Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data and extract key insights
by: Michael Walker
Publisher finelybook 出版社: Packt Publishing (December 11,2020)
Language 语言: English
Print Length 页数: 436 pages
ISBN-10: 1800565666
ISBN-13: 9781800565661
Book Description
Discover how to describe your data in detail,identify data issues,and find out how to solve them using commonly used techniques and tips and tricks
Getting clean data to reveal insights is essential,as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python.
You’ll begin by: getting familiar with the shape of data by: using practices that can be deployed routinely with most data sources. Then,the book teaches you how to manipulate data to get them into a useful form. You’ll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not,along with discovering how to operate on data to address the issues you’ve identified. Moving on,you’ll perform key tasks such as handling missing values,validating errors,removing duplicate data,monitoring high volumes of data,and handling outliers and invalid dates. Next,you’ll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors,and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally,you’ll build functions and classes that you can reuse without modifying when you have new data.
By the end of this Python book,you’ll be equipped with all the key skills that you need to clean data and diagnose problems in it.
What you will learn
Find out how to read and analyze data from a variety of sources
Produce summaries of the attributes of data frames,columns,and rows
Filter data and select columns of interest that satisfy given criteria
Address messy data issues,including working with dates and missing values
Improve your productivity in Python pandas using method chaining
Use visualizations to gain additional insights and identify potential data issues
Enhance your ability to learn what is going on in your data
Build user-defined functions and classes to automate data cleaning
