This chapter covered the basics of loading data from database, disk, and web sources. It also covered basic SQL queries and pandas' access and search functions. The last sections of the chapter introduced common types of plots using Seaborn. You should be able to transfer these basic code examples to many of your analysis projects for data mining. The examples were chosen to demonstrate how to use these libraries, so if your specific need or requirement is not covered, you should be able to replace the method calls with ease, simply by looking up the syntax on the library's website. The next chapter will cover data wrangling and cleaning up data in order to prepare it for analysis.
Python Data Mining Quick Start Guide
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Python Data Mining Quick Start Guide
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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)
Preface
Free Chapter
Data Mining and Getting Started with Python Tools
Basic Terminology and Our End-to-End Example
Collecting, Exploring, and Visualizing Data
Cleaning and Readying Data for Analysis
Grouping and Clustering Data
Prediction with Regression and Classification
Advanced Topics - Building a Data Processing Pipeline and Deploying It
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