入门指南：利用Pandas进行高效数据分析

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"《Learning Pandas_2015》是一本面向Python程序员的数据分析入门书籍，特别适合希望利用pandas库进行数据处理、分析和发现的读者。尽管书中强调一些统计分析经验是有帮助的，但并非强制要求，因此即使是对数据分析初学者也是一份宝贵的资源。作者Michael Heydt以其深入浅出的方式，带领读者掌握这个强大且高效（versatile and high-performance）的Python库。该书详细介绍了pandas的核心概念，如Series和DataFrame的数据结构，数据清洗、合并、重塑、分组、排序等操作方法，以及如何使用pandas进行统计计算、时间序列分析和数据可视化。书中还涵盖了pandas与NumPy、SciPy等其他科学计算库的集成应用，以及如何在大型数据集上优化性能。版权方面，本书受到Packt Publishing的保护，未经出版商书面许可，不得复制、存储或通过任何形式或媒介传播，除非用于学术引用。尽管作者和出版社已尽力确保信息的准确性，但书中提供的所有内容均不带有任何明示或暗示的保修，也不承担因使用本书信息导致的直接或间接损失的责任。在版权信息中，Packt Publishing表示，他们已尽力提供关于书中提及的所有公司和产品的商标信息，但由于市场竞争和信息更新，可能无法保证其完全准确。《Learning Pandas_2015》是Python数据分析师不可或缺的参考资料，无论你是初学者还是进阶用户，都能从中获得实用的技巧和深入理解。"

[ vii ]

Preface

This book is about learning to use pandas, an open source library for Python, which

was created to enable Python to easily manipulate and perform powerful statistical

and mathematical analyses on tabular and multidimensional datasets. The design of

pandas and its power combined with the familiarity of Python have created explosive

growth in its usage over the last several years, particularly among nancial rms as

well as those simply looking for practical tools for statistical and data analysis.

While there exist many excellent examples of using pandas to solve many

domain-specic problems, it can be difcult to nd a cohesive set of examples

in a form that allows one to effectively learn and apply the features of pandas.

The information required to learn practical skills in using pandas is distributed

across many websites, slide shares, and videos, and is generally not in a form

that gives an integrated guide to all of the features with practical examples in

an easy-to-understand and applicable fashion.

This book is therefore intended to be a go-to reference for learning pandas. It will

take you all the way from installation, through to creating one- and two-dimensional

indexed data structures, to grouping data and slicing-and-dicing them, with common

analyses used to demonstrate derivation of useful results. This will include the

loading and saving of data from resources that are local and Internet-based and

creating effective data visualizations that provide instant ability to visually realize

insights into the meaning previously hidden within complex data.

What this book covers

Chapter 1, A Tour of pandas, is a hands-on introduction to the key features of pandas.

It will give you a broad overview of the types of data tasks that can be performed

with pandas. This chapter will set the groundwork for learning as all concepts

introduced in this chapter will be expanded upon in subsequent chapters.

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Preface

[ viii ]

Chapter 2, Installing pandas, will show you how to install Anaconda Python and pandas

on Windows, OS X, and Linux. This chapter also covers using the conda package

manager to upgrade pandas and its dependent libraries to the most recent version.

Chapter 3, NumPy for pandas, will introduce you to concepts in NumPy,

particularly NumPy arrays, which are core for understanding the pandas

Series and DataFrame objects.

Chapter 4, The pandas Series Object, covers the pandas

Series object and how it

expands upon the functionality of the NumPy array to provide richer representation

and manipulation of sequences of data through the use of high-performance indexes.

Chapter 5, The pandas DataFrame Object, introduces the primary data structure of

pandas, the

DataFrame object, and how it forms a two-dimensional representation of

tabular data by aligning multiple Series objects along a common index to provide

seamless access and manipulation across elements in multiple series that are related

by a common index label.

Chapter 6, Accessing Data, shows how data can be loaded and saved from external

sources into both

Series and DataFrame objects. You will learn how to access

data from multiple sources such as les, HTTP servers, database systems, and

web services, as well as how to process data in CSV, HTML, and JSON formats.

Chapter 7, Tidying Up Your Data, instructs you on how to use the various tools

provided by pandas for managing dirty and missing data.

Chapter 8, Combining and Reshaping Data, covers various techniques for combining,

splitting, joining, and merging data located in multiple pandas objects, and

then demonstrates how to reshape data using concepts such as pivots, stacking,

and melting.

Chapter 9, Grouping and Aggregating Data, focuses on how to use pandas to group data

to enable you to perform aggregate operations on grouped data to assist in deriving

analytic results.

Chapter 10, Time-series Data, will instruct you on how to use pandas to represent

sequences of information that is indexed by the progression of time. This chapter

will rst cover how pandas represents dates and time, as well as concepts such as

periods, frequencies, time zones, and calendars. The focus then shifts to time-series

data and various operations such as shifting, lagging, resampling, and moving

window operations.

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Preface

[ ix ]

Chapter 11, Visualization, dives into the integration of pandas with matplotlib to

visualize pandas data. This chapter will demonstrate how to represent and present

many common statistical and nancial data visualizations, including bar charts,

histograms, scatter plots, area plots, density plots, and heat maps.

Chapter 12, Applications to Finance, brings together everything learned through the

previous chapters with practical examples of using pandas to obtain, manipulate,

analyze, and visualize stock data.

What you need for this book

This book assumes some familiarity with programming concepts, but those

without programming experience, or specically Python programming experience,

will be comfortable with the examples as they focus on pandas constructs more

than Python or programming. The examples are based on Anaconda Python 2.7

and pandas 0.15.1. If you do not have either installed, guidance will be given in

Chapter 2, Installing pandas, on installing both on Windows, OS X, and Ubuntu

systems. For those not interested in installing any software, instructions are also

given on using the Warkari.io online Python data analysis service.

Who this book is for

If you are looking to get into data science and want to learn how to use the Python

programming language for data analysis instead of other domain-specic data

science tools such as R, then this book is for you. If you have used other data science

packages and want to learn how to apply that knowledge to Python, then this book

is also for you. Alternately, if you want to learn an additional tool or start with data

science to enhance your career, then this book is for you.

Conventions

In this book, you will nd a number of styles of text that distinguish between

different kinds of information. Here are some examples of these styles, and an

explanation of their meaning.

Code words in text are shown as follows: "This information can be easily imported

into

DataFrame using the pd.read_csv() function as follows."

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Preface

[ xi ]

Downloading the example code

You can download the example code les for all Packt books you have purchased

from your account at http://www.packtpub.com. If you purchased this book

elsewhere, you can visit http://www.packtpub.com/support and register to have

the les e-mailed directly to you. The code examples in the book are also publicly

available on Wakari.io at https://wakari.io/sharing/bundle/LearningPandas/

LearningPandas_Index

Although great efforts are taken to use data that will reproduce

the same output when you execute the samples, there is a

small set of code that uses current data and hence the result of

running those samples may vary from what is published in this

book. These include In [39]: and In [40]: in Chapter 1, A Tour of

pandas, which uses the data of the last three months of Google

stock, as well as a small number of samples used in the later

chapters that demonstrate the usage of date offsets centered on

the current date.

Downloading the color images of this book

We also provide you with a PDF le that has color images of the screenshots/diagrams

used in this book. The color images will help you better understand the changes in

the output. You can download this le from https://www.packtpub.com/sites/

default/files/downloads/5128OS_ColoredImages.pdf

Errata

Although we have taken every care to ensure the accuracy of our content, mistakes

do happen. If you nd a mistake in one of our books — maybe a mistake in the text or

the code — we would be grateful if you would report this to us. By doing so, you can

save other readers from frustration and help us improve subsequent versions of this

book. If you nd any errata, please report them by visiting http://www.packtpub.

com/support

, selecting your book, clicking on the errata submission form link, and

entering the details of your errata. Once your errata are veried, 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.

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