
    i5                       U d dl mZ d dlmZmZmZ d dlZd dlm	Z	 d dl
mZ d dlmZ d dlmZmZ erd dlmZ  G d	 d
e      Z e	d       G d de             ZdZe e	d       G d de                    Ze e	d       G d de                    Ze e	d       G d de                    Ze e	d       G d de                    Ze e	d       G d de                    Ze e	d       G d de                    Ze e	d       G d de                    Ze e	d       G d de                    Z ej<                  ej>                         e        ej<                  ej@                         e        ej<                  ejB                         e        ej<                  ejD                         e        ej<                  ejF                         e        ej<                  ejH                         e        ej<                  ejJ                         e        ej<                  ejL                         e       iZ'd e(d!<   y)"    )annotations)TYPE_CHECKINGAnyClassVarN)
set_module)register_extension_dtype)is_integer_dtype)NumericArrayNumericDtype)Callablec                      e Zd ZU dZdZ ej                  ej                        Ze	Z
ded<   d	dZed
d       Zedd       Zy)IntegerDtypea'  
    An ExtensionDtype to hold a single size & kind of integer dtype.

    These specific implementations are subclasses of the non-public
    IntegerDtype. For example, we have Int8Dtype to represent signed int 8s.

    The attributes name & type are set when these subclasses are created.
       zCallable[[Any], bool]_checkerc                    t         S )zq
        Return the array type associated with this dtype.

        Returns
        -------
        type
        )IntegerArray)selfs    W/app/cer_product_mecsu/.venv/lib/python3.12/site-packages/pandas/core/arrays/integer.pyconstruct_array_typez!IntegerDtype.construct_array_type(   s
         c                    t         S )N)NUMPY_INT_TO_DTYPE)clss    r   _get_dtype_mappingzIntegerDtype._get_dtype_mapping2   s    !!r   c           	        	 |j                  |d|      S # t        $ ra}|j                  ||      }||k(  j                         r|cY d}~S t        d|j                   dt	        j                  |             |d}~ww xY w)z
        Safely cast the values to the given dtype.

        "safe" in this context means the casting is lossless. e.g. if 'values'
        has a floating dtype, each value must be an integer.
        safe)castingcopy)r   Nz"cannot safely cast non-equivalent z to )astype	TypeErroralldtypenp)r   valuesr"   r   errcasteds         r   
_safe_castzIntegerDtype._safe_cast6   s    		==T=BB 	]]5t]4F& %%'4V\\N$rxxPUFWX	s     	B 'A;B /A;;B N)returnztype[IntegerArray])r(   dict[np.dtype, IntegerDtype])r$   
np.ndarrayr"   znp.dtyper   boolr(   r*   )__name__
__module____qualname____doc___internal_fill_valuer#   r"   int64_default_np_dtyper	   r   __annotations__r   classmethodr   r'    r   r   r   r      s\      *&6H#6 " "  r   r   zpandas.arraysc                      e Zd ZdZeZy)r   a  
    Array of integer (optional missing) values.

    Uses :attr:`pandas.NA` as the missing value.

    .. warning::

       IntegerArray is currently experimental, and its API or internal
       implementation may change without warning.

    We represent an IntegerArray with 2 numpy arrays:

    - data: contains a numpy integer array of the appropriate dtype
    - mask: a boolean array holding a mask on the data, True is missing

    To construct an IntegerArray from generic array-like input, use
    :func:`pandas.array` with one of the integer dtypes (see examples).

    See :ref:`integer_na` for more.

    Parameters
    ----------
    values : numpy.ndarray
        A 1-d integer-dtype array.
    mask : numpy.ndarray
        A 1-d boolean-dtype array indicating missing values.
    copy : bool, default False
        Whether to copy the `values` and `mask`.

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

    Methods
    -------
    None

    Returns
    -------
    IntegerArray

    See Also
    --------
    array : Create an array using the appropriate dtype, including ``IntegerArray``.
    Int32Dtype : An ExtensionDtype for int32 integer data.
    UInt16Dtype : An ExtensionDtype for uint16 integer data.

    Examples
    --------
    Create an IntegerArray with :func:`pandas.array`.

    >>> int_array = pd.array([1, None, 3], dtype=pd.Int32Dtype())
    >>> int_array
    <IntegerArray>
    [1, <NA>, 3]
    Length: 3, dtype: Int32

    String aliases for the dtypes are also available. They are capitalized.

    >>> pd.array([1, None, 3], dtype="Int32")
    <IntegerArray>
    [1, <NA>, 3]
    Length: 3, dtype: Int32

    >>> pd.array([1, None, 3], dtype="UInt16")
    <IntegerArray>
    [1, <NA>, 3]
    Length: 3, dtype: UInt16
    N)r,   r-   r.   r/   r   
_dtype_clsr5   r   r   r   r   J   s    DL Jr   r   a  
An ExtensionDtype for {dtype} integer data.

Uses :attr:`pandas.NA` as its missing value, rather than :attr:`numpy.nan`.

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

Methods
-------
None

See Also
--------
Int8Dtype : 8-bit nullable integer type.
Int16Dtype : 16-bit nullable integer type.
Int32Dtype : 32-bit nullable integer type.
Int64Dtype : 64-bit nullable integer type.

Examples
--------
For Int8Dtype:

>>> ser = pd.Series([2, pd.NA], dtype=pd.Int8Dtype())
>>> ser.dtype
Int8Dtype()

For Int16Dtype:

>>> ser = pd.Series([2, pd.NA], dtype=pd.Int16Dtype())
>>> ser.dtype
Int16Dtype()

For Int32Dtype:

>>> ser = pd.Series([2, pd.NA], dtype=pd.Int32Dtype())
>>> ser.dtype
Int32Dtype()

For Int64Dtype:

>>> ser = pd.Series([2, pd.NA], dtype=pd.Int64Dtype())
>>> ser.dtype
Int64Dtype()

For UInt8Dtype:

>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt8Dtype())
>>> ser.dtype
UInt8Dtype()

For UInt16Dtype:

>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt16Dtype())
>>> ser.dtype
UInt16Dtype()

For UInt32Dtype:

>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt32Dtype())
>>> ser.dtype
UInt32Dtype()

For UInt64Dtype:

>>> ser = pd.Series([2, pd.NA], dtype=pd.UInt64Dtype())
>>> ser.dtype
UInt64Dtype()
pandasc                  X    e Zd ZU ej                  ZdZded<   ej                  d      Z
y)	Int8DtypeInt8ClassVar[str]nameint8r"   N)r,   r-   r.   r#   r>   typer=   r3   _dtype_docstringformatr/   r5   r   r   r:   r:      s*     77D D- %%F%3Gr   r:   c                  X    e Zd ZU ej                  ZdZded<   ej                  d      Z
y)
Int16DtypeInt16r<   r=   int16r?   N)r,   r-   r.   r#   rF   r@   r=   r3   rA   rB   r/   r5   r   r   rD   rD      *     88D!D-!%%G%4Gr   rD   c                  X    e Zd ZU ej                  ZdZded<   ej                  d      Z
y)
Int32DtypeInt32r<   r=   int32r?   N)r,   r-   r.   r#   rK   r@   r=   r3   rA   rB   r/   r5   r   r   rI   rI      rG   r   rI   c                  X    e Zd ZU ej                  ZdZded<   ej                  d      Z
y)
Int64DtypeInt64r<   r=   r1   r?   N)r,   r-   r.   r#   r1   r@   r=   r3   rA   rB   r/   r5   r   r   rM   rM      rG   r   rM   c                  X    e Zd ZU ej                  ZdZded<   ej                  d      Z
y)
UInt8DtypeUInt8r<   r=   uint8r?   N)r,   r-   r.   r#   rR   r@   r=   r3   rA   rB   r/   r5   r   r   rP   rP      rG   r   rP   c                  X    e Zd ZU ej                  ZdZded<   ej                  d      Z
y)UInt16DtypeUInt16r<   r=   uint16r?   N)r,   r-   r.   r#   rV   r@   r=   r3   rA   rB   r/   r5   r   r   rT   rT     *     99D"D-"%%H%5Gr   rT   c                  X    e Zd ZU ej                  ZdZded<   ej                  d      Z
y)UInt32DtypeUInt32r<   r=   uint32r?   N)r,   r-   r.   r#   r[   r@   r=   r3   rA   rB   r/   r5   r   r   rY   rY     rW   r   rY   c                  X    e Zd ZU ej                  ZdZded<   ej                  d      Z
y)UInt64DtypeUInt64r<   r=   uint64r?   N)r,   r-   r.   r#   r_   r@   r=   r3   rA   rB   r/   r5   r   r   r]   r]     rW   r   r]   r)   r   ))
__future__r   typingr   r   r   numpyr#   pandas.util._decoratorsr   pandas.core.dtypes.baser   pandas.core.dtypes.commonr	   pandas.core.arrays.numericr
   r   collections.abcr   r   r   rA   r:   rD   rI   rM   rP   rT   rY   r]   r"   r>   rF   rK   r1   rR   rV   r[   r_   r   r3   r5   r   r   <module>rh      sj   "   . < 6
 (.< .b OG< G GTE T H4 4  4 H5 5  5 H5 5  5 H5 5  5 H5 5  5 H6, 6  6 H6, 6  6 H6, 6  6 BHHRWWy{BHHRXX
BHHRXX
BHHRXX
BHHRXX
BHHRYYBHHRYYBHHRYY	4 0 	r   