by_type

lab.connectome.by_type

Collect the values of a table column for each cell type.

in_order(table, order, *, by='type')

Sort the rows of a table by the position of their label.

Parameters:

Name	Type	Description	Default
table	DataFrame	Has the column named by by.	required
order	sequence of str	The labels in the order the rows should come.	required
by	str	Column that holds each row's label.	"type"

Returns:

Type	Description
DataFrame	The rows sorted by the position of their by value in order; rows with the same value keep their order, and a value not in order goes last.

Examples:

>>> import polars as pl
>>> table = pl.DataFrame({"type": ["B", "A", "C"], "n": [1, 2, 3]})
>>> in_order(table, ["A", "B"])["type"].to_list()
['A', 'B', 'C']

Source code in lab/connectome/by_type.py

def in_order(
    table: pl.DataFrame, order: Sequence[str], *, by: str = "type"
) -> pl.DataFrame:
    """Sort the rows of a table by the position of their label.

    Parameters
    ----------
    table : polars.DataFrame
        Has the column named by `by`.
    order : sequence of str
        The labels in the order the rows should come.
    by : str, default "type"
        Column that holds each row's label.

    Returns
    -------
    polars.DataFrame
        The rows sorted by the position of their `by` value in
        `order`; rows with the same value keep their order, and a
        value not in `order` goes last.

    Examples
    --------
    >>> import polars as pl
    >>> table = pl.DataFrame({"type": ["B", "A", "C"], "n": [1, 2, 3]})
    >>> in_order(table, ["A", "B"])["type"].to_list()
    ['A', 'B', 'C']
    """
    return table.sort(
        pl.col(by).replace_strict(
            {value: n for n, value in enumerate(order)}, default=len(order)
        ),
        maintain_order=True,
    )

values_by_type(table, column, types)

Collect the values of a column among the rows of each type.

Parameters:

Name	Type	Description	Default
table	DataFrame	Has a column type and the column named by column.	required
column	str	Column whose values are collected.	required
types	sequence of str	The types to collect, in order.	required

Returns:

Type	Description
dict of str to polars.Series	One series per type; a type with no row has an empty series.

See Also

lab.connectome.overlap.sets_by_type : The same, as sets.

Examples:

>>> import polars as pl
>>> table = pl.DataFrame({"type": ["P", "P", "Q"], "s": [3, 5, 4]})
>>> by_type = values_by_type(table, "s", ["P", "Q"])
>>> {t: values.to_list() for t, values in by_type.items()}
{'P': [3, 5], 'Q': [4]}

Source code in lab/connectome/by_type.py

def values_by_type(
    table: pl.DataFrame, column: str, types: Sequence[str]
) -> dict[str, pl.Series]:
    """Collect the values of a column among the rows of each type.

    Parameters
    ----------
    table : polars.DataFrame
        Has a column ``type`` and the column named by `column`.
    column : str
        Column whose values are collected.
    types : sequence of str
        The types to collect, in order.

    Returns
    -------
    dict of str to polars.Series
        One series per type; a type with no row has an empty series.

    See Also
    --------
    lab.connectome.overlap.sets_by_type : The same, as sets.

    Examples
    --------
    >>> import polars as pl
    >>> table = pl.DataFrame({"type": ["P", "P", "Q"], "s": [3, 5, 4]})
    >>> by_type = values_by_type(table, "s", ["P", "Q"])
    >>> {t: values.to_list() for t, values in by_type.items()}
    {'P': [3, 5], 'Q': [4]}
    """
    return {t: table.filter(pl.col("type") == t)[column] for t in types}