Data analysis was performed using the Python Description:
Still looking for a complete course to control, process, organize, and analyze structured data using Python? "Data Analysis Using Python" contains a large number of practical cases. You will learn how to use various Python libraries (including NumPy, pandas, matplotlib, IPython, etc.) to efficiently solve various data analysis problems.
Because the author Wes McKinney is the main author of the pandas library, this book can also be used as a practical guide to scientific computing using Python to implement data-intensive applications. This book is suitable for analysts who are new to Python and Python programmers who are new to scientific computing.
· Use IPython, the interactive shell, as your primary development environment.
· Learn the basic and advanced knowledge of NumPy (Numerical Python).
· Start with the data analysis tool of the pandas library.
Use high-performance tools to load, clean, transform, merge, and reshape data.
· Use matplotlib to create scatter plots and static or interactive visualization results.
· Use pandas groupby function to slice, dice and summarize the data set.
· Process various time series data.
· Learn how to solve problems in the fields of Web analysis, social sciences, finance, and economics through detailed case studies.
Directory for Data Analysis with Python:
Introduction 1
Chapter 1 Preparation 5
Main Contents of this Book 5
Why Use Python for Data Analysis 6
Important Python Libraries 7
Installation and Setup 10
Communities and Seminars 16
Using this Book 16
Acknowledgements 18
2 Chapter 1 Introduction 20
1.usa.gov bit.ly data from 21 of
MovieLens 1M data sets 29
1880 - 35 2010 between the United States baby name
Brief Summary 47
Chapter 3 IPython: An interactive computing environment and development 48
IPython 49 basis
within Save 51
Use Command History 60
Interact with Operating System 63
Software Development Tools 66
IPython HTML Notebook 75
Some Tips for Using IPython to Improve Code Development Efficiency 77
Advanced IPython Features 79
Acknowledgements 81
Chapter 4 NumPy basics: array and vector calculation 82
NumPy's ndarray: a multi-dimensional array object 83
universal functions: fast element-level array functions 98
use arrays for data processing 100
file input and output for arrays 107
linear algebra 109
random numbers Generate 111
examples: random walk 112
Chapter 5 Introduction to pandas 115 Introduction to
pandas data structure 116
Basic functions 126
Summary and calculation description statistics 142
Handling missing data 148
Hierarchical index 153
Other topics about pandas 158
Chapter 6 Data loading, storage and File format 162
Read and write data in text format 162
Binary data format 179
Use HTML and Web API 181
Use database 182
Chapter 7 Data normalization: cleanup, conversion, merge, reshape 186
Merge data set 186
Reshape and axial rotation 200
Data conversion 204
String operations 217
Example: USDA Food Database 224
Chapter 8 Plotting and Visualization 231
Getting started with matplotlib API 231
Plotting functions in pandas 244
Plotting maps: Graphically displaying Haiti earthquake crisis data 254
Python graphical tools ecosystem 260
Chapter 9 Data aggregation With grouping operations 263
GroupBy technology 264
data aggregation 271
group-level operations and conversions 276
pivot tables and cross tables 288
examples: 2012 Federal Election Commission database 291
Chapter 10 time series 302
date and time data types and tools 303
time series basis 307
date Range, frequency and movement 311
time zone processing 317
periods and their arithmetic operations 322
resampling and frequency conversion 327
time series drawing 334
moving window functions 337
performance and memory usage considerations 342
Chapter 11 financial and economic data applications 344
Topics in data normalization 344
Group transformation and analysis 355
More sample applications 361
Chapter 12 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 Sorted topics 388
NumPy's matrix class 393
Advanced array input and output 395
Performance recommendations 397
Appendix A Python language highlights 401
download link
https://pan.baidu.com/s/1tIFNpeWdzIQSY3EHT0AHGA
-
To extract the code:
[Open WeChat]-> [Scan the QR code below]-> [Follow Data and People] Enter "800123" to get the extraction code
Adhere to the sharing of e-book resources, thank you for your approval!
If you cancel following this public account, even if you follow again, you will not be able to provide this service.