Python End-to-end Data Analysis 英文高清完整.pdf版

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The aim of this book is to develop skills to effectively approach almost any data analysis problem, and extract all of the available information.
Python End-to-end Data Analysis Copyright o 2016 Packt Publishing All rights reserved. No part of this course 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 course to ensure the accuracy of the information presented. However, the information contained in this course 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 course Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this course by the appropriate use of capitals However, Packt Publishing cannot guarantee the accuracy of this information Published on: Mav 2017 Production reference: 1050517 Published by Packt Publishing Ltd Livery place 35 Livery street Birmingham B3 2PB, UK ISBN978-178839469-7 www.packtpub.com Credits Authors Content Development Editor Phuong Vo. TH Aishwarya Pandere Martin Czygan Ivan idris Graphics Magnus vilhelmPersson Jason monteiro Luiz Felipe martins Production coordinator Deepika naik Reviewers Dong CI Hai minh nguyen th emeka odoh Bill chambers Alexey Grigore Dr. Vahidmirjalili Michele usue lli Hang(Harvey) Yu Laurie lugi organ Michele pratusevi Preface The use of Python for data analysis and visualization has only increased in popularity in the last few years 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 What this learning path covers Module 1, Getting Started with Python Data Analysis, shows how to work with time- oriented data in pandas. How do you clean, inspect, reshape, merge or group data these are the concerns in this chapter The library of choice in the course will be Pandas again Module 2, Python Data Analysis Cookbook, demonstrates how to visualize data and mentions frequently encountered pitfalls. Also, discusses statistical probability distributions and correlation between two variables Module 3, Mastering Python Data Analysis, introduces linear, multiple, and logistic regression with in-depth examples of using SciPy and stats models packages to test various hypotheses of relationships between variables Preface What you need for this learning path Module 1 There are not too many requirements to get started. You will need a Python programming environment installed on your system Under Linux and Mac OsX, Python is usually installed by default Installation on Windows is supported by an excellent installer provided and maintained by the community. This book uses a recent Python 2, but many examples will work with Python 3as well The versions of the libraries used in this book are the following: NumPy 1.9.2, Pandas 0. 16.2, matplotlib 1.4.3, tables 3. 2.2, pymongo 3.0.3, redis 2.10.3, and scikit-learn 0. 16. 1. As these packages are all hosted on PyPI, the Python package index, they can e easily installed with pip. To install NumPy, you would write S pip install numpy If you are not using them already we suggest you take a look at virtual environments for managing isolating Python environment on your computer For Python 2, there are two packages of interest there: virtualenv and virtualenvwrapper. Since Python 3.3,thereisatoolinthestandardlibrarycalledpyvenv(https://docs.pythonorg/3/ library/venv. html), which serves the same purpose Most libraries will have an attribute for the version, so if you already have a library installed, you can quickly check its version: >importredis >>redis. version 2.10.3 This works well for most libraries. A few such as pymongo, use a different attribute (pymongo uses just version, without the underscores). While all the examples can be run interactively in a Python shell, we recommend using IPython IPython started as a more versatile Python shell, but has since evolved into a powerful tool for exploration and sharing. We used IPython 4.0.0 with Python 2.7.10. IPython is a great way to work interactively with Python, be it in the terminal or in the browser Module First, you need a Python 3 distribution. I recommend the full Anaconda distribution as it comes with the majority of the software we need i tested the code with python 3.4 and the following packages joblib 0.8.4 IPython 3.2.1 Preface Networkx 19.1 nlTK 3.0.2 Numexpr 2.3.1 pandas 0. 16.2 SciPv0.16.0 seaborn 0.6.0 alchemy 0.9.9 statsmodels 0.6.1 matplotlib 1.5.0 NumPy 1.10.1 scikit-learn 0.17 dautilo.0.1a29 For some recipes you need to install extra software but this is explained whenever the software is required Module 3 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, stats models, Py MC, 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 learning path is for This learning path is for developers, analysts, and data scientists who want to learn data analysis from scratch. This course will provide you with a solid foundation Python (and a strong interest in playing with your data)is recommende e ge of from which to analyze data with varying complexity. a working knowledg Preface Reader feedback Feedback from our readers is always welcome. let us know what you think about this course-what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of Tosendusgeneralfeedback,simplye-mailfeedback@packtpub.com,andmention the course's title in the subject of your message If there is a topic that you have expertise in and you are interested in either writing orcontributingtoabookseeourauthorguideatwww.packlpub.com/authors Customer support Now that you are the proud owner of a Packt course, we have a number of things to help you to get the most from your purchase Downloading the example code You can download the example code files for this course from your account at http://www.packtpub.comIfyoupurchasedthiscourseelsewhereyoucanvisit http://www.packtpub.com/supportandregistertohavethefilese-maileddirectly to you You can download the code files by following these steps Log in or register to our website using your e-mail address and password Hover the mouse pointer on the sUPPORt tab at the top Click on code downloads errata Enter the name of the course in the search box Select the course for which you're looking to download the code files Choose from the drop-down menu where you purchased this course from 7 Click on code download You can also download the code files by clicking on the Code files button on th course's webpage at the Packt Publishing website. This page can be accessed by entering the course's name in the Search box. Please note that you need to be logged into your packt account Preface Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of Winrar /7-Zip for Windows Zipeg/ izip/ UnRarX for mac 7-Zip/PeaZip for linux ThecodebundleforthecourseisalsohostedonGitllubathttps://github.com/ PacktPublishing/Python-End-to-end-Data-Analysis. We also have other code bundlesfromourrichcatalogofbooksvideosandcoursesavailableathttps:// github. com/PacktPublishing/ Check them out Errata Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you find a mistake in one of our courses-maybe a mistake in the text or the code-we would be grateful if you could report this to us. By doing sO,you can save other readers from frustration and help us improve subsequent versionsofthiscourseIfyoufindanyerratapleasereportthembyvisitinghttp:// www.packtpub.com/submit-errata,selectingyourcourseclickingontheerrata Submission Form link, and entering the details of your errata. Once your errata are verified your submission will be accepted and the errata will be uploaded to our website or added to any list of existing errata under the errata section of that title Toviewthepreviouslysubmittederratagotohttps://www.packtpub.com/books/ content/support and enter the name of the course in the search field. The required information will appear under the Errata section Prac cy Piracy of copyrighted material on the internet is an ongoing problem across all media. At Packt, we take the protection of our copyright and licenses very seriously If you come across any illegal copies of our works in any form on the Internet, please provide us with the location address or website name immediately so that we can pursue a remedy Preface Please contact us at copyright@packtpub com with a link to the suspected pirated materia We appreciate your help in protecting our authors and our ability to bring you valuable content Questions If you have a problem with any aspect of this course, you can contact us at questions@packtpub.com,andwewilldoourbesttoaddresstheprobler

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fab_lyd so good so useful for some field.
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