Chapter 2: The Geospatial Python Toolbox¶
Part I — Python for Data Science
Learning Objectives¶
By the end of this chapter, you will have mastered the essential building blocks of Python, enabling you to translate complex geographic problems into clean, efficient code.
{note}
Exercises should mirror real-world datasets (IBGE, OpenStreetMap attributes).
# Standard imports — add chapter-specific imports below
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Data as Coordinates¶
In the world of computing, data refers to any collection of values or information that can be processed, stored, and transformed by a computer. This data can represent a wide variety of information, including text, images, and coordinates. In reality, any information processed by a computer must eventually be translated into numbers, as computers remain, at their core, sophisticated machines for numerical processing. In geography, everything starts with a location. To a computer, a location is simply a set of numbers stored in variables. A variable acts as a labeled container for data. For example, if we want to calculate the distance between two points, we first need to store their coordinates as floats (decimal numbers).
To illustrate this, let’s represent the locations of two iconic cities: Rio de Janeiro and Paris. The following code demonstrates how we assign these numerical values to specific variables:
# Assignment: Placing the coordinates of Rio and Paris into variables
# Rio de Janeiro, Brazil
x1 = -43.1729
y1 = -22.9068
# Paris, France
x2 = 2.3522
y2 = 48.8566
# Now the computer has these 'values' stored in 'labeled containers'
Did you know?
In Python, the equals sign (=) does not represent mathematical equality. Instead, it is an assignment operator. Think of it as an instruction that says: 'Take the value on the right and store it inside the labeled container on the left.'
# Code for section: Series and DataFrames
Making Decisions¶
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# Code for section: Indexing and Selection
Collections¶
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# Code for section: Filtering and Boolean Masks
Automation¶
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# Code for section: GroupBy and Aggregation
Building Models¶
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# Code for section: Merging and Joining
Vectorized Operations¶
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# Code for section: Vectorized Operations
Exercises¶
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# Code for section: Exercises
Further Reading¶
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