Python for Data Analysis
作者：Wesly McKinney
分类：文学
ISBN：9781549329784
出版时间：2013-6-16
出版社：O'Reilly Media
章节目录
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 Acknowledgements Chapter 2 Introductory Examples 1.usa.gov data from bit.ly 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 Credits 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 Broadcasting 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 Functions Files and the operating system
内容简介
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
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