Python数据分析 第2版
作者： McKinney
出版社：东南大学出版社 2018年07月
简介：
本书由Pythopandas项目的创立者Wes McKinney撰写，是一本实用、现代的Python数据科学工具读物，适合新入门的Python分析师和刚接触数据科学及科学计算的Python程序员。数据文件和相关材料在Github上可以获得。
* 将IPythoshell和Jupyter Notebook用于探索式计算
* 学习NumPy(Numerical Python)的基础和高级特性
* 通过pandas库中的数据分析工具入门
* 使用灵活的工具装载、清洗、转换、合并和整形数据
* 用matplotlib创建信息可视化
* 应用pandas groupby功能将数据集切片、切块和汇总
* 分析和操纵规整和不规整时间序列数据
* 通过全面详细的实例学习如何解决真实世界的数据分析问题
“作为在Python数据生态中已成经典的著作，这本新版更新了能提升其独值的多个领域，从Pytho3.6到新的pandas特性。通过阐释Python数据工具的原理和方法，本书帮助读者以新颖而富有创造性的途径学习如何有效利用它们。这是任何现代数据密集型计算库的关键部分。
【目录】
Preface
1. Preliminaries
1.1 What Is This Book About?
What Kinds of Data?
1.2 Why Python for Data Analysis?
Python as Glue
Solving the "Two-Language" Problem
Why Not Python?
1.3 Essential Python Libraries
NumPy
pandas
matplotlib
IPython and Jupyter
SciPy
scikit-learn
statsmodels
1.4 Installation and Setup
Windows
Apple （OS X， macOS）
GNU/Linux
Installing or Updating Python Packages
Python 2 and Python 3
Integrated Development Environments （IDEs） and Text Editors
1.5 Community and Conferences
1.6 Navigating This Book
Code Examples
Data for Examples
Import Conventions
Jargon
2. Python Language Basics， IPython， and Jupyter Notebooks
2.1 The Python Interpreter
2.2 IPython Basics
Running the IPython Shell
Running the Jupyter Notebook
Tab Completion
Introspection
The %run Command
Executing Code from the Clipboard
Terminal Keyboard Shortcuts
About Magic Commands
Matplotlib Integration
2.3 Python Language Basics
Language Semantics
Scalar Types
Control Flow
3. Built-in Data Structures， Functions， and Files
3.1 Data Structures and Sequences
Tuple
List
Built-in Sequence Functions
dict
set
List， Set， and Dict Comprehensions
3.2 Functions
Namespaces， Scope， and Local Functions
Returning Multiple Values
Functions Are Objects
Anonymous （Lambda） Functions
Currying： Partial Argument Application
Generators
Errors and Exception Handling
3.3 Files and the Operating System
Bytes and Unicode with Files
3.4 Conclusion
4. NumPy Basics： Arrays and Vectorized Computation
4.1 The NumPy ndarray： A Multidimensional Array Object
5. Getting Started with pandas.
6. Data Loading， Storage， and File Formats
7. Data Cleanincl and Preparation.
8. Data Wrangling： Join， Combine， and Reshape.
9. Plotting and Visualization.
10. Data Aggregation and Group Operations.
11. Time Series
12. Advanced pandas
13. Introduction to Modeling Libraries in Python
14. Data Analysis Examples
A. Advanced NumPy.
B. More on the IPython System
