string_.py

from typing import TYPE_CHECKING, Type, Union

import numpy as np

from pandas._libs import lib, missing as libmissing
from pandas._typing import Scalar
from pandas.compat.numpy import function as nv

from pandas.core.dtypes.base import ExtensionDtype, register_extension_dtype
from pandas.core.dtypes.common import (
    is_array_like,
    is_bool_dtype,
    is_integer_dtype,
    is_object_dtype,
    is_string_dtype,
    pandas_dtype,
)

from pandas.core import ops
from pandas.core.array_algos import masked_reductions
from pandas.core.arrays import FloatingArray, IntegerArray, PandasArray
from pandas.core.arrays.floating import FloatingDtype
from pandas.core.arrays.integer import _IntegerDtype
from pandas.core.construction import extract_array
from pandas.core.indexers import check_array_indexer
from pandas.core.missing import isna

if TYPE_CHECKING:
    import pyarrow


@register_extension_dtype
class StringDtype(ExtensionDtype):
    """
    Extension dtype for string data.

    .. versionadded:: 1.0.0

    .. warning::

       StringDtype is considered experimental. The implementation and
       parts of the API may change without warning.

       In particular, StringDtype.na_value may change to no longer be
       ``numpy.nan``.

    Attributes
    ----------
    None

    Methods
    -------
    None

    Examples
    --------
    >>> pd.StringDtype()
    StringDtype
    """

    name = "string"

    #: StringDtype.na_value uses pandas.NA
    na_value = libmissing.NA

    @property
    def type(self) -> Type[str]:
        return str

    @classmethod
    def construct_array_type(cls) -> Type["StringArray"]:
        """
        Return the array type associated with this dtype.

        Returns
        -------
        type
        """
        return StringArray

    def __repr__(self) -> str:
        return "StringDtype"

    def __from_arrow__(
        self, array: Union["pyarrow.Array", "pyarrow.ChunkedArray"]
    ) -> "StringArray":
        """
        Construct StringArray from pyarrow Array/ChunkedArray.
        """
        import pyarrow

        if isinstance(array, pyarrow.Array):
            chunks = [array]
        else:
            # pyarrow.ChunkedArray
            chunks = array.chunks

        results = []
        for arr in chunks:
            # using _from_sequence to ensure None is converted to NA
            str_arr = StringArray._from_sequence(np.array(arr))
            results.append(str_arr)

        return StringArray._concat_same_type(results)


class StringArray(PandasArray):
    """
    Extension array for string data.

    .. versionadded:: 1.0.0

    .. warning::

       StringArray is considered experimental. The implementation and
       parts of the API may change without warning.

    Parameters
    ----------
    values : array-like
        The array of data.

        .. warning::

           Currently, this expects an object-dtype ndarray
           where the elements are Python strings or :attr:`pandas.NA`.
           This may change without warning in the future. Use
           :meth:`pandas.array` with ``dtype="string"`` for a stable way of
           creating a `StringArray` from any sequence.

    copy : bool, default False
        Whether to copy the array of data.

    Attributes
    ----------
    None

    Methods
    -------
    None

    See Also
    --------
    array
        The recommended function for creating a StringArray.
    Series.str
        The string methods are available on Series backed by
        a StringArray.

    Notes
    -----
    StringArray returns a BooleanArray for comparison methods.

    Examples
    --------
    >>> pd.array(['This is', 'some text', None, 'data.'], dtype="string")
    <StringArray>
    ['This is', 'some text', <NA>, 'data.']
    Length: 4, dtype: string

    Unlike arrays instantiated with ``dtype="object"``, ``StringArray``
    will convert the values to strings.

    >>> pd.array(['1', 1], dtype="object")
    <PandasArray>
    ['1', 1]
    Length: 2, dtype: object
    >>> pd.array(['1', 1], dtype="string")
    <StringArray>
    ['1', '1']
    Length: 2, dtype: string

    However, instantiating StringArrays directly with non-strings will raise an error.

    For comparison methods, `StringArray` returns a :class:`pandas.BooleanArray`:

    >>> pd.array(["a", None, "c"], dtype="string") == "a"
    <BooleanArray>
    [True, <NA>, False]
    Length: 3, dtype: boolean
    """

    # undo the PandasArray hack
    _typ = "extension"

    def __init__(self, values, copy=False):
        values = extract_array(values)

        super().__init__(values, copy=copy)
        # pandas\core\arrays\string_.py:188: error: Incompatible types in
        # assignment (expression has type "StringDtype", variable has type
        # "PandasDtype")  [assignment]
        self._dtype = StringDtype()  # type: ignore[assignment]
        if not isinstance(values, type(self)):
            self._validate()

    def _validate(self):
        """Validate that we only store NA or strings."""
        if len(self._ndarray) and not lib.is_string_array(self._ndarray, skipna=True):
            raise ValueError("StringArray requires a sequence of strings or pandas.NA")
        if self._ndarray.dtype != "object":
            raise ValueError(
                "StringArray requires a sequence of strings or pandas.NA. Got "
                f"'{self._ndarray.dtype}' dtype instead."
            )

    @classmethod
    def _from_sequence(cls, scalars, *, dtype=None, copy=False):
        if dtype:
            assert dtype == "string"

        from pandas.core.arrays.masked import BaseMaskedArray

        if isinstance(scalars, BaseMaskedArray):
            # avoid costly conversion to object dtype
            na_values = scalars._mask
            result = scalars._data
            result = lib.ensure_string_array(result, copy=copy, convert_na_value=False)
            result[na_values] = StringDtype.na_value

        else:
            # convert non-na-likes to str, and nan-likes to StringDtype.na_value
            result = lib.ensure_string_array(
                scalars, na_value=StringDtype.na_value, copy=copy
            )

        # Manually creating new array avoids the validation step in the __init__, so is
        # faster. Refactor need for validation?
        new_string_array = object.__new__(cls)
        new_string_array._dtype = StringDtype()
        new_string_array._ndarray = result

        return new_string_array

    @classmethod
    def _from_sequence_of_strings(cls, strings, *, dtype=None, copy=False):
        return cls._from_sequence(strings, dtype=dtype, copy=copy)

    def __arrow_array__(self, type=None):
        """
        Convert myself into a pyarrow Array.
        """
        import pyarrow as pa

        if type is None:
            type = pa.string()

        values = self._ndarray.copy()
        values[self.isna()] = None
        return pa.array(values, type=type, from_pandas=True)

    def _values_for_factorize(self):
        arr = self._ndarray.copy()
        mask = self.isna()
        arr[mask] = -1
        return arr, -1

    def __setitem__(self, key, value):
        value = extract_array(value, extract_numpy=True)
        if isinstance(value, type(self)):
            # extract_array doesn't extract PandasArray subclasses
            value = value._ndarray

        key = check_array_indexer(self, key)
        scalar_key = lib.is_scalar(key)
        scalar_value = lib.is_scalar(value)
        if scalar_key and not scalar_value:
            raise ValueError("setting an array element with a sequence.")

        # validate new items
        if scalar_value:
            if isna(value):
                value = StringDtype.na_value
            elif not isinstance(value, str):
                raise ValueError(
                    f"Cannot set non-string value '{value}' into a StringArray."
                )
        else:
            if not is_array_like(value):
                value = np.asarray(value, dtype=object)
            if len(value) and not lib.is_string_array(value, skipna=True):
                raise ValueError("Must provide strings.")

        super().__setitem__(key, value)

    def astype(self, dtype, copy=True):
        dtype = pandas_dtype(dtype)
        if isinstance(dtype, StringDtype):
            if copy:
                return self.copy()
            return self
        elif isinstance(dtype, _IntegerDtype):
            arr = self._ndarray.copy()
            mask = self.isna()
            arr[mask] = 0
            values = arr.astype(dtype.numpy_dtype)
            return IntegerArray(values, mask, copy=False)
        elif isinstance(dtype, FloatingDtype):
            arr = self.copy()
            mask = self.isna()
            arr[mask] = "0"
            values = arr.astype(dtype.numpy_dtype)
            return FloatingArray(values, mask, copy=False)
        elif np.issubdtype(dtype, np.floating):
            arr = self._ndarray.copy()
            mask = self.isna()
            arr[mask] = 0
            values = arr.astype(dtype)
            values[mask] = np.nan
            return values

        return super().astype(dtype, copy)

    def _reduce(self, name: str, *, skipna: bool = True, **kwargs):
        if name in ["min", "max"]:
            return getattr(self, name)(skipna=skipna)

        raise TypeError(f"Cannot perform reduction '{name}' with string dtype")

    def min(self, axis=None, skipna: bool = True, **kwargs) -> Scalar:
        nv.validate_min((), kwargs)
        result = masked_reductions.min(
            values=self.to_numpy(), mask=self.isna(), skipna=skipna
        )
        return self._wrap_reduction_result(axis, result)

    def max(self, axis=None, skipna: bool = True, **kwargs) -> Scalar:
        nv.validate_max((), kwargs)
        result = masked_reductions.max(
            values=self.to_numpy(), mask=self.isna(), skipna=skipna
        )
        return self._wrap_reduction_result(axis, result)

    def value_counts(self, dropna=False):
        from pandas import value_counts

        return value_counts(self._ndarray, dropna=dropna).astype("Int64")

    def memory_usage(self, deep: bool = False) -> int:
        result = self._ndarray.nbytes
        if deep:
            return result + lib.memory_usage_of_objects(self._ndarray)
        return result

    def _cmp_method(self, other, op):
        from pandas.arrays import BooleanArray

        if isinstance(other, StringArray):
            other = other._ndarray

        mask = isna(self) | isna(other)
        valid = ~mask

        if not lib.is_scalar(other):
            if len(other) != len(self):
                # prevent improper broadcasting when other is 2D
                raise ValueError(
                    f"Lengths of operands do not match: {len(self)} != {len(other)}"
                )

            other = np.asarray(other)
            other = other[valid]

        if op.__name__ in ops.ARITHMETIC_BINOPS:
            result = np.empty_like(self._ndarray, dtype="object")
            result[mask] = StringDtype.na_value
            result[valid] = op(self._ndarray[valid], other)
            return StringArray(result)
        else:
            # logical
            result = np.zeros(len(self._ndarray), dtype="bool")
            result[valid] = op(self._ndarray[valid], other)
            return BooleanArray(result, mask)

    _arith_method = _cmp_method

    # ------------------------------------------------------------------------
    # String methods interface
    _str_na_value = StringDtype.na_value

    def _str_map(self, f, na_value=None, dtype=None):
        from pandas.arrays import BooleanArray, IntegerArray, StringArray
        from pandas.core.arrays.string_ import StringDtype

        if dtype is None:
            dtype = StringDtype()
        if na_value is None:
            na_value = self.dtype.na_value

        mask = isna(self)
        arr = np.asarray(self)

        if is_integer_dtype(dtype) or is_bool_dtype(dtype):
            constructor: Union[Type[IntegerArray], Type[BooleanArray]]
            if is_integer_dtype(dtype):
                constructor = IntegerArray
            else:
                constructor = BooleanArray

            na_value_is_na = isna(na_value)
            if na_value_is_na:
                na_value = 1
            result = lib.map_infer_mask(
                arr,
                f,
                mask.view("uint8"),
                convert=False,
                na_value=na_value,
                dtype=np.dtype(dtype),
            )

            if not na_value_is_na:
                mask[:] = False

            return constructor(result, mask)

        elif is_string_dtype(dtype) and not is_object_dtype(dtype):
            # i.e. StringDtype
            result = lib.map_infer_mask(
                arr, f, mask.view("uint8"), convert=False, na_value=na_value
            )
            return StringArray(result)
        else:
            # This is when the result type is object. We reach this when
            # -> We know the result type is truly object (e.g. .encode returns bytes
            #    or .findall returns a list).
            # -> We don't know the result type. E.g. `.get` can return anything.
            return lib.map_infer_mask(arr, f, mask.view("uint8"))