and others.

Matplotlib is a Python library that allows you to create static, animated, and interactive visualizations. Matplotlib makes simple things simple and difficult things possible.

  • Matploitlib publication quality plots.

  • Create interactive figures that can be zoomed, panned, and updated.

  • Customize the visual style and layout.

  • Export to a variety of file formats.

  • Include JupyterLab and Graphical User Interfaces.

  • Use a wide range of third−party packages based on Matplotlib.


YELP created the popular Python package for MapReduce known as mrjob. The library supports Python programmers in developing MapReduce programs. MapReduce Python code produced with mrjob may be tested locally or in the cloud using Amazon EMR (Elastic MapReduce).

Amazon EMR is a Big Data cloud−based web service offered by Amazon Web Services. mrjob is an active framework for MapReduce programming or Hadoop Streaming tasks that provides better documentation for Hadoop with Python than any other library or framework currently available. We can write code for Mapper and Reducer in a single class using mrjob. If we don't have Hadoop installed, we may still run the mrjob programme in our local system environment. Mrjob works with Python 2.7/3.4+.

Installation of mrjob

pip install mrjob (or) pip3 install mrjob #for python3


NumPy is one of the most widely used open−source Python libraries for scientific computation. Its built−in mathematical functions allow for lightning−fast computation and support for multidimensional data and massive matrices. Linear algebra also makes use of it. NumPy Array is frequently preferred over lists because it consumes less memory and is more convenient and efficient.

When it comes to open−source Python libraries, NumPy is among the most popular for doing scientific computations. Since it already has the necessary mathematical functions programmed in, calculations can be performed quickly, and it can handle data in several dimensions as well as large matrices. This is also used in linear algebra. Compared to lists, NumPy Array is often chosen because it is more memory economical and has less overhead requirements.


Pybrain is a Python−implemented open−source library for Machine Learning. The library provides user−friendly training methods, datasets, and trainers for training and testing networks.

The official literature for Pybrain describes it as a Python library for machine learning that is modular. It aims to provide flexible, user−friendly, yet strong algorithms for Machine Learning Tasks as well as a range of predefined settings for testing and comparing your algorithms.

Python−Based Reinforcement Learning, Artificial Intelligence, and Neural Network Library is the abbreviation for PyBrain. In actuality, we came up with the name first and then reverse−engineered this extremely detailed "Backronym."


mlpy is a Python module for machine learning that is constructed using NumPy/SciPy and the GNU Scientific Libraries.

The objective of mlpy is to achieve a reasonable balance between modularity, maintainability, reproducibility, usability, and efficiency through its extensive selection of cutting−edge machine−learning algorithms for supervised and unsupervised situations. mlpy is an open−source, cross−platform Python 2 and 3 libraries provided under the GNU General Public License version 3.


Regression−Least Squares, Ridge Regression, Last Angle Regression, Elastic Net, Kernel Ridge Regression, Support Vector Machines (SVR), Partial Least Squares (PLS).


Scrapy is a Python framework for web scraping on a huge scale. It provides you with all of the tools you need to easily extract data from websites, analyze it as you feel appropriate, and store it in the structure and format you prefer.

Because the internet is so diverse, there is no "one size fits all" technique for extracting data from websites. Ad hoc approaches are frequently used, and if you start writing code for every little work you undertake, you will soon end up building your own scraping framework. Scrapy is that framework.

You don't have to reinvent the wheel using Scrapy.


In this article, we learned about eight very essential Python modules for data mining. Each module performs distinct functions in the data mining process.