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Python Data Mining Quick Start Guide

You're reading from   Python Data Mining Quick Start Guide A beginner's guide to extracting valuable insights from your data

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789800265
Length 188 pages
Edition 1st Edition
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Author (1):
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Nathan Greeneltch Nathan Greeneltch
Author Profile Icon Nathan Greeneltch
Nathan Greeneltch
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Table of Contents (9) Chapters Close

Preface 1. Data Mining and Getting Started with Python Tools 2. Basic Terminology and Our End-to-End Example FREE CHAPTER 3. Collecting, Exploring, and Visualizing Data 4. Cleaning and Readying Data for Analysis 5. Grouping and Clustering Data 6. Prediction with Regression and Classification 7. Advanced Topics - Building a Data Processing Pipeline and Deploying It 8. Other Books You May Enjoy

Setting up Python environments for data mining

A computing setup conducive to advanced data mining requires a comfortable development environment and working libraries for data management, analytics, plotting, and deployment. The popular bundled Python distribution from Anaconda is a perfect fit for the job. It is targeted at scientists and engineers, and includes all the required packages to get started. Conda itself is a package manager for maintaining working Python environments and, of course, is included in the bundle. The package manager will allow you to install/remove combinations of libraries into segregated Python environments, all the while reconciling any version dependencies between the distinct libraries.

It includes an integrated development environment called The Scientific Python Development Environment (Spyder) and a ready-to-use implementation of Jupyter Notebook interface. Both of these development environments use the interactive Python console called IPython. IPython gives you a live console for scripting. You can run a single line of code, check results, then run another line of code in same console in an interactive fashion. A few trial-and-error sessions with IPython will demonstrate very clearly why these Python tools are so beloved by practitioners working in a rapid prototyping environment.

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