finelybook
Pandas Cookbook
by 作者: Ted Petrou
Pages: 390 pages
Edition 版本: 1
Language 语言: English
Publisher Finelybook 出版社: Packt Publishing
Publisher Finelybook 出版日期: 2017-12-06
ISBN-10 书号: 1784393878
ISBN-13 书号: 9781784393878
Book Description
Key Features
Learn to use the power of Pandas to solve most complex scientific computing problems
Leverage fast,robust data structures in Pandas to gain most from your data
Perform various data analysis tasks efficiently with ease
Book Description
Pandas is one of the most efficient scientific computing packages in Python. It has an enormous amount of power and flexibility to tackle any data task in a variety of ways. It is common for advanced users to write "ugly" Pandas code. With this book,you will explore data in Pandas through dozens of practice problems with detailed solutions in iPython notebooks
This book will provide you with clean,clear recipes and solutions on how to handle common data manipulation tasks. You will be introduced to Pandas and its various features. You will learn about working with different types of data sets,data manipulation,and data wrangling. You will explore the power of Pandas DataFrames and find out about Boolean and multi-indexing with Pandas. You will perform statistical,time series computations,and implement them in financial and scientific applications.
By the end of this book,you will know how to perform fast and accurate scientific computing in Python.
What you will learn
Group,aggregate,transform,reshape and filter data to discover meaningful insights
Combine and merge data from different sources through Pandas SQL-like operations
Create beautiful and insightful visualizations through Pandas direct hooks to Matplotlib and Seaborn
Perform efficient and powerful analyses with Pandas time series functionality
Build pipelines to import,clean and prepare real-world messy data sets for machine learning
Create big data workflows for processing data that is too large to fit in the memory
Contents
Chapter 1. Pandas Foundations
Chapter 2. Essential Dataframe Operations
Chapter 3. Beginning Data Analysis
Chapter 4. Selecting Subsets Of Data
Chapter 5. Boolean Indexing
Chapter 6. Index Alignment
Chapter 7. Grouping For Aggregation,Filtration,And Transformation
Chapter 8. Restructuring Data Into A Tidy Form
Chapter 9. Combining Pandas Objects
Chapter 10. Time Series Analysis
Chapter 11. Visualization With Matplotlib,Pandas,And Seaborn
