Book description
Python Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage of pandas, an open source, BSDlicensed library providing highperformance, easytouse data structures and data analysis tools for the Python programming language.
Author Fabio Nelli expertly shows the strength of the Python programming language when applied to processing, managing and retrieving information. Inside, you will see how intuitive and flexible it is to discover and communicate meaningful patterns of data using Python scripts, reporting systems, and data export. This book examines how to go about obtaining, processing, storing, managing and analyzing data using the Python programming language.
You will use Python and other open source tools to wrangle data and tease out interesting and important trends in that data that will allow you to predict future patterns. Whether you are dealing with sales data, investment data (stocks, bonds, etc.), medical data, web page usage, or any other type of data set, Python can be used to interpret, analyze, and glean information from a pile of numbers and statistics.
This book is an invaluable reference with its examples of storing and accessing data in a database; it walks you through the process of report generation; it provides three real world case studies or examples that you can take with you for your everyday analysis needs.
Table of contents
 Cover
 Title
 Copyright
 Contents at a Glance
 Contents
 About the Author
 About the Technical Reviewer
 Acknowledgments
 Chapter 1 : An Introduction to Data Analysis
 Chapter 2 : Introduction to the Python’s World
 Chapter 3 : The NumPy Library

Chapter 4 : The pandas Library—An Introduction
 pandas: The Python Data Analysis Library
 Installation
 Test Your pandas Installation
 Getting Started with pandas
 Introduction to pandas Data Structures
 Other Functionalities on Indexes
 Operations between Data Structures
 Function Application and Mapping
 Sorting and Ranking
 Correlation and Covariance
 “Not a Number” Data
 Hierarchical Indexing and Leveling
 Conclusions

Chapter 5 : pandas: Reading and Writing Data
 I/O API Tools
 CSV and Textual Files
 Reading Data in CSV or Text Files
 Reading and Writing HTML Files
 Reading Data from XML
 Reading and Writing Data on Microsoft Excel Files
 JSON Data
 The Format HDF5
 Pickle—Python Object Serialization
 Interacting with Databases
 Reading and Writing Data with a NoSQL Database: MongoDB
 Conclusions
 Chapter 6 : pandas in Depth: Data Manipulation

Chapter 7 : Data Visualization with matplotlib
 The matplotlib Library
 Installation
 IPython and IPython QtConsole
 matplotlib Architecture
 pyplot
 Using the kwargs
 Adding Further Elements to the Chart
 Saving Your Charts
 Handling Date Values
 Chart Typology
 Line Chart
 Histogram
 Bar Chart
 Pie Charts
 Advanced Charts
 mplot3d
 MultiPanel Plots
 Conclusions
 Chapter 8 : Machine Learning with scikitlearn
 Chapter 9 : An Example—Meteorological Data
 Chapter 10 : Embedding the JavaScript D3 Library in IPython Notebook
 Chapter 11 : Recognizing Handwritten Digits
 Appendix A: Writing Mathematical Expressions with LaTeX
 Appendix B: Open Data Sources
 Index
Product information
 Title: Python Data Analytics: Data Analysis and Science Using Pandas, matplotlib, and the Python Programming Language
 Author(s):
 Release date: August 2015
 Publisher(s): Apress
 ISBN: 9781484209585
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