Python数据分析入门

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"Python For Data Analysis 是一本由Wes McKinney编写的关于使用Python进行数据分析的书籍，涵盖了Python在数据处理、操作、清洗、聚合、可视化等领域的应用。本书旨在帮助读者掌握如何有效地利用Python工具包解决实际数据分析问题。" Python在数据科学领域的应用日益广泛，而《Python for Data Analysis》正是针对这一主题的权威指南。作者Wes McKinney是Pandas库的主要开发者，Pandas是Python中用于数据分析的核心库。书中深入浅出地介绍了如何利用Python进行数据处理和分析，尤其强调了Pandas库的使用方法。该书内容丰富，包括以下几个主要知识点： 1. 数据结构：书中详细讲解了Pandas的DataFrame和Series数据结构，这两个结构是进行数据处理的基础。DataFrame类似于电子表格或SQL表，而Series则类似于一维数组。 2. 数据清洗：书中介绍了如何处理缺失值、重复值、异常值，以及数据类型转换等常见的数据清洗问题。 3. 数据操作：讨论了合并、连接、分组、排序和切片等数据操作技巧，帮助读者高效地处理大型数据集。 4. 时间序列分析：由于时间序列数据在许多领域都很常见，因此书中专门有一章介绍如何使用Pandas处理时间序列数据。 5. 数据聚合和分组：通过示例展示了如何使用Pandas的groupby函数进行数据聚合，以及计算统计量。 6. 数据重塑和透视：介绍了pivot_table功能，让数据以不同的方式排列，以揭示隐藏的模式。 7. 数据可视化：探讨了使用Matplotlib和Seaborn库进行数据可视化的技术，包括制作折线图、柱状图、散点图等。 8. 数据集成：讨论了如何与其他数据源（如Excel、SQL数据库、CSV文件）进行交互，以及如何读取和写入数据。 9. 性能优化：提供了关于如何优化代码以处理大规模数据集的建议。 10. 进阶话题：书中还涉及了一些高级主题，如并行计算和分布式系统，以及如何将Python与Hadoop等大数据框架结合。此外，书中还包括了大量的实战案例，覆盖了从导入数据到得出结论的完整分析流程，让读者能够边学边练，提高实际操作能力。这本书对于想要提升Python数据分析技能的初学者和专业人士来说都是宝贵的资源。

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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Python数据分析入门

《Python For Data Analysis》数据集

PDF Python for Data Analysis. 中文+英文+pdf

有关python大数据分析技术的文献及其作者和出处

C:\Users\86152\AppData\Local\Programs\Python\Python39\python.exe

["data.name = 'test2'", 'data.sex = 1']这种格式通过python 拿到data.name

C:\Users\lee\AppData\Local\Programs\Python\Python37\python.exe'

No Python at 'C:\Users\zhr\AppData\Local\Programs\Python\Python39\python.exe'

用python随机生成data.txt

python中的data.sample怎么用

利用 python 讀取 data1.txt 的內容，並輸出以下含冒泡排序

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