With Colab you can harness the full power of popular Python libraries to analyze and visualize data. The code cell below uses numpy to generate some random data, and uses matplotlib to visualize it.

With Colab you can harness the full power of popular Python libraries to analyse and visualise data. The code cell below uses numpy to generate some random data, and uses matplotlib to visualise it.

In this practical, we will learn about the programming language Python as well as NumPy and Matplotlib, two fundamental tools for data science and machine learning in Python.

Colab provides automatic completions to explore attributes of Python objects, as well as to quickly view documentation strings. As an example, first run the following cell to import the numpy module.

To run the following cell, your API key must be stored it in a Colab Secret named GOOGLE_API_KEY. If you don't already have an API key, or you're not sure how to create a Colab Secret, see the …

This tutorial is designed to run as a Python notebook on Colab. We’ll take a closer look at Colab and its features in a separate tutorial, but for now, here is what you need to know: When you see a “cell” …

You can now embed live Google Sheets in Colab with the InteractiveSheet library. This means you can create and edit data in Google Sheets and seamlessly incorporate it into your notebook with Pandas …

This section will describe four primary ways you can run Python code: the Python interpreter, the IPython interpreter, via Self-contained Scripts, or in the Jupyter notebook.

This is key for enabling dynamic agentic workflows, allowing models to independently research, analyze articles, and synthesize information from the web as part of their reasoning process.

In this practical, we will learn about the programming language Python as well as NumPy and Matplotlib, two fundamental tools for data science and machine learning in Python.