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This setting allows for project-specific environment variables. Directory paths for each can be added sequentially and separated by a semicolon. Note the variable name "PYTHONPATH".

Confirm variable setup by outputting the path in Bash terminal, which returns the directory path.

Note that Power Save mode disables code inspection.

Check bottom right corner for memory usage notification. Right-click bottom toolbar and enable Memory Indicator.

Access Help > Change Memory Settings for modifications from default.

Source:

By default, wrap text is not enabled in viewport, but can be via:

View > Active Editor > Soft-Wrap

Subfolders within the Content Root folder can be configured to be noted as Resources, Sources, Templates, or Excluded. Note there are 2 ways to assign this configuration to a folder: via Settings and by right-clicking the folder for options.

Source: https://www.jetbrains.com/help/pycharm/configuring-project-structure.html

In this example, the Python stub file created by UE4 when the Python plugin is enabled is linked to the Python virtual environment so that it can be accessed from within PyCharm.

The filesize of the stub file may prevent PyCharm from recognizing the data, but this can be modified via Help > Edit Custom Properties..., which opens the idea.properties file and it's filesize parameter. Increasing it and saving the file may support PyCharm accessing the stub file.

Source: https://www.jetbrains.com/help/pycharm/stubs.html

Edit Configurations > Configuration tab > Parameters

Source: https://intellij-support.jetbrains.com/hc/en-us/community/posts/360004963019-Using-IntelliJ-as-Git-Bash-Core-Editor-on-Windows

Default is 2500 KB

Configure the following to change: Help > Edit Custom Properties

idea.properties file opens (and creates a new one if one did not already exist)

Add the following and update file size value accordingly:

Save and close the idea.properties file.

In PyCharm, File > Invalidate Caches / Restart

This can also be updated via the Registry.

Source: https://www.jetbrains.com/help/objc/configuring-file-size-limit.html#file-length-limit

Even though Python is an interpreted language (vs. compiled), the interpreter compiles source code to 'byte code' for use in the virtual machine.

Corresponding .pyc files are generated when .py files are imported for use in other files.

Source: https://stackoverflow.com/questions/2998215/if-python-is-interpreted-what-are-pyc-files

Verify that Add Anaconda3 to my PATH environment variable is not enabled.

Disable Register Anaconda3 as my default Python since there are multiple versions of Anaconda and Python running on system.

Install to following location. This can vary, but all installations are collected here for personal preference.

Source: https://docs.anaconda.com/anaconda/install/windows/

Source: https://docs.anaconda.com/anaconda/user-guide/tasks/pycharm/

While PyCharm has the option to install and coordinate environment packages, the installed Anaconda Navigator has a similar user-friendly UI for managing packages in all Conda environments installed on the system. As part of the setup for the Conda environment in PyCharm, verify that the following packages are installed, per the Anaconda documentation link directly above.

Installing packages via the Anaconda Navigator will also update the packages list ready by the Conda environment in PyCharm, confirming that they were installed and recognized. The image below shows the above packages as well as others, like NumPy being installed via the Anaconda Navigator.

The source link below outlines all of the operations in PyCharm to use Jupyter notebooks. The notes that follow are specific instances of encountered issues with operating Jupyter notebooks in PyCharm and resolutions, as well as some key tips.

Source: https://www.jetbrains.com/help/pycharm/jupyter-notebook-support.html

Automatic (default)

Manual

Sources

Verify upstream code cells are run and executed before referencing them in downstream code cells, otherwise they will not run or have the latest updates.

With the Jupyter Server relying on the Java Virtual Machine (JVM), the memory allocations within PyCharm need to be monitored. The following link outlines memory management and most importantly, memory usage notifications when thresholds are being reached or exceeded.

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