The Python Standard Library

In the previous section, you learned how to write and import your own custom scripts. While building your own tools is a vital skill, you rarely need to invent everything from scratch.

When you install Python, it automatically comes pre-packaged with the Python Standard Library. This is a substantial collection of highly optimized, ready-to-use modules. Whether you need to run complex statistical math, generate random coordinate samples, or navigate your computer’s file system, the tools are already sitting on your machine. You do not need to download or install anything extra.

You just have to unlock them using the import command.

1. Importing modules¶

To use a module from the Standard Library, you must import it at the top of your script or notebook. Once imported, you can access all the functions inside it by typing the module name, a dot, and the function name.

If you only need a specific tool, you can import just that part. This allows you to use the tool directly without typing the module name every time.

2. Advanced math and statistics¶

The math module provides access to advanced mathematical functions and constants. A great spatial data science example is calculating the exact distance between two coordinates on the spherical Earth using the Haversine formula.

If you need to analyze lists of numbers, the statistics module is incredibly useful. It provides functions for calculating the mean, median, and mode of your data without writing loops yourself.

Concept check¶

Predict the result of these two common rounding operations. Remember to think about what “up” and “down” mean on a number line.

3. Adding unpredictability¶

The random module is used to generate random numbers and make random selections. This is especially useful in spatial sampling, simulations, or creating dummy data for testing.

Concept check¶

We need to pick two test sites. Predict which method guarantees you will select two unique cities.

import random
cities = ["Zurich", "Geneva", "Basel", "Bern"]

# Method A (requires two function calls)
site_1 = random.choice(cities)
site_2 = random.choice(cities)

# Method B (requires one function call)
sites_group = random.sample(cities, k=2)

4. Navigating your computer¶

When working with local data, you often need to know exactly which folder your Python code is running in, or you need to build paths to your data files. The os module (Operating System) and the pathlib module help you interact with your computer’s file system.

While os is the older, traditional way to handle paths using text strings, modern Python developers prefer pathlib because it treats paths as smart objects that automatically handle the messy differences between Windows and Mac/Linux folders.

Concept check¶

Examine how the / operator works. Predict the type and content of the final_path variable.

from pathlib import Path

# Assume our current folder is: C:/project/data/
my_folder = Path.cwd()

# The division operator works on smart objects!
final_path = my_folder.parent / "output" / "map.tif"

5. Formatting data with JSON¶

JSON (JavaScript Object Notation) is a lightweight text format used to store and transport data. It looks almost exactly like Python dictionaries and lists. The json module allows you to convert text data into usable Python objects.

This is a critical skill for working with Web APIs, which almost always return data in JSON format.

Concept check¶

You have successfully used json.loads() to convert an API response into a Python dictionary named data.

Examine the raw JSON structure. Predict the nested key access required to extract the longitude (the first value in the coordinates list).

{
  "properties": {
    "name": "Eiffel Tower",
    "location": {
      "city": "Paris",
      "coordinates": [2.29, 48.85]
    }
    ...
  }
}

# Predict the correct indexing path here. Fill in the blanks:
# Hint: You will need multiple sets of []
longitude_val = data[...][...][...][...]

6. Exercises¶

To practice using the Standard Library, complete the following tasks.

Exercise 1: Random coordinates¶

Using the random module, write a loop that generates 3 random coordinate pairs (representing latitude and longitude). Assume the latitude must be a decimal between 45.0 and 47.0, and the longitude must be a decimal between 6.0 and 9.0. Print each pair rounded to 4 decimal places.

import random

for i in range(3):
    # Use random.uniform to generate floating-point numbers
    lat = random.uniform(45.0, 47.0)
    lon = random.uniform(6.0, 9.0)

    # Note: latitude display is rounded (.4f) while longitude is not,
    # to highlight the difference between rounded and full-precision coordinates
    # You can adjust it, as you like.
    print(f"Coordinate {i+1}: [{lat:.4f}, {lon}]")

Exercise 2: Parsing JSON¶

You downloaded a small JSON string representing a feature collection. Use the json module to parse it into a Python dictionary, then print the name of the feature.