Python数据分析入门指南

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"Python电子书教程，适合初学者学习Python" 本书《Python for Data Analysis》是Wes McKinney所著，由O'Reilly Media出版，针对的是想要掌握Python数据处理的初学者。书中深入探讨了Python在数据分析领域的应用，提供了一个全面的指南，帮助读者理解和运用Python工具解决实际的数据分析问题。 Python作为一门易于学习且功能强大的编程语言，被广泛应用于数据科学、机器学习和大数据处理等领域。这本书特别适合那些没有编程背景但对数据感兴趣的读者，同时也适用于有一定Python基础，希望提升数据分析能力的读者。书中可能涵盖以下关键知识点： 1. **Python基础**：介绍Python的基本语法，包括变量、数据类型（如列表、元组、字典和集合）、控制结构（如if语句和循环）、函数以及模块的使用。 2. **NumPy**：Python中的科学计算库，用于处理大型多维数组和矩阵，支持各种数学运算。NumPy是数据分析的基础，书中会详细讲解其核心概念和操作。 3. **Pandas**：Pandas是Python中用于数据操作和分析的库，提供了高效的数据结构DataFrame和Series。书中将讲解如何读取和写入数据，数据清洗，数据合并，时间序列分析等实用技巧。 4. **数据清洗和预处理**：数据往往不完美，书中会教读者如何处理缺失值、异常值，以及进行数据转换和规范化。 5. **数据分析工具**：涵盖统计分析的基础知识，如描述性统计、假设检验、回归分析等，并演示如何在Python中实现这些分析。 6. **数据可视化**：利用Matplotlib、Seaborn等库进行数据可视化，帮助读者更好地理解数据并传达分析结果。 7. **数据导入与导出**：学习如何从CSV、Excel、数据库等多种来源导入数据，以及如何将处理后的数据保存为合适格式。 8. **性能优化**：讨论如何处理大规模数据集，包括内存管理和并行计算策略。 9. **案例研究**：通过真实世界的数据分析项目，展示Python在实际工作中的应用，帮助读者巩固理论知识并提升实战能力。这本书是Python数据分析领域的重要参考资料，不仅适合自学，也适合作为教学用书。通过阅读，读者可以系统地学习Python数据分析的各个方面，从而提升自己的数据处理技能。

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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