Chapter 2: Mastering Pandas¶

Part I — Python for Data Science

Learning Objectives¶

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

• Work confidently with Series and DataFrames
• Index, filter, group, and aggregate tabular data
• Apply vectorized operations instead of Python loops

{note}
Exercises should mirror real-world datasets (IBGE, OpenStreetMap attributes).

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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

Series and DataFrames¶

TODO: write content.

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# Code for section: Series and DataFrames

# Code for section: Series and DataFrames

Indexing and Selection¶

TODO: write content.

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# Code for section: Indexing and Selection

# Code for section: Indexing and Selection

Filtering and Boolean Masks¶

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# Code for section: Filtering and Boolean Masks

# Code for section: Filtering and Boolean Masks

GroupBy and Aggregation¶

TODO: write content.

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# Code for section: GroupBy and Aggregation

# Code for section: GroupBy and Aggregation

Merging and Joining¶

TODO: write content.

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# Code for section: Merging and Joining

# Code for section: Merging and Joining

Vectorized Operations¶

TODO: write content.

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# Code for section: Vectorized Operations

# Code for section: Vectorized Operations

Exercises¶

TODO: write content.

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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.