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Developer tools · #spyder
Learn how to choose between 'Current Console' and 'Dedicated Console' in Spyder to balance iteration speed with environment isolation and avoid ghost variables.
Explains how Spyder’s Variable Explorer isolates the kernel, uses lazy Qt modeling, handles serialization limits, and recovers from failures.
When Spyder’s IPython console exits instantly or never starts, this guide walks you through common causes, step‑by‑step checks, corrective actions, and when to seek help. Covers interpreter misconfigurations, corrupted config files, missing packages, resource limits, and kernel spec issues.
Spyder's Variable Explorer stays responsive on huge NumPy arrays and DataFrames by design: event-driven kernel updates, metadata-first rendering, and dtype-aware viewers that load data only on demand.
Spyder lets you choose between its built‑in IPython console and an external Jupyter kernel. This guide compares the two, shows trade‑offs, and walks through configuration and performance checks to help you pick the right console for your workflow.
Learn how to diagnose and fix Spyder's IPython console when it fails to start or becomes unresponsive, with step‑by‑step checks and fixes.
An architecture note on Spyder’s Variable Explorer: requirements, minimal Qt‑based design, trust boundaries, operational checks, failure modes, and design‑change conditions.
Choosing the right code‑completion engine in Spyder can boost productivity. This guide explains when to pick Jedi or Rope, compares their features, and walks you through configuration and validation steps.
Spyder's Variable Explorer turns the IPython kernel namespace into a live, editable table with lazy loading, bidirectional sync, and built-in plotting—making interactive data inspection faster than print statements for scientific Python workflows.
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