The Anaconda Distribution of Python

Anaconda provides a way of managing multiple Python environments; a Python environment is a specific version of Python with a set of packages. For a given project, it is best practice to maintain a separate environment; this allows us to specify a Python version and set of packages required to run the programs in the project. Anaconda provides a framework in which we can create an environment, called a conda environment.

We will creat our own environment in the next section.

Hello World and the Spyder IDE

When it is first loaded, the Spyder IDE looks something like what is shown in fig. ¿fig:spyder0?.

The left pane is the code editor. It has a default Python file, which conventionally has extension .py, already queued up. Create a new Python file by selecting the menu item FileNew file…. Save this file (FileSave) as hello_world.py in a dedicated directory.¹

The hello_world.py file already contains a triple-quoted string with basic information about the file. Below the ending quotes, add the following statement:

print("Hello World!")

Save the file and run it with the menu selection RunRun or the key F5.

The console pane on the lower right shows the result of the execution of the file, which is the output

Hello World!

Now let’s edit the program as follows:

greeting = "Hello World!"
print(greeting)

This should yield the same result in the console. In the upper-right pane, select the Variable Explorer tab. This shows variable names, types, and values in the current kernel. A kernel is a computing process that runs programs. In most environments, when a program runs, a kernel is created at the start and destroyed at the end of execution. However, Spyder maintains the same kernel between runs. This is convenient for debugging purposes. For instance, we can interact with the program(s) run in the current kernel by entering commands in the console; try entering

greeting

This will return the value of the variable greeting. The console is a convenient place to try out statements as we work on our program. For instance, we may want to append some text to the greeting string. In the console, try

greeting + "It's a beautiful day"

This returns, Hello World!It's a beautiful day, which is close but not quite what we wanted. We should add a space character to the beginning of our addendum. So, trying it out in the console allowed us to quickly debug our code.

The persistent kernel can also cause problems. Sometimes we may want to create a new kernel by selecting the menu item ConsolesRestart kernel, which clears all variables and unloads any packages. Similarly, to clear all variables in the kernel, we can execute the console magic command

%reset

We will be asked to confirm, which we can do by entering y.

Configuring the Spyder IDE for Anaconda

In subsection 1.4.3, we used the Python distribution that Spyder has built in. We here configure Spyder to use the Anaconda distribution installed in subsection 1.4.1. First, we must install the spyder-kernels package in the base Anaconda environment. On a Windows PC, open the Anaconda Prompt application; on MacOS or Linux, open the Terminal application. To ensure you have activated the base environment, enter the following prompt:

conda activate base

Now install the spyder-kernels package with the command

conda install spyder-kernels

Enter y if prompted. After successful installation, conda list should display the packages installed in the base environment, including spyder-kernels. Finally, enter the command

which python

Copy or record the returned path.

In Spyder, open preferences with Ctrl+,. Navigate to the tab Python Interpreter and check Use the following Python interpreter. Either paste the path copied above in the text field or click the Select file button, then navigate to the path in question, selecting the python program. Click OK to complete the configuration.

You may need to restart Spyder for the changes to take effect.

The Anaconda Distribution of Python

Anaconda provides a way of managing multiple Python environments; a Python environment is a specific version of Python with a set of packages. For a given project, it is best practice to maintain a separate environment; this allows us to specify a Python version and set of packages required to run the programs in the project. Anaconda provides a framework in which we can create an environment, called a conda environment.

Configuring VS Code with Anaconda

To configure VS Code to use the Anaconda distribution of Python, follow these steps:

Install the Python and Jupyter extensions

After installing VS Code, open the application. Choose the menu item ViewExtensions and install the Python extension by Microsoft. This extension provides Python language support, debugging, and additional tools for Python programming. Also install the Jupyter extension, which provides notebook support in VS Code.

Open a working directory

Open a directory in which to save your Python programs by selecting the menu item FileOpen Folder…. Navigate to a convenient location and create a new directory, such as engineering_computing. Select this directory and click Open. You will save your Python programs in subdirectories of this directory.

Create a conda environment

Open a terminal in VS Code by selecting the menu item TerminalNew Terminal. In the terminal, enter the following command to create a new conda environment named ec:

conda create --name ec python=3.12 jupyter numpy matplotlib sympy

The python=3.12 argument specifies the Python version, and the packages jupyter, numpy, matplotlib, and sympy are installed in the environment.

Select the Python interpreter

Open the Command Palette by selecting the menu item ViewCommand Palette…. Type Python: Select Interpreter and select the corresponding command that appears. Select the Python interpreter that corresponds to the ec environment of Anaconda created in the previous step. This will allow VS Code to use the Anaconda distribution of Python, and this interpreter will be used for all Python programs in the open directory (i.e., you will not need to select the interpreter for each program).

Hello World and VS Code

Create a new Python file by selecting the menu item FileNew Text File. Save this file (FileSave) as hello_world.py in a dedicated directory.²

Add the following statement:

print("Hello World!")

Save the file and run it with the menu selection RunRun or the key F5.

A new terminal will open in the lower pane of the VS Code window and displayed will be the output

Hello World!

Now let’s edit the program as follows:

greeting = "Hello World!"
print(greeting)

This should yield the same result in the terminal. Now right-click anywhere in the editor pane and select Run in Interactive WindowRun Current File in Interactive Window. This will open an interactive window that will execute the program and display the output. This is different from the terminal in that it allows us to interact with the program after it has run.

In the interactive window, select the Jupyter Variables button. This shows variable names, types, and values in the current kernel. A kernel is a computing process that runs programs. In most environments, when a program runs, a kernel is created at the start and destroyed at the end of execution. However, the interactive window maintains the same kernel between runs. This is convenient for debugging purposes.

A prompt appears at the bottom of the interactive window, where we can enter Python commands that are executed in the context of the program that was run. For instance, we can interact with the program(s) run in the current kernel by entering commands in the prompt; try entering

greeting

This will return the value of the variable greeting. The console is a convenient place to try out statements as we work on our program. For instance, we may want to append some text to the greeting string. In the console, try

greeting + "It's a beautiful day"

This returns, Hello World!It's a beautiful day, which is close but not quite what we wanted. We should add a space character to the beginning of our addendum. So, trying it out in the console allowed us to quickly debug our code.

The persistent kernel can also cause problems. Sometimes we may want to create a new kernel by clicking the button Restart, which clears all variables and unloads any packages. Similarly, to clear all variables in the kernel, we can execute the magic command

%reset

We will be asked to confirm, which we can do by entering y. Note that magic commands like this are only available in the interactive window, and cannot be used in Python programs. The interactive window is a great place to test out code snippets and debug programs. It is, in fact, a form of a Jupyter notebook, which is a powerful tool for data analysis and visualization. See appendix A for more information on Jupyter notebooks.