Chapter 13: The Performance Problem¶

Part III — Discrete Spatial Modeling

Learning Objectives¶

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

• Profile Python code with cProfile and line_profiler
• Understand why row-by-row GeoDataFrame iteration is slow
• Contrast naive iteration with vectorized NumPy operations

{note}
Benchmark table: 10×10, 50×50, 100×100, 500×500. Motivates Ch 14–15.

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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

Profiling Tools: cProfile and line_profiler¶

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# Code for section: Profiling Tools: cProfile and line_profiler

# Code for section: Profiling Tools: cProfile and line_profiler

Measuring the Naive CA¶

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# Code for section: Measuring the Naive CA

Why Python Loops Are Slow¶

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# Code for section: Why Python Loops Are Slow

Vectorization with NumPy¶

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# Code for section: Vectorization with NumPy

# Code for section: Vectorization with NumPy

Benchmarking: Loop vs Array¶

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# Code for section: Benchmarking: Loop vs Array

# Code for section: Benchmarking: Loop vs Array

What We Still Need¶

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# Code for section: What We Still Need

Summary¶

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Key concepts introduced¶

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Further Reading¶

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