Update PyCharm
By default, PyCharm is configured to check for updates automatically and notify you when a new version is available. Updates are usually patch-based : they are applied to the existing installation and only require you to restart the IDE. However, sometimes patch updates are not available, and a new version of PyCharm must be installed.
When PyCharm updates to a new major release, it opens the What’s New in PyCharm tab in the editor with information about the changes, improvements, and fixes. To open this tab manually, select Help | What’s New in PyCharm .
If PyCharm does not have HTTP access outside your local network, it will not be able to check for updates and apply patches. In this case, you have to download new versions of the IDE and install them manually as described in Standalone installation. On macOS, you can delete the outdated version of PyCharm from Application and then drag the newly installed version to Application .
Toolbox App
If you installed PyCharm using the Toolbox App, it will suggest that you update the IDE when a new version is available.
Automatically update all managed tools
- Open the Toolbox App and click the Toolbox App menu icon in the top right corner.
- In the Toolbox App Settings dialog, expand Tools and select Update all tools automatically .
If you disable this option, you will need to click Update next to any instance when a newer version comes out.
You can also configure the update policy for every managed IDE instance separately.
Configure the update policy for a specific instance
- Open the Toolbox App, click next to the relevant IDE instance, and select Settings .
- In the instance settings dialog, select the update channel to use for this IDE instance:
- Release : Update only to stable releases that are recommended for production.
- Early Access Program : Includes updates to release candidates , beta releases, and EAP builds, which are not recommended for production and include feature previews.
Standalone instance
If you installed PyCharm manually, the standalone IDE instance will manage its own updates. It will notify you when a new version is available (show you a notification in the Event Log tool window). You can choose to update the current instance, download and install the new version as a separate instance, postpone the notification, or ignore the update entirely.
On Linux, it is not recommended to manually update an existing standalone PyCharm installation to a newer major version. You should always extract the downloaded tarball to a clean directory. For more information, refer to Standalone installation.
If you installed PyCharm using root privileges, you should use the same level of privileges to update the IDE.
Configure the update policy
To manage the PyCharm update policy, open Settings Control+Alt+S and select Appearance & Behavior | System Settings | Updates .
If the IDE instance is managed by the Toolbox App, these settings will affect only plugin updates.
The Updates page contains the following settings:
Check IDE updates for
Select whether you want PyCharm to check for updates automatically and choose an update channel.
- Early Access Program : Provides all updates, including major version EAP builds and minor version Preview builds. This channel is not recommended for production development. PyCharm can be updated only to a minor Preview version, but not to a major EAP build. For example, you can update PyCharm 2021.1.1 to 2021.1.2 , but not to 2021.2 EAP . The 2021.2 EAP version in this case will be installed as an additional instance. EAP versions can be updated to both newer EAP and stable PyCharm versions. If an EAP version is updated to a stable version at some point, the name of the original installation directory does not change.
- Beta Releases or Public Previews : Includes stable releases, release candidates, and beta releases. Some updates in this channel may contain minor bugs and feature previews.
- Stable Releases : Includes only stable releases that are recommended for production.
You can choose the update channel only if you are using a stable version . For EAP builds, the channel is always set to Early Access Program .
Check for plugin updates
Select whether you want PyCharm to check for new versions of plugins automatically.
Check for Updates
Check for updates immediately.
Alternatively, go to Help | Check for Updates on Windows or Linux, or PyCharm | Check for Updates on macOS.
Manage ignored updates…
Show the list of updates that were ignored. These updates will not be suggested until you remove them from the list of ignored updates.
If you remove several updates from the ignored list, only the most recent will be offered for download when you check for updates.
Show What’s New in the editor after an IDE update
Open a tab with information about new features and improvements after a major IDE update.
Snap package
If you installed PyCharm as a snap package, it will manage updates automatically. All snaps are updated automatically in the background every day. You can also get the latest version of all snaps manually at any time by running the following command:
sudo snap refresh
Or if you want to update only the PyCharm snap:
Python
If you are a Windows user, we recommend that you install Python for Windows.
Supported versions
- Python 2: version 2.7
- Python 3: from the version 3.6 up to the version 3.12
The following features are available as part of Python 3.12 support:
- PPEP 695 – Type Parameter Syntax
- PEP 669 – Low Impact Monitoring for CPython
- PEP 701 – Syntactic formalization of f-strings
- PEP 698 – Override Decorator for Static Typing
The following features are available as part of Python 3.11 support:
- PEP 673 – Self Type
- PEP 646 – Variadic Generics
- PEP 654 – Exception Groups and except*
- PEP 655 – Marking individual TypedDict items as required or potentially-missing
The following features are available as part of Python 3.10 support:
- PEP 647 – User-Defined Type Guards
- PEP 604 – Allow writing union types as X | Y
- PEP 612 – Parameter Specification Variables
- PEP 613 – Explicit Type Aliases
- PEP 634 – Structural Pattern Matching: Specification
- PEP 635 – Structural Pattern Matching: Motivation and Rationale
- PEP 636 – Structural Pattern Matching: Tutorial
The following features are available as part of Python 3.9 support:
- PEP 585 – Type Hinting Generics In Standard Collections
- PEP 593 – Flexible function and variable annotations
- PEP 614 – Relaxing Grammar Restrictions On Decorators
The following features are available as part of Python 3.8 support
- PEP-570 – Python Positional-Only Parameters
- PEP-572 – Assignment Expressions
- PEP-586 – Literal Types
- PEP-589 – TypedDict: Type Hints for Dictionaries with a Fixed Set of Keys
- PEP-591 – Adding a final qualifier to typing
- Python bug tracker task #36817 – Support = expressions in f-strings
PyCharm integrates the following features of Python 3.7:
- PEP-561 – Distributing and Packaging Type Information
- PEP-544 – Protocols: Structural subtyping (static duck typing)
- PEP-557 – Data Classes
- PEP-560 – Core support for typing module and generic types
- PEP-562 – Module __getattr__ and __dir__
- PEP-563 – Postponed Evaluation of Annotations
Python support in PyCharm
PyCharm provides the following features to help you work with Python:
Update Project dialog (Mercurial)
In this dialog, select how you want to synchronize your local repository with the central storage.
Select this option to pull new changesets from the remote repository to the local one. This option can be deselected if the pull operation is performed by other means, for example via a script. The result is identical with that of running the hg pull command.
In this section, select the synchronization method. This strategy will be applied to all Mercurial version control roots. The available options are:
- Only Update : select this option to apply the update strategy. The local working directory will be updated to the latest available changeset. The result is identical with that of running the hg update command. It is recommended to select this option only if there are no conflicting changes or multiple heads, and if the latest changeset is a descendant or ancestor of the working directory’s parent. Otherwise, the update operation will be aborted with errors.
- Merge : select this option to apply the merge strategy. The latest changeset from the central repository will be incorporated into the current tip in your working directory. The result is identical with that of running the hg merge command.
- Commit after merge without conflicts : select this option if you want to commit the resulting changeset after the merge operation has completed successfully.
Do not show this dialog in the future
Select this option to have PyCharm update your project silently in the future using the specified update strategy.
To invoke this dialog before an update, open the the Version Control | Confirmation page of the IDE settings Control+Alt+S , and then select Update in the Display option dialogs when these commands are invoked area.
Configure a Python interpreter
To work with your Python code in PyCharm, you need to configure at least one Python interpreter. You can use a system interpreter that is available with your Python installation. You can also create a Virtualenv, pipenv, Poetry, or conda virtual environment . A virtual environment consists of a base interpreter and the installed packages.
With PyCharm Professional , you can also configure interpreters to execute your Python code on remote environments by using SSH, Vagrant, Docker, Docker Compose, or WSL (only for Windows).
When you configure a Python interpreter , you need to specify the path to the Python executable in your system. So, before configuring a Python interpreter, you need to ensure that you’ve downloaded Python and installed it in your system and you’re aware of a path to it.
You can create several Python interpreters based on the same Python executable. This is helpful when you need to create different virtual environments for developing different types of applications. For example, you can create one virtual environment based on Python 3.6 to develop Django applications and another virtual environment based on the same Python 3.6 to work with scientific libraries.
Python interpreters can be configured for a new project or for the current project (you can create a new interpreter or use one of the existing interpreters).
Configuring an existing Python interpreter
At any time, you can switch the Python interpreter either by using the Python Interpreter selector or in Settings .
Switch the Python interpreter using the Python Interpreter selector
- The Python Interpreter selector is located on the status bar. It is the most convenient and quickest way to switch the Python interpreter. Just click it and select the target interpreter:
Switch the Python interpreter in the IDE settings
- Press Control+Alt+S to open the IDE settings and then select Project | Python Interpreter .
- Click the drop-down and select the desired Python interpreter:
- If it’s not on the list, click Show All . Then select the desired interpreter in the left pane and click OK . When PyCharm stops supporting any of the outdated Python versions, the corresponding Python interpreter is marked as unsupported.
When you change the project interpreter and select an SSH interpreter, you might need to synchronize the local content with the target server. Mind a notification balloon in the lower-right corner:
You can choose to enable the automatic uploading of files to the server:
- Click Auto-upload files to start uploading on the next save.
- Click Sync and auto-upload files to immediately sync the files and upload them on every save in future.
Modify a Python interpreter
- Press Control+Alt+S to open the IDE settings and then select Project | Python Interpreter .
- Expand the list of the available interpreters and click Show All .
- You can modify the path to the Python executable in the Interpreter path field. When the Associate this virtual environment with the current project checkbox is enabled, the interpeter is available only in the current PyCharm project. To change the interpreter name, select the target interpreter and click .
The Python interpreter name specified in the Name field, becomes visible in the list of available interpreters. Click OK to apply the changes. 
Remove a Python interpreter
If you no longer need a Python interpreter for a project, you can remove it from the project settings.
- Do one of the following:
- Press Control+Alt+S to open the IDE settings and then select Project | Python Interpreter .
- Click the Python Interpreter selector and choose Interpreter Settings .
- Expand the list of the available interpreters and click Show All .
- Choose the interpreter that you want to remove and click .
Creating a new Python interpreter
Configuring local Python interpreters
To configure a local Python interpreter for the current project, follow one of the procedures below:
Configure a system interpreter
- Ensure that you have downloaded and installed Python on your computer. Installing Python on Windows from Microsoft Store If you are on Windows, you can download Python from the Microsoft Store and install it as a Python interpreter. Once the Python application is downloaded from the Microsoft Store, it becomes available in the list of the Python executables. Note that interpreters added from the Microsoft Store installations come with some limitations. Because of restrictions on Microsoft Store apps, Python scripts may not have full write access to shared locations such as TEMP and the registry.
- Do one of the following:
- Click the Python Interpreter selector and choose Add New Interpreter .
- Press Control+Alt+S to open Settings and go to Project: | Python Interpreter . Click the Add Interpreter link next to the list of the available interpreters.
- Click the Python Interpreter selector and choose Interpreter Settings . Click the Add Interpreter link next to the list of the available interpreters.
- Select Add Local Interpreter .
- In the left-hand pane of the Add Python Interpreter dialog, select System Interpreter .
- In the Interpreter drop-down, select one of the Python interpreters that have been installed in your system, or click and in the Select Python Interpreter dialog that opens, choose the desired Python executable.
You will need admin privileges to install, remove, and upgrade packages for the system interpreter. When attempting to install an interpreter package through an intention action, you might receive the following error message: 
As prompted, consider using a virtual environment for your project. - Click OK to complete the task.
Create a virtualenv environment
- Do one of the following:
- Click the Python Interpreter selector and choose Add New Interpreter .
- Press Control+Alt+S to open Settings and go to Project: | Python Interpreter . Click the Add Interpreter link next to the list of the available interpreters.
- Click the Python Interpreter selector and choose Interpreter Settings . Click the Add Interpreter link next to the list of the available interpreters.
- Select Add Local Interpreter .
- In the left-hand pane of the Add Python Interpreter dialog, select Virtualenv Environment .
- The following actions depend on whether you want to create a new virtual environment or to use an existing one. New virtual environment
- Specify the location of the new virtual environment in the Location field, or click and browse for the desired location in your file system. The directory for the new virtual environment should be empty.
- Choose the base interpreter from the list, or click and find the desired Python executable in your file system.
- Select the Inherit global site-packages checkbox if you want all packages installed in the global Python on your machine to be added to the virtual environment you’re going to create. This checkbox corresponds to the —system-site-packages option of the virtualenv tool.
Existing virtual environment
- Choose the desired interpreter from the list.
- If the desired interpreter is not on the list, click , and then browse for the desired Python executable (for example, venv/bin/python on macOS or venv\Scripts\python.exe on Windows).
The selected virtual environment will be reused for the current project.
If PyCharm displays the Invalid environment warning, it means that the specified Python binary cannot be found in the file system, or the Python version is not supported. Check the Python path and install a new version, if needed.
Create a conda environment
- Ensure that Anaconda or Miniconda is downloaded and installed on your computer, and you’re aware of a path to its executable file. For more information, refer to the installation instructions.
- Do one of the following:
- Click the Python Interpreter selector and choose Add New Interpreter .
- Press Control+Alt+S to open Settings and go to Project: | Python Interpreter . Click the Add Interpreter link next to the list of the available interpreters.
- Click the Python Interpreter selector and choose Interpreter Settings . Click the Add Interpreter link next to the list of the available interpreters.
- Select Add Local Interpreter .
- In the left-hand pane of the Add Python Interpreter dialog, select Conda Environment .
- The following actions depend on whether you want to create a new conda environment or to use an existing one. New conda environment
- Select the Python version from the list.
- Normally, PyCharm will detect conda installation. Otherwise, specify the location of the conda executable, or click to browse for it.
- Specify the environment name.
Existing conda environment
- Select the environment from the list.
The selected conda environment will be reused for the current project.
Create a pipenv environment
- Do one of the following:
- Click the Python Interpreter selector and choose Add New Interpreter .
- Press Control+Alt+S to open Settings and go to Project: | Python Interpreter . Click the Add Interpreter link next to the list of the available interpreters.
- Click the Python Interpreter selector and choose Interpreter Settings . Click the Add Interpreter link next to the list of the available interpreters.
- Select Add Local Interpreter .
- In the left-hand pane of the Add Python Interpreter dialog, select Pipenv Environment .
- Choose the base interpreter from the list, or click and find the desired Python executable in your file system.
- If your project contains Pipfile , you can choose whether you want to install the packages listed in it by enabling or disabling the Install packages from Pipfile checkbox. By default, the checkbox is enabled.
- If you have added the base binary directory to your PATH environmental variable, you don’t need to set any additional options: the path to the pipenv executable will be autodetected. If the pipenv executable is not found, follow the pipenv installation procedure to discover the executable path, and then specify it in the dialog.
- Click OK to complete the task.
When you have set the pipenv virtual environment as a Python interpreter, all available packages are added from the source defined in Pipfile . The packages are installed, removed, and updated in the list of the packages through pipenv rather than through pip.
Create a Poetry environment
- Do one of the following:
- Click the Python Interpreter selector and choose Add New Interpreter .
- Press Control+Alt+S to open Settings and go to Project: | Python Interpreter . Click the Add Interpreter link next to the list of the available interpreters.
- Click the Python Interpreter selector and choose Interpreter Settings . Click the Add Interpreter link next to the list of the available interpreters.
- Select Add Local Interpreter .
- In the left-hand pane of the Add Python Interpreter dialog, select Poetry Environment .
- The following actions depend on whether you want to create a new Poetry environment or to use an existing one. New Poetry environment
- Select Poetry Environment .
- Choose the base interpreter from the list, or click and find the desired Python executable in your file system.
- If your project contains pyproject.toml , you can choose whether you want to install the packages listed in it by enabling or disabling the Install packages from pyproject.toml checkbox. By default, the checkbox is enabled.
- If PyCharm doesn’t detect the poetry executable, specify the following path in the dialog, replacing jetbrains with your username:
/Users/jetbrains/Library/Application Support/pypoetry/venv/bin/poetry
C:\Users\jetbrains\AppData\Roaming\pypoetry\venv\Scripts\poetry.exe
/home/jetbrains/.local/bin/poetry- Make sure that the project directory contains a pyproject.toml file.
- Select Existing environment . Then expand the Interpreter list and choose the desired interpreter.
- If the desired interpreter is not on the list, click, and then browse for the Python executable within the previously configured Poetry environment.
The selected Poetry environment will be reused for the current project.
Configuring remote Python interpreters
When a remote Python interpreter is added, at first the PyCharm helpers are copied to the remote host. PyCharm helpers are needed to run remotely the packaging tasks, debugger, tests and other PyCharm features.
Next, the skeletons for binary libraries are generated and copied locally. Also, all the Python library sources are collected from the Python paths on a remote host and copied locally along with the generated skeletons. Storing skeletons and all Python library sources locally is required for resolve and completion to work correctly.
PyCharm checks the remote helpers version on every remote run, so if you update your PyCharm version, the new helpers will be uploaded automatically, and you don’t need to recreate remote interpreter.
You can only add a remote interpreter for your Project . A remote interpreter cannot be set as the default interpreter for a Workspace .
Configure a WSL interpreter
- Do one of the following:
- Click the Python Interpreter selector and choose Add New Interpreter .
- Press Control+Alt+S to open Settings and go to Project: | Python Interpreter . Click the Add Interpreter link next to the list of the available interpreters.
- Click the Python Interpreter selector and choose Interpreter Settings . Click the Add Interpreter link next to the list of the available interpreters.
- Select On WSL .
- Wait until PyCharm detects Linux on your machine and completes introspection. Click Next to proceed:
- In the left-hand pane of the dialog, select the type of the WSL interpreter you want to create: Virtual Environment , Conda Environment , or System Interpreter .
For a system interpreter, just provide the path to the Python executable in the selected Linux distribution. For virtual and conda environments, you can provide a path to a Python executable of an existing environment in the selected Linux distribution or create a new environment based on the specified Python.
Once done, the new interpreter will be added to your project, and the default mnt mappings will be set.
Configure an interpreter using Vagrant
- Ensure that the following prerequisites are met (outside of PyCharm):
- One of supported Vagrant providers is installed on your computer.
- Vagrant is installed on your computer, and all the necessary infrastructure is created.
- The parent folders of the following executable files have been added to the system PATH variable:
- vagrant.bat or vagrant from your Vagrant installation. This should be done automatically by the installer.
- VBoxManage.exe or VBoxManage from your Oracle’s VirtualBox installation.
- The required virtual boxes are created.
- Make sure that the Vagrant plugin is enabled.
- Ensure that you have properly initiated and started Vagrant. Basically, you need to open the Terminal window and execute the following commands:
$ vagrant init ubuntu/trusty64
$ vagrant upFor more information, refer to Vagrant documentation.
- Do one of the following:
- Click the Python Interpreter selector and choose Add New Interpreter .
- Press Control+Alt+S to open Settings and go to Project: | Python Interpreter . Click the Add Interpreter link next to the list of the available interpreters.
- Click the Python Interpreter selector and choose Interpreter Settings . Click the Add Interpreter link next to the list of the available interpreters.
- Select On Vagrant .
- Specify the path to the Vagrant instance folder in Vagrant Instance Folder . Wait until you see a link in Vagrant Host URL .
- In the New Target: Vagrant dialog, click the browse icon next to the Vagrant Instance Folder field, and specify the desired Vagrant instance folder. This results in showing the link to Vagrant Host URL .
- In the next field, PyCharm will display the path to the Python executable. Press «Next» to proceed.
- You can create a virtual environment (venv or conda) or use a system Python interpreter for the target Vagrant instance. Note that virtual environment must be configured and available in the specified Vagrant instance folder. Otherwise, the corresponding lists will be empty.
Clik Create to complete the task. - Ensure that there is an SSH server running on a remote host, since PyCharm runs remote interpreters via ssh-sessions.
- Do one of the following:
- Click the Python Interpreter selector and choose Add New Interpreter .
- Press Control+Alt+S to open Settings and go to Project: | Python Interpreter . Click the Add Interpreter link next to the list of the available interpreters.
- Click the Python Interpreter selector and choose Interpreter Settings . Click the Add Interpreter link next to the list of the available interpreters.
- Select On SSH .
- Select an option to create a new SSH connection, then specify server information (host, port, and username).
Alternatively, you can select Existing and choose any available SSH configuration from the list. To create a new SSH configuration, follow the steps below: Creating an SSH configuration
- Click next to the list of configurations:
- Click, disable the Visible only for this project checkbox, and fill in the required fields:
- Once done, the newly created SSH configuration will appear in the list of available configurations. It will also become available in the SSH Deployment Configurations settings. Click Next to proceed:
- Click next to the list of configurations:
- In the next dialog window, provide the authentication details to connect to the target server.
Select Password or Key pair (OpenSSH or PuTTY) and enter your password or passphrase. If Key pair (OpenSSH or PuTTY) is selected, specify:
- Private key : location of the file with a private key
- Passphrase : similar to a password, it serves to encrypt the private key.
- Wait until PyCharm completes the introspection of the SSH server.
- In the next dialog, select a type of Python environment to configure on the SSH server.
You can create a new virtual environment, select an existing one, or use a system interpreter.
- Select the Inherit global site-packages checkbox if you want all packages installed in the global Python on your machine to be added to the virtual environment you’re going to create. This checkbox corresponds to the —system-site-packages option of the virtualenv tool.
- If you need to execute your Python code on the SSH server as a sudo user, enable the Execute code with root privileges via sudo checkbox.
- You can configure the path mappings between your local project and the server. To do that, click the Browse icon in the Sync folders field and enter the path to the local project folder and the path to the folder on the remote server.
Click Create to complete adding the interpreter.
Configure an interpreter using Docker
- Do one of the following:
- Click the Python Interpreter selector and choose Add New Interpreter .
- Press Control+Alt+S to open Settings and go to Project: | Python Interpreter . Click the Add Interpreter link next to the list of the available interpreters.
- Click the Python Interpreter selector and choose Interpreter Settings . Click the Add Interpreter link next to the list of the available interpreters.
- Select On Docker .
- Select an existing Docker configuration in the Docker server dropdown. Alternatively, click and perform the following steps to create a new Docker configuration: Create a Docker configuration Click to add a Docker configuration and specify how to connect to the Docker daemon. The connection settings depend on your Docker version and operating system. For more information, refer to Docker connection settings. The Connection successful message should appear at the bottom of the dialog.
For more information about mapping local paths to the virtual machine running the Docker daemon when using Docker on Windows or macOS, refer to Virtual machine path mappings for Windows and macOS hosts. You will not be able to use volumes and bind mounts for directories outside of the mapped local path. This table is not available on a Linux host, where Docker runs natively and you can mount any directory to the container. - The following actions depend on whether you want to pull a pre-built image from a Docker registry or to build an image locally from a Dockerfile. Pull a Docker image Select Pull or use existing and specify the tag of the desired image in the Image tag field.
Build a Docker image Select Build and change the default values in the Dockerfile and Context folder fields if necessary. 
If required, expand the Optional section and specify the following:
Image tag Specify an optional name and tag for the built image. This can be helpful for referring to the image in the future. If you leave the field blank, the image will have only a random unique identifier. Build options Set supported docker build options. For example, you can specify metadata for the built image with the —label option. Build args Specify the values for build-time variables that can be accessed like regular environment variables during the build process but do not persist in the intermediate or final images. This is similar to using the —build-args option with the docker build command. These variables must be defined in the Dockerfile with the ARG instruction. For example, you can define a variable for the version of the base image that you are going to use: ARG PY_VERSION=latest FROM python:$PY_VERSION
The PY_VERSION variable in this case will default to latest and the Dockerfile will produce an image with the latest available version of Python, unless you redefine it as a build-time argument. If you set, PY_VERSION=3.10 , Docker will pull python:3.10 instead, which will run a container with Python version 3.10. Redefining the PY_VERSION argument is similar to setting the following command-line option:
—build-arg PY_VERSION=3.10
- Wait for PyCharm to connect to the Docker daemon and complete the container introspection.
- Next, select an interpreter to use in the Docker container. You can choose any virtualenv or conda environment that is already configured in the container or select a system interpreter.
- Click OK . The configured remote interpreter is added to the list.
- Do one of the following:
- Click the Python Interpreter selector and choose Add New Interpreter .
- Press Control+Alt+S to open Settings and go to Project: | Python Interpreter . Click the Add Interpreter link next to the list of the available interpreters.
- Click the Python Interpreter selector and choose Interpreter Settings . Click the Add Interpreter link next to the list of the available interpreters.
- Select On Docker Compose .
- Select an existing Docker configuration in the Server dropdown. Alternatively, select Create new and perform the following steps to create a new Docker configuration: Create a Docker configuration Click to add a Docker configuration and specify how to connect to the Docker daemon. The connection settings depend on your Docker version and operating system. For more information, refer to Docker connection settings. The Connection successful message should appear at the bottom of the dialog. For more information about mapping local paths to the virtual machine running the Docker daemon when using Docker on Windows or macOS, refer to Virtual machine path mappings for Windows and macOS hosts. You will not be able to use volumes and bind mounts for directories outside of the mapped local path. This table is not available on a Linux host, where Docker runs natively and you can mount any directory to the container.
- In Configuration files , specify the docker-compose.yml file. Also select the service.
Optionally, specify environment variables and edit the Compose project name in Project name . - Wait until PyCharm creates and configures a new target:
- Next, select an interpreter to use in the container. You can choose any virtualenv or conda environment that is already configured in the container, or select a system interpreter.
- Click OK . The configured remote interpreter is added to the list.
- Go to File | New Projects Setup | Settings for New Projects (on Window and Linux) or File | New Projects Setup | Preferences for New Projects (on macOS).
- Select Python Interpreter settings. Then either choose an existing interpreter from the Python interpreter list of click to add a new interpreter. Click OK to save the changes. The change will become effective for all newly created projects in PyCharm.
- How to install a package
- How to upgrade the package
- How to uninstall the package
Configure an interpreter using SSH
Click Next to proceed.
Configure an interpreter using Docker Compose
Setting the default interpreter
In PyCharm, you can specify an interpreter that will be automatically set for all newly created projects.
Managing interpreter packages
For each interpreter, you can install, upgrade, and delete Python packages. By default, PyCharm uses pip to manage project packages. For conda environments you can use the conda package manager.
PyCharm smartly tracks the status of packages and recognizes outdated versions by showing the number of the currently installed package version (column Version ), and the latest available version (column Latest version ). When a newer version of a package is detected, PyCharm marks it with the arrow sign and suggests to upgrade it.
By default, the Latest version column shows only stable versions of the packages. If you want to extend the scope of the latest available versions to any pre-release versions (such as beta or release candidate ), click Show early releases .
You can upgrade several packages at once. Hold Cmd (macOS) or Ctrl on (Unix or Windows), left-click to select several items in the list of packages, and then click Upgrade .
See the detailed instructions:
If you are looking for a more convenient way to search for Python packages, preview the documentation, and manage Python package repositories, try the Python Packages tool window. For more information, refer to Manage packages in the Python Packages tool window.