Python数据可视化宝典：60个实战配方引领你的大数据分析之旅

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《Python Data Visualization Cookbook》是一本专为Python开发者设计的数据可视化指南，由Igor Milovanović编著，由BIRMINGHAM-MUMBAI出版社出版。该书于2013年版权保护，旨在帮助读者通过Python中最流行的库学习如何创建吸引人的数据可视化图表。书中包含超过60个实用的配方，涵盖了从基础到高级的技术，让读者能够深入理解和掌握Python在大数据分析领域的视觉表达能力。本书的核心知识点包括但不限于： 1. Python数据科学库：书中详细介绍了NumPy、Pandas、Matplotlib、Seaborn、Plotly、Bokeh等Python主要数据处理和可视化库的使用方法，这些库是数据科学家和分析师日常工作的核心工具。 2. 数据清洗与预处理：讲解如何清洗和整理数据，使其适合用于可视化，包括缺失值处理、数据类型转换、数据重塑等。 3. 统计图形：涵盖了直方图、折线图、散点图、柱状图、饼图等各种基本图形的制作技巧，以及如何使用这些图形展示趋势、比较和关联性。 4. 高级可视化：涉及交互式可视化、地图绘制、热力图、网络图等复杂主题，让读者能创建动态和具有深度的可视化作品。 5. 仪表板和报告：学习如何创建和部署交互式仪表板，以便将多个图表组合成一个可视化的报告，提高决策支持的效率。 6. 数据故事叙述：书中还强调了如何通过数据可视化讲述故事，引导读者理解如何选择合适的图表类型和布局，以有效地传达信息。 7. 实践案例：每条配方都配有详细的步骤和示例代码，确保读者能够通过实际操作加深理解，并能在项目中快速应用所学知识。 8. 代码优化与性能：探讨如何优化可视化过程，提高代码执行效率，尤其是在大数据集上的性能提升策略。尽管这本书提供了丰富的信息，但需注意的是，版权法规定未经版权所有者Packt Publishing事先书面许可，不能复制、存储或通过任何方式传播书中的内容。同时，作者和出版商不对由于本书内容导致的直接或间接损失承担法律责任。《Python Data Visualization Cookbook》是一本实用且全面的数据可视化指南，适合Python初学者和进阶者深入学习和实践，提升在数据分析领域的可视化技能。

Preface

Code words in text are shown as follows: "We packed our little demo in class DemoPIL,

so that we can extend it easily, while sharing the common code around the demo function,

run_fixed_filters_demo."

A block of code is set as follows:

def _load_image(self, imfile):

self.im = mplimage.imread(imfile)

When we wish to draw your attention to a particular part of a code block, the relevant lines or

items are set in bold:

# tidy up tick labels size

all_axes = plt.gcf().axes

for ax in all_axes:

for ticklabel in ax.get_xticklabels() + ax.get_yticklabels():

ticklabel.set_fontsize(10)

Any command-line input or output is written as follows:

$ sudo python setup.py install

New terms and important words are shown in bold. Words that you see on the screen, in

menus or dialog boxes for example, appear in the text like this: "We then set up a label for

the stem plot and the position of baseline, which defaults to 0."

Warnings or important notes appear in a box like this.

Tips and tricks appear like this.

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Preface

If there is a topic that you have expertise in and you are interested in either writing or

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Preparing Your Working Environment

Installing matplotlib, NumPy, and SciPy

This chapter describes several ways of installing matplotlib and required dependencies

under Linux.

Getting ready

We assume that you already have Linux (preferably Debian/Ubuntu or RedHat/SciLinux)

installed and Python installed on it. Usually, Python is already installed on the mentioned

Linux distributions and, if not, it is easily installable through standard means. We assume

that Python 2.7+ Version is installed on your workstation.

Almost all code should work with Python 3.3+ Versions, but because most

operating systems still deliver Python 2.7 (some even Python 2.6) we decided

to write the Python 2.7 Version code. The differences are small, mainly in

version of packages and some code (xrange should be substituted with range

in Python 3.3+).

We also assume that you know how to use your OS package manager in order to install

software packages and know how to use a terminal.

Build requirements must be satised before matplotlib can be built.

matplotlib requires NumPy, libpng, and freetype as build dependencies. In order to be able to

build matplotlib from source, we must have installed NumPy. Here's how to do it:

Install NumPy (at least 1.4+, or 1.5+ if you want to use it with Python 3) from

http://www.numpy.org/.

NumPy will provide us with data structures and mathematical functions for

using it with large datasets. Python's default data structures such as tuples,

lists, or dictionaries are great for insertions, deletions, and concatenation.

NumPy's data structures support "vectorized" operations and are very efcient

for use and for executions. They are implemented with Big Data in mind and

rely on C implementations that allow efcient execution time.

SciPy, building on top of NumPy, is the de facto standard's scientic and

numeric toolkit for Python comprising great selection of special functions and

algorithms, most of them actually implemented in C and Fortran, coming from

the well-known Netlib repository (see http://www.netlib.org).

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