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Dataspell's built-in DataFrame viewer lets you sort, filter, and export pandas DataFrames directly in the debugger—no print statements, no CSV round-trips. Here's how to use it, where it struggles with large frames, and a quick verification checklist.
Learn how to use Streamlit's st.cache_data to speed up expensive operations, avoid stale data, and tune cache size and TTL.
Dataspell’s built‑in Jupyter notebooks let you prototype, test, and visualize data without leaving the IDE, boosting productivity while keeping code and results together.
Learn how to lock a Conda environment with environment.yml, avoid platform issues, and keep data‑science workflows reproducible across machines.
Stop relying on df.head() for every check. Learn how to use DataSpell's Variable View and remote kernels to streamline data exploration and offload heavy compute.
Resource Management in Managed Notebooks Kaggle Notebooks provide GPU acceleration through a weekly quota system, utilizing accelerators such as the T4 or P100. These resources are managed at the account level rather than the individual notebook level. Concurrency and Quota Constraints When multiple notebooks are active simultaneously, the platform must bala
pandas
dataspell
streamlit
jupyter
kaggle