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, BSD-licensed library providing high-performance, easy-to-use 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
- Multi-Panel Plots
- Conclusions
- Chapter 8 : Machine Learning with scikit-learn
- 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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