Visual Studio Code Numpy | Install Numpy In Vs Code

Free Python and Data Science lessons

NASA-inspired lessons

This learning path enables students to use Python to explore doing analyses and projects inspired from real-world problems faced by National Aeronautics and Space Administration (NASA) scientists. View full details of the lessons under NASA-inspired Lessons.

Learn Python with Over The Moon

These space-themed lessons were inspired by the Netflix film, Over the Moon, and will introduce students to data science, machine learning, and artificial intelligence using Python and Azure. View full details on Learn Python with Over The Moon.

Wonder Woman-inspired lessons

Give an introduction to Python with “Wonder Woman 1984”-inspired lessons that help students learn about the basics like conditionals and variables. Get full lesson details under Learn Python with Wonder Woman.

Python in Notebooks

Learn the basics of Python. View the full lesson at Write basic Python in Notebooks in Visual Studio Code.

Set up your Python beginner development environment

A step-by-step guide to installing and setting up your Python and VS Code environment. View the full lesson at Set up your Python beginner development environment with Visual Studio Code.

Getting Started with Python in VS Code

In this tutorial, you will learn how to use Python 3 in Visual Studio Code to create, run, and debug a Python “Roll a dice” application, work with virtual environments, use packages, and more! By using the Python extension, you turn VS Code into a great, lightweight Python editor.

If you are new to programming, check out the Visual Studio Code for Education – Introduction to Python course. This course offers a comprehensive introduction to Python, featuring structured modules in a ready-to-code browser-based development environment.

To gain a deeper understanding of the Python language, you can explore any of the programming tutorials listed on python.org within the context of VS Code.

For a Data Science focused tutorial with Python, check out our Data Science section.

Create a virtual environment

A best practice among Python developers is to use a project-specific
virtual environment
. Once you activate that environment, any packages you then install are isolated from other environments, including the global interpreter environment, reducing many complications that can arise from conflicting package versions. You can create non-global environments in VS Code using Venv or Anaconda with Python: Create Environment.

Open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), start typing the Python: Create Environment command to search, and then select the command.

The command presents a list of environment types, Venv or Conda. For this example, select Venv.

The command then presents a list of interpreters that can be used for your project. Select the interpreter you installed at the beginning of the tutorial.

After selecting the interpreter, a notification will show the progress of the environment creation and the environment folder (
/.venv
) will appear in your workspace.

Ensure your new environment is selected by using the Python: Select Interpreter command from the Command Palette.

Note: For additional information about virtual environments, or if you run into an error in the environment creation process, see Environments.

How To Install NumPy in Visual Studio Code on Windows 11 | Setup NumPy Project in VSCode

Checking the NumPy Version Installed on Your System

Before we proceed with using NumPy in our projects, it’s essential to know the version of NumPy installed on your system. There are multiple ways to check the NumPy version, and we will discuss two common methods below.

Method 1: Using Python’s Interactive Shell

Open your terminal or command prompt.
Type
python
or
python3
, depending on your Python installation, and press Enter to launch the Python interactive shell.
Import the NumPy library by typing the following command and pressing Enter:

import numpy as np

To check the NumPy version, type the following command and press Enter:

print(np.__version__)

This command will display the NumPy version installed on your system.

Method 2: Using a Python Script

Create a new Python file named
check_numpy_version.py
.
Open the file in your favorite code editor and add the following code:

import numpy as np def main(): numpy_version = np.__version__ print("NumPy version:", numpy_version) if __name__ == "__main__": main()

Save the file and run the script using the terminal or command prompt by typing the following command and pressing Enter:

python check_numpy_version.py

This command will display the NumPy version installed on your system.

Conclusion

These are the steps to install the NumPy module on Microsoft VS Code. I hope you have liked this tutorial. If you are unable to install it and have another query then you can contact us for more help.

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You may not have numpy installed on the version of python you are running.

Try this:

import sys

print(sys.version)

Is the printed version Anaconda? If you installed Anaconda python, it should come with numpy already installed. If it turns out to be another version of python you are accessing inside Visual Studio Code that doesn’t have numpy installed, then that’s what you need to fix.

The version of python that is called depends on which version of python comes up in your PATH variable first. Type into a terminal:
echo $PATH
.
The output should look like this with Anaconda bin first:
/Users/jlzhang/anaconda/bin:/usr/local/bin:/usr/bin:/bin

If you do not have Anaconda bin first, you can add this to your ~/.bashrc file:
echo

# Use Anaconda python

export PATH=”/Users/jlzhang/anaconda/bin:$PATH”

Restart a terminal and Visual Studio Code and see if you are now running Anaconda python.

Hope it helps/ Did it work?

You may not have numpy installed on the version of python you are running.

Try this:

import sys

print(sys.version)

If you do not have Anaconda bin first, you can add this to your ~/.bashrc file:
echo

# Use Anaconda python

export PATH=”/Users/jlzhang/anaconda/bin:$PATH”

Restart a terminal and Visual Studio Code and see if you are now running Anaconda python.

Hope it helps/ Did it work?

How to Install Numpy in Visual Studio (2023)

FAQ

Q: What is NumPy?

A: NumPy is a popular library for scientific computing in Python. It provides a high-performance multidimensional array object and tools for working with these arrays.

Q: How do I check the NumPy version installed on my system?

A: You can check the NumPy version by importing the library in the Python interactive shell or a Python script and using the
np.__version__
attribute.

Q: How do I upgrade NumPy to the latest version?

A: You can upgrade NumPy using the command
pip install --upgrade numpy
in your terminal or command prompt.

Q: Why should I use NumPy for data analysis?

A: NumPy provides performance, ease of use, compatibility, and extensive documentation, making it an essential tool for data analysis and scientific computing in Python.

For more information on NumPy, visit the official NumPy documentation.

We hope that this guide has helped you understand how to check the NumPy version installed on your system and upgrade it if necessary. Happy coding with NumPy on codedamn!

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Python in Visual Studio Code

Visual Studio Code is a free source code editor that fully supports Python and useful features such as real-time collaboration. It’s highly customizable to support your classroom the way you like to teach.

“Visual Studio Code is the best balance of authenticity and accessibility… Visual Studio Code doesn’t feel ‘fake’, it’s what real software developers use. Plus, Visual Studio Code works on every OS!” – Professor Zachary Dodds from Harvey Mudd College

Read below for recommendations for extensions, settings, and links to free lessons that you can use in your classes.

Install NumPy with VSCode - Windows — Install NumPy with VSCode – Windows

Benefits of Using NumPy and Python for Data Analysis

NumPy provides several benefits when working with large datasets and performing complex mathematical operations:

Performance: NumPy’s ndarray object is optimized for performance, providing a faster alternative to Python’s built-in list object for numerical operations.
Ease of use: NumPy simplifies complex mathematical operations with easy-to-read syntax and built-in functions.
Compatibility: NumPy is compatible with a wide range of other Python libraries, making it an essential tool for data analysis and scientific computing.
Extensive documentation: NumPy has well-maintained documentation and an active community, making it easy to find resources and support for your projects.

Run Python code

Click the Run Python File in Terminal play button in the top-right side of the editor.

The button opens a terminal panel in which your Python interpreter is automatically activated, then runs
python3 hello.py
(macOS/Linux) or
python hello.py
(Windows):

There are three other ways you can run Python code within VS Code:

Right-click anywhere in the editor window and select Run > Python File in Terminal (which saves the file automatically):
Select one or more lines, then press Shift+Enter or right-click and select Run Selection/Line in Python Terminal. This command is convenient for testing just a part of a file.
From the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), select the Python: Start REPL command to open a REPL terminal for the currently selected Python interpreter. In the REPL, you can then enter and run lines of code one at a time.

Congrats, you just ran your first Python code in Visual Studio Code!

How To Install NumPy in Visual Studio Code (Windows 11)

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.

Disclaimer: Some pages on this site may include an affiliate link. This does not effect our editorial in any way.

Python NumPy is a general-purpose array processing package that provides tools for handling n-dimensional arrays. It provides various computing tools such as comprehensive mathematical functions, and linear algebra routines. NumPy provides both the flexibility of Python and the speed of well-optimized compiled C code. Its easy-to-use syntax makes it highly accessible and productive for programmers from any background. In this article, we will see how to install NumPy as well as how to import Numpy in Python.

Pre-requisites:

Install NumPy in VS Code

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

Fire up VS Code. If, by chance, you don’t have it already download a copy from the official website.
Click on the Extension tab. You can find it on the left side of the window, denoted by a four-squared icon.
Type “Python” into the extension search bar.
Select “Python from Microsoft” from the results.
Click on the blue “Install” button.
Go back to the main menu.
Select “Terminal.”
Choose “New Terminal.”
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.

How to install Python Libraries in Visual Studio Code

Common Ways to Use NumPy

Here are some examples of how to use NumPy in your projects:

Creating arrays: You can create NumPy arrays using various functions, such as
np.array()
,
np.zeros()
, and
np.ones()
.

import numpy as np # Creating a 1D array arr1 = np.array([1, 2, 3, 4, 5]) print("1D array:", arr1) # Creating a 2D array filled with zeros arr2 = np.zeros((3, 3)) print("2D array filled with zeros:", arr2) # Creating a 3D array filled with ones arr3 = np.ones((2, 2, 2)) print("3D array filled with ones:", arr3)

Performing mathematical operations: NumPy makes it easy to perform element-wise mathematical operations on arrays.

import numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) # Element-wise addition sum = a + b print("Sum:", sum) # Element-wise multiplication product = a * b print("Product:", product) # Squaring elements in the array squared = a**2 print("Squared:", squared)

Configure and run the debugger

Let’s now try debugging our Python program. Debugging support is provided by the Python Debugger extension, which is automatically installed with the Python extension. To ensure it has been installed correctly, open the Extensions view (⇧⌘X (Windows, Linux Ctrl+Shift+X)) and search for
@installed python debugger
. You should see the Python Debugger extension listed in the results.

Next, set a breakpoint on line 2 of
hello.py
by placing the cursor on the

Next, to initialize the debugger, press F5. Since this is your first time debugging this file, a configuration menu will open from the Command Palette allowing you to select the type of debug configuration you would like for the opened file.

Note: VS Code uses JSON files for all of its various configurations;

launch.json
is the standard name for a file containing debugging configurations.

Select Python File, which is the configuration that runs the current file shown in the editor using the currently selected Python interpreter.

The debugger will start, and then stop at the first line of the file breakpoint. The current line is indicated with a yellow arrow in the left margin. If you examine the Local variables window at this point, you can see that the
msg
variable appears in the Local pane.

A debug toolbar appears along the top with the following commands from left to right: continue (F5), step over (F10), step into (F11), step out (⇧F11 (Windows, Linux Shift+F11)), restart (⇧⌘F5 (Windows, Linux Ctrl+Shift+F5)), and stop (⇧F5 (Windows, Linux Shift+F5)).

The Status Bar also changes color (orange in many themes) to indicate that you’re in debug mode. The Python Debug Console also appears automatically in the lower right panel to show the commands being run, along with the program output.

To continue running the program, select the continue command on the debug toolbar (F5). The debugger runs the program to the end.

Tip Debugging information can also be seen by hovering over code, such as variables. In the case of

msg
, hovering over the variable will display the string
Roll a dice!
in a box above the variable.

You can also work with variables in the Debug Console (If you don’t see it, select Debug Console in the lower right area of VS Code, or select it from the … menu.) Then try entering the following lines, one by one, at the > prompt at the bottom of the console:

msg msg.capitalize() msg.split()

Select the blue Continue button on the toolbar again (or press F5) to run the program to completion. “Roll a dice!” appears in the Python Debug Console if you switch back to it, and VS Code exits debugging mode once the program is complete.

If you restart the debugger, the debugger again stops on the first breakpoint.

To stop running a program before it’s complete, use the red square stop button on the debug toolbar (⇧F5 (Windows, Linux Shift+F5)), or use the Run > Stop debugging menu command.

For full details, see Debugging configurations, which includes notes on how to use a specific Python interpreter for debugging.

Tip: Use Logpoints instead of print statements: Developers often litter source code with

How To Install NumPy in Visual Studio Code (Mac)

How to check the version of the Numpy in visual Studio code

After installing the module you can check whether the Numpy is installed or not. It’s a must check as you can get the ModuleNotFoundError: no module named numpy even after installing it. Go to your sample.py and use the following lines of code to check the version of the numpy.

import numpy as np print(np.__version__)

You can also check the version of Python using the terminal of the Visual Studio Code. Go to the terminal and type the following command to know the version of the python.

If python 3 has been installed then use the python3 command otherwise just python.

For Python 3. xx

python3 --version

For Python 2.xx

python --version

Introduction to NumPy

NumPy (short for Numerical Python) is a popular library for scientific computing in Python. It provides a high-performance multidimensional array object and tools for working with these arrays. NumPy is widely used in various fields such as data analysis, machine learning, image processing, and more. It allows you to perform complex mathematical operations on large data sets with ease and efficiency.

Install Python Library NumPy In Windows10 | Visual Studio Code

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.

Python3

import
numpy as np

arr
np.array( [[
],

]] )

print
"Array is of type: "
type
(arr))

print
"No. of dimensions: "
, arr.ndim)

print
"Shape of array: "
, arr.shape)

print
"Size of array: "
, arr.size)

print
"Array stores elements of type: "
, arr.dtype)

Output:

Array is of type: No. of dimensions: 2Shape of array: (2, 3)Size of array: 6Array stores elements of type: int64

Install and use packages

Let’s build upon the previous example by using packages.

In Python, packages are how you obtain any number of useful code libraries, typically from PyPI, that provide additional functionality to your program. For this example, you use the
numpy
package to generate a random number.

Return to the Explorer view (the top-most icon on the left side, which shows files), open
hello.py
, and paste in the following source code:

import numpy as np msg = "Roll a dice" print(msg) print(np.random.randint(1,9))

Tip: If you enter the above code by hand, you may find that auto-completions change the names after the

as
keywords when you press Enter at the end of a line. To avoid this, type a space, then Enter.

Next, run the file in the debugger using the “Python: Current file” configuration as described in the last section.

You should see the message, “ModuleNotFoundError: No module named ‘numpy'”. This message indicates that the required package isn’t available in your interpreter. If you’re using an Anaconda distribution or have previously installed the
numpy
package you may not see this message.

To install the
numpy
package, stop the debugger and use the Command Palette to run Terminal: Create New Terminal (⌃⇧` (Windows, Linux Ctrl+Shift+`)). This command opens a command prompt for your selected interpreter.

To install the required packages in your virtual environment, enter the following commands as appropriate for your operating system:

Install the packages

# Don't use with Anaconda distributions because they include matplotlib already. # macOS python3 -m pip install numpy # Windows (may require elevation) py -m pip install numpy # Linux (Debian) apt-get install python3-tk python3 -m pip install numpy
Now, rerun the program, with or without the debugger, to view the output!

Congrats on completing the Python tutorial! During the course of this tutorial, you learned how to create a Python project, create a virtual environment, run and debug your Python code, and install Python packages. Explore additional resources to learn how to get the most out of Python in Visual Studio Code!

NumPy with VS Code Extensions

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.

How to run Python in Visual Studio Code on Windows 10/11 [ 2024 Update ] Python Developers

Intro to CS at Harvey Mudd College

Professor Zachary Dodds is a Computer Science professor at Harvey Mudd College who teaches several introductory classes both for students new to Computer Science and students from a non-Computer Science background. He co-created the popular introduction to Computer Science class CS5, which attracts students from all backgrounds to develop programming and problem-solving skills and to build “a coherent, intellectually compelling picture of Computer Science”. The class is taught with Python and uses VS Code as the recommended editor.

Why Visual Studio Code?

Professor Dodds has been recommending and using Visual Studio Code in his classes since it debuted in 2015.

“Visual Studio Code is the best balance of authenticity and accessibility… Visual Studio Code doesn’t feel ‘fake’, it’s what real software developers use. Plus, Visual Studio Code works on every OS!”

VS Code runs on Windows, macOS, Linux, and even Chromebooks.

Classroom settings

Since VS Code is easy to customize, Professor Dodds is able to tailor the editor for his students, preferring to hide IntelliSense, or code completion suggestions, so they can learn from what they type and reinforce the conceptual models being built.

Here are the settings his students use:

"editor.quickSuggestions": false, "editor.acceptSuggestionOnCommitCharacter": false, "editor.suggest.filterGraceful": true, "editor.suggestOnTriggerCharacters": false, "editor.acceptSuggestionOnEnter": "on", "editor.suggest.showIcons": false, "editor.suggest.maxVisibleSuggestions": 7, "editor.hover.enabled": false, "editor.hover.sticky": false, "editor.suggest.snippetsPreventQuickSuggestions": false, "editor.parameterHints.enabled": false, "editor.wordBasedSuggestions": "matchingDocuments", "editor.tabCompletion": "on", "extensions.ignoreRecommendations": true, "files.autoSave": "afterDelay",

You can find the most up-to-date settings on his course website: CS5 – Python Tips.

Integrated Terminal

Professor Dodds also utilizes the built-in terminal heavily in his class as an introduction to running programs from the command line and navigating around their machine all within Visual Studio Code. He appreciates how “the built-in terminal panel does not try to automate too much (which, if it did, would deprive newcomers of the experience of the information-flow that’s going on).”

In the video below, the student does all of their command line and coding work in one place, such as installing Python libraries, while working on Lab 3 from the CS5 class:

Thank you, Professor Dodds, for sharing your story! If you’re interested in using VS Code to teach Python in your classes, you can get started with the Python Education Extension Pack below!

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:

Open the Command Palette (Ctrl+Shift+P).
Find the command “
Create: New Jupyter Notebook
.”
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.

HƯỚNG DẪN PHÁT HIỆN SÓNG NGÀNH VÀ CỔ PHIẾU MẠNH VỚI AMIBROKER

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.

Next steps

To learn how to build web apps with popular Python web frameworks, see the following tutorials:

There is then much more to explore with Python in Visual Studio Code:

Python profile template – Create a new profile with a curated set of extensions, settings, and snippets
Editing code – Learn about autocomplete, IntelliSense, formatting, and refactoring for Python.
Linting – Enable, configure, and apply a variety of Python linters.
Debugging – Learn to debug Python both locally and remotely.
Testing – Configure test environments and discover, run, and debug tests.
Settings reference – Explore the full range of Python-related settings in VS Code.
Deploy Python to Azure App Service
Deploy Python to Container Apps

Numpy is a Python library that allows you to do mathematical calculations very easily. It has many functions that allow you to perform these calculations. Many data science learner readers have asked that they are unable to install numpy in visual studio code. In this tutorial, you will know how to install numpy in visual studio code through the steps.

Python for Data Science - Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib) — Python for Data Science – Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib)

Python Extension Pack

Unsure which extensions to recommend to your students? You can point your students to the Python Education Extension Pack that contains essential and helpful extensions for the classroom. You can download the extension pack from the VS Code Marketplace:

The extension pack contains:

Python for basic Python functionality like compiling, debugging support, linting, Jupyter Notebooks, unit tests, and more.
Live Share to enable real-time collaboration.
Remote – SSH to work on remote projects (for example, to access lab machines) through SSH with full VS Code functionality.
Markdown+Math for full LaTeX support in Markdown.
Python Test Explorer for Visual Studio Code to visualize and run Python tests in the side bar.
Code Runner to run snippets (selected code) and single files of any code with a single click.

Upgrading Your NumPy Setup

To ensure that you have the latest features and bug fixes, it’s essential to keep your NumPy installation up-to-date. To upgrade NumPy, follow these steps:

Open your terminal or command prompt.
Run the following command to upgrade NumPy to the latest version:

pip install --upgrade numpy

This command will update NumPy to the latest available version.

Create a Python source code file

From the File Explorer toolbar, select the New File button on the
hello
folder:

Name the file
hello.py
, and VS Code will automatically open it in the editor:

By using the
.py
file extension, you tell VS Code to interpret this file as a Python program, so that it evaluates the contents with the Python extension and the selected interpreter.

Note: The File Explorer toolbar also allows you to create folders within your workspace to better organize your code. You can use the New folder button to quickly create a folder.

Now that you have a code file in your Workspace, enter the following source code in
hello.py
:

msg = "Roll a dice" print(msg)

When you start typing

IntelliSense and auto-completions work for standard Python modules as well as other packages you’ve installed into the environment of the selected Python interpreter. It also provides completions for methods available on object types. For example, because the
msg
variable contains a string, IntelliSense provides string methods when you type
msg.
:

Finally, save the file (⌘S (Windows, Linux Ctrl+S)). At this point, you’re ready to run your first Python file in VS Code.

For full details on editing, formatting, and refactoring, see Editing code. The Python extension also has full support for Linting.

Installing Numpy on Windows

Below are the ways by which we can install NumPy on Windows and later on import Numpy in Python:

Install Numpy Using Conda

If you want the installation to be done through conda, you can use the below command:

conda install -c anaconda numpy

You will get a similar message once the installation is complete

Make sure you follow the best practices for installation using conda as:

Use an environment for installation rather than in the base environment using the below command:

conda create -n my-envconda activate my-env

Note: If your preferred method of installation is conda-forge, use the below command:

conda config –env –add channels conda-forge

Installing Numpy For PIP Users

Users who prefer to use pip can use the below command to install NumPy:

pip install numpy

You will get a similar message once the installation is complete:

Now that we have installed Numpy successfully in our system, let’s take a look at few simple examples.

Example of Numpy

In this example, a 2D NumPy array named
arr
is created, and its characteristics are demonstrated: the array type, number of dimensions (2), shape (2 rows, 3 columns), size (6 elements), and the data type of its elements (int64).

Learn NUMPY in 5 minutes - BEST Python Library! — Learn NUMPY in 5 minutes – BEST Python Library!

Install NumPy in VS Code

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

Fire up VS Code. If, by chance, you don’t have it already download a copy from the official website.
Click on the Extension tab. You can find it on the left side of the window, denoted by a four-squared icon.
Type “Python” into the extension search bar.
Select “Python from Microsoft” from the results.
Click on the blue “Install” button.
Go back to the main menu.
Select “Terminal.”
Choose “New Terminal.”
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.

Start VS Code in a workspace folder

By starting VS Code in a folder, that folder becomes your “workspace”.

Using a command prompt or terminal, create an empty folder called “hello”, navigate into it, and open VS Code (
code
) in that folder () by entering the following commands:

mkdir hello cd hello code .

Note: If you’re using an Anaconda distribution, be sure to use an Anaconda command prompt.

Alternately, you can create a folder through the operating system UI, then use VS Code’s File > Open Folder to open the project folder.

Install Numpy FAQ

Q: How do I install NumPy?

You can install NumPy by using the pip package installer. Open your command prompt or terminal and run the following command: pip install numpy. This will download and install the latest version of NumPy from PyPI.

Q: Do I need to install any dependencies for NumPy?

NumPy has a few dependencies, such as the Python development headers and a C compiler. However, when you install NumPy using pip, it automatically handles the dependencies for you.

Q: Can I install a specific version of NumPy?

Yes, you can install a specific version of NumPy by specifying the version number in the pip install command. For example, to install version 1.19.5, you would run: pip install numpy==1.19.5.

Q: I encountered an error related to building or compilingWhat should I do?

Building NumPy from source requires certain development tools. On Windows, you might need to install Microsoft Visual C++ Build Tools. On macOS, you may need to install the Xcode Command Line Tools. On Linux, you may need to install the build-essential package. Refer to the NumPy documentation for detailed instructions based on your operating system.

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Last Updated :
07 Dec, 2023

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How to check NumPy version installed on your system

NumPy is an essential library for anyone working with data in Python. It provides a powerful array object, known as the ndarray, which simplifies mathematical operations and makes it easier to work with large amounts of data. In this blog post, we will discuss how to check the NumPy version installed on your system and provide a step-by-step guide to updating your NumPy setup if necessary. We will also cover the benefits of using NumPy and Python for data analysis and provide some examples of common ways to use NumPy in your code.

Steps to install numpy in Visual Studio Code

In this section, you will know all the steps that you will follow for better understanding.

Step 1: Open your visual studio code and go to the extension tab. You will find it on the left side of the window.

Step 2: Go to the search bar and type Python. There you will see the Python by Mircosoft.

Step 3: Click on the install button to install. It will be a blue in color.

Step 4: After the installation of Python. Go to the terminal and click on the new terminal.

Step 5: Type the following command on it to install the numpy.

pip install numpy

In my system, the numpy is already installed that’s why I am getting the message requirement already satisfied. In your case, it will start downloading numpy and install it.

That’s all you have to do to install the numpy module in Visual Studio code.

How To Install Python Libraries In Visual Studio Code (Windows 11)

Write Great Code With NumPy

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

Disclaimer: Some pages on this site may include an affiliate link. This does not effect our editorial in any way.

How to Install NumPy in VS Code

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 a Python interpreter

Along with the Python extension, you need to install a Python interpreter. Which interpreter you use is dependent on your specific needs, but some guidance is provided below.

Windows

Install Python from python.org. Use the Download Python button that appears first on the page to download the latest version.

Note: If you don’t have admin access, an additional option for installing Python on Windows is to use the Microsoft Store. The Microsoft Store provides installs of supported Python versions.

For additional information about using Python on Windows, see Using Python on Windows at Python.org

macOS

The system install of Python on macOS is not supported. Instead, a package management system like Homebrew is recommended. To install Python using Homebrew on macOS use
brew install python3
at the Terminal prompt.

Note: On macOS, make sure the location of your VS Code installation is included in your PATH environment variable. See these setup instructions for more information.

Linux

The built-in Python 3 installation on Linux works well, but to install other Python packages you must install
pip
with get-pip.py.

Other options

Data Science: If your primary purpose for using Python is Data Science, then you might consider a download from Anaconda. Anaconda provides not just a Python interpreter, but many useful libraries and tools for data science.
Windows Subsystem for Linux: If you are working on Windows and want a Linux environment for working with Python, the Windows Subsystem for Linux (WSL) is an option for you. If you choose this option, you’ll also want to install the WSL extension. For more information about using WSL with VS Code, see VS Code Remote Development or try the Working in WSL tutorial, which will walk you through setting up WSL, installing Python, and creating a Hello World application running in WSL.

Note: To verify that you’ve installed Python successfully on your machine, run one of the following commands (depending on your operating system):

Linux/macOS: open a Terminal Window and type the following command:

python3 --version

Windows: open a command prompt and run the following command:

py -3 --version

If the installation was successful, the output window should show the version of Python that you installed. Alternatively, you can use the

py -0
command in the VS Code integrated terminal to view the versions of python installed on your machine. The default interpreter is identified by an asterisk (*).

Python vs C/C++ vs Assembly side-by-side comparison

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

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

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

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