get_filter_df_by_prefixes_index

get_filter_df_by_prefixes_index(df: DataframeOrSeries, *, column: str | int | None = None, prefixes: str | Collection[str], method: PrefixIndexMethod | None = None, validate: bool = False, converter: Converter | None = None) → pd.Series[bool][source]

Get an index of CURIEs in the given column that start with the prefix(es).

Parameters:

df – A dataframe or series. If a dataframe is given, the column must not be none.
column – The column to check, if a dataframe was passed. If a series was passed, this can be left as none.
prefixes – The prefix or set of prefixes to identify
method – The indexing method
validate – Should the prefixes be validated against the converter?
converter – A converter for validating CURIEs

Returns:

A pandas boolean series that corresponds to the rows of the dataframe or series provided

Raises:

ValueError – If validation is set to true but no converter is passed

Example usage:

import pandas as pd
from curies.dataframe import get_filter_df_by_prefixes_index

rows = [
    ("DOID:0080795", "skos:exactMatch", "EFO:0003029", "semapv:ManualMappingCuration"),
    ("DOID:0080795", "skos:exactMatch", "mesh:D015471", "semapv:ManualMappingCuration"),
    ("DOID:0080799", "skos:exactMatch", "EFO:1000527", "semapv:ManualMappingCuration"),
    (
        "DOID:0080808",
        "skos:exactMatch",
        "mesh:D000069295",
        "semapv:ManualMappingCuration",
    ),
]
df = pd.DataFrame(
    rows, columns=["subject_id", "predicate_id", "object_id", "mapping_justification"]
)
idx = get_filter_df_by_prefixes_index(df, column="object_id", prefixes=["EFO"])
filtered_df = df[idx]