Visual studio python

Visual studio python DEFAULT

Python in Visual Studio Code

Working with Python in Visual Studio Code, using the Microsoft Python extension, is simple, fun, and productive. The extension makes VS Code an excellent Python editor, and works on any operating system with a variety of Python interpreters. It leverages all of VS Code's power to provide auto complete and IntelliSense, linting, debugging, and unit testing, along with the ability to easily switch between Python environments, including virtual and conda environments.

This article provides only an overview of the different capabilities of the Python extension for VS Code. For a walkthrough of editing, running, and debugging code, use the button below.

Python Hello World Tutorial

Install Python and the Python extension

The tutorial guides you through installing Python and using the extension. You must install a Python interpreter yourself separately from the extension. For a quick install, use Python from and install the extension from the VS Code Marketplace.

Once you have a version of Python installed, activate it using the Python: Select Interpreter command. If VS Code doesn't automatically locate the interpreter you're looking for, refer to Environments - Manually specify an interpreter.

You can configure the Python extension through settings. Learn more in the Python Settings reference.

Windows Subsystem for Linux: If you are on Windows, WSL is a great way to do Python development. You can run Linux distributions on Windows and Python is often already installed. When coupled with the Remote - WSL extension, you get full VS Code editing and debugging support while running in the context of WSL. To learn more, go to Developing in WSL or try the Working in WSL tutorial.

The Insiders program allows you to try out and automatically install new versions of the Python extension prior to release, including new features and fixes.

If you'd like to opt into the program, you can either open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and select Python: Switch to Insiders Daily/Weekly Channel or else you can open settings (⌘, (Windows, Linux Ctrl+,)) and look for Python: Insiders Channel to set the channel to "daily" or "weekly".

Run Python code

To experience Python, create a file (using the File Explorer) named and paste in the following code:

The Python extension then provides shortcuts to run Python code in the currently selected interpreter (Python: Select Interpreter in the Command Palette):

  • In the text editor: right-click anywhere in the editor and select Run Python File in Terminal. If invoked on a selection, only that selection is run.
  • In Explorer: right-click a Python file and select Run Python File in Terminal.

You can also use the Terminal: Create New Terminal command to create a terminal in which VS Code automatically activates the currently selected interpreter. See Environments below. The Python: Start REPL activates a terminal with the currently selected interpreter and then runs the Python REPL.

For a more specific walkthrough on running code, see the tutorial.

Autocomplete and IntelliSense

The Python extension supports code completion and IntelliSense using the currently selected interpreter. IntelliSense is a general term for a number of features, including intelligent code completion (in-context method and variable suggestions) across all your files and for built-in and third-party modules.

IntelliSense quickly shows methods, class members, and documentation as you type, and you can trigger completions at any time with ⌃Space (Windows, Linux Ctrl+Space). You can also hover over identifiers for more information about them.

IntelliSense and autocomplete for Python code

Tip: Check out the IntelliCode extension for VS Code (preview). IntelliCode provides a set of AI-assisted capabilities for IntelliSense in Python, such as inferring the most relevant auto-completions based on the current code context.


Linting analyzes your Python code for potential errors, making it easy to navigate to and correct different problems.

The Python extension can apply a number of different linters including Pylint, pycodestyle, Flake8, mypy, pydocstyle, prospector, and pylama. See Linting.


No more statement debugging! Set breakpoints, inspect data, and use the debug console as you run your program step by step. Debug a number of different types of Python applications, including multi-threaded, web, and remote applications.

For Python-specific details, including setting up your configuration and remote debugging, see Debugging. General VS Code debugging information is found in the debugging document. The Django and Flask tutorials also demonstrate debugging in the context of those web apps, including debugging Django page templates.


The Python extension automatically detects Python interpreters that are installed in standard locations. It also detects conda environments as well as virtual environments in the workspace folder. See Configuring Python environments. You can also use the setting to point to an interpreter anywhere on your computer.

The current environment is shown on the left side of the VS Code Status Bar:

Selected Python interpreter in the Status Bar

The Status Bar also indicates if no interpreter is selected:

Status bar showing no selected Python interpreter

The selected environment is used for IntelliSense, auto-completions, linting, formatting, and any other language-related feature other than debugging. It is also activated when you use run Python in a terminal.

To change the current interpreter, which includes switching to conda or virtual environments, select the interpreter name on the Status Bar or use the Python: Select Interpreter command.

Python: Select Interpreter command

VS Code prompts you with a list of detected environments as well as any you've added manually to your user settings (see Configuring Python environments).

Installing packages

Packages are installed using the Terminal panel and commands like (Windows) and (macOS/Linux). VS Code installs that package into your project along with its dependencies. Examples are given in the Python tutorial as well as the Django and Flask tutorials.

Jupyter notebooks

If you open a Jupyter notebook file () in VS Code, you can use the Jupyter Notebook Editor to directly view, modify, and run code cells.

Jupyter notebook running in VS code in the Notebook Editor

You can also convert and open the notebook as a Python code file. The notebook's cells are delimited in the Python file with comments, and the Python extension shows Run Cell or Run All Cells CodeLens. Selecting either CodeLens starts the Jupyter server and runs the cell(s) in the Python interactive window:

Jupyter notebook running in VS Code and the Python interactive window

Opening a notebook as a Python file allows you to use all of VS Code's debugging capabilities. You can then save the notebook file and open it again as a notebook in the Notebook Editor, Jupyter, or even upload it to a service like Azure Notebooks.

Using either method, Notebook Editor or a Python file, you can also connect to a remote Jupyter server for running the code. For more information, see Jupyter support.


The Python extension supports testing with unittest and pytest.

To run tests, you enable one of the frameworks in settings. Each framework also has specific settings, such as arguments that identify paths and patterns for test discovery.

Once discovered, VS Code provides a variety of commands (on the Status Bar, the Command Palette, and elsewhere) to run and debug tests, including the ability to run individual test files and individual methods.


The Python extension provides a wide variety of settings for its various features. These are described on their relevant topics, such as Editing code, Linting, Debugging, and Testing. The complete list is found in the Settings reference.

Other popular Python extensions

The Microsoft Python extension provides all of the features described previously in this article. Additional Python language support can be added to VS Code by installing other popular Python extensions.

  1. Open the Extensions view (⇧⌘X (Windows, Linux Ctrl+Shift+X)).
  2. Filter the extension list by typing 'python'.

The extensions shown above are dynamically queried. Click on an extension tile above to read the description and reviews to decide which extension is best for you. See more in the Marketplace.

Next steps



Getting Started with Python in VS Code

In this tutorial, you use Python 3 to create the simplest Python "Hello World" application in Visual Studio Code. By using the Python extension, you make VS Code into a great lightweight Python IDE (which you may find a productive alternative to PyCharm).

This tutorial introduces you to VS Code as a Python environment, primarily how to edit, run, and debug code through the following tasks:

  • Write, run, and debug a Python "Hello World" Application
  • Learn how to install packages by creating Python virtual environments
  • Write a simple Python script to plot figures within VS Code

This tutorial is not intended to teach you Python itself. Once you are familiar with the basics of VS Code, you can then follow any of the programming tutorials on within the context of VS Code for an introduction to the language.

If you have any problems, feel free to file an issue for this tutorial in the VS Code documentation repository.


To successfully complete this tutorial, you need to first setup your Python development environment. Specifically, this tutorial requires:

  • VS Code
  • VS Code Python extension
  • Python 3

Install Visual Studio Code and the Python Extension

  1. If you have not already done so, install VS Code.

  2. Next, install the Python extension for VS Code from the Visual Studio Marketplace. For additional details on installing extensions, see Extension Marketplace. The Python extension is named Python and it's published by Microsoft.

    Python extension on Marketplace

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.


Install Python from You can typically 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 Python 3.7, Python 3.8, Python 3.9, and Python 3.10. Be aware that you might have compatibility issues with some packages using this method.

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


The system install of Python on macOS is not supported. Instead, an installation through Homebrew is recommended. To install Python using Homebrew on macOS use 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.


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

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 Remote - 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.

Verify the Python installation

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:

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

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

Note You can use the 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 (*).

Start VS Code in a project (workspace) folder

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

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

By starting VS Code in a folder, that folder becomes your "workspace". VS Code stores settings that are specific to that workspace in , which are separate from user settings that are stored globally.

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

Select a Python interpreter

Python is an interpreted language, and in order to run Python code and get Python IntelliSense, you must tell VS Code which interpreter to use.

From within VS Code, select a Python 3 interpreter by opening the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), start typing the Python: Select Interpreter command to search, then select the command. You can also use the Select Python Environment option on the Status Bar if available (it may already show a selected interpreter, too):

No interpreter selected

The command presents a list of available interpreters that VS Code can find automatically, including virtual environments. If you don't see the desired interpreter, see Configuring Python environments.

Note: When using an Anaconda distribution, the correct interpreter should have the suffix , for example .

Selecting an interpreter sets which interpreter will be used by the Python extension for that workspace.

Note: If you select an interpreter without a workspace folder open, VS Code sets in User scope instead, which sets the default interpreter for VS Code in general. The user setting makes sure you always have a default interpreter for Python projects. The workspace settings lets you override the user setting.

Create a Python Hello World source code file

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

File Explorer New File

Name the file , and it automatically opens in the editor:

File Explorer

By using the 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 :

When you start typing , notice how IntelliSense presents auto-completion options.

IntelliSense appearing for Python code

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 variable contains a string, IntelliSense provides string methods when you type :

IntelliSense appearing for a variable whose type provides methods

Feel free to experiment with IntelliSense some more, but then revert your changes so you have only the variable and the call, and save the file (⌘S (Windows, Linux Ctrl+S)).

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

Run Hello World

It's simple to run with Python. Just click the Run Python File in Terminal play button in the top-right side of the editor.

Using the run python file in terminal button

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

Program output in a Python terminal

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):

    Run Python File in Terminal command in the Python editor

  • 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.

Configure and run the debugger

Let's now try debugging our simple Hello World program.

First, set a breakpoint on line 2 of by placing the cursor on the call and pressing F9. Alternately, just click in the editor's left gutter, next to the line numbers. When you set a breakpoint, a red circle appears in the gutter.

Setting a breakpoint in

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.

Debug configurations after launch.json is created

Note: VS Code uses JSON files for all of its various configurations; is the standard name for a file containing debugging configurations.

These different configurations are fully explained in Debugging configurations; for now, just select Python File, which is the configuration that runs the current file shown in the editor using the currently selected Python interpreter.

You can also start the debugger by clicking on the down-arrow next to the run button on the editor, and selecting Debug Python File in Terminal.

Using the debug Python file in terminal button

The debugger will 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 will see now defined variable appears in the Local pane.

Debugging step 2 - variable defined

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)).

Debugging toolbar

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 , hovering over the variable will display the string 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:

Debugging step 3 - using the debug console

Select the blue Continue button on the toolbar again (or press F5) to run the program to completion. "Hello World" 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 statements to quickly inspect variables without necessarily stepping through each line of code in a debugger. In VS Code, you can instead use Logpoints. A Logpoint is like a breakpoint except that it logs a message to the console and doesn't stop the program. For more information, see Logpoints in the main VS Code debugging article.

Install and use packages

Let's now run an example that's a little more interesting. In Python, packages are how you obtain any number of useful code libraries, typically from PyPI. For this example, you use the and packages to create a graphical plot as is commonly done with data science. (Note that cannot show graphs when running in the Windows Subsystem for Linux as it lacks the necessary UI support.)

Return to the Explorer view (the top-most icon on the left side, which shows files), create a new file called , and paste in the following source code:

Tip: If you enter the above code by hand, you may find that auto-completions change the names after the keywords when you press Enter at the end of a line. To avoid this, type a space, then Enter.

Next, try running the file in the debugger using the "Python: Current file" configuration as described in the last section.

Unless you're using an Anaconda distribution or have previously installed the package, you should see the message, "ModuleNotFoundError: No module named 'matplotlib'". Such a message indicates that the required package isn't available in your system.

To install the package (which also installs as a dependency), 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.

A best practice among Python developers is to avoid installing packages into a global interpreter environment. You instead use a project-specific that contains a copy of a global interpreter. Once you activate that environment, any packages you then install are isolated from other environments. Such isolation reduces many complications that can arise from conflicting package versions. To create a virtual environment and install the required packages, enter the following commands as appropriate for your operating system:

Note: For additional information about virtual environments, see Environments.

  1. Create and activate the virtual environment

    Note: When you create a new virtual environment, you should be prompted by VS Code to set it as the default for your workspace folder. If selected, the environment will automatically be activated when you open a new terminal.

    Virtual environment dialog

    For Windows

    If the activate command generates the message "Activate.ps1 is not digitally signed. You cannot run this script on the current system.", then you need to temporarily change the PowerShell execution policy to allow scripts to run (see About Execution Policies in the PowerShell documentation):

    For macOS/Linux

  2. Select your new environment by using the Python: Select Interpreter command from the Command Palette.

  3. Install the packages

  4. Rerun the program now (with or without the debugger) and after a few moments a plot window appears with the output:

    matplotlib output

  5. Once you are finished, type in the terminal window to deactivate the virtual environment.

For additional examples of creating and activating a virtual environment and installing packages, see the Django tutorial and the Flask tutorial.

Next steps

You can configure VS Code to use any Python environment you have installed, including virtual and conda environments. You can also use a separate environment for debugging. For full details, see Environments.

To learn more about the Python language, follow any of the programming tutorials listed on within the context of VS Code.

To learn to build web apps with the Django and Flask frameworks, see the following tutorials:

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


  1. Peacock images painting
  2. Bad company lyrics
  3. John macarthur audio books
  4. Corvette c8 engine specs

Visual Studio Code for Python Programmers

Introduction xix

Part I Welcome to Visual Studio Code 1

Chapter 1 Getting Started 3

Installing Visual Studio Code 4

The Visual Studio Code User Interface 4

Activity Bar 5

Side Bar 6

Editor 7

Panels 11

Status Bar 12

Command Palette 12

Extensions 14

Customizations 15

Settings 16

Color Themes and Icons 18

Keybindings 18

Display Langage 18

Summary 19

Chapter 2 Hello World for Python 21

Installing a Python Interpreter 21

macOS 22

Linux 22

Windows 22

Installing the Python Extension for Visual Studio Code 22

Creating a Python File 23

Selecting an Interpreter 24

Setting a Default Interpreter 26

Settings Editor 26

settings.json File 26

Selecting a Linter 26

Editing a Python File 27

Running a Python File 29

Workflow Recap 30

Summary 31

Chapter 3 Editing Code 33

Quick Fixes 34

Code Completion, Definitions, and Declarations 35

Formatting 38

Edit Formatting Settings in the Settings Editor 39

Edit Formatting Settings in settings.json 40

Linting 41

Enable and Disable Linting 41

Run Linting 42

Linting Settings 43

Refactoring 44

Extract Variable 44

Extract Method 45

Sort Imports 46

Snippets 47

Summary 48

Part II Additional Visual Studio Code Features 51

Chapter 4 Managing Projects and Collaboration 53

Files and Folders 53

Open a Project 54

Navigate Files 56

Search across Files 57

Close a File or Folder 60

Environments 60

Virtual Environments 61

Conda Environments 61

Source Control 63

Initialize a Repository 65

Commit Changes 66

Branches 69

Remotes 70

Gutter Indicators 71

View Diffs 71

Push and Merge Commits 73

Pull Requests 74

Live Share 74

Install Live Share 75

Sign In to Live Share 76

Share a Project 76

Join a Session 78

Editing and Collaboration 80

Follow a Participant 80

Share a Terminal 81

Summary 82

Chapter 5 Debugging 83

Starting a Debug Session 84

Debug Commands 89

Continue 89

Step Over 90

Step Into 90

Step Out 91

Stop 91

Restart 92

Call Stack 92

Triggering a Breakpoint 93

Logpoints 95

Watch 96

The Debug Console 98

Launch Configurations 101

Summary 104

Chapter 6 Unit Testing 105

Enable and Discover Tests 105

Run Tests 109

Debug Tests 113

Summary 115

Chapter 7 Jupyter Notebook 117

Creating and Opening a Jupyter Notebook 118

Code Cell Modes 120

Adding Cells 121

Editing Cells 122

Running a Cell 124

Running a Single Cell 124

Running All Code Cells 124

Running Cells Above and Below a Code Cell 125

Additional Commands 126

Viewing Variables and Data 126

Viewing Plots 128

Debugging a Jupyter Notebook 129

Connecting to a Remote Server 130

Exporting a Notebook 131

Summary 132

Chapter 8 Using Git and GitHub with Visual Studio Code 135

Getting Started 135

GitHub Pull Requests and Issues Extension 136

Publish a Project to GitHub 139

Push Changes to GitHub 141

Manage Pull Requests and Issues 143

Pull Requests 144

Issues 147

Clone Repository 152

Timeline View 154

Summary 156

Chapter 9 Deploy a Django App to Azure App Service with the Azure App Service Extension 157

Getting Started 157

Creating a Django Project 159

Creating an App 161

Creating a Home Page 163

Creating Website Pages 166

Deploying to Azure 168

Summary 175

Chapter 10 Create and Debug a Flask App 177

Getting Started 177

Create a Flask App 178

Create and Render a Template 180

Debug the Flask App 184

Summary 187 

Chapter 11 Create and Deploy a Container with Azure Container Registry and Azure App Service 189

Getting Started 189

Create a Container 191

Add Docker Files to the Project 191

Build an Image 193

Build and Run a Container 195

Debug a Container 197

Push an Image to the Registry 197

Create an Azure Container Registry 198

Determine the Image’s Registry Location 199

Deploy the Container Image to Azure 201

Make Changes to the App and Deploy 205

Multicontainer Apps 206

Summary 207

Chapter 12 Deploy an Azure Function Trigger by a Timer 209

Getting Started 210

Create an Azure Function 211

Invoke the Function Locally 213

Add the Code to the Function 214

Deploy the Function to Azure 215

Summary 220

Appendix Getting Started with Azure 221

Index 225

Visual Studio 2019 and Python

Sonechka looked at the tip with her mouth slightly open. It was mesmerizing. Seeing the white drop, she smiled. -Yes this.

Python visual studio

Mishka thought. She will go again!" This time he did it right, just perfect. He left the toilet, went into the room and showed Kolka with gestures that nothing had worked out so far. They sat for about another hour and a half, then Katka said that it was time for her and began to get ready.

Visual Studio 2019 Launch: Python development with Visual Studio

Yeah. in a pack of ten pieces: Enough for a couple of days: Although I will have my period soon. And so safe. Then I'll leave them with you: Ok. The children quietly walked into another hall of the boarding house.

Now discussing:

I don't understand what you mean. Harry paused for a moment, looking through Ron Weasley. I seem to have figured out exactly how the Demo Kratius spell works.

487 488 489 490 491