Python for Data Analysis, 3rd Edition
豆瓣 Eggplant.place
Data Wrangling With Pandas, Numpy, and Jupyter
Wes McKinney
简介
Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.9 and pandas 1.2, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You�?�¢??ll learn the latest versions of pandas, NumPy, and Jupyter in the process.
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It�?�¢??s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.
Use the Jupyter notebook and IPython shell for exploratory computing
Learn basic and advanced features in NumPy
Get started with data analysis tools in the pandas library
Use flexible tools to load, clean, transform, merge, and reshape data
Create informative visualizations with matplotlib
Apply the pandas groupby facility to slice, dice, and summarize datasets
Analyze and manipulate regular and irregular time series data
Learn how to solve real-world data analysis problems with thorough, detailed examples
contents
Preface
1 Preliminaries
2 Python Language Basics, IPython, and Jupyter Notebooks
3 Built-In Data Structures, Functions, and Files
4 NumPy Basics: Arrays and Vectorized Computation
5 Getting Started with pandas
6 Data Loading, Storage, and File Formats
7 Data Cleaning and Preparation
8 Data Wrangling: Join, Combine, and Reshape
9 Plotting and Visualization
10 Data Aggregation and Group Operations
11 Time Series
12 Introduction to Modeling Libraries in Python
13 Data Analysis Examples
