Introduction to Data Cleaning and Preprocessing
Chapter 1: The Importance of Clean Data
What is Data Cleaning?
What is Data Preprocessing?
Common Sources of Dirty Data
Impact of Poor Data Quality
The Data Cleaning Workflow Overview
Quiz for Chapter 1
Chapter 2: Identifying and Handling Missing Data
What Are Missing Values?
Methods for Detecting Missing Data
Visualizing Missing Data Patterns
Strategy 1: Deleting Rows (Listwise Deletion)
Strategy 2: Deleting Columns
Strategy 3: Basic Imputation (Mean/Median/Mode)
Considerations for Choosing a Strategy
Handling Missing Data: Hands-on Practical
Quiz for Chapter 2
Chapter 3: Dealing with Duplicate Data
What Constitutes Duplicate Data?
Why Remove Duplicates?
Identifying Complete Duplicate Rows
Identifying Duplicates Based on Specific Columns
Removing Duplicate Rows
Handling Duplicates: Practice
Quiz for Chapter 3
Chapter 4: Correcting Data Types
Common Data Types in Datasets
Why Correct Data Types Matter
Identifying Incorrect Data Types
Converting to Numeric Types (Integer, Float)
Handling Errors During Numeric Conversion
Converting to Datetime Types
Converting to Categorical or String Types
Data Type Correction: Hands-on Practical
Quiz for Chapter 4
Chapter 5: Basic Data Formatting and Standardization
Importance of Consistent Formatting
Standardizing Text Case (Upper/Lower)
Removing Leading/Trailing Whitespace
Simple String Replacements
Basic Unit Conversion Example
Formatting Practice
Quiz for Chapter 5
Removing Leading/Trailing Whitespace
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- pandas.Series.str.strip, pandas development team, 2024 - Official documentation explaining the use and behavior of string stripping methods within the pandas library for data cleaning.
- Python for Data Analysis: Data Wrangling with pandas, NumPy, and IPython, Wes McKinney, 2022 (O'Reilly Media) - A foundational textbook for data manipulation in Python, offering practical instructions on data preprocessing, including string operations with pandas, relevant to resolving data inconsistencies.
- Data Cleaning: A Practical Guide, Ihab F. Ilyas and Xu Chu, 2019 (Morgan & Claypool Publishers) DOI: 10.2200/S00910ED1V01Y201903DBS017 - This book offers a systematic overview of data cleaning problems and solutions, providing context for text data standardization as a critical component of data quality.