Setting Up Your Python Environment

Practically applying mathematical objects such as vectors and matrices requires a computational environment. While many programming languages can handle numbers, Python has become the standard for machine learning and data science. Its simple syntax, combined with powerful specialized libraries, makes it an excellent tool for translating mathematical theory into practical application.

In this section, we will walk through setting up a complete Python environment for numerical computing. We will focus on using the Anaconda distribution, as it simplifies the management of packages and environments, allowing you to focus on learning linear algebra instead of wrestling with installation issues.

The Anaconda Distribution

The best way for a newcomer to get started with Python for data science is by installing Anaconda. Anaconda is not just Python; it is a full distribution that bundles the Python interpreter with a package manager (called conda) and a collection of the most popular libraries for scientific computing, including NumPy, SciPy, pandas, and Matplotlib.

Why Use Anaconda?

You could install Python directly from python.org and then install each library individually using a tool like pip. However, managing dependencies between libraries can sometimes be complicated. Anaconda simplifies this process immensely by managing all the packages for you in a cohesive environment. It ensures that the libraries you install are compatible with each other, which prevents many common setup problems.

Here are the steps to get Anaconda installed on your system:

1. Download the Installer: Navigate to the Anaconda Distribution website. Download the installer for your operating system (Windows, macOS, or Linux). You should choose the latest Python 3 version.
2. Run the Installer: Locate the downloaded file and run it. You will be guided by an installation wizard. For most users, the default settings are perfect. There is no need to change the installation location or add Anaconda to your system's PATH variable unless you are an advanced user who understands the implications.
3. Complete Installation: Follow the on-screen instructions to complete the installation. This may take a few minutes.

Once finished, you will have Python, the conda package manager, and all the necessary libraries ready to go.

The Anaconda ecosystem provides the core tools needed for this course.

Core Tools: NumPy and Jupyter Notebook

With Anaconda installed, you automatically have the two most important tools for this course:

• NumPy (Numerical Python): This is the fundamental library for numerical computation in Python. It provides a high-performance multidimensional array object, which is the data structure we will use to create vectors and matrices. Nearly all data science libraries in Python are built on top of NumPy.
• Jupyter Notebook: This is an interactive, browser-based application that allows you to create and share documents containing live code, equations, visualizations, and explanatory text. It is an ideal environment for learning and experimenting, as you can run small blocks of code one at a time and see the results immediately. We will use Jupyter Notebooks for all our hands-on practicals.

Verifying Your Setup

Let's make sure everything is working correctly. The most straightforward way to start is by launching a Jupyter Notebook.

1. Open Anaconda Navigator: Find and open the Anaconda Navigator application. This is a graphical interface that shows all the applications included with your installation.
2. Launch Jupyter Notebook: From the Navigator home screen, find the "Jupyter Notebook" application and click the "Launch" button. This will open a new tab in your web browser showing the Jupyter file browser.
3. Create a New Notebook: In the top right of the file browser, click "New" and select "Python 3" (or a similar kernel name). This will open a new notebook, which is your interactive coding environment.

Now, in the first cell of your new notebook, type the following code and run it by pressing Shift + Enter:

import numpy as np

print(f"NumPy version: {np.__version__}")

# Create a simple vector to test
my_vector = np.array([5, 10, 15])

print(f"Successfully created a NumPy array: {my_vector}")

If your installation was successful, you should see output similar to this:

NumPy version: 1.23.5
Successfully created a NumPy array: [ 5 10 15]

The version number might be different, but seeing a version number and the confirmation message means your environment is properly configured. You have successfully installed Python, launched a Jupyter Notebook, and confirmed that the NumPy library is ready for use. With this setup complete, you are now prepared to move from theory to practice and begin creating vectors and matrices in code.