Function value_counts_internal

pandas/core/algorithms.py:841–932

(
    values,
    sort: bool = True,
    ascending: bool = False,
    normalize: bool = False,
    bins=None,
    dropna: bool = True,
)

def value_counts_internal(
    values,
    sort: bool = True,
    ascending: bool = False,
    normalize: bool = False,
    bins=None,
    dropna: bool = True,
) -> Series:
    from pandas import (
        DatetimeIndex,
        Index,
        Series,
        TimedeltaIndex,
    )

    index_name = getattr(values, "name", None)
    name = "proportion" if normalize else "count"

    if bins is not None:
        from pandas.core.reshape.tile import cut

        if isinstance(values, Series):
            values = values._values

        try:
            ii = cut(values, bins, include_lowest=True)
        except TypeError as err:
            raise TypeError("bins argument only works with numeric data.") from err

        # count, remove nulls (from the index), and but the bins
        result = ii.value_counts(dropna=dropna)
        result.name = name
        result = result[result.index.notna()]
        result.index = result.index.astype("interval")
        result = result.sort_index()

        # if we are dropna and we have NO values
        if dropna and (result._values == 0).all():
            result = result.iloc[0:0]

        # normalizing is by len of all (regardless of dropna)
        normalize_denominator = len(ii)

    else:
        normalize_denominator = None
        if is_extension_array_dtype(values):
            # handle Categorical and sparse,
            result = Series(values, copy=False)._values.value_counts(dropna=dropna)
            result.name = name
            result.index.name = index_name

        elif isinstance(values, ABCMultiIndex):
            # GH49558
            levels = list(range(values.nlevels))
            result = (
                Series(index=values, name=name)
                .groupby(level=levels, dropna=dropna)
                .size()