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[6]_nat_scalar_rulesPyObject *c_nat_stringsc_NaTpandas._libs.tslibs.timezonesPyDateTime_TZInfo *utc_pytzchecknullisnaobjint (PyObject *)is_null_datetime64is_null_timedelta64convert_to_tsobjectchecknull_with_natis_offset_objectpandas._libs.tslibs.periodis_period_objectconvert_to_timedelta64tz_comparenumpy.core.multiarray_ARRAY_API_ARRAY_API not found_ARRAY_API is NULL pointer__init__.pxdnumpy.import_arraygetbuffer(obj, view, flags)init pandas._libs.libis_integer_arrayis_float_arrayis_datetime64_arrayis_period_arrayis_interval_arraybase__reduce_cython____setstate_cython__pandas._libs.lib.memoryviewstridessuboffsetsndimitemsizenbytesis_c_contigis_f_contigcopycopy_fortranpandas._libs.lib.Enumpandas._libs.lib.arraymemview__getattr__pandas._libs.lib.Validatorpandas._libs.lib.Seennumeric_is_boolis_float_or_complexto_object_array_tuplestuples_to_object_arrayinfer_datetimelike_arrayis_periodis_complexis_integeris_floatclean_index_listmaybe_booleans_to_sliceitem_from_zerodimis_iteratoris_scalarmemory_usage_of_objectsconst uint8_tconst float64_tconst float32_tconst int64_tuint64_tdouble complexpûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ`ûÿ°‘ûÿ°‘ûÿ ‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ‘ûÿ€‘ûÿ°‘ûÿ°‘ûÿp‘ûÿ°‘ûÿ°‘ûÿ`‘ûÿP‘ûÿ@‘ûÿ°‘ûÿ°‘ûÿ0‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ°‘ûÿ ‘ûÿ‘ûÿ‘ûÿ°‘ûÿèûÿАûÿûÿ°ûÿ°‘ûÿ°‘ûÿ ûÿ°‘ûÿ°‘ûÿ°‘ûÿûÿ€ûÿ°‘ûÿûÿPøûÿ€øûÿ€øûÿPøûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿ`øûÿ@øûÿ€øûÿ€øûÿ0øûÿ€øûÿ€øûÿ0øûÿ0øûÿ0øûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿ€øûÿPøûÿPøûÿ0øûÿ€øûÿ@øûÿpøûÿ`øûÿ@øûÿ€øûÿ€øûÿ0øûÿ€øûÿ€øûÿ€øûÿPøûÿ0øûÿ€øûÿPøûÿ"üûÿ,üûÿ,üûÿ"üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿ"üûÿ"üûÿ,üûÿ,üûÿ"üûÿ,üûÿ,üûÿùûÿùûÿ"üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿ,üûÿüûÿúûûÿüûÿ,üûÿüûÿüûÿüûÿüûÿ,üûÿ,üûÿüûÿ,üûÿ,üûÿ,üûÿüûÿüûÿ,üûÿüûÿèúûÿøúûÿøúûÿèúûÿøúûÿøúûÿøúûÿøúûÿøúûÿ¨ùûÿØúûÿøúûÿøúûÿØúûÿøúûÿøúûÿ˜øûÿ˜øûÿ˜øûÿøúûÿøúûÿøúûÿøúûÿøúûÿøúûÿøúûÿøúûÿøúûÿøúûÿøúûÿøúûÿøúûÿøúûÿøúûÿøúûÿèúûÿèúûÿ(úûÿøúûÿúûÿ@úûÿ¨ùûÿØúûÿøúûÿøúûÿØúûÿøúûÿøúûÿøúûÿèúûÿ˜øûÿøúûÿèúûÿÔúûÿ$úûÿ$úûÿÔúûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿÄúûÿüúûÿ$úûÿ$úûÿ´úûÿ$úûÿ$úûÿ´úûÿ´úûÿ´úûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿ$úûÿÔúûÿÔúûÿûûÿ$úûÿ$ûûÿäúûÿÄúûÿüúûÿ$úûÿ$úûÿ´úûÿ$úûÿ$úûÿ$úûÿÔúûÿ´úûÿ$úûÿÔúûÿýûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿ0üûÿHüûÿHüûÿ0üûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿ0üûÿðþûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHûûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿHüûÿ 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(got %d and %d)construct_1d_object_array_from_listlikeUnable to convert item to objectThe values in the array are unorderable. Pass `sort=False` to suppress this warning.Out of bounds on buffer access (axis %d)Non-native byte order not supportedInvalid mode, expected 'c' or 'fortran', got %sInvalid length for values or for binnerIndirect dimensions not supportedIncompatible checksums (%s vs 0xb068931 = (name))Format string allocated too short, see comment in numpy.pxdEmpty shape tuple for cython.arrayCannot create writable memory view from read-only memoryviewCannot assign to read-only memoryviewCan only create a buffer that is contiguous in memory.Buffer view does not expose stridesunknown dtype code in numpy.pxd (%d)numpy.core.multiarray failed to importis_datetime_with_singletz_array child class must define is_value_typed child class must define is_valid_null
    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
    --------
    >>> 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
    
    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])
    
    Efficiently infer the type of a passed val, or list-like
    array of values. Return a string describing the type.

    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

    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

    Examples
    --------
    >>> 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])
    'mixed'

    >>> 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'
    
    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.

        .. versionadded:: 0.24.0

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

    Examples
    --------
    >>> is_list_like([1, 2, 3])
    True
    >>> is_list_like({1, 2, 3})
    True
    >>> is_list_like(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
    
    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
    --------
    >>> is_iterator((x for x in []))
    True
    >>> is_iterator([1, 2, 3])
    False
    >>> is_iterator(datetime(2017, 1, 1))
    False
    >>> is_iterator("foo")
    False
    >>> is_iterator(1)
    False
    <strided and direct or indirect>all arrays must be same lengthunable to allocate array data.fast_unique_multiple_list_genValues falls before first binndarray is not C contiguousitemsize <= 0 for cython.arrayconvert_to_nullable_integerValues falls after last binitem_from_zerodim (line 222)fast_unique_multiple_listInvalid shape in axis %d: %d.infer_datetimelike_arrayEmpty string encounteredCannot index with type '%s'pandas.core.dtypes.castmemory_usage_of_objectsmaybe_booleans_to_slice<contiguous and indirect>array_equivalent_object<MemoryView of %r at 0x%x>tuples_to_object_arrayto_object_array_tuplesmaybe_indices_to_slice<MemoryView of %r object>tz-naive and tz-awaremaybe_convert_objectsmaybe_convert_numericis_list_like (line 963)infer_dtype (line 1205)<contiguous and direct>cannot infer type for IntegerFloatValidatorAnyTimedeltaValidator<strided and indirect>pandas/_libs/lib.pyxis_iterator (line 191)fast_unique_multipleInteger out of range.mixed-integer-floatis_float_or_complexget_reverse_indexerNotImplementedErrorDatetime64Validator<strided and direct>pandas.core.arrayspandas._libs.tslibis_scalar (line 118)generate_bins_dt64cline_in_tracebackTimedeltaValidatorIntegerNaValidator__pyx_unpickle_Enumitem_from_zerodimconvert_timedeltaarray_to_datetimeTemporalValidatorIntervalValidatorDatetimeValidatorpandas._libs.libget_level_sorterconvert_na_valueconvert_datetimeclean_index_listIntegerValidatorto_object_array__setstate_cython____pyx_PickleErrorgenerate_slicesdtype_is_objectallocate_bufferView.MemoryViewStringValidatorPeriodValidatortimedelta64[ns]seen_timedeltamap_infer_maskdicts_to_arraycount_level_2dcoerce_numericastype_intsafeRuntimeWarningFloatValidatorBytesValidatorAttributeErrorsorted_labelsseen_tz_naiveseen_tz_awareseen_datetime__reduce_cython____pyx_getbuffermixed-integerinferred_typefast_multigetdatetime64[ns]convert_emptyTimeValidatorOverflowErrorDatetimeIndexDateValidatorBoolValidatorstringsourceright_closed__pyx_checksumis_list_likeindices_fastRuntimeErrorIntegerArraytimedelta64rev_indexeritimedeltasis_iteratoris_datelikeinfer_dtypehas_infs_f8has_infs_f4collectionscategorical at position PickleErrorMemoryErrorImportErrortimedeltasseen_pdnat__pyx_vtable____pyx_resultno_defaultmaybe_intsis_integeris_complexinteger-naidatetimesgroup_sizedatetime64complex256complex128allow_setsall_arraysValueErrorIndexErrortry_floattimedeltaseen_date__reduce_ex____pyx_statenew_dtypenat_countna_valuesmin_widthmergesortmaybe_intmap_inferlabel_arris_scalaris_periodenumeratedatetimescomplexescomplex64ValidatorTypeErrorwarnings__setstate__seen_valseen_nat__pyx_typeobject_ndarraysna_valueitemsizeis_floatintervalinferred__getstate__floatingfloat128finishedfast_zipdatetimecategorybase_val_TYPE_MAPIterableEllipsisuniquesstartedobjectsnumeric_ngroupsmemviewmax_lenmax_binmappingis_boolintegerindicesindexerhasnansfortranfloat64float32float16defaultdecimalconvertcomplexcolumnsbytes_booleanbase_tzasarrayargsortDecimalvstartvaluesupdateunpackuint64uint32uint16tzinfotuplesstructstringstatusstartsskipna__sizeof__result__reduce__pickleperiodpandasoutputobjectneginf__name__lenidxlengthlenbinlabelsinsert__import__formatfloatserrorsencodecountsclosedboolbinnerastypeas_intarrayszerosvlastvalueuintsuint8tablestartslobjshaperightravelrangeraiser_binordernumpymixedm8[ns]listslabell_binint64int32int16indexflagserroremptydtypedicts__class__bytesboolsarrayM8[ns]ASCIIwarnviewtypetime__test__stubstopstepsortsizeseensaferowspackonanobjsndimnamemodemask__main__leftkindkeysitemintsint8__hash__fvalfullfnanends__dict__date_datacopybool_binsbaseaxisSeenval_typtuptmpsumstr_rowoutobj__new__natnan__len__labinfidxgenerrendcurcolc16bufarrallabcSetNaNvcu8u1tznsnpitidi8f8bcyxvurnmlkjifcbUSOMKBuffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'memviewslice is already initialized!%.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 PyObjectUnable to initialize pickling for %s%.200s does not export expected C variable %.200sC variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)%.200s does not export expected C function %.200sC function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)Does not understand character buffer dtype format string ('%c')Argument '%.200s' has incorrect type (expected %.200s, got %.200s)Cannot convert %.200s to %.200sView.MemoryView.memoryview.__str__View.MemoryView.memoryview.size.__get__View.MemoryView.memoryview.nbytes.__get__View.MemoryView.memoryview.itemsize.__get__View.MemoryView.memoryview.ndim.__get__View.MemoryView.memoryview.suboffsets.__get__View.MemoryView.memoryview.shape.__get__View.MemoryView.memoryview.setitem_indexedView.MemoryView.array.memview.__get__View.MemoryView.array.__getattr__View.MemoryView.array.__getitem__pandas._libs.lib.TemporalValidator.is_validpandas._libs.lib.Validator.is_valid_skipnaView.MemoryView.array.__setitem__Subscript deletion not supported by %.200spandas._libs.lib.Validator.is_validpandas._libs.lib.Validator.validate%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'%.200s() takes %.8s %zd positional argument%.1s (%zd given)pandas._libs.lib.Seen.__cinit__pandas._libs.lib.fast_unique_multiple_listobject of type 'NoneType' has no len()pandas._libs.lib.fast_unique_multiple_list_gencalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionpandas._libs.lib.Seen.__reduce_cython__pandas._libs.lib.Seen.__setstate_cython__pandas._libs.lib.Validator.__reduce_cython__pandas._libs.lib.Validator.__setstate_cython__pandas._libs.lib.BoolValidator.__reduce_cython__pandas._libs.lib.BoolValidator.__setstate_cython__pandas._libs.lib.IntegerValidator.__reduce_cython__pandas._libs.lib.IntegerValidator.__setstate_cython__pandas._libs.lib.IntegerNaValidator.__reduce_cython__pandas._libs.lib.IntegerNaValidator.__setstate_cython__pandas._libs.lib.IntegerFloatValidator.__reduce_cython__pandas._libs.lib.IntegerFloatValidator.__setstate_cython__pandas._libs.lib.FloatValidator.__reduce_cython__pandas._libs.lib.FloatValidator.__setstate_cython__pandas._libs.lib.StringValidator.__reduce_cython__pandas._libs.lib.StringValidator.__setstate_cython__pandas._libs.lib.BytesValidator.__reduce_cython__pandas._libs.lib.BytesValidator.__setstate_cython__pandas._libs.lib.TemporalValidator.__reduce_cython__pandas._libs.lib.TemporalValidator.__setstate_cython__pandas._libs.lib.DatetimeValidator.__reduce_cython__pandas._libs.lib.DatetimeValidator.__setstate_cython__pandas._libs.lib.Datetime64Validator.__reduce_cython__pandas._libs.lib.Datetime64Validator.__setstate_cython__pandas._libs.lib.TimedeltaValidator.__reduce_cython__pandas._libs.lib.TimedeltaValidator.__setstate_cython__pandas._libs.lib.AnyTimedeltaValidator.__reduce_cython__pandas._libs.lib.AnyTimedeltaValidator.__setstate_cython__pandas._libs.lib.DateValidator.__reduce_cython__pandas._libs.lib.DateValidator.__setstate_cython__pandas._libs.lib.TimeValidator.__reduce_cython__pandas._libs.lib.TimeValidator.__setstate_cython__pandas._libs.lib.PeriodValidator.__reduce_cython__pandas._libs.lib.PeriodValidator.__setstate_cython__pandas._libs.lib.IntervalValidator.__reduce_cython__pandas._libs.lib.IntervalValidator.__setstate_cython__View.MemoryView.array.__reduce_cython__View.MemoryView.array.__setstate_cython__View.MemoryView.memoryview.__reduce_cython__View.MemoryView.memoryview.__setstate_cython__View.MemoryView.memoryview.strides.__get__View.MemoryView._memoryviewslice.__reduce_cython__View.MemoryView._memoryviewslice.__setstate_cython__PyObject_GetBuffer: view==NULL argument is obsoleteView.MemoryView.array.__getbuffer__View.MemoryView.memoryview.__getbuffer____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)View.MemoryView.memoryview.assign_item_from_objectView.MemoryView._memoryviewslice.assign_item_from_objectAcquisition count is %d (line %d)Unexpected format string character: '%c'Expected a dimension of size %zu, got %zuExpected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'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 ')'Buffer 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)pandas._libs.lib.maybe_indices_to_sliceOut of bounds on buffer access (axis %d)pandas._libs.lib.is_interval_arraypandas._libs.lib.is_period_arraypandas._libs.lib.is_integer_na_arraypandas._libs.lib.is_integer_float_arraypandas._libs.lib.is_float_arraypandas._libs.lib.is_integer_arraypandas._libs.lib.is_timedelta_or_timedelta64_arraypandas._libs.lib.is_datetime64_arraypandas._libs.lib.Seen.is_bool.__get__View.MemoryView.array.get_memviewView.MemoryView.memoryview_cwrapperItem size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s)Buffer exposes suboffsets but no stridesC-contiguous buffer is not indirect in dimension %dBuffer and memoryview are not contiguous in the same dimension.Buffer is not indirectly contiguous in dimension %d.Buffer is not indirectly accessible in dimension %d.Buffer not compatible with direct access in dimension %d.pandas._libs.lib.is_datetime_arraypandas._libs.lib.is_date_arraypandas._libs.lib.is_time_arraypandas._libs.lib.is_string_arraypandas._libs.lib.is_bool_arraypandas._libs.lib.Validator.__cinit__pandas._libs.lib.TemporalValidator.__cinit__pandas._libs.lib.Validator._validatepandas._libs.lib.Validator._validate_skipnaView.MemoryView.memoryview.is_sliceView.MemoryView.memoryview.__repr__View.MemoryView.memoryview_fromsliceView.MemoryView.memoryview_copy_from_sliceView.MemoryView.Enum.__reduce_cython__pandas._libs.lib.IntegerValidator.is_array_typedpandas._libs.lib.IntegerFloatValidator.is_array_typedpandas._libs.lib.BytesValidator.is_array_typedpandas._libs.lib.StringValidator.is_array_typedpandas._libs.lib.FloatValidator.is_array_typedpandas._libs.lib.BoolValidator.is_array_typedpandas._libs.lib.memory_usage_of_objectspandas._libs.lib.Validator.is_value_typedpandas._libs.lib.TemporalValidator.is_valid_nullc-string too long to convert to Pythonvalue too large to convert to intcan't convert negative value to npy_uint64pandas._libs.tslibs.util.is_nanpandas._libs.lib.infer_datetimelike_arrayvalue too large to convert to npy_uint8can't convert negative value to npy_uint8pandas._libs.lib.item_from_zerodim'NoneType' object is not subscriptablehasattr(): attribute name must be stringView.MemoryView.__pyx_unpickle_Enum__set_stateView.MemoryView.Enum.__setstate_cython__pandas._libs.lib.maybe_booleans_to_sliceCannot transpose memoryview with indirect dimensionsView.MemoryView.transpose_memsliceView.MemoryView.memoryview_copyView.MemoryView.memoryview.T.__get__View.MemoryView.copy_data_to_tempView.MemoryView.memoryview_copy_contentsCannot copy memoryview slice with indirect dimensions (axis %d)View.MemoryView.array_cwrapperView.MemoryView.memoryview.copy_fortranView.MemoryView.memoryview.copypandas._libs.lib.TemporalValidator.is_valid_skipnaView.MemoryView.__pyx_unpickle_Enuminteger division or modulo by zerovalue too large to perform divisionView.MemoryView.pybuffer_indexView.MemoryView.memoryview.get_item_pointerlocal variable '%s' referenced before assignmentpandas._libs.lib.is_datetime_with_singletz_arrayView.MemoryView.get_slice_from_memviewView.MemoryView.memoryview.is_f_contigView.MemoryView.memoryview.is_c_contigView.MemoryView.assert_direct_dimensionsView.MemoryView.memoryview.setitem_slice_assign_scalarView.MemoryView.memoryview.convert_item_to_objectView.MemoryView._memoryviewslice.convert_item_to_objectpandas._libs.lib.c_is_list_likeView.MemoryView.array.__cinit__Argument '%.200s' must not be Noneexpected bytes, NoneType foundtoo many values to unpack (expected %zd)need more than %zd value%.1s to unpack'NoneType' object is not iterableView.MemoryView.memoryview.__setitem__pandas._libs.lib.get_reverse_indexerpandas._libs.lib._try_infer_mapBuffer acquisition failed on assignment; and then reacquiring the old buffer failed too!pandas._libs.lib.fast_unique_multiplepandas._libs.lib.astype_intsafepandas._libs.lib.array_equivalent_objectpandas._libs.lib.tuples_to_object_arraypandas._libs.lib.dicts_to_arraypandas._libs.lib.get_level_sorterpandas._libs.lib.clean_index_listpandas._libs.lib.map_infer_maskpandas._libs.lib.fast_multigetpandas._libs.lib.generate_slicesView.MemoryView.memoryview.__cinit__View.MemoryView.memoryview.setitem_slice_assignmentpandas._libs.lib.count_level_2dpandas._libs.lib.to_object_arraypandas._libs.lib.ensure_string_arrayAll dimensions preceding dimension %d must be indexed and not slicedView.MemoryView.slice_memviewsliceStep may not be zero (axis %d)View.MemoryView.memoryview.__getitem__pandas._libs.lib.to_object_array_tuplespandas._libs.lib.is_bytes_arraypandas._libs.lib.generate_bins_dt64pandas._libs.lib.maybe_convert_objectspandas._libs.lib.maybe_convert_numericcompiletime version %s of module '%.100s' does not match runtime version %spandas._libs.tslibs.conversionpandas._libs.tslibs.timedeltasinvalid vtable found for imported typestruct __pyx_obj_6pandas_5_libs_7missing_C_NAType *struct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *int (PyObject *, int __pyx_skip_dispatch)PyArrayObject *(PyArrayObject *, int __pyx_skip_dispatch)PyObject *(PyObject *, PyDateTime_TZInfo *, PyObject *, int, int, struct __pyx_opt_args_6pandas_5_libs_6tslibs_10conversion_convert_to_tsobject *__pyx_optional_args)PyObject *(PyObject *, PyObject *)int (PyDateTime_TZInfo *, PyDateTime_TZInfo *, int __pyx_skip_dispatch)numpy.core.multiarray failed to import_ARRAY_API is not PyCapsule objectmodule 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%xFATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimeis_timedelta_or_timedelta64_arraypandas._libs.lib._memoryviewsliceInternal class for passing memoryview slices to Pythonpandas._libs.lib.IntervalValidatorpandas._libs.lib.PeriodValidatorpandas._libs.lib.TimeValidatorpandas._libs.lib.DateValidatorpandas._libs.lib.AnyTimedeltaValidatorpandas._libs.lib.TimedeltaValidatorpandas._libs.lib.Datetime64Validatorpandas._libs.lib.DatetimeValidatorpandas._libs.lib.TemporalValidatorpandas._libs.lib.BytesValidatorpandas._libs.lib.StringValidatorpandas._libs.lib.FloatValidatorpandas._libs.lib.IntegerFloatValidatorpandas._libs.lib.IntegerNaValidatorpandas._libs.lib.IntegerValidatorpandas._libs.lib.BoolValidator
    Class for keeping track of the types of elements
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    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
    -------
    numpy array of the object dtype.
    
    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
    -------
    numpy array of the object dtype.
    
    Substitute for np.vectorize with pandas-friendly dtype inference.

    Parameters
    ----------
    arr : ndarray
    f : function

    Returns
    -------
    ndarray
    
    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
    -------
    ndarray
    
    Type inference function-- convert object array to proper dtype

    Parameters
    ----------
    values : ndarray
        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_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_to_nullable_integer : bool, default False
        If an array-like object contains only integer values (and NaN) is
        encountered, whether to convert and return an IntegerArray.

    Returns
    -------
    Array of converted object values to more specific dtypes if applicable.
    
    Convert object array to a numeric array if possible.

    Parameters
    ----------
    values : ndarray
        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.

    Returns
    -------
    Array of converted object values to numerical ones.
    
    Infer with timedeltas and/or nat/none.
    
    Check values have the same tzinfo attribute.
    Doesn't check values are datetime-like types.
    
    Infer if we have a datetime or timedelta array.
    - date: we have *only* date and maybe strings, nulls
    - datetime: we have *only* datetimes and maybe strings, nulls
    - timedelta: we have *only* timedeltas and maybe strings, nulls
    - nat: we do not have *any* date, datetimes or timedeltas, but do have
      at least a NaT
    - mixed: other objects (strings, a mix of tz-aware and tz-naive, or
                            actual objects)

    Parameters
    ----------
    arr : object array

    Returns
    -------
    str: {datetime, timedelta, date, nat, mixed}
    
    Efficiently infer the type of a passed val, or list-like
    array of values. Return a string describing the type.

    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

    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

    Examples
    --------
    >>> 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])
    'mixed'

    >>> 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'
    
    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.

        .. versionadded:: 0.24.0

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

    Examples
    --------
    >>> is_list_like([1, 2, 3])
    True
    >>> is_list_like({1, 2, 3})
    True
    >>> is_list_like(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
    
    Return a boolean if this is a Period object.

    Returns
    -------
    bool
    
    Returns
    -------
    bool
    
    Returns
    -------
    bool
    
    Returns
    -------
    bool
    
    Returns
    -------
    bool
    
    Parameters
    ----------
    index : ndarray
    labels : ndarray[int64]
    keys : list
    sorted_labels : list[ndarray[int64]]
    
    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([label[starts[i]:starts[i+1]].argsort(kind='mergesort')
            + starts[i] for i in range(len(starts) - 1)])
    
    Int64 (datetime64) version of generic python version in ``groupby.py``.
    
    Utility used in ``pandas.core.indexes.api.ensure_index``.
    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
        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
    -------
    ndarray
        An array with the input array's elements casted to str or nan-like.
    
    Perform an element by element comparison on 1-d object arrays
    taking into account nan positions.
    
    Reverse indexing operation.

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

        indexer_inv[indexer[x]] = x

    .. note:: If indexer is not unique, only first occurrence is accounted.
    
    For zipping multiple ndarrays into an ndarray of tuples.
    
    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
    
    Generate a list of unique values from a list of arrays.

    Parameters
    ----------
    list : array-like
        List of array-like objects.
    sort : bool
        Whether or not to sort the resulting unique list.

    Returns
    -------
    list of unique values
    
    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])
    
    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
    --------
    >>> is_iterator((x for x in []))
    True
    >>> is_iterator([1, 2, 3])
    False
    >>> is_iterator(datetime(2017, 1, 1))
    False
    >>> is_iterator("foo")
    False
    >>> is_iterator(1)
    False
    
    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
    --------
    >>> 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
    
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    Does not include the actual bytes of the pointers
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