Pandas 1.x Cookbook: Practical recipes for scientific computing,time series analysis,and exploratory data analysis using Python,2nd Edition
by: Matt Harrison and Theodore Petrou
Print Length 页数: 626 pages
Publisher finelybook 出版社: Packt Publishing (February 27,2020)
Language 语言: English
ISBN-10: 1839213108
ISBN-13: 9781839213106
Book Description
Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x.
The pandas library is massive,and it’s common for frequent users to be unaware of many of its more impressive features. The official pandas documentation,while thorough,does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you,as if you were looking over the shoulder of an expert,through situations that you are highly likely to encounter.
This new updated and revised edition provides you with unique,idiomatic,and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles,or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset,uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.
What you will learn
Master data exploration in pandas through dozens of practice problems
Group,aggregate,transform,reshape,and filter data
Merge data from different sources through pandas SQL-like operations
Create visualizations via pandas hooks to matplotlib and seaborn
Use pandas,time series functionality to perform powerful analyses
Import,clean,and prepare real-world datasets for machine learning
Create workflows for processing big data that doesn’t fit in memory
Contents
Preface
Chapter 1: Pandas Foundations
Chapter 2: Essential DataFrame Operations
Chapter 3: Creating and Persisting DataFrames
Chapter 4: Beginning Data Analysis
Chapter 5: Exploratory Data Analysis
Chapter 6: Selecting Subsets of Data
Chapter 7: Filtering Rows
Chapter 8: Index Alignment
Chapter 9: Grouping for Aggregation,Filtration,and Transformation
Chapter 10: Restructuring Data into a Tidy Form
Chapter 11: Combining Pandas Objects
Chapter 12: Time Series Analysis
Chapter 13: Visualization with Matplotlib,Pandas,and Seaborn
Chapter 14: Debugging and Testing Pandas
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