Claude Code prepares filtered research CSV files

Papanda, a neurosurgeon writing about research work, describes using Claude Code to prepare datasets for statistical software. The previous process involved opening a CSV, selecting qualifying rows, checking missing values and grouping records. A changed inclusion condition could force the preparation to begin again.

The published example gives the agent explicit age and hospital-stay thresholds, requests grouping by gender and asks it to save a statistics-ready file. Papanda reports a prepared file on the desktop within minutes. A follow-up instruction adds a BMI threshold and produces a revised file. The source does not identify the study, disclose patient records or establish that the illustrative thresholds define a medically appropriate analysis.

Papanda retains the validation step: compare output with the original data, check that extraction conditions were applied correctly and inspect missing-value handling. A clean-looking file is insufficient evidence of correct content. The stated time reduction is a personal experience rather than an independently measured benchmark. The practical result is faster iteration on a clearly specified data-preparation task, with the researcher still responsible for interpretation and correctness. The copyable pattern is to make selection, grouping and output requirements explicit and review the resulting rows before using them in an analysis.

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Source: Blog · ぱぱんだ (Papanda), neurosurgeon and research blogger