Python数据分析实战指南：pandas作者麦金尼力作

需积分: 50 45 浏览量更新于2024-07-19 收藏 16.02MB PDF 举报

《Python数据分析(影印版)》是由Wes McKinney编著的一本专业书籍，McKinney本人是知名Python数据分析库pandas的主要开发者。这本书籍在IT领域中占有重要地位，尤其对于那些想要利用Python进行数据处理、科学计算和数据分析的初学者以及寻求转型为数据密集型应用的Python程序员来说，是一本极具实践价值的指南。本书的核心内容围绕Python在数据分析中的应用展开，涵盖了从基础到进阶的知识点，包括数据清洗、数据转换、数据探索、数据可视化，以及与Pandas库深度集成的各种操作。读者可以通过这本书学习如何有效地处理结构化数据，如CSV、Excel和SQL数据库，掌握使用Pandas进行高效数据操作的方法，如DataFrame和Series等核心数据结构的使用技巧。 Wes McKinney以其丰富的经验，将理论与实际案例相结合，使得学习者能够迅速理解和掌握Python数据分析的实战技能。此外，书中还强调了Python在科学计算中的灵活性和可扩展性，帮助读者构建自己的数据分析工具链。《Python for Data Analysis》不仅适合数据分析新手入门，也适合有一定Python基础的开发人员进一步提升数据分析能力。它不仅提供了一个全面的教程，而且提供了许多实用的例子和代码示例，便于读者在实践中快速上手并解决实际问题。值得注意的是，本书的版权信息显示于2013年首次出版，但该书持续更新和维护，以适应不断发展的数据科学领域。如果你需要获取最新的版本或查看修订历史，可以访问在线资源如www.it-ebooks.info，或者访问O'Reilly Media的官方网站获取最新资讯和电子版内容。《Python数据分析(影印版)》是一本不可或缺的数据分析教材，无论是为了提升个人职业技能还是推动团队在大数据分析项目中的工作，都能从中受益匪浅。通过深入理解这本书，读者将能熟练运用Python进行高效、灵活的数据处理和分析工作。

www.it-ebooks.info

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

www.it-ebooks.info

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

www.it-ebooks.info

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

www.it-ebooks.info

剩余469页未读，继续阅读

libraacrab

粉丝: 0
资源: 3

Python数据分析实战指南：pandas作者麦金尼力作

Python数据分析英文版

Python数据分析与挖掘实战.zip_Python 数据挖掘_python_python 案例_python数据分析_挖掘实战

Python进行数据分析完整版.pdf_python_数据分析_

python数据分析 第二版 python for data analysis

python数据分析课程设计

python数据分析pandas教程

Python数据分析 PPT课件

Python数据分析常用方法手册

python 数据分析与可视化

Python数据分析与应用资源

最新资源

python数据分析第二版 python for data analysis