+
    Ŝi5                      a  0 t $ ^ RIHt ^ RIHtHtHt ^ RIt^ RI	H
t
 ^ RIHt ^ RIHt ^ RIHtHt ]'       d   ^ RIHt  ! R	 R
]4      t]
! R4       ! R R]4      4       tRt]]
! R4       ! R R]4      4       4       t]]
! R4       ! R R]4      4       4       t]]
! R4       ! R R]4      4       4       t]]
! R4       ! R R]4      4       4       t]]
! R4       ! R R]4      4       4       t]]
! R4       ! R R]4      4       4       t]]
! R4       ! R R]4      4       4       t]]
! R4       ! R R]4      4       4       t]P>                  ! ]P@                  4      ]! 4       ]P>                  ! ]PB                  4      ]! 4       ]P>                  ! ]PD                  4      ]! 4       ]P>                  ! ]PF                  4      ]! 4       ]P>                  ! ]PH                  4      ]! 4       ]P>                  ! ]PJ                  4      ]! 4       ]P>                  ! ]PL                  4      ]! 4       ]P>                  ! ]PN                  4      ]! 4       /t(R ])R!&   R# )"    )annotations)TYPE_CHECKINGAnyClassVarN)
set_module)register_extension_dtype)is_integer_dtype)NumericArrayNumericDtype)Callablec                      ] tR t^t$ Rt^t]P                  ! ]P                  4      t	]
tR]R&   R R lt]R R l4       t]R R	 l4       tR
tR# )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                   V ^8  d   QhRR/# )   returnztype[IntegerArray] )formats   "c/Users/mibo/.openclaw/workspace/.venv-ak/lib/python3.14/site-packages/pandas/core/arrays/integer.py__annotate__IntegerDtype.__annotate__(   s      &8     c                    \         # )zI
Return the array type associated with this dtype.

Returns
-------
type
)IntegerArray)selfs   &r   construct_array_type!IntegerDtype.construct_array_type(   s
     r   c                   V ^8  d   QhRR/# )r   r   dict[np.dtype, IntegerDtype]r   )r   s   "r   r   r   3   s     " "#? "r   c                	    \         # )N)NUMPY_INT_TO_DTYPE)clss   &r   _get_dtype_mappingIntegerDtype._get_dtype_mapping2   s    !!r   c               (    V ^8  d   QhRRRRRRRR/# )r   valuesz
np.ndarraydtypeznp.dtypecopyboolr   r   )r   s   "r   r   r   7   s(      
 8 4 J r   c           	         VP                  VRVR7      #   \         dg   pTP                  Y#R7      pYQ8H  P                  4       '       d   Tu Rp?# \        RTP                   R\        P                  ! T4       24      ThRp?ii ; i)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)castingr(   )r(   Nz"cannot safely cast non-equivalent z to )astype	TypeErrorallr'   np)r"   r&   r'   r(   errcasteds   &&&&  r   
_safe_castIntegerDtype._safe_cast6   s    		==T=BB 	]]5]4F %%''4V\\N$rxxPUFWX	s     B+BB0BBr   N)__name__
__module____qualname____firstlineno____doc___internal_fill_valuer0   r'   int64_default_np_dtyper	   r   __annotations__r   classmethodr#   r3   __static_attributes__r   r   r   r   r      sZ     *&6H#6 " "  r   r   zpandas.arraysc                      ] tR t^JtRt]tRtR# )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
r   N)r5   r6   r7   r8   r9   r   
_dtype_clsr?   r   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                  b    ] tR t^t$ ]P
                  tRtR]R&   ]	P                  RR7      tRtR# )	Int8DtypeInt8ClassVar[str]nameint8r'   r   N)r5   r6   r7   r8   r0   rH   typerG   r=   _dtype_docstringr   r9   r?   r   r   r   rD   rD      s*     77D D- %%F%3Gr   rD   c                  b    ] tR t^t$ ]P
                  tRtR]R&   ]	P                  RR7      tRtR# )
Int16DtypeInt16rF   rG   int16rI   r   N)r5   r6   r7   r8   r0   rO   rJ   rG   r=   rK   r   r9   r?   r   r   r   rM   rM      *     88D!D-!%%G%4Gr   rM   c                  b    ] tR t^t$ ]P
                  tRtR]R&   ]	P                  RR7      tRtR# )
Int32DtypeInt32rF   rG   int32rI   r   N)r5   r6   r7   r8   r0   rT   rJ   rG   r=   rK   r   r9   r?   r   r   r   rR   rR      rP   r   rR   c                  b    ] tR t^t$ ]P
                  tRtR]R&   ]	P                  RR7      tRtR# )
Int64DtypeInt64rF   rG   r;   rI   r   N)r5   r6   r7   r8   r0   r;   rJ   rG   r=   rK   r   r9   r?   r   r   r   rV   rV      rP   r   rV   c                  b    ] tR t^t$ ]P
                  tRtR]R&   ]	P                  RR7      tRtR# )
UInt8DtypeUInt8rF   rG   uint8rI   r   N)r5   r6   r7   r8   r0   r[   rJ   rG   r=   rK   r   r9   r?   r   r   r   rY   rY      rP   r   rY   c                  b    ] tR tRt$ ]P
                  tRtR]R&   ]	P                  RR7      tRtR# )	UInt16Dtypei  UInt16rF   rG   uint16rI   r   N)r5   r6   r7   r8   r0   r_   rJ   rG   r=   rK   r   r9   r?   r   r   r   r]   r]     *     99D"D-"%%H%5Gr   r]   c                  b    ] tR tRt$ ]P
                  tRtR]R&   ]	P                  RR7      tRtR# )	UInt32Dtypei  UInt32rF   rG   uint32rI   r   N)r5   r6   r7   r8   r0   rd   rJ   rG   r=   rK   r   r9   r?   r   r   r   rb   rb     r`   r   rb   c                  b    ] tR tRt$ ]P
                  tRtR]R&   ]	P                  RR7      tRtR# )	UInt64Dtypei  UInt64rF   rG   uint64rI   r   N)r5   r6   r7   r8   r0   rh   rJ   rG   r=   rK   r   r9   r?   r   r   r   rf   rf     r`   r   rf   r   r!   )*__conditional_annotations__
__future__r   typingr   r   r   numpyr0   pandas.util._decoratorsr   pandas.core.dtypes.baser   pandas.core.dtypes.commonr	   pandas.core.arrays.numericr
   r   collections.abcr   r   r   rK   rD   rM   rR   rV   rY   r]   rb   rf   r'   rH   rO   rT   r;   r[   r_   rd   rh   r!   r=   )ri   s   @r   <module>rr      s^   " "   . < 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 HHRWWy{HHRXX
HHRXX
HHRXX
HHRXX
HHRYYHHRYYHHRYY	4 0 	r   