Python数据分析实战指南：Wes McKinney解密

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《Python for Data Analysis》是一本由Wes McKinney编著的专业书籍，专注于使用Python进行数据分析。该书是2013年出版的第一版，版权归Wes McKinney所有，享有保留权利。本书在美国印刷，由O'Reilly Media公司发行，该公司位于加利福尼亚州塞巴斯托波尔，提供在线版本供读者选择。读者可以通过800-998-9938或corporate@oreilly.com了解更多关于教育、商业或销售推广用途的购买信息。编辑方面，Julie Steele和Meghan Blanchette共同负责本书的内容编辑，Melanie Yarbrough担任生产编辑，Teresa Exley负责校对工作，而BIM Publishing Services负责了索引制作。封面设计由Karen Montgomery操刀，内部设计则由David Futato负责，Rebecca Demarest负责插图。这本书首次发布于2012年10月5日，并在O'Reilly官方网站上提供了错误修正记录。《Python for Data Analysis》旨在帮助读者掌握Python在数据处理、清洗、转换和探索方面的强大功能，以及如何构建复杂的数据分析项目。书中涵盖了Pandas库（由作者创建）的核心概念，Pandas是Python中最常用的数据分析工具之一，以其高效的数据结构DataFrame和Series著称，能够轻松处理大量数据。此外，书中还可能涉及NumPy、Matplotlib、SciPy等其他与数据分析相关的Python库，以及如何利用这些工具进行统计分析、数据可视化和数据挖掘。书中内容包括但不限于：如何导入和清洗数据，数据重塑与整合，数据聚合与分组，时间序列分析，数据透视表的创建，缺失值处理，数据可视化，以及如何将数据导出到不同的格式，如CSV、Excel、SQL数据库等。对于初学者和经验丰富的数据分析人员来说，这都是一本深入理解Python数据分析技术的实用指南。通过阅读这本书，读者不仅能提升Python编程技能，还能学习到如何在实际工作中高效地解决数据分析问题，无论是数据科学竞赛、商业报告还是学术研究，都能从中获益匪浅。因此，《Python for Data Analysis》不仅是一本技术手册，也是一本持续学习和进阶的数据分析教程。

Why Python for Data Analysis?

For many people (myself among them), the Python language is easy to fall in love with.

Since its first appearance in 1991, Python has become one of the most popular dynamic,

programming languages, along with Perl, Ruby, and others. Python and Ruby have

become especially popular in recent years for building websites using their numerous

web frameworks, like Rails (Ruby) and Django (Python). Such languages are often

called scripting languages as they can be used to write quick-and-dirty small programs,

or scripts. I don’t like the term “scripting language” as it carries a connotation that they

cannot be used for building mission-critical software. Among interpreted languages

Python is distinguished by its large and active scientific computing community. Adop-

tion of Python for scientific computing in both industry applications and academic

research has increased significantly since the early 2000s.

For data analysis and interactive, exploratory computing and data visualization, Python

will inevitably draw comparisons with the many other domain-specific open source

and commercial programming languages and tools in wide use, such as R, MATLAB,

SAS, Stata, and others. In recent years, Python’s improved library support (primarily

pandas) has made it a strong alternative for data manipulation tasks. Combined with

Python’s strength in general purpose programming, it is an excellent choice as a single

language for building data-centric applications.

Python as Glue

Part of Python’s success as a scientific computing platform is the ease of integrating C,

C++, and FORTRAN code. Most modern computing environments share a similar set

of legacy FORTRAN and C libraries for doing linear algebra, optimization, integration,

fast fourier transforms, and other such algorithms. The same story has held true for

many companies and national labs that have used Python to glue together 30 years’

worth of legacy software.

Most programs consist of small portions of code where most of the time is spent, with

large amounts of “glue code” that doesn’t run often. In many cases, the execution time

of the glue code is insignificant; effort is most fruitfully invested in optimizing the

computational bottlenecks, sometimes by moving the code to a lower-level language

like C.

In the last few years, the Cython project (http://cython.org) has become one of the

preferred ways of both creating fast compiled extensions for Python and also interfacing

with C and C++ code.

Solving the “Two-Language” Problem

In many organizations, it is common to research, prototype, and test new ideas using

a more domain-specific computing language like MATLAB or R then later port those

2 | Chapter 1: Preliminaries

ideas to be part of a larger production system written in, say, Java, C#, or C++. What

people are increasingly finding is that Python is a suitable language not only for doing

research and prototyping but also building the production systems, too. I believe that

more and more companies will go down this path as there are often significant organ-

izational benefits to having both scientists and technologists using the same set of pro-

grammatic tools.

Why Not Python?

While Python is an excellent environment for building computationally-intensive sci-

entific applications and building most kinds of general purpose systems, there are a

number of uses for which Python may be less suitable.

As Python is an interpreted programming language, in general most Python code will

run substantially slower than code written in a compiled language like Java or C++. As

programmer time is typically more valuable than CPU time, many are happy to make

this tradeoff. However, in an application with very low latency requirements (for ex-

ample, a high frequency trading system), the time spent programming in a lower-level,

lower-productivity language like C++ to achieve the maximum possible performance

might be time well spent.

Python is not an ideal language for highly concurrent, multithreaded applications, par-

ticularly applications with many CPU-bound threads. The reason for this is that it has

what is known as the global interpreter lock (GIL), a mechanism which prevents the

interpreter from executing more than one Python bytecode instruction at a time. The

technical reasons for why the GIL exists are beyond the scope of this book, but as of

this writing it does not seem likely that the GIL will disappear anytime soon. While it

is true that in many big data processing applications, a cluster of computers may be

required to process a data set in a reasonable amount of time, there are still situations

where a single-process, multithreaded system is desirable.

This is not to say that Python cannot execute truly multithreaded, parallel code; that

code just cannot be executed in a single Python process. As an example, the Cython

project features easy integration with OpenMP, a C framework for parallel computing,

in order to to parallelize loops and thus significantly speed up numerical algorithms.

Essential Python Libraries

For those who are less familiar with the scientific Python ecosystem and the libraries

used throughout the book, I present the following overview of each library.

Essential Python Libraries | 3

NumPy

NumPy, short for Numerical Python, is the foundational package for scientific com-

puting in Python. The majority of this book will be based on NumPy and libraries built

on top of NumPy. It provides, among other things:

• A fast and efficient multidimensional array object ndarray

• Functions for performing element-wise computations with arrays or mathematical

operations between arrays

• Tools for reading and writing array-based data sets to disk

• Linear algebra operations, Fourier transform, and random number generation

• Tools for integrating connecting C, C++, and Fortran code to Python

Beyond the fast array-processing capabilities that NumPy adds to Python, one of its

primary purposes with regards to data analysis is as the primary container for data to

be passed between algorithms. For numerical data, NumPy arrays are a much more

efficient way of storing and manipulating data than the other built-in Python data

structures. Also, libraries written in a lower-level language, such as C or Fortran, can

operate on the data stored in a NumPy array without copying any data.

pandas

pandas provides rich data structures and functions designed to make working with

structured data fast, easy, and expressive. It is, as you will see, one of the critical in-

gredients enabling Python to be a powerful and productive data analysis environment.

The primary object in pandas that will be used in this book is the DataFrame, a two-

dimensional tabular, column-oriented data structure with both row and column labels:

>>> frame

total_bill tip sex smoker day time size

1 16.99 1.01 Female No Sun Dinner 2

2 10.34 1.66 Male No Sun Dinner 3

3 21.01 3.5 Male No Sun Dinner 3

4 23.68 3.31 Male No Sun Dinner 2

5 24.59 3.61 Female No Sun Dinner 4

6 25.29 4.71 Male No Sun Dinner 4

7 8.77 2 Male No Sun Dinner 2

8 26.88 3.12 Male No Sun Dinner 4

9 15.04 1.96 Male No Sun Dinner 2

10 14.78 3.23 Male No Sun Dinner 2

pandas combines the high performance array-computing features of NumPy with the

flexible data manipulation capabilities of spreadsheets and relational databases (such

as SQL). It provides sophisticated indexing functionality to make it easy to reshape,

slice and dice, perform aggregations, and select subsets of data. pandas is the primary

tool that we will use in this book.

4 | Chapter 1: Preliminaries

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