Aside from this troubleshooting guide, there are a number of other sources of documentation and troubleshooting information you can check before submitting an issue, as they might already offer an answer, or at least help you better understand the problem.
Even if we aren’t able to help you, these places might.
Spyder’s documentation has a FAQ section, where you might find the answers that you are looking for.
Our YouTube channel contains helpful videos that will guide you through many of Spyder’s features, and provide a starting point for newer users.
We have a public Gitter room where you can chat directly with the Spyder developers.
If you’ve got a quick question to ask the team and are looking for a quick answer, this is a great place to go.
Our Google Group is great for help-related questions, particularly those you aren’t sure are a full-on Spyder issue.
The Spyder website contains basic information about the IDE and links to many other helpful resources.
Stack Overflow is a great place to start searching and posting, particularly if your question is more programming-related, or has to do with something specific to your own code.
It has an vibrant Spyder community as well, with new questions posted every day, and the developers (especially Carlos Cordoba, the lead maintainer) are active in answering them.
The Python help page is a great resource that lists a number of places you can get assistance, support and learning resources for the language and its packages.
The Python documentation site can help you understand a number of issues that can be caused by quirks in the language itself, or misunderstandings as to how it behaves.
r/python and r/learnpython are resources you can use to ask about and discuss issues with Python and its packages.
The former is aimed more at general Python usage, and the latter more specifically at beginners.
The Anaconda help site offers free community help and documentation for the Anaconda applications, installing the Anaconda distribution, and using the Conda package and environment manager; along with paid support options.
The Scipy website is the the central home of the SciPy stack, with information, documentation, help, and bug tracking for many of the core packages used with Spyder, including NumPy, SciPy, Matplotlib, Pandas, Sympy and IPython.
Project Jupyter is the development hub for IPython, Spyder’s QtConsole, Jupyter notebooks used with the Spyder-Notebook plugin, and more.
The Data Science site in Stack Exchange can be very useful for questions that relate more to data science than programming specifically.