Python办公自动化脚本实战指南

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《Python for Data Analysis》是一本由Wes McKinney撰写的实用指南，专为数据分析师和爱好者设计，它在办公自动化领域尤其有用。本书是2013年出版的第一版，版权由Wes McKinney持有，并获得了全面的全球发行权，包括美国、北京、剑桥、法纳姆、科隆、塞巴斯托波尔和东京等地。O'Reilly Media出版社出版，强调了教育、商业或销售推广用途，电子版也广泛提供。该书的核心内容围绕Python语言在数据分析中的应用，Python因其强大的数据处理能力、简洁的语法以及丰富的库（如Pandas和NumPy）而备受推崇。通过阅读这本书，读者可以学习如何使用Python进行数据清洗、数据转换、数据分析和可视化，从而提高工作效率。McKinney以其深入浅出的方式讲解了如何利用Python解决实际问题，无论是初学者还是进阶用户都能从中获益匪浅。书中涵盖了以下关键知识点： 1. **Pandas库**：Pandas是Python中用于数据操作和分析的核心库，它提供了高效的数据结构DataFrame和Series，以及一系列强大的数据处理函数，如合并、重塑、分组和过滤等。 2. **数据清洗**：作者会介绍如何处理缺失值、异常值、重复值等问题，确保数据质量，这对于数据科学家来说是至关重要的预处理步骤。 3. **数据导入与导出**：书中会展示如何从各种数据源（如CSV、Excel、SQL数据库等）读取数据，并将其保存到不同的格式，如JSON或HDF5。 4. **数据可视化**：Python的数据可视化工具如Matplotlib和Seaborn将教授读者如何创建专业的图表和图形，以有效地呈现数据故事。 5. **高级数据分析技术**：涵盖统计分析、时间序列分析、数据建模以及机器学习的基础，帮助读者深入了解数据背后的模式和规律。 6. **实践案例与示例**：书中包含大量实战项目，让读者通过实际操作加深理解和应用所学知识。 7. **代码优化与性能调优**：探讨如何优化代码以提高计算效率，尤其是在大数据处理场景下的性能改进策略。 8. **最新技术和最佳实践**：随着Python生态系统的发展，书中会同步更新最新的库和工具，确保读者跟上数据分析领域的前沿动态。《Python for Data Analysis》不仅是一本教程，还是一本实用手册，它引导读者通过Python探索数据世界，提升数据分析技能，是每个数据工作者或希望转型数据分析的个人的理想选择。如果你正在寻找一本全面且深入的Python数据分析入门书籍，这本书无疑是你不可或缺的参考资料。

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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