category_rank

category_rank(source, categories, name=None, metadata=None)

Return percentile ranks within each category and row.

Parameters

source : Panel | Graph

Input numeric Panel or single-output Graph. categories : Panel | Graph

Matching CategoryPanel containing row-wise group labels. name : str | None, default None

Optional graph-node name. A generated name is used when omitted. metadata : Mapping[str, Any] | None, default None

Optional metadata stored on the graph node.

Returns

Graph

Lazy single-output graph. Call .compute() to materialize a Panel.

Examples

import pandas as pd

from bagelquant_core import CategoryPanel, Domain, Panel
from bagelquant_core.transformer import category_rank

domain = Domain(calendar=pd.to_datetime(["2024-01-02"]), universe=["a", "b", "c"])
factor = Panel.from_domain(pd.DataFrame({"a": [1.0], "b": [3.0], "c": [8.0]}, index=domain.sessions), domain)
industry = CategoryPanel.from_domain(pd.DataFrame({"a": ["tech"], "b": ["tech"], "c": ["bank"]}, index=domain.sessions), domain)

result = category_rank(factor, industry).compute().data
print(result)

Notes

Rows represent time and columns represent assets.

Missing group labels are excluded from the group calculation.