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¤¶x¨¼º7FWiap9HVinfpandas/_libs/lib.pyxstringsourcesvaluesunicodearrendOut of bounds on buffer access (axis %d)namepandas._libs.lib__reduce____module____doc____name____qualname____dict__|ASCIIAttributeErrorBooleanArrayBuffer view does not expose stridesCan only create a buffer that is contiguous in memory.Cannot assign to read-only memoryviewCannot create writable memory view from read-only memoryviewCannot index with type '%s'Cannot specify 'dtype_if_all_nat' without convert_datetime=True and convert_timedelta=True
    Check if the object is an iterator.

    This is intended for generators, not list-like objects.

    Parameters
    ----------
    obj : The object to check

    Returns
    -------
    is_iter : bool
        Whether `obj` is an iterator.

    Examples
    --------
    >>> import datetime
    >>> from pandas.api.types import is_iterator
    >>> is_iterator((x for x in []))
    True
    >>> is_iterator([1, 2, 3])
    False
    >>> is_iterator(datetime.datetime(2017, 1, 1))
    False
    >>> is_iterator("foo")
    False
    >>> is_iterator(1)
    False
    
    Check if the object is list-like.

    Objects that are considered list-like are for example Python
    lists, tuples, sets, NumPy arrays, and Pandas Series.

    Strings and datetime objects, however, are not considered list-like.

    Parameters
    ----------
    obj : object
        Object to check.
    allow_sets : bool, default True
        If this parameter is False, sets will not be considered list-like.

    Returns
    -------
    bool
        Whether `obj` has list-like properties.

    Examples
    --------
    >>> import datetime
    >>> from pandas.api.types import is_list_like
    >>> is_list_like([1, 2, 3])
    True
    >>> is_list_like({1, 2, 3})
    True
    >>> is_list_like(datetime.datetime(2017, 1, 1))
    False
    >>> is_list_like("foo")
    False
    >>> is_list_like(1)
    False
    >>> is_list_like(np.array([2]))
    True
    >>> is_list_like(np.array(2))
    False
    DatetimeIndexDecimalEllipsisEmpty shape tuple for cython.arrayEmpty string encounteredEnumExpected at least %d argument%s, got %dFunction call with ambiguous argument types_GenericAlias
    If the value is a zerodim array, return the item it contains.

    Parameters
    ----------
    val : object

    Returns
    -------
    object

    Examples
    --------
    >>> item_from_zerodim(1)
    1
    >>> item_from_zerodim('foobar')
    'foobar'
    >>> item_from_zerodim(np.array(1))
    1
    >>> item_from_zerodim(np.array([1]))
    array([1])
    ImportErrorIncompatible checksums (0x%x vs (0xb068931, 0x82a3537, 0x6ae9995) = (name))IndexErrorIndirect dimensions not supportedIntegerArrayInteger out of range.IntervalIndexInvalid length for values or for binnerInvalid mode, expected 'c' or 'fortran', got %sInvalid shape in axis %d: %d.LiteralMM8[ns]MemoryError<MemoryView of %r at 0x%x><MemoryView of %r object>NO_DEFAULTNaN_NoDefaultNoDefault_NoDefault.__repr__No matching signature foundNotImplementedErrorOOutOfBoundsDatetimeOutOfBoundsTimedeltaOverflowErrorPeriodPeriodIndexPickleError
    Return True if given object is scalar.

    Parameters
    ----------
    val : object
        This includes:

        - numpy array scalar (e.g. np.int64)
        - Python builtin numerics
        - Python builtin byte arrays and strings
        - None
        - datetime.datetime
        - datetime.timedelta
        - Period
        - decimal.Decimal
        - Interval
        - DateOffset
        - Fraction
        - Number.

    Returns
    -------
    bool
        Return True if given object is scalar.

    Examples
    --------
    >>> import datetime
    >>> dt = datetime.datetime(2018, 10, 3)
    >>> pd.api.types.is_scalar(dt)
    True

    >>> pd.api.types.is_scalar([2, 3])
    False

    >>> pd.api.types.is_scalar({0: 1, 2: 3})
    False

    >>> pd.api.types.is_scalar((0, 2))
    False

    pandas supports PEP 3141 numbers:

    >>> from fractions import Fraction
    >>> pd.api.types.is_scalar(Fraction(3, 5))
    True
    
    Return a string label of the type of a scalar or list-like of values.

    Parameters
    ----------
    value : scalar, list, ndarray, or pandas type
    skipna : bool, default True
        Ignore NaN values when inferring the type.

    Returns
    -------
    str
        Describing the common type of the input data.
    Results can include:

    - string
    - bytes
    - floating
    - integer
    - mixed-integer
    - mixed-integer-float
    - decimal
    - complex
    - categorical
    - boolean
    - datetime64
    - datetime
    - date
    - timedelta64
    - timedelta
    - time
    - period
    - mixed
    - unknown-array

    Raises
    ------
    TypeError
        If ndarray-like but cannot infer the dtype

    Notes
    -----
    - 'mixed' is the catchall for anything that is not otherwise
      specialized
    - 'mixed-integer-float' are floats and integers
    - 'mixed-integer' are integers mixed with non-integers
    - 'unknown-array' is the catchall for something that *is* an array (has
      a dtype attribute), but has a dtype unknown to pandas (e.g. external
      extension array)

    Examples
    --------
    >>> import datetime
    >>> infer_dtype(['foo', 'bar'])
    'string'

    >>> infer_dtype(['a', np.nan, 'b'], skipna=True)
    'string'

    >>> infer_dtype(['a', np.nan, 'b'], skipna=False)
    'mixed'

    >>> infer_dtype([b'foo', b'bar'])
    'bytes'

    >>> infer_dtype([1, 2, 3])
    'integer'

    >>> infer_dtype([1, 2, 3.5])
    'mixed-integer-float'

    >>> infer_dtype([1.0, 2.0, 3.5])
    'floating'

    >>> infer_dtype(['a', 1])
    'mixed-integer'

    >>> infer_dtype([Decimal(1), Decimal(2.0)])
    'decimal'

    >>> infer_dtype([True, False])
    'boolean'

    >>> infer_dtype([True, False, np.nan])
    'boolean'

    >>> infer_dtype([pd.Timestamp('20130101')])
    'datetime'

    >>> infer_dtype([datetime.date(2013, 1, 1)])
    'date'

    >>> infer_dtype([np.datetime64('2013-01-01')])
    'datetime64'

    >>> infer_dtype([datetime.timedelta(0, 1, 1)])
    'timedelta'

    >>> infer_dtype(pd.Series(list('aabc')).astype('category'))
    'categorical'
    SSet_TYPE_MAPTimedeltaIndexTypeErrorUUnable to convert item to objectValueErrorValues falls after last binValues falls before first binView.MemoryView()abcallall_arraysall arrays must be same lengthallocate_bufferallow_null_in_intallow_setsargsargsortarrayarray_equivalentas_intasarrayastype at position bbase_valbcbinnerbinsbool_boolbooleanboolsbufbytes_categoricalcategorycheck_na_tuples_nonequal child class must define is_valid_null child class must define is_value_typed__class__cline_in_tracebackclosedcoerce_numericcolcollectionscomplexcomplex128complex256complex64complexesconstruct_1d_object_array_from_listlike<contiguous and direct><contiguous and indirect>convertconvert_datetimeconvert_emptyconvert_intervalconvert_na_valueconvert_numericconvert_periodconvert_timedeltaconvert_to_masked_nullableconvert_to_nullable_dtypecountscur_datadatetime64datetime64[ns]decimaldecodedefaultdefaultsdti_dtype_dtype_codedtype_if_all_natdtype_is_objectemptyencodeendsenumenumerateerrerrorff8finishedfirstflagsfloat128float16float32float64floatsfnanfullfvalgen__getstate__got differing extents in dimension %d (got %d and %d)group_size__hash__hasnansii8i8maxididxignore_naii__import__indexerinfer_dtype (line 1369)inferredinferred_typeinsertint16int32int64int8integer-naintervalintpintsis_iterator (line 261)is_list_like (line 1098)is_scalar (line 167)isnaititemitem_from_zerodim (line 294)itemsize <= 0 for cython.arrayitemsize_max__iter__jkkeykindkwargsllablabels__len__lenbinlengthlenidxmm8[ns]__main__max_binmax_lenmaybe_intmaybe_intsmergesort__metaclass__mimin_widthmixedmixed-integermixed-integer-floatmodenna_valuenannat_count_ndarrayndarray[Python object,ndim=1]ndarray[Python object,ndim=2]neginf__new__ngroups<no_default>no_defaultno default __reduce__ due to non-trivial __cinit__npnsnumpy.core.multiarray failed to importnumpy.core.umath failed to importobjectobject_onanoutoutputpackpandaspandas._libs.tslibspandas.core.arrayspandas.core.dtypes.castpandas.core.dtypes.missingperiodpipickle__prepare____pyx_PickleError__pyx_checksum__pyx_getbuffer__pyx_result__pyx_state__pyx_type__pyx_vtable__rr_binrange__reduce_ex__resresultretreturnrev_indexerright_closedrowrowssafeseenseen_pdnatseen_valself__setstate__signatures__sizeof__skipnasortsplitstartstartedstartsstepstopstr_<strided and direct><strided and direct or indirect><strided and indirect>stringstripstructstubsumttabletdi__test__timedelta64timedelta64[ns]tmpto_numpytry_floattsobjtup_typtypingtzuu1u8maxuint16uint32uint64uint8uintsunable to allocate array data.unable to allocate shape and strides.uniquesunknown-arrayunpackupdatevvalueviewvlastvstartxyzerosnumpydtype__reduce_cython____setstate_cython__numeric_is_boolis_bool_or_namemviewshapeformatbytescopycfortranbasendimitemsizesizetypendarrayintegerfloatingcomplexfloatingdatetimedatetimetimedeltatzinfopandas._libs.missingpandas._libs.tslibs.periodmemory_usage_of_objectsis_scalaris_iteratoritem_from_zerodimfast_unique_multiple_listlistsfast_unique_multiple_list_gendicts_to_arraydictscolumnsfast_zipndarraysget_reverse_indexerfloatdoublehas_infsmaybe_indices_to_sliceindicesmaybe_booleans_to_slicemaskarray_equivalent_objectleftrightis_range_indexerint64_tint32_tconvert_nans_to_NAis_all_arraylikeobjgenerate_bins_dt64get_level_sortercodescount_level_2dgenerate_slicesindices_fastindexkeyssorted_labelsis_floatis_integeris_complexis_periodis_list_likeinfer_dtypevalis_datetime_with_singletz_arraybase_tzmaybe_convert_numericna_values00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899maybe_convert_objectsobjects__repr__map_infer_maskmap_inferto_object_arraytuples_to_object_arraytuplesto_object_array_tuplesfast_multigetmappingis_bool_listdtypes_all_equaltypes__pyx_unpickle_Enumÿÿÿÿÿÿÿÿ ¤p=
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ºقnQ:B)#ÊåÈv3t¬<{¬ ÈëóÌáInvalid object type-inf+infinfinity-infinity+infinityUnable to parse string "%s"libensure_string_arrayis_decimalis_intervalis_bool_arrayis_integer_arrayis_float_arrayis_string_arrayis_datetime_arrayis_datetime64_arrayis_timedelta_or_timedelta64_arrayis_date_arrayis_time_arrayis_interval_arrayeq_NA_compatpandas._libs.lib.ensure_string_array%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'%s() got multiple values for keyword argument '%U'venv3.12-ios_13_0_arm64_iphoneos/cross/lib/python3.12/site-packages/numpy/__init__.pxdat leastat mostexactly%.200s() takes %.8s %zd positional argument%.1s (%zd given)%s (%s:%d)pandas/_libs/lib.chasattr(): attribute name must be string while calling a Python objectNULL result without error in PyObject_CallMissing type objectCannot convert %.200s to %.200sname '%U' is not definedpandas._libs.lib.is_decimalpandas._libs.lib.is_intervalpandas._libs.lib.is_bool_arrayArgument '%.200s' has incorrect type (expected %.200s, got %.200s)pandas._libs.lib.is_integer_arraypandas._libs.lib.is_float_arraypandas._libs.lib.is_string_arraypandas._libs.lib.is_datetime_arraypandas._libs.lib.is_datetime64_arraypandas._libs.lib.is_timedelta_or_timedelta64_arraypandas._libs.lib.is_date_arraypandas._libs.lib.is_time_arraypandas._libs.lib.is_interval_arrayExpected %.16s, got %.200scalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionpandas._libs.tslibs.util.is_nanpandas._libs.lib.eq_NA_compatBuffer has wrong number of dimensions (expected %d, got %d)Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)buffer dtypeBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T''Buffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s''bool''char''signed char''unsigned char''short''unsigned short''int''unsigned int''long''unsigned long''long long''unsigned long long''complex float''float''complex double''double''complex long double''long double'a structPython objecta pointera stringunparseable format stringExpected a dimension of size %zu, got %zuExpected %d dimensions, got %dBuffer dtype mismatch; next field is at offset %zd but %zd expectedPython does not define a standard format string size for long double ('g')..Unexpected format string character: '%c'Cannot handle repeated arrays in format stringExpected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'Does not understand character buffer dtype format string ('%c')uint8_tvalue too large to convert to npy_uint8can't convert negative value to npy_uint8intan integer is required__int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__%.4s__ returned non-%.4s (type %.200s)loader__loader__origin__file__parent__package__submodule_search_locations__path__Interpreter change detected - this module can only be loaded into one interpreter per process.Module 'lib' has already been imported. Re-initialisation is not supported.builtinscython_runtime__builtins__getbuffer(obj, view, flags)init pandas._libs.lib%d.%dcompiletime version %s of module '%.100s' does not match runtime version %s_cython_0_29_37Shared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompilingcython_function_or_method<cyfunction %U at %p>unbound method %.200S() needs an argument%.200s() takes no arguments (%zd given)%.200s() takes exactly one argument (%zd given)Bad call flags in __Pyx_CyFunction_Call. METH_OLDARGS is no longer supported!%.200s() takes no keyword argumentsfunc_docfunc_name__self__func_dictfunc_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations____name__ must be set to a string object__qualname__ must be set to a string objectfunction's dictionary may not be deletedsetting function's dictionary to a non-dict__defaults__ must be set to a tuple object__kwdefaults__ must be set to a dict object__annotations__ must be set to a dict objectfused_cython_functionFunction is not fusedNeed at least one argument, 0 given.First argument should be of type %.200s, got %.200s.__signatures__co_argcountco_posonlyargcountco_kwonlyargcountco_nlocalsco_stacksizeco_flagsco_codeco_constsco_namesco_varnamesco_freevarsco_cellvarsco_linetablereplacec_is_list_likeint (PyObject *, int)PyArrayObject *(PyArrayObject *, PyObject *, int __pyx_skip_dispatch)__pyx_capi__pandas._libs.lib.c_is_list_like%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectpandas._libs.lib.Seen.saw_intpandas._libs.lib.Seen
    Class for keeping track of the types of elements
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    pandas._libs.lib.Seen.__reduce_cython__pandas._libs.lib.Seen.__setstate_cython__pandas._libs.lib.Seen.is_bool.__get__pandas._libs.lib.Seen.is_bool_or_na.__get____cinit__pandas._libs.lib.Seen.__cinit__Unable to initialize pickling for %spandas._libs.lib.Validator.validatepandas._libs.lib.Validator._validatepandas._libs.lib.Validator._validate_skipnapandas._libs.lib.Validator.is_validpandas._libs.lib.Validator.is_valid_skipnapandas._libs.lib.Validator.is_value_typedpandas._libs.lib.Validatorpandas._libs.lib.Validator.__reduce_cython__pandas._libs.lib.Validator.__setstate_cython__pandas._libs.lib.Validator.__cinit__pandas._libs.lib.BoolValidator.is_array_typedpandas._libs.lib.BoolValidatorpandas._libs.lib.BoolValidator.__reduce_cython__pandas._libs.lib.BoolValidator.__setstate_cython__pandas._libs.lib.IntegerValidator.is_array_typedpandas._libs.lib.IntegerValidatorpandas._libs.lib.IntegerValidator.__reduce_cython__pandas._libs.lib.IntegerValidator.__setstate_cython__pandas._libs.lib.IntegerNaValidatorpandas._libs.lib.IntegerNaValidator.__reduce_cython__pandas._libs.lib.IntegerNaValidator.__setstate_cython__pandas._libs.lib.IntegerFloatValidator.is_array_typedpandas._libs.lib.IntegerFloatValidatorpandas._libs.lib.IntegerFloatValidator.__reduce_cython__pandas._libs.lib.IntegerFloatValidator.__setstate_cython__pandas._libs.lib.FloatValidator.is_array_typedpandas._libs.lib.FloatValidatorpandas._libs.lib.FloatValidator.__reduce_cython__pandas._libs.lib.FloatValidator.__setstate_cython__pandas._libs.lib.ComplexValidator.is_array_typedpandas._libs.lib.ComplexValidatorpandas._libs.lib.ComplexValidator.__reduce_cython__pandas._libs.lib.ComplexValidator.__setstate_cython__pandas._libs.lib.DecimalValidatorpandas._libs.lib.DecimalValidator.__reduce_cython__pandas._libs.lib.DecimalValidator.__setstate_cython__pandas._libs.lib.StringValidator.is_array_typedpandas._libs.lib.StringValidatorpandas._libs.lib.StringValidator.__reduce_cython__pandas._libs.lib.StringValidator.__setstate_cython__pandas._libs.lib.BytesValidator.is_array_typedpandas._libs.lib.BytesValidatorpandas._libs.lib.BytesValidator.__reduce_cython__pandas._libs.lib.BytesValidator.__setstate_cython__pandas._libs.lib.TemporalValidator._validate_skipnapandas._libs.lib.TemporalValidator.is_validpandas._libs.lib.TemporalValidator.is_valid_skipnapandas._libs.lib.TemporalValidator.is_valid_nullpandas._libs.lib.TemporalValidatorpandas._libs.lib.TemporalValidator.__reduce_cython__pandas._libs.lib.TemporalValidator.__setstate_cython__pandas._libs.lib.TemporalValidator.__cinit__pandas._libs.lib.DatetimeValidatorpandas._libs.lib.DatetimeValidator.__reduce_cython__pandas._libs.lib.DatetimeValidator.__setstate_cython__pandas._libs.lib.Datetime64Validatorpandas._libs.lib.Datetime64Validator.__reduce_cython__pandas._libs.lib.Datetime64Validator.__setstate_cython__pandas._libs.lib.AnyDatetimeValidator.is_value_typedpandas._libs.lib.AnyDatetimeValidatorpandas._libs.lib.AnyDatetimeValidator.__reduce_cython__pandas._libs.lib.AnyDatetimeValidator.__setstate_cython__pandas._libs.lib.TimedeltaValidatorpandas._libs.lib.TimedeltaValidator.__reduce_cython__pandas._libs.lib.TimedeltaValidator.__setstate_cython__pandas._libs.lib.AnyTimedeltaValidatorpandas._libs.lib.AnyTimedeltaValidator.__reduce_cython__pandas._libs.lib.AnyTimedeltaValidator.__setstate_cython__pandas._libs.lib.DateValidatorpandas._libs.lib.DateValidator.__reduce_cython__pandas._libs.lib.DateValidator.__setstate_cython__pandas._libs.lib.TimeValidatorpandas._libs.lib.TimeValidator.__reduce_cython__pandas._libs.lib.TimeValidator.__setstate_cython__View.MemoryView.array.get_memviewpandas._libs.lib.arrayView.MemoryView.array.__getitem__Subscript deletion not supported by %.200sView.MemoryView.array.__setitem__View.MemoryView.array.__getattr____getattr__View.MemoryView.array.__reduce_cython__View.MemoryView.array.__setstate_cython__View.MemoryView.array.memview.__get__View.MemoryView.array.__cinit__Argument '%.200s' must not be Noneobject of type 'NoneType' has no len()expected bytes, NoneType foundinteger division or modulo by zerovalue too large to perform divisionpandas._libs.lib.EnumView.MemoryView.Enum.__reduce_cython__tupleView.MemoryView.Enum.__setstate_cython__'NoneType' object is not subscriptableView.MemoryView.__pyx_unpickle_Enum__set_state__init__View.MemoryView.Enum.__init__View.MemoryView.memoryview.get_item_pointerView.MemoryView.pybuffer_indexView.MemoryView.memoryview.is_sliceView.MemoryView.memoryview.setitem_slice_assignmentView.MemoryView.get_slice_from_memviewvalue too large to convert to intDimension %d is not directView.MemoryView.memoryview_copy_contentsView.MemoryView._err_extentsView.MemoryView._err_dimc-string too long to convert to PythonView.MemoryView.copy_data_to_tempView.MemoryView._errCannot transpose memoryview with indirect dimensionsView.MemoryView.transpose_memsliceView.MemoryView.memoryview.setitem_slice_assign_scalarView.MemoryView.assert_direct_dimensionsView.MemoryView.memoryview.setitem_indexedView.MemoryView.memoryview.convert_item_to_object'NoneType' is not iterableView.MemoryView.memoryview.assign_item_from_objectpandas._libs.lib.memoryviewView.MemoryView.memoryview.__repr__View.MemoryView.memoryview.__getitem__View.MemoryView._unellipsifytoo many values to unpack (expected %zd)need more than %zd value%.1s to unpack'NoneType' object is not iterablememviewsliceobjView.MemoryView.memview_sliceIndex out of bounds (axis %d)Step may not be zero (axis %d)All dimensions preceding dimension %d must be indexed and not slicedView.MemoryView.slice_memviewslicelocal variable '%s' referenced before assignmentView.MemoryView.memoryview_fromsliceAcquisition count is %d (line %d)__pyx_fatalerrorView.MemoryView.memoryview.__setitem__View.MemoryView.memoryview.__str__is_c_contigis_f_contigcopy_fortranView.MemoryView.memoryview.is_c_contigView.MemoryView.memoryview.is_f_contigView.MemoryView.memoryview.copyCannot copy memoryview slice with indirect dimensions (axis %d)View.MemoryView.array_cwrapperView.MemoryView.memoryview_cwrappermemviewslice is already initialized!View.MemoryView.memoryview_copy_from_sliceView.MemoryView.memoryview.copy_fortranView.MemoryView.memoryview.__reduce_cython__View.MemoryView.memoryview.__setstate_cython__TstridessuboffsetsnbytesView.MemoryView.memoryview.T.__get__View.MemoryView.memoryview_copyView.MemoryView.memoryview.shape.__get__View.MemoryView.memoryview.strides.__get__View.MemoryView.memoryview.suboffsets.__get__View.MemoryView.memoryview.ndim.__get__View.MemoryView.memoryview.itemsize.__get__View.MemoryView.memoryview.nbytes.__get__View.MemoryView.memoryview.size.__get__View.MemoryView.memoryview.__cinit__View.MemoryView._memoryviewslice.convert_item_to_objectView.MemoryView._memoryviewslice.assign_item_from_objectpandas._libs.lib._memoryviewsliceInternal class for passing memoryview slices to PythonView.MemoryView._memoryviewslice.__reduce_cython__View.MemoryView._memoryviewslice.__setstate_cython__flatiterbroadcastgenericnumbersignedintegerunsignedintegerinexactflexiblecharacterufuncC_NATypepandas._libs.tslibs.baseABCTimestamppandas._libs.tslibs.offsetsBaseOffsetpandas._libs.tslibs.timestamps_Timestamppandas._libs.tslibs.conversion_TSObjectpandas._libs.tslibs.nattype_NaTpandas._libs.tslibs.timedeltas_Timedeltainvalid vtable found for imported typeC_NAstruct __pyx_obj_6pandas_5_libs_7missing_C_NAType *pandas._libs.tslibs.timezonesutc_stdlibPyDateTime_TZInfo *NPY_NAT__pyx_t_5numpy_int64_tc_nat_stringsPyObject *c_NaTstruct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *%.200s does not export expected C variable %.200sC variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)is_matching_naint (PyObject *, PyObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_6pandas_5_libs_7missing_is_matching_na *__pyx_optional_args)checknullint (PyObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_6pandas_5_libs_7missing_checknull *__pyx_optional_args)is_null_datetime64int (PyObject *)is_null_timedelta64is_offset_objecttz_compareint (PyDateTime_TZInfo *, PyDateTime_TZInfo *, int __pyx_skip_dispatch)convert_to_tsobjectstruct __pyx_obj_6pandas_5_libs_6tslibs_10conversion__TSObject *(PyObject *, PyDateTime_TZInfo *, PyObject *, int, int, struct __pyx_opt_args_6pandas_5_libs_6tslibs_10conversion_convert_to_tsobject *__pyx_optional_args)checknull_with_natis_period_objectconvert_to_timedelta64PyObject *(PyObject *, PyObject *)%.200s does not export expected C function %.200sC function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)cannot import name %Sdatetime.datetime_CAPInumpy.import_arraynumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is not PyCapsule object_ARRAY_API is NULL pointermodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule compiled against API version 0x%x but this version of numpy is 0x%x . Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem .FATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimepandas._libs.lib.memory_usage_of_objectspandas._libs.lib.item_from_zerodimpandas._libs.lib.fast_unique_multiple_listlistpandas._libs.lib.fast_unique_multiple_list_genpandas._libs.lib.dicts_to_arraydictBuffer acquisition failed on assignment; and then reacquiring the old buffer failed too!pandas._libs.lib.fast_zippandas._libs.lib.get_reverse_indexerItem size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s)Buffer is not indirectly contiguous in dimension %d.Buffer and memoryview are not contiguous in the same dimension.C-contiguous buffer is not contiguous in dimension %dC-contiguous buffer is not indirect in dimension %dBuffer exposes suboffsets but no stridesBuffer not compatible with direct access in dimension %d.Buffer is not indirectly accessible in dimension %d.Buffer not C contiguous.const intp_tintp_t__pyx_fuse_0has_infspandas._libs.lib.has_infs__pyx_fuse_1has_infs__pyx_fused_cpdefpandas._libs.lib.__pyx_fused_cpdefonly single character unicode strings can be converted to Py_UCS4, got length %zdord() expected string of length 1, but %.200s foundord() expected a character, but string of length %zd founddictionary changed size during iterationpandas._libs.lib.maybe_indices_to_slicepandas._libs.lib.maybe_booleans_to_slicepandas._libs.lib.array_equivalent_objectnumpy.PyArray_MultiIterNew2__pyx_fuse_0is_range_indexerpandas._libs.lib.is_range_indexer__pyx_fuse_1is_range_indexer__pyx_fuse_0convert_nans_to_NApandas._libs.lib.convert_nans_to_NA__pyx_fuse_1convert_nans_to_NApandas._libs.lib.is_all_arraylikepandas._libs.lib.generate_bins_dt64const int64_tpandas._libs.lib.get_level_sorterpandas._libs.lib.count_level_2dpandas._libs.lib.generate_slicespandas._libs.lib.indices_fastpandas._libs.lib.is_list_likepandas._libs.lib.infer_dtypepandas._libs.lib._try_infer_mappandas._libs.lib.is_integer_float_arraypandas._libs.lib.is_integer_na_arraypandas._libs.lib.is_decimal_arraypandas._libs.lib.is_complex_arraypandas._libs.lib.is_bytes_arraypandas._libs.lib.is_period_arraypandas._libs.lib.is_datetime_with_singletz_arraypandas._libs.lib.maybe_convert_numericfloat64_tdouble complexuint64_tcan't convert negative value to npy_uint64join() result is too long for a Python stringpandas._libs.lib.maybe_convert_objectspandas._libs.lib.get_itemsize__float__ returned non-float (type %.200s)pandas._libs.lib.is_datetime_or_datetime64_array'%.200s' object does not support slice %.10sassignmentdeletionmetaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases'%.200s' object is not subscriptablecannot fit '%.200s' into an index-sized integerpandas._libs.lib.map_infer_maskconst uint8_tpandas._libs.lib.map_inferpandas._libs.lib.to_object_arraypandas._libs.lib.tuples_to_object_arraypandas._libs.lib.to_object_array_tuplespandas._libs.lib.fast_multigetpandas._libs.lib.is_bool_listpandas._libs.lib.dtypes_all_equalPyObject_GetBuffer: view==NULL argument is obsoleteView.MemoryView.array.__getbuffer__View.MemoryView.memoryview.__getbuffer__View.MemoryView.__pyx_unpickle_Enumiout of memoryBuffer overflow caught - possible malformed input file.
TRUEFALSEExpected %lld fields in line %llu, saw %lld
Skipping line %llu: expected %lld fields, saw %lld
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ðÙ<øÙ<Ú,BøÏBPË£<Ëþ=Úþ=Ú~B8Ì¨<Ú¯< ÚI>(ÚI>XÎ²<0Ú´<°Ì·<@Î½<HÎÄ<PÎË<8ÎÒ<8ÚØ<@ÚÞ<HÚý<&ÏãPÚ#=XÚ+=`Ú9=hÚ@=pÚG=xÚ°@ÚI=`ËÜÚO=ÚT=ÚZ= Úa=¨Úc=°Úe=èô$0<HT`lx¨´ÀÌØäðü ,8DP\ht¤°¼ÈÔàìø(4@LXdp| ¬¸ÄÐÜèô$0<HT`lx¨´ÀÌØäðü ,8DP\ht¤°¼ÈÔàìø(4@LXdp| ¬¸ÄÐÜèô$0<HT`lx¨´ÀÌØäðü ,8DP\ht¤°¼ÈÔàìø(47MFàG;MäBHOMìkZM8lfMltM oM¼rMds¤Mv¶MyÊM¸|	KìMÔúMðN:KN´KàPã
    Returns a new numpy array with object dtype and only strings and na values.

    Parameters
    ----------
    arr : array-like
        The values to be converted to str, if needed.
    na_value : Any, default np.nan
        The value to use for na. For example, np.nan or pd.NA.
    convert_na_value : bool, default True
        If False, existing na values will be used unchanged in the new array.
    copy : bool, default True
        Whether to ensure that a new array is returned.
    skipna : bool, default True
        Whether or not to coerce nulls to their stringified form
        (e.g. if False, NaN becomes 'nan').

    Returns
    -------
    np.ndarray[object]
        An array with the input array's elements casted to str or nan-like.
    
    Infer with timedeltas and/or nat/none.
    
    Is this an ndarray of Interval (or np.nan) with a single dtype?
    
    Check for `arr == key`, treating all values as not-equal to pd.NA.

    key is assumed to have `not isna(key)`
    8Ê@ÊHÊPÊXÊ`ËXÊ`ËXÊ`ËXÊ`ËXÊ`ËXÊ`ËXÊ`ËXÊ`ËXÊÿÿÿÿÿÿÿÿãAä^2= H\8°H8°Q\hPhYè[\d\°øf°øn\{\\°\° \ԍª\ԍ³\ühÁ\ühÎ\tÝ\d  ^¸w=ìü=äý=Üþ¦=(ÿ®=ØÓw==|	8×ÊXÊw=ä
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w==w= =w=4=,w=H=@w=\=Tw=p=hw==|w= !="8×ÊXÊw=´#=¬$w=È%=À&w=Ü'=Ô(w=ð)=è*w=,=ü,w=.=/w=,0=$1¸EÄE¸EPFXGÄ{¾pHHw=I=J¼=K Ù(Ö Õ(×ØÒw=PO=UÊ8X8ä, ~¾x´Êx8µ×=`¶Öx¸w=¤º=»¨z Çæ=hÊÄ=Êªz(Ì²z Îë=¸Ðð=Ñ½zhÑù=ÄÒ¸×ÕÔw=ü×=ôØæ=ìÙ
    Return the memory usage of an object array in bytes.

    Does not include the actual bytes of the pointers
    
    Return True if given object is scalar.

    Parameters
    ----------
    val : object
        This includes:

        - numpy array scalar (e.g. np.int64)
        - Python builtin numerics
        - Python builtin byte arrays and strings
        - None
        - datetime.datetime
        - datetime.timedelta
        - Period
        - decimal.Decimal
        - Interval
        - DateOffset
        - Fraction
        - Number.

    Returns
    -------
    bool
        Return True if given object is scalar.

    Examples
    --------
    >>> import datetime
    >>> dt = datetime.datetime(2018, 10, 3)
    >>> pd.api.types.is_scalar(dt)
    True

    >>> pd.api.types.is_scalar([2, 3])
    False

    >>> pd.api.types.is_scalar({0: 1, 2: 3})
    False

    >>> pd.api.types.is_scalar((0, 2))
    False

    pandas supports PEP 3141 numbers:

    >>> from fractions import Fraction
    >>> pd.api.types.is_scalar(Fraction(3, 5))
    True
    
    Check if the object is an iterator.

    This is intended for generators, not list-like objects.

    Parameters
    ----------
    obj : The object to check

    Returns
    -------
    is_iter : bool
        Whether `obj` is an iterator.

    Examples
    --------
    >>> import datetime
    >>> from pandas.api.types import is_iterator
    >>> is_iterator((x for x in []))
    True
    >>> is_iterator([1, 2, 3])
    False
    >>> is_iterator(datetime.datetime(2017, 1, 1))
    False
    >>> is_iterator("foo")
    False
    >>> is_iterator(1)
    False
    
    If the value is a zerodim array, return the item it contains.

    Parameters
    ----------
    val : object

    Returns
    -------
    object

    Examples
    --------
    >>> item_from_zerodim(1)
    1
    >>> item_from_zerodim('foobar')
    'foobar'
    >>> item_from_zerodim(np.array(1))
    1
    >>> item_from_zerodim(np.array([1]))
    array([1])
    °Ö8Ù
    Generate a list of unique values from a generator of lists.

    Parameters
    ----------
    gen : generator object
        Generator of lists from which the unique list is created.
    sort : bool
        Whether or not to sort the resulting unique list.

    Returns
    -------
    list of unique values
    HÕ8Ù
    For zipping multiple ndarrays into an ndarray of tuples.
    
    Reverse indexing operation.

    Given `indexer`, make `indexer_inv` of it, such that::

        indexer_inv[indexer[x]] = x

    Parameters
    ----------
    indexer : np.ndarray[np.intp]
    length : int

    Returns
    -------
    np.ndarray[np.intp]

    Notes
    -----
    If indexer is not unique, only first occurrence is accounted.
    
    Perform an element by element comparison on N-d object arrays
    taking into account nan positions.
    ¸Ë¸Ø
    Perform an element by element comparison on 1-d integer arrays, meant for indexer
    comparisons
    
    Helper for StringArray that converts null values that
    are not pd.NA(e.g. np.nan, None) to pd.NA. Assumes elements
    have already been validated as null.
    
    Should we treat these as levels of a MultiIndex, as opposed to Index items?
    
    Int64 (datetime64) version of generic python version in ``groupby.py``.
    
    Argsort for a single level of a multi-index, keeping the order of higher
    levels unchanged. `starts` points to starts of same-key indices w.r.t
    to leading levels; equivalent to:
        np.hstack([codes[starts[i]:starts[i+1]].argsort(kind='mergesort')
            + starts[i] for i in range(len(starts) - 1)])

    Parameters
    ----------
    codes : np.ndarray[int64_t, ndim=1]
    starts : np.ndarray[intp, ndim=1]

    Returns
    -------
    np.ndarray[np.int, ndim=1]
    
    Parameters
    ----------
    index : ndarray[intp]
    labels : ndarray[int64]
    keys : list
    sorted_labels : list[ndarray[int64]]
    
    Return True if given object is float.

    Returns
    -------
    bool
    
    Return True if given object is integer.

    Returns
    -------
    bool
    
    Return True if given object is boolean.

    Returns
    -------
    bool
    
    Return True if given object is complex.

    Returns
    -------
    bool
    
    Return True if given object is Period.

    Returns
    -------
    bool
    
    Check if the object is list-like.

    Objects that are considered list-like are for example Python
    lists, tuples, sets, NumPy arrays, and Pandas Series.

    Strings and datetime objects, however, are not considered list-like.

    Parameters
    ----------
    obj : object
        Object to check.
    allow_sets : bool, default True
        If this parameter is False, sets will not be considered list-like.

    Returns
    -------
    bool
        Whether `obj` has list-like properties.

    Examples
    --------
    >>> import datetime
    >>> from pandas.api.types import is_list_like
    >>> is_list_like([1, 2, 3])
    True
    >>> is_list_like({1, 2, 3})
    True
    >>> is_list_like(datetime.datetime(2017, 1, 1))
    False
    >>> is_list_like("foo")
    False
    >>> is_list_like(1)
    False
    >>> is_list_like(np.array([2]))
    True
    >>> is_list_like(np.array(2))
    False
    ¸×èÒ
    Return a string label of the type of a scalar or list-like of values.

    Parameters
    ----------
    value : scalar, list, ndarray, or pandas type
    skipna : bool, default True
        Ignore NaN values when inferring the type.

    Returns
    -------
    str
        Describing the common type of the input data.
    Results can include:

    - string
    - bytes
    - floating
    - integer
    - mixed-integer
    - mixed-integer-float
    - decimal
    - complex
    - categorical
    - boolean
    - datetime64
    - datetime
    - date
    - timedelta64
    - timedelta
    - time
    - period
    - mixed
    - unknown-array

    Raises
    ------
    TypeError
        If ndarray-like but cannot infer the dtype

    Notes
    -----
    - 'mixed' is the catchall for anything that is not otherwise
      specialized
    - 'mixed-integer-float' are floats and integers
    - 'mixed-integer' are integers mixed with non-integers
    - 'unknown-array' is the catchall for something that *is* an array (has
      a dtype attribute), but has a dtype unknown to pandas (e.g. external
      extension array)

    Examples
    --------
    >>> import datetime
    >>> infer_dtype(['foo', 'bar'])
    'string'

    >>> infer_dtype(['a', np.nan, 'b'], skipna=True)
    'string'

    >>> infer_dtype(['a', np.nan, 'b'], skipna=False)
    'mixed'

    >>> infer_dtype([b'foo', b'bar'])
    'bytes'

    >>> infer_dtype([1, 2, 3])
    'integer'

    >>> infer_dtype([1, 2, 3.5])
    'mixed-integer-float'

    >>> infer_dtype([1.0, 2.0, 3.5])
    'floating'

    >>> infer_dtype(['a', 1])
    'mixed-integer'

    >>> infer_dtype([Decimal(1), Decimal(2.0)])
    'decimal'

    >>> infer_dtype([True, False])
    'boolean'

    >>> infer_dtype([True, False, np.nan])
    'boolean'

    >>> infer_dtype([pd.Timestamp('20130101')])
    'datetime'

    >>> infer_dtype([datetime.date(2013, 1, 1)])
    'date'

    >>> infer_dtype([np.datetime64('2013-01-01')])
    'datetime64'

    >>> infer_dtype([datetime.timedelta(0, 1, 1)])
    'timedelta'

    >>> infer_dtype(pd.Series(list('aabc')).astype('category'))
    'categorical'
    ÚXÊ
    Check values have the same tzinfo attribute.
    Doesn't check values are datetime-like types.
    
    Convert object array to a numeric array if possible.

    Parameters
    ----------
    values : ndarray[object]
        Array of object elements to convert.
    na_values : set
        Set of values that should be interpreted as NaN.
    convert_empty : bool, default True
        If an empty array-like object is encountered, whether to interpret
        that element as NaN or not. If set to False, a ValueError will be
        raised if such an element is encountered and 'coerce_numeric' is False.
    coerce_numeric : bool, default False
        If initial attempts to convert to numeric have failed, whether to
        force conversion to numeric via alternative methods or by setting the
        element to NaN. Otherwise, an Exception will be raised when such an
        element is encountered.

        This boolean also has an impact on how conversion behaves when a
        numeric array has no suitable numerical dtype to return (i.e. uint64,
        int32, uint8). If set to False, the original object array will be
        returned. Otherwise, a ValueError will be raised.
    convert_to_masked_nullable : bool, default False
        Whether to return a mask for the converted values. This also disables
        upcasting for ints with nulls to float64.
    Returns
    -------
    np.ndarray
        Array of converted object values to numerical ones.

    Optional[np.ndarray]
        If convert_to_masked_nullable is True,
        returns a boolean mask for the converted values, otherwise returns None.
    `Ë@×èÓÓÔ
    Type inference function-- convert object array to proper dtype

    Parameters
    ----------
    objects : ndarray[object]
        Array of object elements to convert.
    try_float : bool, default False
        If an array-like object contains only float or NaN values is
        encountered, whether to convert and return an array of float dtype.
    safe : bool, default False
        Whether to upcast numeric type (e.g. int cast to float). If set to
        True, no upcasting will be performed.
    convert_numeric : bool, default True
        Whether to convert numeric entries.
    convert_datetime : bool, default False
        If an array-like object contains only datetime values or NaT is
        encountered, whether to convert and return an array of M8[ns] dtype.
    convert_timedelta : bool, default False
        If an array-like object contains only timedelta values or NaT is
        encountered, whether to convert and return an array of m8[ns] dtype.
    convert_period : bool, default False
        If an array-like object contains only (homogeneous-freq) Period values
        or NaT, whether to convert and return a PeriodArray.
    convert_interval : bool, default False
        If an array-like object contains only Interval objects (with matching
        dtypes and closedness) or NaN, whether to convert to IntervalArray.
    convert_to_nullable_dtype : bool, default False
        If an array-like object contains only integer or boolean values (and NaN) is
        encountered, whether to convert and return an Boolean/IntegerArray.
    dtype_if_all_nat : np.dtype, ExtensionDtype, or None, default None
        Dtype to cast to if we have all-NaT.

    Returns
    -------
    np.ndarray or ExtensionArray
        Array of converted object values to more specific dtypes if applicable.
    È×èÙèØøÓàÓÔÔðÓÔÔ
    Substitute for np.vectorize with pandas-friendly dtype inference.

    Parameters
    ----------
    arr : ndarray
    f : function
    mask : ndarray
        uint8 dtype ndarray indicating values not to apply `f` to.
    convert : bool, default True
        Whether to call `maybe_convert_objects` on the resulting ndarray
    na_value : Any, optional
        The result value to use for masked values. By default, the
        input value is used
    dtype : numpy.dtype
        The numpy dtype to use for the result ndarray.

    Returns
    -------
    np.ndarray
    
    Substitute for np.vectorize with pandas-friendly dtype inference.

    Parameters
    ----------
    arr : ndarray
    f : function
    convert : bint
    ignore_na : bint
        If True, NA values will not have f applied

    Returns
    -------
    np.ndarray
    
    Convert a list of lists into an object array.

    Parameters
    ----------
    rows : 2-d array (N, K)
        List of lists to be converted into an array.
    min_width : int
        Minimum width of the object array. If a list
        in `rows` contains fewer than `width` elements,
        the remaining elements in the corresponding row
        will all be `NaN`.

    Returns
    -------
    np.ndarray[object, ndim=2]
    
    Convert a list of tuples into an object array. Any subclass of
    tuple in `rows` will be casted to tuple.

    Parameters
    ----------
    rows : 2-d array (N, K)
        List of tuples to be converted into an array.

    Returns
    -------
    np.ndarray[object, ndim=2]
    
    Check if this list contains only bool or np.bool_ objects.

    This is appreciably faster than checking `np.array(obj).dtype == bool`

    obj1 = [True, False] * 100
    obj2 = obj1 * 100
    obj3 = obj2 * 100
    obj4 = [True, None] + obj1

    for obj in [obj1, obj2, obj3, obj4]:
        %timeit is_bool_list(obj)
        %timeit np.array(obj).dtype.kind == "b"

    340 ns ± 8.22 ns
    8.78 µs ± 253 ns

    28.8 µs ± 704 ns
    813 µs ± 17.8 µs

    3.4 ms ± 168 µs
    78.4 ms ± 1.05 ms

    48.1 ns ± 1.26 ns
    8.1 µs ± 198 ns
    
    Faster version for:

    first = types[0]
    all(is_dtype_equal(first, t) for t in types[1:])

    And assuming all elements in the list are np.dtype/ExtensionDtype objects

    See timings at https://github.com/pandas-dev/pandas/pull/44594
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