Python Data Cleaning Cookbook – Second Edition

Python Data Cleaning Cookbook – Second Edition – Ebook PDF Instant Download/Delivery ISBN: 9781800564596, 1800564597

Product details:

• ISBN 10:1800564597
• ISBN 13: 9781800564596
• Author:Michael Walker

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 it 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 modification when you have new data.

Table contents:

Chapter 1: Anticipating Data Cleaning Issues when Importing Tabular Data into pandas

Chapter 2: Anticipating Data Cleaning Issues when Importing HTML and JSON into pandas

Chapter 3: Taking the Measure of Your Data

Chapter 4: Identifying Missing Values and Outliers in Subsets of Data

Chapter 5: Using Visualizations for the Identification of Unexpected Values

Chapter 6: Cleaning and Exploring Data with Series Operations

Chapter 7: Fixing Messy Data when Aggregating

Chapter 8: Addressing Data Issues When Combining DataFrames

Chapter 9: Tidying and Reshaping Data

Chapter 10: User-Defined Functions and Classes to Automate Data Cleaning

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