Python数据分析实战指南：开源模块详解

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"Python Data Analysis 是一本由 Ivan Idris 所著的专业书籍，旨在向读者介绍如何利用流行的开源 Python 模块进行强大的数据分析。本书是 2014 年 Packt Publishing 出版的作品，版权受到严格保护，未经许可禁止任何形式的复制、存储或传播，除非是在批评性文章或评论中引用短小片段。《Python Data Analysis》的内容涵盖了数据处理、清洗、可视化以及挖掘等方面的核心技能，适合对数据分析感兴趣的Python开发者、数据科学家以及想要提升数据分析能力的专业人士。书中详细介绍了NumPy、Pandas、Matplotlib、Scikit-Learn等Python库，这些都是数据科学领域不可或缺的基础工具。作者通过实例和深入浅出的讲解，帮助读者掌握如何运用这些工具来处理大量数据，进行高效的数据操作，以及如何构建和优化数据分析模型。此外，书中还强调了版权法的重要性，提醒读者在使用第三方资源时必须尊重知识产权。尽管作者和出版商已尽力确保书中信息的准确性，但书中的内容并不保证在所有情况下都能达到预期效果，因为技术和市场环境可能随着时间变化。Packt Publishing 在提到商标时，尽可能地使用大写以表示尊重，但并不能保证所有信息的准确性。本书于2014年10月首次出版，生产参考编号为12110，表明它是一个经过精心策划和制作的产品。《Python Data Analysis》是一本实用且权威的指南，对于希望在Python数据分析领域深入学习的人来说，它是一份宝贵的参考资料。"

Preface

"Data analysis is Python's killer app."

– Unknown

Data analysis has a rich history in the natural, biomedical, and social sciences.

You may have heard of Big Data. Although, it's hard to give a precise denition

of Big Data, we should be aware of its impact on data analysis efforts. Currently,

we have the following trends associated with Big Data:

• The world's population continues to grow

• More and more data is collected and stored

• The number of transistors that can be put on a computer chip cannot

grow indenitely

• Governments, scientists, industry, and individuals have a growing

need to learn from data

Data analysis has gained popularity lately due to the hype around Data Science.

Data analysis and Data Science attempt to extract information from data. For that

purpose, we use techniques from statistics, machine learning, signal processing,

natural language processing, and computer science.

A mind map visualizing Python software that can be used for data analysis can be

found at

http://www.xmind.net/m/WvfC/

. The rst thing that we should notice

is that the Python ecosystem is very mature. It includes famous packages such as

NumPy, SciPy, and matplotlib. This should not come as a surprise since Python

has been around since 1989. Python is easy to learn and use, less verbose than other

programming languages, and very readable. Even if you don't know Python, you

can pick up the basics within days, especially if you have experience in another

programming language. To enjoy this book, you don't need more than the basics.

There are plenty of books, courses, and online tutorials that teach Python.

Preface

[

]

What this book covers

This book starts as a tutorial on NumPy, SciPy, matplotlib, and pandas. These are

open source Python packages useful for numerical work, data wrangling, and

visualization. Combined, they can compete with MATLAB, Mathematica, and R.

The second half of the book teaches more advanced topics such as signal processing,

databases, text analysis, machine learning, interoperability, and performance tuning.

Chapter 1, Getting Started with Python Libraries, guides us to achieve a successful

installation of the numerical Python software and set it up step by step. Also,

we will create a small application.

Chapter 2, NumPy Arrays, introduces us to NumPy fundamentals and arrays.

By the end of this chapter, we will have basic understanding of NumPy arrays

and the associated functions.

Chapter 3, Statistics and Linear Algebra, gives a quick overview of linear algebra

and statistical functions.

Chapter 4, pandas Primer, provides a tutorial on basic pandas functionality where

we learn about pandas data structures and operations.

Chapter 5, Retrieving, Processing, and Storing Data, explains how to acquire data in

various formats and how to clean raw data and store it.

Chapter 6, Data Visualization, teaches how to plot data with matplotlib.

Chapter 7, Signal Processing and Time Series, contains time series and signal processing

examples using sunspot cycles data. The examples mostly use NumPy/SciPy, along

with statsmodels in at least one example.

Chapter 8, Working with Databases, provides information about various databases

(relational and NoSQL) and related APIs.

Chapter 9, Analyzing Textual Data and Social Media, analyzes texts for sentiment

analysis and topics extraction. A small example is also given of network analysis.

Chapter 10, Predictive Analytics and Machine Learning, explains articial intelligence

with weather prediction as a running example and mostly uses scikit-learn.

However, some machine learning algorithms are not covered by scikit-learn,

so for those, we use other APIs.

Chapter 11, Environments Outside the Python Ecosystem and Cloud Computing,

gives various examples on how to integrate existing code not written in Python.

Also, setup in the Cloud will be demonstrated.

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