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descriptive-statistics helpers — summary (mean/std/median), quantile, zscore normalization, and Pearson correlation on plain Python number lists (no pandas/numpy).
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SOC 職業分類に基づく
Score an LLM's biological-protocol reasoning on the BioProBench benchmark: protocol QA, step ordering, error detection, protocol generation, and LLM-judged error reasoning; or generate the responses.
Run the reference end-to-end research pass — fixed database query, local analysis, versioned artifacts with lineage, then an exported evidence package that verifies in a clean environment. Use as the first-run demonstration, as a benchmark case, or when a result must be handed to someone who was not there when it ran.
industrial retrosynthesis planning pipeline for target SMILES; run AiZynthFinder, normalize route JSON, query route molecules, rank routes, render figure-style route dashboards, and write analyst reports with retrosynthetic rationale.
ADMET-guided genetic molecule optimization workflow from seed SMILES; use when the agent needs to build or run an RDKit/SA-Score/ADMET-AI GA pipeline for molecule optimization, enforce molecule lineage logs, render optimization-history HTML dashboards, and write candidate triage reports.
Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency pLDDT, ipTM, and RMSD, or to run a quick MSA-backed prediction using the public MMseqs2 server.
Audit tabular datasets before analysis or training for schema drift, missing values, duplicate rows or IDs, target imbalance, and entity or group leakage across splits using pure-stdlib helpers.
| name | example_stats |
| description | descriptive-statistics helpers — summary (mean/std/median), quantile, zscore normalization, and Pearson correlation on plain Python number lists (no pandas/numpy). |
| origin | personal |
Lightweight descriptive-statistics helpers. Use this when a task needs quick summary statistics, quantiles, correlation, or z-score normalization on plain Python number lists — no pandas/numpy required.
from example_stats.kernel import summary, quantile, zscore, correlation
Describe a dataset in one call:
data = [4, 8, 15, 16, 23, 42]
s = summary(data)
print(s) # {'n':6,'mean':18.0,'std':13.49...,'min':4,'max':42,'median':15.5}
Get an arbitrary quantile (0..1):
p90 = quantile(data, 0.90)
print(p90)
Standardize values (z-scores, sample std):
z = zscore(data)
print(z) # list of z-scores, mean~0 std~1
Pearson correlation between two equal-length series:
r = correlation([1, 2, 3, 4], [2, 4, 6, 8])
print(r) # 1.0
summary uses SAMPLE std (n-1 denominator). Pass population=True for the
n-denominator version.ValueError on empty input.