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Developer tools · #spyder
Learn to use Spyder's Variable Explorer for debugging Python scientific code. Covers conditional breakpoints, post-mortem debugging, and verification steps.
Spyder's IPython kernel integration uses a lightweight client-server model over ZeroMQ. This guide explains the minimal design, trust boundaries, operational checks, failure modes, and when to change the architecture for remote kernels or protocol updates.
Learn how to configure Spyder's IPython console and Variable Explorer to maintain session state, perform inline data editing, and inspect Python objects in real-time.
Spyder’s Variable Explorer lets you inspect and edit NumPy arrays live in the editor. Learn how it syncs with the IPython console, configure memory‑sensitive settings, and use a step‑by‑step example to boost debugging speed.
Inspect and edit live Python variables in Spyder's Variable Explorer, with caveats around pickle persistence and silent state mutation.
Move from print‑debugging to interactive inspection in Spyder with the Variable Explorer and cell‑based script execution. Learn a practical example, trade‑offs, and how to avoid common pitfalls.
Spyder's IPython console hangs on connecting or crashes after an environment change. This diagnostic guide isolates kernel failures from Spyder configuration using ordered checks, targeted fixes, and clear escalation criteria.
How Spyder keeps its Qt GUI responsive by running IPython in a separate kernel process, plus trust boundaries, operational checks, and failure modes.
Spyder's editor and the process running your code are different programs. That explains most "which Python am I in?" confusion — and enables remote execution.
jupyter
numpy
python