Book Image

Python Data Mining Quick Start Guide

By : Nathan Greeneltch
Book Image

Python Data Mining Quick Start Guide

By: Nathan Greeneltch

Overview of this book

Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle.
Table of Contents (9 chapters)

Python-specific deployment concerns

Python is not a compiled language. It is interpreted at the time of execution. It is important to remember that, when you follow the steps in this chapter, you are not pickling an executable program. You are simply pickling an object. At load time, the environment must be compatible with the contents of the object. Often, that means matching versions, as libraries change over time. Also, the default serialization protocol for pickle is not compatible with Python 2, so you will have to change the protocol if switching Python versions.

Lastly, the pickled object is similar to a ZIP file in that anyone can bundle up anything inside it and you will not know it until you unpickle/unzip it. Security should always be a concern with any file types that are not transparent.

You should read the main pickle doc page for descriptions of compatibility...