Chapter 4: Exploratory Data Analysis¶

Part I — Python for Data Science

Learning Objectives¶

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

• Explore distributions, relationships, and outliers visually
• Use matplotlib and seaborn for quick validation plots
• Establish visual validation as a prerequisite for spatial analysis

{note}
Introduce the idea that bad EDA leads to bad spatial models.

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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

Univariate Distributions¶

TODO: write content.

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# Code for section: Univariate Distributions

# Code for section: Univariate Distributions

Bivariate Relationships¶

TODO: write content.

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# Code for section: Bivariate Relationships

# Code for section: Bivariate Relationships

Outlier Detection¶

TODO: write content.

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# Code for section: Outlier Detection

# Code for section: Outlier Detection

EDA Checklist¶

TODO: write content.

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# Code for section: EDA Checklist

# Code for section: EDA Checklist

Exercises¶

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# Code for section: Exercises

# Code for section: Exercises

Summary¶

TODO: summarise key takeaways.

Key concepts introduced¶

• TODO
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Further Reading¶

• TODO: add references.