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Chapter 2: Data Wrangling: Preprocessing

Learning Objectives

•Outline the processes of data wrangling, focusing on their role in preparing data for analysis and storytelling.

•Apply strategies for addressing missing data within a dataset.

•Find and address anomalous values that may distort data analysis.

•Transform quantitative and categorical variables to enhance data structure and analysis.

•Reshape data into a tidy format, ensuring that each column represents a variable and each row an observation.

•Describe approaches to combining multiple datasets for comprehensive data analysis.

Google Drive For Chapter 2 Review Questions

Google Drive For Chapter 2 "Try It Yourself" Python Notebooks

Google Drive For Chapter 2 Data Sets

Section 2.1: What Is Data Wrangling?

Section 2.2: Cleaning Missing Data

Try It Yourself: Identify Missing Data

Try It Yourself: Impute Missing Values from Internal Data

Section 2.3: Cleaning Anomalous Values

Try It Yourself: Identify Extreme Data Values

Try It Yourself: Clean Incorrect Formats

Section 2.4: Transforming Quantitative Variables

Try It Yourself: Create a Categorical Variable from a Quantitative Variable

Section 2.5: Transforming Categorical Variables

Try It Yourself: Create a Boolean Variable That Represents a Categorical Variable

Try It Yourself: Splitting a Variable into Multiple Variables

Section 2.6: Reshaping a Dataset

Section 2.7: Combining Datasets

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