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Wes McKinney 著
出版社: 东南大学出版社 ISBN:9787564175191 版次:1 商品编码:12310353 包装:平装 外文名称:Python for Data Analysis 开本:16开 出版时间:2018-02-01 用纸:胶版纸
* 将IPython shell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特价值的多个领域,从Python 3.6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。
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
Python数据分析 第2版(影印版) [Python for Data Analysis] 电子书 下载 mobi epub pdf txt
Python数据分析 第2版(影印版) [Python for Data Analysis]-so88
Python数据分析 第2版(影印版) [Python for Data Analysis] pdf epub mobi txt 电子书 下载 2022
图书介绍
☆☆☆☆☆
||
Wes McKinney 著
出版社: 东南大学出版社 ISBN:9787564175191 版次:1 商品编码:12310353 包装:平装 外文名称:Python for Data Analysis 开本:16开 出版时间:2018-02-01 用纸:胶版纸
编辑推荐
获得关于用Python语言操纵、处理、清洗和压缩数据集的完整介绍。这本容易上手的指南第二版为Python 3.6而升级,其中包括一些实用的案例研究,展示了如何有效解决各种数据分析问题。你将从中学到新版pandas、NumPy、IPython和Jupyter的处理方法。
内容简介
本书由Python pandas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IPython shell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特价值的多个领域,从Python 3.6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。
作者简介
Wes McKinney是流行开源Python数据分析库pandas的创立者。他是一位公共演讲者和开源Python及C++开发者,活跃于Python数据科学社区和Apache软件基金会。他在纽约从事软件架构师工作。
目录
Preface1. 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
Python数据分析 第2版(影印版) [Python for Data Analysis] 电子书 下载 mobi epub pdf txt
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