Chapter 3: Data Cleaning and Preparation¶

Part I — Python for Data Science

Learning Objectives¶

By the end of this chapter you will be able to:

• Handle missing values, duplicates, and type errors
• Manipulate strings and dates programmatically
• Build reproducible data pipelines

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# Standard imports — add chapter-specific imports below
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Standard imports — add chapter-specific imports below import numpy as np import pandas as pd import matplotlib.pyplot as plt

Missing Values¶

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# Code for section: Missing Values

# Code for section: Missing Values

Type Conversion¶

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# Code for section: Type Conversion

# Code for section: Type Conversion

String Manipulation¶

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# Code for section: String Manipulation

# Code for section: String Manipulation

Merging Messy Datasets¶

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# Code for section: Merging Messy Datasets

# Code for section: Merging Messy Datasets

Building a Reproducible Pipeline¶

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# Code for section: Building a Reproducible Pipeline

# Code for section: Building a Reproducible Pipeline

Exercises¶

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# Code for section: Exercises

# Code for section: Exercises

Summary¶

TODO: summarise key takeaways.

Key concepts introduced¶

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Further Reading¶

• TODO: add references.