Getting Started with Python Data Analysis

Phuong Vo.T.H, Martin Czygan

• Publisher: Packt Publishing
• Year: 2015
• ISBN:
  9781785285110
• Edition: 1
• Topic: Computer Science
• Subtopic: Programming in Python
• Index: Computer Science

Learn to use powerful Python libraries for effective data processing and analysis

About This Book

• Learn the basic processing steps in data analysis and how to use Python in this area through supported packages, especially Numpy, Pandas, and Matplotlib
• Create, manipulate, and analyze your data to extract useful information to optimize your system
• A hands-on guide to help you learn data analysis using Python

Who This Book Is For

If you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.

What You Will Learn

• Understand the importance of data analysis and get familiar with its processing steps
• Get acquainted with Numpy to use with arrays and array-oriented computing in data analysis
• Create effective visualizations to present your data using Matplotlib
• Process and analyze data using the time series capabilities of Pandas
• Interact with different kind of database systems, such as file, disk format, Mongo, and Redis
• Apply the supported Python package to data analysis applications through examples
• Explore predictive analytics and machine learning algorithms using Scikit-learn, a Python library

In Detail

Data analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It's often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.

With this book, we will get you started with Python data analysis and show you what its advantages are.

The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems.

Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples.

Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn.

Style and approach

This is an easy-to-follow, step-by-step guide to get you familiar with data analysis and the libraries supported by Python. Topics are explained with real-world examples wherever required.

Getting Started with Python Data Analysis

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Table of Contents

Getting Started with Python Data Analysis

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Introducing Data Analysis and Libraries

Data analysis and processing

An overview of the libraries in data analysis

Python libraries in data analysis

NumPy

Pandas

Matplotlib

PyMongo

The scikit-learn library

Summary

2. NumPy Arrays and Vectorized Computation

NumPy arrays

Data types

Array creation

Indexing and slicing

Fancy indexing

Numerical operations on arrays

Array functions

Data processing using arrays

Loading and saving data

Saving an array

Loading an array

Linear algebra with NumPy

NumPy random numbers

Summary

3. Data Analysis with Pandas

An overview of the Pandas package

The Pandas data structure

Series

The DataFrame

The essential basic functionality

Reindexing and altering labels

Head and tail

Binary operations

Functional statistics

Function application

Sorting

Indexing and selecting data

Computational tools

Working with missing data

Advanced uses of Pandas for data analysis

Hierarchical indexing

The Panel data

Summary

4. Data Visualization

The matplotlib API primer

Line properties

Figures and subplots

Exploring plot types

Scatter plots

Bar plots

Contour plots

Histogram plots

Legends and annotations

Plotting functions with Pandas

Additional Python data visualization tools

Bokeh

MayaVi

Summary

5. Time Series

Time series primer

Working with date and time objects

Resampling time series

Downsampling time series data

Upsampling time series data

Time zone handling

Timedeltas

Time series plotting

Summary

6. Interacting with Databases

Interacting with data in text format

Reading data from text format

Writing data to text format

Interacting with data in binary format

HDF5

Interacting with data in MongoDB

Interacting with data in Redis

The simple value

List

Set

Ordered set

Summary

7. Data Analysis Application Examples

Data munging

Cleaning data

Filtering

Merging data

Reshaping data

Data aggregation

Grouping data

Summary

8. Machine Learning Models with scikit-learn

An overview of machine learning models

The scikit-learn modules for different models

Data representation in scikit-learn

Supervised learning – classification and regression

Unsupervised learning – clustering and dimensionality reduction

Measuring prediction performance

Summary

Index

Getting Started with Python Data Analysis

Copyright © 2015 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

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First published: October 2015

Production reference: 1231015

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78528-511-0

www.packtpub.com

Credits

Authors

Phuong Vo.T.H

Martin Czygan

Reviewers

Don...

Table of contents

• Getting Started with Python Data Analysis