Python数据分析实战

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"Python for Data Analysis 是一本由 Wes McKinney 编写的英文书籍，专注于使用 Python 进行数据处理、操作、清洗和可视化。本书旨在帮助读者掌握如何利用 Python 库，如 Pandas、NumPy 和 Matplotlib 等，有效地进行数据分析工作。" 在 Python for Data Analysis 一书中，Wes McKinney 深入探讨了 Python 在数据科学领域的应用，特别是针对那些在实际工作中需要处理大量数据的数据分析师。这本书主要涵盖以下几个关键知识点： 1. **Pandas库**：Pandas 是 Python 中最常用的数据分析库，提供了高效的数据结构，如 DataFrame 和 Series。书中详细介绍了如何创建、操作和理解这些数据结构，以及如何进行数据清洗和预处理。 2. **数据操作**：包括数据的合并（merge）、拼接（concatenate）、重塑（reshape）、切片（slicing）等，这些都是数据分析中不可或缺的步骤。McKinney 阐述了如何在实际场景中灵活运用这些操作。 3. **数据清洗**：书中详细讲解了处理缺失值、异常值和重复值的方法，以及数据类型转换等数据预处理技巧。 4. **统计分析**：Python 提供了丰富的统计函数，如描述性统计、假设检验、回归分析等。本书会介绍如何利用这些工具进行统计建模和分析。 5. **时间序列分析**：由于数据往往包含时间信息，书中专门讨论了时间序列数据的处理，包括日期和时间的处理、频率转换、滑动窗口统计等。 6. **数据可视化**：通过 Matplotlib 和 Seaborn 库，读者将学习如何创建直观且美观的数据图表，如折线图、柱状图、散点图和热力图，以帮助理解和展示数据。 7. **数据导入与导出**：书中涵盖了多种数据格式的读取和写入，如 CSV、Excel、SQL 数据库等，以及如何使用 pandas 与其他数据源（如 Hadoop 或大数据平台）进行交互。 8. **性能优化**：随着数据规模的增长，性能优化变得至关重要。书中将介绍如何利用 NumPy 数组运算提升效率，以及如何利用 Dask 等并行计算库处理大规模数据。 9. **案例研究**：通过真实的数据集和案例，读者可以学习到如何将所学知识应用于实际数据分析项目。 10. **最佳实践**：作者分享了自己的经验，提供了一些在实际数据分析工作中应该遵循的最佳实践和技巧。 Python for Data Analysis 是一本面向实践的数据科学家和工程师的指南，它不仅教授了 Python 数据分析的基本技能，还强调了如何解决实际问题和提高工作效率。无论是对初学者还是有经验的开发者，这本书都是一份宝贵的参考资料。

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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

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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

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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.

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