How to Install NumPy in VS Code

If you’re a Python developer, chances are you’ve heard of NumPy, the must-have package for scientific computing in Python. But do you know how to get it running in Visual Studio Code (VS Code), the go-to code editor for many developers?

This article will take an in-depth look at the NumPy installation in VS Code and some other related topics that might happen to be interesting.

Install NumPy in VS Code

Without delay, here are all the steps you’ll need to take to install NumPy:

1. Fire up VS Code. If, by chance, you don’t have it already download a copy from the official website.
   
2. Click on the Extension tab. You can find it on the left side of the window, denoted by a four-squared icon.
   
3. Type “Python” into the extension search bar.
   
4. Select “Python from Microsoft” from the results.
   
5. Click on the blue “Install” button.
   
6. Go back to the main menu.
7. Select “Terminal.”
   
8. Choose “New Terminal.”
   
9. To get started with NumPy, enter this command into the Terminal you just opened: pip install numpy.
   

This tells the Python package installer to download NumPy and install it on your computer. The process from then on is automatic.

The Python extension you downloaded also gives an abundance of support for your other Python projects, such as IntelliSense, linting, or debugging.

If you run into any roadblocks that mention “no module named numpy” as an error message, double-check if you selected the right Python interpreter. You can adjust this by navigating to “Python” in the lower region of the screen and selecting the interpreter which has pip and NumPy.

Make the Most of NumPy in VS Code

With NumPy up and running, you can benefit from Visual Studio Code’s features to make your programming more effective and enjoyable.

Debugging

Programming is intricate and delicate and you’re bound to debug code that won’t do what it’s supposed to. VS Code has a comprehensive debugging environment specifically for Python applications and those that use NumPy for scientific computing. Here are some tips:

• If you do identify a possible bug, to get to the root of the issue, you need to pull out the Python file you are working on and open it up. After that, click on “Run” from the top menu and choose “Start Debugging.” This will kick off the debugging.
• Debugging your code can be much easier if you set breakpoints. These markers let the debugger pause your program, so you can inspect the state of your program at that exact point of execution. Try setting these breakpoints by clicking on the margin next to the line of code you’d like to pause or hovering your cursor over the line and pressing F9.
• When the program is on pause, the debugging controls at the top of the screen will let you move through the code. If you press F10, you can skip over functions; with F11, you dive deeper into them, and pressing both Shift and F11 will step out of a function.
• You can also look closely at the variables with the “Variables” pane. It will show up in the “Run and Debug” sidebar when your program is on pause, displaying all of the values in the local scope. If you need more detail, hover over any variable in the code to get an exact value.

Using Jupyter Notebooks

A savvy data analyst can benefit from the interactive environment of Jupyter Notebooks, accessible directly from VS Code. This way, you can construct, execute, and debug code within a seamless interface.

If you want to create a new Jupyter Notebook in Visual Studio Code:

1. Open the Command Palette (Ctrl+Shift+P).
   
2. Find the command “Create: New Jupyter Notebook.”
   
3. Alternatively, open your workspace and create a new file with the “.ipynb” extension.
   

Once you create a notebook, you can type Python code into its cells and execute those commands by clicking the “Run Cell” button that appears when your cursor hovers over it. The results of your cell will then show up beneath it so that you can use them in other calculations or operations.

You can choose the Python interpreter for each Notebook you make by picking from the kernel picker in the top right. This option comes in handy, particularly if you have more than one Python environment on your computer and would like to use all of them, depending on the work.

Using IntelliSense

VS Code’s IntelliSense is your best friend and companion to NumPy coding. This powerful set of features grants intelligent code completion as you type. You don’t have to overthink function names, variables, etc. IntelliSense can figure it out from the code’s context.

For example, if you need to write a function, start typing the name, and IntelliSense will provide you with all of the available functions from NumPy and other modules. Select one that fits, and the tool will add it to the code. You’ll also get a correct list of arguments for each function, which also brings great speed to coding.

NumPy with VS Code Extensions

VS Code’s extensibility is one of its most powerful features. With the correct extensions, you can supercharge both Python and NumPy. Here are a few extensions that can be particularly useful along with NumPy.

• The Python extension by Microsoft brings intuitive coding to VS Code. It lets you write code faster, with helpful features like IntelliSense, real-time linting and formatting, debugging tools, and more.
• The Python Docstring Generator is a lifesaver for developers who have to work on complex NumPy code. This extension will generate detailed docstrings to document the code in seconds, which saves genuinely countless hours of manual writing and formatting.
• Python Test Explorer can run your Python tests right from the sidebar and get instant feedback on the results. There is no need to switch back and forth between applications, making it another worthwhile timesaver.
• MagicPython is also terrific for Python developers who need to analyze and debug complicated code. Thanks to improved syntax highlighting and indentation, you’ll be able to read NumPy expressions on the fly with MagicPython.
• Kite’s Autocomplete for Python is yet another more thoughtful way to code. It uses machine learning so that Kite can give context-aware completions for Python scripts. Instead of spending time looking up functions and methods from the library (or the Internet), you’ll find intelligent suggestions with Kite as you type. It even supports complex operations.

Write Great Code With NumPy

As you can tell, installing NumPy into VS Code is not intimidating at all, and its features are a great help for coding and analyzing data science. And within Visual Studio Code, you can take NumPy development further when you combine it with other powerful tools and extensions.

Did you get NumPy working in your VS Code? What’s its most helpful functionality for you? Tell us in the comments.