Python数据分析：Wes McKinney著

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"Addison Wesley - Programming In Python 3 2E (2012)" 和 "Oreilly.Python.for.Data.Analysis." 这两本书分别聚焦于Python 3编程和Python在数据分析领域的应用。"Programming In Python 3 2E" 是Addison Wesley出版社出版的第二版Python 3编程教程，由经验丰富的程序员撰写，旨在帮助读者深入理解Python 3语言的核心概念和语法。它涵盖了从基础语法如变量、控制流到高级特性如异常处理、模块化编程等全面的知识，旨在使读者能够熟练地运用Python 3进行软件开发。而"Python for Data Analysis"是O'Reilly Media出版的一本专著，作者是Wes McKinney，他是pandas库的主要开发者之一。这本书主要面向数据科学家和分析师，详细介绍了如何利用Python进行数据清洗、操作、探索和可视化。pandas是Python中一个强大的数据处理库，它提供了高效的数据结构，如DataFrame，使得处理大型数据集变得简单易行。书中讨论的关键知识点包括： 1. **Python 3基础知识**：包括变量、数据类型（如整型、浮点型、字符串、列表、元组、字典和集合）、控制结构（如if语句和循环）、函数定义与调用、类和对象等。 2. **pandas库**：介绍DataFrame和Series对象，以及它们的创建、索引、切片和操作方法。此外，还包括数据合并、分组、排序和缺失值处理。 3. **数据清洗**：讲解如何处理不完整或不一致的数据，如缺失值的填充、异常值的识别和处理，以及数据类型转换。 4. **数据分析**：涵盖统计分析的基础知识，如描述性统计量、假设检验和推断统计。 5. **数据操作**：介绍如何使用pandas进行复杂的数据重塑，如透视表、时间序列分析和数据聚合。 6. **数据可视化**：使用matplotlib和seaborn库创建数据图表，包括散点图、直方图、箱线图等，以帮助理解数据分布和关系。 7. **数据输入和输出**：学习读取和写入各种数据格式，如CSV、Excel、SQL数据库等。 8. **集成其他库**：讨论如何与其他Python库（如NumPy、SciPy、scikit-learn等）结合使用，进行更深入的数据科学工作，如数值计算、机器学习和优化算法。 9. **性能优化**：探讨如何通过适当的方法和工具提高代码执行效率，例如使用Dask进行并行计算。 10. **实战案例**：书中可能包含实际的数据分析项目，展示如何将所学应用于解决真实世界的问题。这两本书对于想要学习Python 3编程或提升数据处理能力的人来说，都是宝贵的资源。无论是初学者还是有经验的开发者，都可以从中获取到有价值的知识和技巧。

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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