"Data analysis using Python". (Wes McKinney). [Pdf].pdf

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Content Introduction· · · · · ·


"The Scientific Computing and data analysis community has been waiting for this book for many years: a number of concrete practical recommendations, and a number of integrated application approaches. This book will certainly be a definitive guide to technical computing in the Python field over the next few years. ”

--fernando Pérez, University of California, Berkeley research scientist, founder of Ipython

"About the content"

Still looking for a complete course to control, process, organize, and analyze structured data with Python? This book contains a number of practical cases where you will learn how to efficiently address a wide range of data analysis issues using a variety of Python libraries, including NumPy, Pandas, Matplotlib, and Ipython.

Because author Wes McKinney is the main author of the Pandas Library, this book can also be used as a practical guide to scientific computing for data-intensive applications using Python. This book is suitable for analysts who have just contacted Python and Python programmers who have just come into contact with scientific computing.

Ipython this interactive shell as your primary development environment.

Learn the basics and advanced knowledge of numpy (numerical Python).

? Start with the data analysis tool from the Pandas library.

Use high-performance tools to load, clean, transform, merge, and reshape data.

? use Matplotlib to create scatter plots, as well as static or interactive visualization results.

Use Pandas's groupby function to slice, dice, and summarize datasets.

Handle a variety of time series data.

? Learn how to solve problems in web analytics, social sciences, finance, and the economy through detailed case studies.

about the author· · · · · ·

Wes McKinney, a senior data analysis expert, has deep research on various Python libraries (including NumPy, pandas, matplotlib, Ipython, etc.), and has accumulated rich experience in a great deal of practice. A large number of classic articles related to Python data analysis are being reproduced by various technology communities and are recognized as one of the authoritative figures in Python and the open source technology community. Developed the well-known open source Python library--pandas for data analysis, which has been widely praised by users. Prior to creating Lambda Foundry, a company dedicated to enterprise data analysis, he was a quantitative analyst at AQR Capital Management.

Preface 1
Chapter 1th Preparation of work 5
Main contents of this book 5
Why use Python for data analysis 6
Important Python Library 7
Setup and Setup 10
Communities and Seminars 16
Using this book 16
Acknowledgements 18
Chapter 2nd Introduction 20
1.usa.gov data from bit.ly 21
Movielens 1M Data Set 29
1880-2010 All-American baby name 35
Summary and Outlook 47
3rd Chapter IPython: An interactive computing and development environment 48
Ipython Foundation 49
Introspection 51
Using the command history 60
Interacting with the operating system 63
Software Development Tools 66
IPython HTML Notebook 75
Some tips on using Ipython to improve code development efficiency 77
Advanced Ipython Features 79
Acknowledgements 81
4th NumPy Basics: Arrays and vector calculations 82
NumPy's Ndarray: A multidimensional Array object 83
General functions: Fast element Progression Group function 98
Using arrays for data processing 100
File input and output for arrays 107
Linear algebra 109
Random number Generation 111
Example: Random Walk 112
5th. Pandas Introduction 115
Pandas introduction to Data structure 116
Basic features 126
Summarize and calculate descriptive statistics 142
Handling Missing Data 148
Hierarchical Index 153
Other topics related to Pandas 158
6th data loading, storage and file format 162
Read and write data in text Format 162
Binary data Format 179
Using HTML and Web API 181
Working with Databases 182
7th. Data normalization: Clean, transform, merge, reshape 186
Merging data sets 186
Reshaping and axial rotation 200
Data Conversion 204
String Manipulation 217
Example: USDA Food Database 224
8th. Drawing and Visualization 231
Matplotlib API Primer 231
Drawing functions in Pandas 244
Draw a map: Graphically visualize Haiti earthquake crisis data 254
Python Graphical tools Ecosystem 260
Chapter 9th Data Aggregation and grouping operations 263
GroupBy Technology 264
Data Aggregation 271
Grouping-level operations and conversions 276
Pivot table and cross-table 288
Example: 2012 federal Election Commission database 291
The 10th Chapter time series 302
Date and time data types and tools 303
Time Series Basics 307
range, frequency, and movement of dates 311
Time Zone Processing 317
Time and its arithmetic operations 322
Resampling and Frequency Conversion 327
Time Series Drawing 334
Moving window Functions 337
Performance and memory usage considerations 342
Chapter 11th application of financial and economic data 344
Topics in Data Normalization 344
Grouping transformations and Analysis 355
More Example Applications 361
12th Chapter NumPy Advanced Applications 368
Internal mechanism of Ndarray objects 368
Advanced Array Operations 370
Broadcast 378
Ufunc Advanced Applications 383
Structured and recorded arrays 386
More about sorting topics 388
NumPy's Matrix class 393
Advanced array input and output 395
Performance Recommendations 397
Appendix A Python language Essentials 401

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