Colab

1. Introduction¶

Google Colab is a cloud based Jupyter notebook that runs entirely in your browser. Your code executes on remote machines, not on your laptop, which means you can start working immediately without installing anything.

In this course, Colab is a low barrier entry point to programming with spatial data. It allows you to focus on learning Python, understanding geospatial workflows, and experimenting with code, rather than spending time on software setup and configuration. When your local environment is not ready or something breaks, Colab is your reliable fallback.

Colab is also a shared learning space. Notebooks can be opened, run, and edited by anyone with a link. This makes it well suited for live demonstrations, guided exercises, peer collaboration, and asking for help.

Throughout this course, you can use Colab to run all course notebooks, modify code examples, and explore spatial data workflows in a clean, consistent, and reproducible environment.

2. Learning Objectives¶

After working through this section, you should be able to:

• Open and run course notebooks in Colab

• Install and use required Python packages

• Manage files and data in Colab using Google Drive

These objectives focus on using Colab as a practical learning and experimentation environment. You will use these skills repeatedly when working through examples and practicals in the course.

3. Getting Started¶

Getting started with Colab is simple and only takes a few minutes.

First, open Colab. You only need a web browser and a Google account.

Once you are signed in, create a new notebook by clicking (File -> New notebook in Drive). This opens an empty Jupyter notebook on your Google Drive where you can start writing and running Python code immediately.

Each notebook consists of code cells and text cells. Code cells are used to run Python. Text cells are used to explain what your code does. You run a cell with Shift Enter.

Colab saves your notebook automatically, but the running session is temporary. This means you can always reopen the notebook later, but you may need to rerun cells when you come back to it.

For now, the key idea is simple. Open Colab, create a notebook, write code in cells, and run them from top to bottom.

4. The Notebook Environment¶

A Colab notebook is made up of cells that are executed one after another. Each cell can contain either Python code or explanatory text. The order in which you run cells matters, because variables and results are stored in memory.

Colab runs your code in a session. This session holds all variables, imported libraries, and loaded data. If the session stops or resets, everything in memory is lost and cells need to be run again.

The execution order is not always the same as the visual order of cells. If you run cells out of order, results may be confusing or incorrect. When something behaves strangely, a good habit is to restart the session and run all cells from top to bottom.

The runtime defines where your code runs and which resources are available. Most of the time, the default runtime is sufficient (Python 3 on a CPU). In later project, you may switch runtimes for more demanding tasks (Runtime -> Change Runtime type, GPU or TPU).

Colab also makes it easy to work together on the same notebook. Sharing works much like sharing a Google Doc. Each notebook has sharing settings that control who can view or edit it. You can choose to share with specific people or allow access to anyone with the link.

5. Managing Python Packages¶

Python packages define what your notebook can do. In Colab, packages are installed into the current runtime, not permanently. This means installs are fast, but they disappear when the session resets.

A good habit is to make all package installs explicit and place them near the top of the notebook. This helps others run your code and helps you recover quickly after a reset.

Inspect installed packages¶

You can check which packages are already available in the current runtime.

# Shows all Python packages currently installed in this Colab runtime
%pip list

Colab includes common geospatial packages (e.g. gdal, geopandas, and xarray), but you should never rely on this without checking.

Installing packages with pip¶

In Colab, pip commands usually start with a percent sign. This tells the notebook to run the command in a way that is safe and consistent with the current Python runtime.

# Installs the required packages into the active runtime
%pip install geopandas rasterio leafmap

You may also see pip commands that start with an exclamation mark. This runs the command at the system level.

# Installs the package using a system command
!pip install pygis

Both approaches work in Colab. In this course, prefer the percent pip version because it avoids confusion when working with different runtimes. Do not use Conda in Colab. Always use pip.

Reducing output¶

Package installation can produce a lot of text. To keep notebooks readable, you can reduce the output (-q =) --quiet .

# Installs the package while reducing console output
%pip install -q pygis

Check package versions¶

When code behaves differently than expected, checking the version is often the first step.

# Displays version number and installation path of the package
%pip show geemap

Different versions can lead to different results, especially in geospatial workflows.

Uninstall packages¶

If a package causes conflicts or you want to start fresh, you can remove it.

# Removes the package without asking for confirmation (-y = --yes)
%pip uninstall geemap -y

6. Working with Files and Data¶

Understanding where your data lives is essential when working in Colab. Files stored inside the Colab session exist only temporarily. If the session resets, those files are lost.

To avoid losing work, important files and results should be stored in Google Drive. Drive provides persistent storage that stays available across sessions and devices.

Using Google Drive for storage¶

Before accessing Drive, you need to mount it in your notebook.

# Connects your Google Drive to the Colab session
from google.colab import drive
drive.mount('/content/gdrive')

After mounting, your personal Drive is available under MyDrive. A good practice is to create a course folder and keep notebooks, data, and outputs organized there.

# Lists the contents of your Google Drive
import os
os.listdir('/content/gdrive/MyDrive')

Uploading and downloading data¶

You can upload files from your local computer directly into the current session. This is useful for small datasets or quick tests.

# Opens a file picker to upload files from your computer
from google.colab import files
uploaded = files.upload()

To download results back to your computer, use the following command.

# Downloads a file from the session to your local machine
from google.colab import files
files.download('results.csv')

Remember that uploaded files stored only in the session should be moved to Drive if you want to keep them.

Accessing data from the web¶

Many datasets can be accessed directly from the web. This avoids manual downloads and keeps workflows reproducible.

# Downloads a dataset from a public URL
!wget https://opengeos.org/data/world/world_cities.csv

Course code and example data are often stored on GitHub. You can clone a repository directly into your session.

# Copies a GitHub repository into the Colab session
!git clone https://github.com/giswqs/intro-gispro.git

Working directly with online sources makes it easier to rerun notebooks and share them with others.

8. Running Course Notebooks¶

All notebooks in this course can be run directly in Google Colab. This allows you to start working immediately without setting up a local environment.

When a page in this Jupyter Book provides an Open in Colab link, use it. The notebook will open in Colab and is ready to run. This is the recommended way to get started.

You can also open notebooks directly from the GitHub repository. Colab can load notebooks straight from GitHub without downloading anything.

Update the link!!! Example: Go to https://colab.research.google.com/github/giswqs/intro-gispro/blob/main

1. Open https://colab.research.google.com/github/

2. Navigate to the course repository

3. Select the notebook you want to run

Once the notebook is open, run the cells from top to bottom. This ensures that all package installs, imports, and data loading steps are executed in the correct order.

If the notebook modifies files or produces results, save a copy to Google Drive. This ensures your work persists even if the session resets.

Running notebooks in Colab is meant to support learning and experimentation. Feel free to change code, add cells, and explore, knowing that you can always reopen a clean version from the book or repository.

9. Exercises¶

The following exercises help you practice the learning objectives of this section. They focus on running notebooks in Colab, managing packages, and working with data in a session based environment.

Exercise 1: Colab setup¶

Objective
Get comfortable with Colab as a working environment for this course.

Tasks

1. Open Google Colab and create a new notebook.

2. Check which runtime is active and confirm that it uses Python.

3. Mount your Google Drive.

4. Install the required Python packages for this course.

5. Create a folder in your Drive for course notebooks and data.

6. Add a short text cell that explains what this notebook is for.

7. Save the notebook with a clear and descriptive name.

This exercise focuses on opening notebooks, installing packages, and using Drive for persistent storage.

Exercise 2: Working with files and data¶

Objective
Practice moving data between the Colab session, Google Drive, and the web.

Tasks

1. Download a small dataset from the web using wget.

2. Upload a second file from your local computer to Colab.

3. Move both datasets into your course folder in Google Drive.

4. Load one dataset into a Pandas DataFrame or a GeoPandas GeoDataFrame.

5. Inspect the data and print the first few rows.

6. Save a processed result to Drive.

7. Download the result back to your local computer.

This exercise reinforces how data lives in Colab sessions and how to make results persistent.

Exercise 3: Running and sharing a course notebook¶

Objective
Run a complete notebook and prepare it for collaboration.

Tasks

1. Open one course notebook in Colab using the provided link.

2. Run all cells from top to bottom without errors.

3. Modify one code cell in a meaningful way.

4. Add a text cell explaining what you changed and why.

5. Save your own copy to Google Drive.

6. Change the sharing settings so others can view the notebook.

7. Share the link with a peer and ask them to run it.

This exercise focuses on reproducible execution, documentation, and sharing notebooks with others.