Pip Visual Studio Code | Other Popular Python Extensions

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.

Downgrading PIP Version

Downgrading may be necessary if a new version of PIP starts performing undesirably. To downgrade PIP to a prior version, specify the version you want.

To downgrade PIP, use the following syntax:


python -m pip install pip==[version_number]


For example, to downgrade to version 18.1, run:


python -m pip install pip==18.1


You should now see the version of PIP that you specified.

Note: Learn how to update packages using winget upgrade command.

Conclusion

After reading this guide, you’ve installed PIP and can manage your Python packages on Windows.

Next, check out our guide and learn how to install NumPy using PIP.

Step 5: Install packages in your Python environment

Previous step: Run code in the debugger

The Python developer community has produced thousands of useful packages that you can incorporate into your own projects. Visual Studio provides a UI to manage packages in your Python environments.

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.

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

Introduction

PIP is a package management system that installs and manages software packages written in Python. It stands for “Preferred Installer Program” or “Pip Installs Packages.” The utility manages PyPI package installations from the command line.

Installing PIP on Windows is simple. It requires downloading the installation package, opening the command line, and launching the installer.

This tutorial will show how to install PIP on Windows using two methods. We will also show you how to check, upgrade, and configure PIP.

Note: The latest versions of Python come with PIP pre-installed, but older versions require manual installation. The following guide is for version 3.4 and above. If you are using an older version of Python, follow our guide to upgrade Python.

Prerequisites

• Access to Command Prompt.
• Python installed and added to PATH.

Feedback

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Python is a popular programming language used for web development, data analysis, machine learning, and more. If you’re just starting with Python, one of the first things you’ll need to do is install pip. Pip is a package manager for Python that makes it easy to install, update, and manage Python packages and dependencies.

Frequently Asked Questions

Q: What is a package manager?A: A package manager is a tool that makes it easy to install, update, and manage software packages and dependencies.

Q: What is PyPI?A: PyPI stands for Python Package Index. It is a repository of software packages for the Python programming language.

Q: Can I use Pip with other code editors besides Visual Studio Code?A: Yes, Pip can be used with other code editors and IDEs, such as PyCharm, Sublime Text, and Atom.

Environment variables

Environment variable definitions file

An environment variable definitions file is a text file containing key-value pairs in the form of

environment_variable=value

, with used for comments. Multiline values aren’t supported, but references to previously defined environment variables are allowed. Environment variable definitions files can be used for scenarios such as debugging and tool execution (including linters, formatters, IntelliSense, and testing tools), but aren’t applied to the terminal.

Note: Environment variable definitions files are not necessarily cross-platform. For instance, while Unix uses

as a path separator in environment variables, Windows uses. There is no normalization of such operating system differences, and so you need to make sure any environment definitions file use values that are compatible with your operating system.

By default, the Python extension looks for and loads a file named

.env

in the current workspace folder, then applies those definitions. The file is identified by the default entry

"python.envFile": "${workspaceFolder}/.env"

in your user settings (see General Python settings). You can change the

python.envFile

setting at any time to use a different definitions file.

Note: Environment variable definitions files are not used in all situations where environment variables are available for use. Unless Visual Studio Code documentation states otherwise, these only affect certain scenarios as per their definition. For example, the extension doesn’t use environment variable definitions files when resolving setting values.

A debug configuration also contains an

envFile

property that also defaults to the

.env

file in the current workspace (see Debugging – Set configuration options). This property allows you to easily set variables for debugging purposes that replace variables specified in the default

.env

file.

For example, when developing a web application, you might want to easily switch between development and production servers. Instead of coding the different URLs and other settings into your application directly, you could use separate definitions files for each. For example:

dev.env file


# dev.env - development configuration # API endpoint MYPROJECT_APIENDPOINT=https://my.domain.com/api/dev/ # Variables for the database MYPROJECT_DBURL=https://my.domain.com/db/dev MYPROJECT_DBUSER=devadmin MYPROJECT_DBPASSWORD=!dfka**213=


prod.env file


# prod.env - production configuration # API endpoint MYPROJECT_APIENDPOINT=https://my.domain.com/api/ # Variables for the database MYPROJECT_DBURL=https://my.domain.com/db/ MYPROJECT_DBUSER=coreuser MYPROJECT_DBPASSWORD=kKKfa98*11@


You can then set the

python.envFile

setting to

${workspaceFolder}/prod.env

, then set the

envFile

property in the debug configuration to

${workspaceFolder}/dev.env

.

Note: When environment variables are specified using multiple methods, be aware that there is an order of precedence. All


env

variables defined in the

launch.json

file will override variables contained in the

.env

file, specified by the

python.envFile

setting (user or workspace). Similarly,

env

variables defined in the

launch.json

file will override the environment variables defined in the

envFile

that are specified in

launch.json

.

Use of the PYTHONPATH variable

The PYTHONPATH environment variable specifies additional locations where the Python interpreter should look for modules. In VS Code, PYTHONPATH can be set through the terminal settings (

terminal.integrated.env.*

) and/or within an

.env

file.

When the terminal settings are used, PYTHONPATH affects any tools that are run within the terminal by a user, as well as any action the extension performs for a user that is routed through the terminal such as debugging. However, in this case when the extension is performing an action that isn’t routed through the terminal, such as the use of a linter or formatter, then this setting won’t have an effect on module look-up.

Jupyter notebooks

To enable Python support for Jupyter notebook files (

.ipynb

) in VS Code, you can install the Jupyter extension. The Python and Jupyter extensions work together to give you a great Notebook experience in VS Code, providing you the ability to directly view and modify code cells with IntelliSense support, as well as run and debug them.

You can also convert and open the notebook as a Python code file through the Jupyter: Export to Python Script command. The notebook’s cells are delimited in the Python file with

#%%

comments, and the Jupyter extension shows Run Cell or Run Below CodeLens. Selecting either CodeLens starts the Jupyter server and runs the cell(s) in the Python interactive window:

You can also connect to a remote Jupyter server to run your notebooks. For more information, see Jupyter support.

Python profile template

Profiles let you quickly switch your extensions, settings, and UI layout depending on your current project or task. To help you get started with Python development, you can use the Python profile template, which is a curated profile with useful extensions, settings, and snippets. You can use the profile template as is or use it as a starting point to customize further for you own workflows.

You select a profile template through the Profiles > Create Profile… dropdown:

Once you select a profile template, you can review the settings and extensions, and remove individual items if you don’t want to include them in your new Profile. After creating the new profile based on the template, changes made to settings, extensions, or UI are persisted in your profile.

More Python resources

• Getting Started with Python in VS Code – Learn how to edit, run, and debug code in VS Code.
• Virtual Environments and Packages (Python.org) – Learn more about virtual environments and packages.
• Installing Python Modules (Python.org) – Learn how to install Python modules.
• Python tutorial (Python.org) – Learn more about the Python language.

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.

Working with Python interpreters

Select and activate an environment

The Python extension tries to find and then select what it deems the best environment for the workspace. If you would prefer to select a specific environment, use the Python: Select Interpreter command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)).

Note: If the Python extension doesn’t find an interpreter, it issues a warning. On macOS 12.2 and older, the extension also issues a warning if you’re using the OS-installed Python interpreter as it is known to have compatibility issues. In either case, you can disable these warnings by setting


python.disableInstallationCheck

to

true

in your user settings.

The Python: Select Interpreter command displays a list of available global environments, conda environments, and virtual environments. (See the Where the extension looks for environments section for details, including the distinctions between these types of environments.) The following image, for example, shows several Anaconda and CPython installations along with a conda environment and a virtual environment (

env

) that’s located within the workspace folder:

Note: On Windows, it can take a little time for VS Code to detect available conda environments. During that process, you may see “(cached)” before the path to an environment. The label indicates that VS Code is presently working with cached information for that environment.

If you have a folder or a workspace open in VS Code and you select an interpreter from the list, the Python extension will store that information internally. This ensures that the same interpreter will be used when you reopen the workspace.

The selected environment is used by the Python extension for running Python code (using the Python: Run Python File in Terminal command), providing language services (auto-complete, syntax checking, linting, formatting, etc.) when you have a

.py

file open in the editor, and opening a terminal with the Terminal: Create New Terminal command. In the latter case, VS Code automatically activates the selected environment.

Tip: To prevent automatic activation of a selected environment, add


"python.terminal.activateEnvironment": false

to your

settings.json

file (it can be placed anywhere as a sibling to the existing settings).

Tip: 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):


Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope Process


Note: By default, VS Code uses the interpreter selected for your workspace when debugging code. You can override this behavior by specifying a different path in the


python

property of a debug configuration. See Choose a debugging environment.

The selected interpreter version will show on the right side of the Status Bar.

The Status Bar also reflects when no interpreter is selected.

In either case, clicking this area of the Status Bar is a convenient shortcut for the Python: Select Interpreter command.

Tip: If you have any problems with VS Code recognizing a virtual environment, please file an issue so we can help determine the cause.

Manually specify an interpreter

If VS Code doesn’t automatically locate an interpreter you want to use, you can browse for the interpreter on your file system or provide the path to it manually.

You can do so by running the Python: Select Interpreter command and select the Enter interpreter path… option that shows on the top of the interpreters list:

You can then either enter the full path of the Python interpreter directly in the text box (for example, “.venv/Scripts/python.exe”), or you can select the Find… button and browse your file system to find the python executable you wish to select.

If you want to manually specify a default interpreter that will be used when you first open your workspace, you can create or modify an entry for the

python.defaultInterpreterPath

setting.

Note: Changes to the


python.defaultInterpreterPath

setting are not picked up after an interpreter has already been selected for a workspace; any changes to the setting will be ignored once an initial interpreter is selected for the workspace.

Additionally, if you’d like to set up a default interpreter to all of your Python applications, you can add an entry for

python.defaultInterpreterPath

manually inside your User Settings. To do so, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and enter Preferences: Open User Settings. Then set

python.defaultInterpreterPath

, which is in the Python extension section of User Settings, with the appropriate interpreter.

How the extension chooses an environment automatically

If an interpreter hasn’t been specified, then the Python extension automatically selects the interpreter with the highest version in the following priority order:

1. Virtual environments located directly under the workspace folder.
2. Virtual environments related to the workspace but stored globally. For example, Pipenv or Poetry environments that are located outside of the workspace folder.
3. Globally installed interpreters. For example, the ones found in
   
/usr/local/bin

   ,
   
C:\\python38

   , etc.

Note: The interpreter selected may differ from what


python

refers to in your terminal.

If Visual Studio Code doesn’t locate your interpreter automatically, you can manually specify an interpreter.

Where the extension looks for environments

The extension automatically looks for interpreters in the following locations, in no particular order:

• Standard install paths such as
  
/usr/local/bin

  ,
  
/usr/sbin

  ,
  
/sbin

  ,
  
c:\\python36

  , etc.
• Virtual environments located directly under the workspace (project) folder.
• Virtual environments located in the folder identified by the
  
python.venvPath

  setting (see General Python settings), which can contain multiple virtual environments. The extension looks for virtual environments in the first-level subfolders of
  
venvPath

  .
• Virtual environments located in a
  
~/.virtualenvs

  folder for virtualenvwrapper.
• Interpreters created by pyenv, Pipenv, and Poetry.
• Virtual environments located in the path identified by
  
WORKON_HOME

  (as used by virtualenvwrapper).
• Conda environments found by
  
conda env list

  . Conda environments which do not have an interpreter will have one installed for them upon selection.
• Interpreters installed in a
  
.direnv

  folder for direnv under the workspace folder.

Environments and Terminal windows

After using Python: Select Interpreter, that interpreter is applied when right-clicking a file and selecting Python: Run Python File in Terminal. The environment is also activated automatically when you use the Terminal: Create New Terminal command unless you change the

python.terminal.activateEnvironment

setting to

false

.

Please note that launching VS Code from a shell in which a specific Python environment is activated doesn’t automatically activate that environment in the default Integrated Terminal.

Note: conda environments cannot be automatically activated in the integrated terminal if PowerShell is set as the integrated shell. See Integrated terminal – Terminal profiles for how to change the shell.

Changing interpreters with the Python: Select Interpreter command doesn’t affect terminal panels that are already open. Thus, you can activate separate environments in a split terminal: select the first interpreter, create a terminal for it, select a different interpreter, then use the split button (⌘\ (Windows, Linux Ctrl+Shift+5)) in the terminal title bar.

Choose a debugging environment

By default, the debugger will use the Python interpreter chosen with the Python extension. However, if there is a

python

property specified in the debug configuration of

launch.json

, it takes precedence. If this property is not defined, it will fall back to using the Python interpreter path selected for the workspace.

For more details on debug configuration, see Debugging configurations.

Checking if You Have PIP Installed on Windows

PIP is automatically installed with Python 3.4.x+. However, depending on how Python was installed, PIP may not be available on the system automatically. Before installing PIP on Windows, check if it is already installed:

1. Launch the command prompt window by pressing Windows Key + X and clicking Run.

2. Type in cmd.exe and hit enter.

Alternatively, type cmd in the Windows search bar and click the “Command Prompt” icon.

3. Type the following command in the command prompt:


pip help


If PIP responds with an error message saying the command is not recognized, follow one of the methods below to install it.

Note: Check out our other guides to learn how to install PIP on other operating systems:

Using Pip in Visual Studio Code

Once you have installed Pip in Visual Studio Code, you can start using it to install and manage Python packages and dependencies. Here are some basic commands you can use with Pip:

• pip install package_name: This command installs a Python package.
• pip uninstall package_name: This command uninstalls a Python package.
• pip freeze: This command displays a list of installed packages and their versions.
• pip list: This command displays a list of installed packages.
• pip search package_name: This command searches for a Python package in the Python Package Index (PyPI).

To use Pip in Visual Studio Code, simply open a Python file and open a terminal window. From there, you can use the above commands to install, uninstall, and manage Python packages.

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:

1. 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


2. 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!

Creating environments

Using the Create Environment command

To create local environments in VS Code using virtual environments or Anaconda, you can follow these steps: open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)), search for the Python: Create Environment command, and select it.

The command presents a list of environment types: Venv or Conda.

If you are creating an environment using Venv, the command presents a list of interpreters that can be used as a base for the new virtual environment.

If you are creating an environment using Conda, the command presents a list of Python versions that can be used for your project.

After selecting the desired interpreter or Python version, a notification will show the progress of the environment creation and the environment folder will appear in your workspace.

Note: The command will also install necessary packages outlined in a requirements/dependencies file, such as


requirements.txt

,

pyproject.toml

, or

environment.yml

, located in the project folder. It will also add a

.gitignore

file to the virtual environment to help prevent you from accidentally committing the virtual environment to source control.

Create a virtual environment in the terminal

If you choose to create a virtual environment manually, use the following command (where “.venv” is the name of the environment folder):


# macOS/Linux # You may need to run `sudo apt-get install python3-venv` first on Debian-based OSs python3 -m venv .venv # Windows # You can also use `py -3 -m venv .venv` python -m venv .venv


Note: To learn more about the


venv

module, read Creation of virtual environments on Python.org.

When you create a new virtual environment, a prompt will be displayed in VS Code to allow you to select it for the workspace.

Tip: Make sure to update your source control settings to prevent accidentally committing your virtual environment (in for example


.gitignore

). Since virtual environments are not portable, it typically does not make sense to commit them for others to use.

Create a conda environment in the terminal

The Python extension automatically detects existing conda environments. We recommend you install a Python interpreter into your conda environment, otherwise one will be installed for you after you select the environment. For example, the following command creates a conda environment named

env-01

with a Python 3.9 interpreter and several libraries:


conda create -n env-01 python=3.9 scipy=0.15.0 numpy


Note: For more information on the conda command line, you can read Conda environments.

Additional notes:

• If you create a new conda environment while VS Code is running, use the refresh icon on the top right of the Python: Select Interpreter window; otherwise you may not find the environment there.

• To ensure the environment is properly set up from a shell perspective, use an Anaconda prompt and activate the desired environment. Then, you can launch VS Code by entering the

  
code .

  command. Once VS Code is open, you can select the interpreter either by using the Command Palette or by clicking on the status bar.

• Although the Python extension for VS Code doesn’t currently have direct integration with conda

  
environment.yml

  files, VS Code itself is a great YAML editor.

• Conda environments can’t be automatically activated in the VS Code Integrated Terminal if the default shell is set to PowerShell. To change the shell, see Integrated terminal – Terminal profiles.

• You can manually specify the path to the

  
conda

  executable to use for activation (version 4.4+). To do so, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and run Preferences: Open User Settings. Then set
  
python.condaPath

  , which is in the Python extension section of User Settings, with the appropriate path.

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.

Method 2: Install PIP on Windows Using ensurepip

The second method uses Python to install PIP without downloading or running any scripts. Enter the following command in the command prompt:


python -m ensurepip --upgrade


Wait for the installation to complete. Check that PIP is installed correctly with the following command:


pip --version


The command outputs the PIP version to the console.

Note: If the command is not found, add Python to the Path environment variable. Or, try using the command as

pip3

instead of

pip

.

Next steps

• Python Hello World tutorial – Get started with Python in VS Code.
• Editing Python – Learn about auto-completion, formatting, and refactoring for Python.
• Basic Editing – Learn about the powerful VS Code editor.
• Code Navigation – Move quickly through your source code.
• Django tutorial
• Flask tutorial