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Python for Data Analysis

Python for Data Analysis

Wesly McKinney
数据分析 Python
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    这本书主要是用 pandas 连接 SciPy 和 NumPy,用pandas做数据处理是Pycon2012上一个很热门的话题。另一个功能强大的东西是Sage,它将很多开源的软件集成到统一的 Python 接口。    Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.    Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientific computing.    Use the IPython interactive shell as your primary development environment    Learn basic and advanced NumPy (Numerical Python) features    Get started with data analysis tools in the pandas library    Use high-performance tools to load, clean, transform, merge, and reshape data    Create scatter plots and static or interactive visualizations with matplotlib    Apply the pandas groupby facility to slice, dice, and summarize datasets    Measure data by points in time, whether it’s specific instances, fixed periods, or intervals    Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

Chapter 1 Preliminaries   
What Is This Book About?   
Why Python for Data Analysis?   
Essential Python Libraries   
Installation and Setup   
Community and Conferences   
Navigating This Book   
Chapter 2 Introductory Examples data from   
MovieLens 1M Data Set   
US Baby Names 1880-2010   
Conclusions and The Path Ahead   
Chapter 3 IPython: An Interactive Computing and Development Environment   
IPython Basics   
Using the Command History   
Interacting with the Operating System   
Software Development Tools   
IPython HTML Notebook   
Tips for Productive Code Development Using IPython   
Advanced IPython Features   
Chapter 4 NumPy Basics: Arrays and Vectorized Computation   
The NumPy ndarray: A Multidimensional Array Object   
Universal Functions: Fast Element-wise Array Functions   
Data Processing Using Arrays   
File Input and Output with Arrays   
Linear Algebra   
Random Number Generation   
Example: Random Walks   
Chapter 5 Getting Started with pandas   
Introduction to pandas Data Structures   
Essential Functionality   
Summarizing and Computing Descriptive Statistics   
Handling Missing Data   
Hierarchical Indexing   
Other pandas Topics   
Chapter 6 Data Loading, Storage, and File Formats   
Reading and Writing Data in Text Format   
Binary Data Formats   
Interacting with HTML and Web APIs   
Interacting with Databases   
Chapter 7 Data Wrangling: Clean, Transform, Merge, Reshape   
Combining and Merging Data Sets   
Reshaping and Pivoting   
Data Transformation   
String Manipulation   
Example: USDA Food Database   
Chapter 8 Plotting and Visualization   
A Brief matplotlib API Primer   
Plotting Functions in pandas   
Plotting Maps: Visualizing Haiti Earthquake Crisis Data   
Python Visualization Tool Ecosystem   
Chapter 9 Data Aggregation and Group Operations   
GroupBy Mechanics   
Data Aggregation   
Group-wise Operations and Transformations   
Pivot Tables and Cross-Tabulation   
Example: 2012 Federal Election Commission Database   
Chapter 10 Time Series   
Date and Time Data Types and Tools   
Time Series Basics   
Date Ranges, Frequencies, and Shifting   
Time Zone Handling   
Periods and Period Arithmetic   
Resampling and Frequency Conversion   
Time Series Plotting   
Moving Window Functions   
Performance and Memory Usage Notes   
Chapter 11 Financial and Economic Data Applications   
Data Munging Topics   
Group Transforms and Analysis   
More Example Applications   
Chapter 12 Advanced NumPy   
ndarray Object Internals   
Advanced Array Manipulation   
Advanced ufunc Usage   
Structured and Record Arrays   
More About Sorting   
NumPy Matrix Class   
Advanced Array Input and Output   
Performance Tips   
Appendix Python Language Essentials   
The Python Interpreter   
The Basics   
Data Structures and Sequences   
Files and the operating system