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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Become an expert at using Python for advanced statistical analysis of data using real-world examples
About This Book
- Clean, format, and explore data using graphical and numerical summaries
- Leverage the IPython environment to efficiently analyze data with Python
- Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data
Who This Book Is For
If you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you. Familiarity with the basics of applying Python libraries to data sets is assumed.
What You Will Learn
- Read, sort, and map various data into Python and Pandas
- Recognise patterns so you can understand and explore data
- Use statistical models to discover patterns in data
- Review classical statistical inference using Python, Pandas, and SciPy
- Detect similarities and differences in data with clustering
- Clean your data to make it useful
- Work in Jupyter Notebook to produce publication ready figures to be included in reports
In Detail
Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want!
Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You'll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making.
You'll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you'll move on to review statistical inference using Python, Pandas, and SciPy. After that, we'll focus on performing regression using computational tools and you'll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you'll discover how to use Python's tools for supervised machine learning.
Style and approach
This book takes a step-by-step approach to reading, processing, and analyzing data in Python using various methods and tools. Rich in examples, each topic connects to real-world examples and retrieves data directly online where possible. With this book, you are given the knowledge and tools to explore any data on your own, encouraging a curiosity befitting all data scientists.
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Seitenzahl: 286
Veröffentlichungsjahr: 2016
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Table of Contents
Mastering Python Data Analysis
Mastering Python Data Analysis
Copyright © 2016 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.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Publishing Month: June 2016
Production reference: 1230616
Published by Packt Publishing Ltd.
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ISBN 978-1-78355-329-7
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Credits
Authors
Magnus Vilhelm Persson
Luiz Felipe Martins
Copy Editor
Tasneem Fatehi
Reviewers
Hang (Harvey) Yu
Laurie Lugrin
Chris Morgan
Michele Pratusevich
Project Coordinator
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Commissioning Editor
Akram Hussain
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Acquisition Editor
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Indexer
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Graphics
Kirk D'Penha
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Technical Editors
Bharat Patil
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Production Coordinator
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About the Authors
Magnus Vilhelm Persson is a scientist with a passion for Python and open source software usage and development. He obtained his PhD in Physics/Astronomy from Copenhagen University’s Centre for Star and Planet Formation (StarPlan) in 2013. Since then, he has continued his research in Astronomy at various academic institutes across Europe. In his research, he uses various types of data and analysis to gain insights into how stars are formed. He has participated in radio shows about Astronomy and also organized workshops and intensive courses about the use of Python for data analysis.
You can check out his web page at http://vilhelm.nu.
This book would not have been possible without the great work that all the people at Packt are doing. I would like to highlight Arun, Bharat, Vinay, and Pranil's work. Thank you for your patience during the whole process. Furthermore, I would like to thank Packt for giving me the opportunity to develop and write this book, it was really fun and I learned a lot. There where times when the work was little overwhelming, but at those times, my colleague and friend Alan Heays always had some supporting words to say. Finally, my wife, Mihaela, is the most supportive partner anyone could ever have. For all the late evenings and nights where you pushed me to continue working on this to finish it, thank you. You are the most loving wife and best friend anyone could ever ask for.
Luiz Felipe Martins holds a PhD in applied mathematics from Brown University and has worked as a researcher and educator for more than 20 years. His research is mainly in the field of applied probability. He has been involved in developing code for open source homework system, WeBWorK, where he wrote a library for the visualization of systems of differential equations. He was supported by an NSF grant for this project. Currently, he is an associate professor in the department of mathematics at Cleveland State University, Cleveland, Ohio, where he has developed several courses in applied mathematics and scientific computing. His current duties include coordinating all first-year calculus sessions.
About the Reviewer
Hang (Harvey) Yu is a data scientist in Silicon Valley. He works on search engine development and model optimization. He has ample experience in big data and machine learning. He graduated from the University of Illinois at Urbana-Champaign with a background in data mining and statistics. Besides this book, he has also reviewed multiple other books and papers including Mastering Python Data Visualization and R Data Analysis Cookbook both by Packt Publishing. When Harvey is not coding, he is playing soccer, reading fiction books, or listening to classical music. You can get in touch with him at [email protected] or on LinkedIn at http://www.linkedin.com/in/hangyu1.
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Preface
The use of Python for data analysis and visualization has only increased in popularity in the last few years. One reason for this is the availability and continued development of a number of excellent tools for conducting advanced data analysis and visualization. Another reason is the possibility of rapid and easy development, deployment, and sharing of code. For these reasons, Python has become one of the most widely used programming and scripting language for data analysis in many industries.
The aim of this book is to develop skills to effectively approach almost any data analysis problem, and extract all of the available information. This is done by introducing a range of varying techniques and methods such as uni- and multi-variate linear regression, cluster finding, Bayesian analysis, machine learning, and time series analysis. Exploratory data analysis is a key aspect to get a sense of what can be done and to maximize the insights that are gained from the data. Additionally, emphasis is put on presentation-ready figures that are clear and easy to interpret.
Knowing how to explore data and present results and conclusions from data analysis in a meaningful way is an important skill. While the theory behind statistical analysis is important to know, to be able to quickly and accurately perform hands-on sorting, reduction, analysis, and subsequently present the insights gained, is a make or break for today's quickly evolving business and academic sector.
What this book covers
Chapter 1, Tools of the Trade, provides an overview of the tools available for data analysis in Python and details the packages and libraries that will be used in the book with some installation tips. A quick example highlights the common data structure used in the Pandas package.
Chapter 2, Exploring Data, introduces methods for initial exploration of data, including numeric summaries and distributions, and various ways of displaying data, such as histograms, Kernel Density Estimation (KDE) plots, and box plots.
Chapter 3, Learning About Models, covers the concept of models in data analysis and how using the cumulative distribution function and probability density function can help characterize a variable. Furthermore, it shows how to make point estimates and generate random numbers with a given distribution.
Chapter 4, Regression, introduces linear, multiple, and logistic regression with in-depth examples of using SciPy and statsmodels packages to test various hypotheses of relationships between variables.
Chapter 5, Clustering, explains some of the theory behind cluster finding analysis and goes through some more complex examples using the K-means and hierarchical clustering algorithms available in SciPy.
Chapter 6, Bayesian Methods, explains how to construct and test a model using Bayesian analysis in Python using the PyMC package. It covers setting up stochastic and deterministic variables with prior information, constructing the model, running the Markov Chain Monte Carlo (MCMC) sampler, and interpreting the results. In addition, a short bonus section covers how to plot coordinates on maps using both the basemap and cartopy packages, which are important for presenting and analyzing data with geographical coordinate information.
Chapter 7, Supervised and Unsupervised Learning, looks at linear regression, clustering, and classification with two machine learning analysis techniques available in the Scikit-learn package.
Chapter 8, Time Series Analysis, examines various aspects of time series modeling using Pandas and statsmodels. Initially, the important concepts of smoothing, resampling, rolling estimates, and stationarity are covered. Later, autoregressive (AR), moving average (MA), and combined ARIMA models are explained and applied to one of the data sets, including making shorter forecasts using the constructed models.
Appendix, More on Jupyter Notebook and matplotlib Styles, shows some convenient extensions of Jupyter Notebook and some useful keyboard shortcuts to make the Jupyter workflow more efficient. The matplotlib style files are explained and how to customize plots even further to make beautiful figures ready for inclusion in reports. Lastly, various useful online resources are listed and described.
What you need for this book
All you need to follow through the examples in this book is a computer running any recent version of Python. While the examples use Python 3, they can easily be adapted to work with Python 2, with only minor changes. The packages used in the examples are NumPy, SciPy, matplotlib, Pandas, statsmodels, PyMC, Scikit-learn. Optionally, the packages basemap and cartopy are used to plot coordinate points on maps. The easiest way to obtain and maintain a Python environment that meets all the requirements of this book is to download a prepackaged Python distribution. In this book, we have checked all the code against Continuum Analytics' Anaconda Python distribution and Ubuntu Xenial Xerus (16.04) running Python 3.
To download the example data and code, an Internet connection is needed.
Who this book is for
This book is intended for professionals with a beginner to intermediate level of Python programming knowledge who want to move in the direction of solving more sophisticated problems and gain deeper insights through advanced data analysis. Some experience with the math behind basic statistics is assumed, but quick introductions are given where required. If you want to learn the breadth of statistical analysis techniques in Python and get an overview of the methods and tools available, you will find this book helpful. Each chapter consists of a number of examples using mostly real-world data to highlight various aspects of the topic and teach how to conduct data analysis from start to finish.
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Questions
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Chapter 1. Tools of the Trade
This chapter gives you an overview of the tools available for data analysis in Python, with details concerning the Python packages and libraries that will be used in this book. A few installation tips are given, and the chapter concludes with a brief example. We will concentrate on how to read data files, select data, and produce simple plots, instead of delving into numerical data analysis.
Before you start
We assume that you have familiarity with Python and have already developed and run some scripts or used Python interactively, either in the shell or on another interface, such as the Jupyter Notebook (formerly known as the IPython notebook). Hence, we also assume that you have a working installation of Python. In this book, we assume that you have installed Python 3.4 or later.
We also assume that you have developed your own workflow with Python, based on needs and available environment. To follow the examples in this book, you are expected to have access to a working installation of Python 3.4 or later. There are two alternatives to get started, as outlined in the following list:
Tip
Even if you have a working Python installation, you might want to try one of the prepackaged distributions. They contain a well-rounded collection of packages and modules suitable for data analysis and scientific computing. If you choose this path, all the libraries in the next list are included by default.
We also assume that you have the libraries in the following list:
Notice that each of the libraries in the preceding list may have several dependencies, which must also be separately installed. To test the availability of any of the packages, start a Python shell and run the corresponding import statement. For example, to test the availability of NumPy, run the following command:
import numpyIf NumPy is not installed in your system, this will produce an error message. An alternative approach that does not require starting a Python shell is to run the command line:
python -c 'import numpy'We also assume that you have either a programmer's editor or Python IDE. There are several options, but at the basic level, any editor capable of working with unformatted text files will do.
Using the notebook interface
Most examples in this book will use the Jupyter Notebook interface. This is a browser-based interface that integrates computations, graphics, and other forms of media. Notebooks can be easily shared and published, for example, http://nbviewer.ipython.org/ provides a simple publication path.
It is not, however, absolutely necessary to use the Jupyter interface to run the examples in this book. We strongly encourage, however, that you at least experiment with the notebook and its many features. The Jupyter Notebook interface makes it possible to mix formatted, descriptive text with code cells that evaluate at the same time. This feature makes it suitable for educational purposes, but it is also useful for personal use as it makes it easier to add comments and share partial progress before writing a full report. We will sometimes refer to a Jupyter Notebook as just a notebook.
To start the notebook interface, run the following command line from the shell or Anaconda command prompt:
jupyter notebookThe notebook server will be started in the directory where the command is issued. After a while, the notebook interface will appear in your default browser. Make sure that you are using a standards-compliant browser, such as Chrome, Firefox, Opera, or Safari. Once the Jupyter dashboard shows on the browser, click on the New button on the upper-right side of the page and select Python 3. After a few seconds, a new notebook will open in the browser. A useful place to learn about the notebook interface is http://jupyter.org.
Imports
There are some modules that we will need to load at the start of every project. Assuming that you are running a Jupyter Notebook, the required imports are as follows:
%matplotlib inlineimport matplotlib.pyplot as pltimport numpy as npimport pandas as pdEnter all the preceding commands in a single notebook cell and press Shift + Enter to run the whole cell. A new cell will be created when there is none after the one you are running; however, if you want to create one yourself, the menu or keyboard shortcut Ctrl +M+A/B is handy (A for above, B for below the current cell). In Appendix, More on Jupyter Notebook and matplotlib Styles, we cover some of the keyboard shortcuts available and installable extensions (that is, plugins) for Jupyter Notebook.
The statement %matplotlib inline is an example of Jupyter Notebook magic and sets up the interface to display plots inline, that is, embedded in the notebook. This line is not needed (and causes an error) in scripts. Next, optionally, enter the following commands:
import osplt.style.use(os.path.join(os.getcwd(), 'mystyle.mplstyle') )As before, run the cell by pressing Shift +Enter. This code has the effect of selecting matplotlib stylesheet mystyle.mplstyle. This is a custom style sheet that I created, which resides in the same folder as the notebook. It is a rather simple example of what can be done; you can modify it to your liking. As we gain experience in drawing figures throughout the book, I encourage you to play around with the settings in the file. There are also built-in styles that you can by typing plt.style.available in a new cell.
This is it! We are all set to start the fun part!
