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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)%.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)Interpreter change detected - this module can only be loaded into one interpreter per process.unbound method %.200S() needs an argument%.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 PyObjectUnexpected format string character: '%c'Acquisition count is %d (line %d)%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'ord() expected string of length 1, but %.200s foundord() expected a character, but string of length %zd foundShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompilinginvalid vtable found for imported typemultiple bases have vtable conflict: '%.200s' and '%.200s'join() result is too long for a Python string__annotations__ must be set to a dict object__name__ must be set to a string object__qualname__ must be set to a string object__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function calls__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function callsfunction's dictionary may not be deletedsetting function's dictionary to a non-dictCannot convert %.200s to %.200sinstance exception may not have a separate valuecalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionBuffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'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 stringDoes not understand character buffer dtype format string ('%c')Expected 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)Argument '%.200s' has incorrect type (expected %.200s, got %.200s)value too large to convert to npy_uint8can't convert negative value to npy_uint8%s() got multiple values for keyword argument '%U'value too large to convert to intcan't convert negative value to npy_uint64cannot fit '%.200s' into an index-sized integer'%.200s' object is not subscriptableUnable to initialize pickling for %.200spandas/_libs/lib.cpython-312-x86_64-linux-gnu.so.p/pandas/_libs/lib.pyx.cOut of bounds on buffer access (axis %d)%.200s() takes %.8s %zd positional argument%.1s (%zd given)View.MemoryView.memoryview_cwrapperItem size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s)Buffer not compatible with direct access in dimension %d.Buffer is not indirectly accessible in dimension %d.pandas._libs.lib._NoDefault.__repr__pandas._libs.tslibs.util.is_nanpandas._libs.lib._try_infer_maphasattr(): attribute name must be stringpandas._libs.lib.c_is_list_likepandas._libs.lib.has_only_ints_or_nan'NoneType' object is not subscriptableView.MemoryView.__pyx_unpickle_Enum__set_stateView.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.strides.__get__View.MemoryView.memoryview.shape.__get__View.MemoryView.memoryview.base.__get__View.MemoryView.transpose_memsliceView.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 divisionView.MemoryView._allocate_bufferView.MemoryView._err_no_memoryView.MemoryView.copy_data_to_tempView.MemoryView.memoryview_copy_contentsView.MemoryView.get_slice_from_memviewView.MemoryView.memoryview.is_f_contigView.MemoryView.memoryview.is_c_contigPyObject_GetBuffer: view==NULL argument is obsoleteView.MemoryView.memoryview.__getbuffer__View.MemoryView.memoryview.__str__'NoneType' object is not iterabletoo many values to unpack (expected %zd)need more than %zd value%.1s to unpackView.MemoryView.memoryview.__setitem__Subscript deletion not supported by %.200sView.MemoryView.memoryview.__repr__View.MemoryView.memoryview.assign_item_from_objectView.MemoryView._memoryviewslice.assign_item_from_objectView.MemoryView.memoryview.convert_item_to_objectView.MemoryView._memoryviewslice.convert_item_to_objectView.MemoryView.memoryview.setitem_indexedView.MemoryView.assert_direct_dimensionsView.MemoryView.memoryview.setitem_slice_assign_scalarView.MemoryView.memoryview.is_sliceView.MemoryView.pybuffer_indexView.MemoryView.memoryview.get_item_pointerView.MemoryView.Enum.__setstate_cython__View.MemoryView.Enum.__reduce_cython__View.MemoryView.array.memview.__get__View.MemoryView.array.__getattr__View.MemoryView.array.__setitem__View.MemoryView.array.__getitem__View.MemoryView.array.get_memviewpandas._libs.lib.Validator.__cinit__pandas._libs.lib.TemporalValidator.__cinit__pandas._libs.lib.TimedeltaValidator.is_valid_nullpandas._libs.lib.AnyDatetimeValidator.is_value_typedpandas._libs.lib.DatetimeValidator.is_valid_nullstrings are too large to concatpandas._libs.lib.TemporalValidator.is_valid_nullpandas._libs.lib.TemporalValidator.is_validpandas._libs.lib.Validator.is_value_typedpandas._libs.lib.Validator.is_valid_skipnapandas._libs.lib.Validator.is_validpandas._libs.lib.Validator._validate_skipnapandas._libs.lib.TemporalValidator._validate_skipnapandas._libs.lib.Validator._validatepandas._libs.lib.Validator.validatepandas._libs.lib.Seen.__cinit__pandas._libs.lib.Seen.is_bool_or_na.__get__pandas._libs.lib.Seen.is_bool.__get__Module 'lib' has already been imported. Re-initialisation is not supported.compile time Python version %d.%d of module '%.100s' %s runtime version %d.%dPyArrayObject *(PyArrayObject *, PyObject *, int __pyx_skip_dispatch)pandas._libs.tslibs.timestampspandas._libs.tslibs.conversionpandas._libs.tslibs.timedeltasstruct __pyx_obj_6pandas_5_libs_7missing_C_NAType *struct __pyx_obj_6pandas_5_libs_6tslibs_7nattype__NaT *int (PyObject *, PyObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_6pandas_5_libs_7missing_is_matching_na *__pyx_optional_args)int (PyObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_6pandas_5_libs_7missing_checknull *__pyx_optional_args)int (PyDateTime_TZInfo *, PyDateTime_TZInfo *, int __pyx_skip_dispatch)struct __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)PyObject *(PyObject *, PyObject *)_ARRAY_API is not PyCapsule objectmodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule was compiled against NumPy C-API version 0x%x (NumPy 1.20) but the running NumPy has C-API version 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 runtime__mro_entries__ must return a tuplemetaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its basespandas._libs.lib.dtypes_all_equalpandas._libs.lib.tuples_to_object_arraypandas._libs.lib.TimeValidator.__setstate_cython__pandas._libs.lib.TimeValidator.__reduce_cython__pandas._libs.lib.DateValidator.__setstate_cython__pandas._libs.lib.DateValidator.__reduce_cython__pandas._libs.lib.AnyTimedeltaValidator.__setstate_cython__pandas._libs.lib.AnyTimedeltaValidator.__reduce_cython__pandas._libs.lib.TimedeltaValidator.__setstate_cython__pandas._libs.lib.TimedeltaValidator.__reduce_cython__pandas._libs.lib.AnyDatetimeValidator.__setstate_cython__pandas._libs.lib.AnyDatetimeValidator.__reduce_cython__pandas._libs.lib.Datetime64Validator.__setstate_cython__pandas._libs.lib.Datetime64Validator.__reduce_cython__pandas._libs.lib.DatetimeValidator.__setstate_cython__pandas._libs.lib.DatetimeValidator.__reduce_cython__pandas._libs.lib.TemporalValidator.__setstate_cython__pandas._libs.lib.TemporalValidator.__reduce_cython__pandas._libs.lib.BytesValidator.__setstate_cython__pandas._libs.lib.BytesValidator.__reduce_cython__pandas._libs.lib.StringValidator.__setstate_cython__pandas._libs.lib.StringValidator.__reduce_cython__pandas._libs.lib.DecimalValidator.__setstate_cython__pandas._libs.lib.DecimalValidator.__reduce_cython__pandas._libs.lib.ComplexValidator.__setstate_cython__pandas._libs.lib.ComplexValidator.__reduce_cython__pandas._libs.lib.FloatValidator.__setstate_cython__pandas._libs.lib.FloatValidator.__reduce_cython__pandas._libs.lib.IntegerFloatValidator.__setstate_cython__pandas._libs.lib.IntegerFloatValidator.__reduce_cython__pandas._libs.lib.IntegerNaValidator.__setstate_cython__pandas._libs.lib.IntegerNaValidator.__reduce_cython__pandas._libs.lib.IntegerValidator.__setstate_cython__pandas._libs.lib.IntegerValidator.__reduce_cython__pandas._libs.lib.BoolValidator.__setstate_cython__pandas._libs.lib.BoolValidator.__reduce_cython__pandas._libs.lib.Validator.__setstate_cython__pandas._libs.lib.Validator.__reduce_cython__pandas._libs.lib.is_datetime64_arraypandas._libs.lib.is_timedelta_or_timedelta64_arrayis_timedelta_or_timedelta64_arraypandas._libs.lib.is_integer_arraypandas._libs.lib.is_datetime_arraypandas._libs.lib.is_date_arraypandas._libs.lib.is_time_arraypandas._libs.lib.is_float_arraypandas._libs.lib.is_integer_float_arraypandas._libs.lib.is_integer_na_arraypandas._libs.lib.is_bool_arraypandas._libs.lib.is_string_arraypandas._libs.lib.is_period_arraypandas._libs.lib.is_interval_arraypandas._libs.lib.maybe_convert_objects'%.200s' object does not support slice %.10s__float__ returned non-float (type %.200s)pandas._libs.lib.is_datetime_or_datetime64_arraypandas._libs.lib.Seen.__setstate_cython__pandas._libs.lib.Seen.__reduce_cython__pandas._libs.lib.is_pyarrow_arraylocal variable '%s' referenced before assignmentpandas._libs.lib.is_decimal_arraypandas._libs.lib.is_bytes_arraypandas._libs.lib.is_complex_arraypandas._libs.lib.maybe_convert_numericpandas._libs.lib.is_int_or_nonepandas._libs.lib.is_all_arraylikepandas._libs.lib.convert_nans_to_NApandas._libs.lib.is_range_indexerpandas._libs.lib.maybe_booleans_to_slicepandas._libs.lib.maybe_indices_to_slicepandas._libs.lib.dicts_to_arraypandas._libs.lib.fast_unique_multiple_listpandas._libs.lib.item_from_zerodimView.MemoryView.__pyx_unpickle_EnumView.MemoryView._memoryviewslice.__setstate_cython__View.MemoryView._memoryviewslice.__reduce_cython__View.MemoryView.memoryview.__setstate_cython__View.MemoryView.memoryview.__reduce_cython__View.MemoryView.memoryview.__cinit__View.MemoryView.memoryview_fromslicepandas._libs.lib.map_infer_maskView.MemoryView.array.__setstate_cython__View.MemoryView.array.__reduce_cython__Cannot copy memoryview slice with indirect dimensions (axis %d)View.MemoryView.array_cwrapperView.MemoryView.memoryview_copy_from_sliceView.MemoryView.memoryview.copy_fortranpandas._libs.lib.get_reverse_indexerpandas._libs.lib.get_level_sorterpandas._libs.lib.count_level_2dpandas._libs.lib._map_infer_maskpandas._libs.lib.fast_multigetView.MemoryView.slice_memviewsliceView.MemoryView.memoryview.__getitem__View.MemoryView.memoryview_copyView.MemoryView.memoryview.T.__get__View.MemoryView.memoryview.copypandas._libs.lib.DecimalValidator.is_value_typedpandas._libs.lib.Validator.is_valid_nullpandas._libs.lib.TemporalValidator.is_valid_skipnais_datetime_with_singletz_arraypandas._libs.lib.is_datetime_with_singletz_arraypandas._libs.lib.IntegerNaValidator.is_value_typedpandas._libs.lib.ComplexValidator.is_value_typedonly single character unicode strings can be converted to Py_UCS4, got length %zdpandas._libs.lib.__pyx_fused_cpdefdictionary changed size during iteration'NoneType' object has no attribute '%.30s'pandas._libs.lib.fast_unique_multiple_list_genpandas._libs.lib.to_object_arraypandas._libs.lib.memory_usage_of_objectspandas._libs.lib.to_object_array_tuplespandas._libs.lib.ensure_string_arraypandas._libs.lib.array_equivalent_objectView.MemoryView.array.__getbuffer__pandas._libs.lib.generate_slicespandas._libs.lib.generate_bins_dt64Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!View.MemoryView.memoryview.setitem_slice_assignment_cython_3_0_5.fused_cython_function_cython_3_0_5.cython_function_or_methodpandas._libs.lib._memoryviewsliceInternal class for passing memoryview slices to Pythonpandas._libs.lib.TimeValidatorpandas._libs.lib.DateValidatorpandas._libs.lib.AnyTimedeltaValidatorpandas._libs.lib.TimedeltaValidatorpandas._libs.lib.AnyDatetimeValidatorpandas._libs.lib.Datetime64Validatorpandas._libs.lib.DatetimeValidatorpandas._libs.lib.TemporalValidatorpandas._libs.lib.BytesValidatorpandas._libs.lib.StringValidatorpandas._libs.lib.DecimalValidatorpandas._libs.lib.ComplexValidatorpandas._libs.lib.FloatValidatorpandas._libs.lib.IntegerFloatValidatorpandas._libs.lib.IntegerNaValidatorpandas._libs.lib.IntegerValidatorpandas._libs.lib.BoolValidator Class for keeping track of the types of elements encountered when trying to perform type conversions. __pyx_fuse_1convert_nans_to_NA__pyx_fuse_0convert_nans_to_NA__pyx_fuse_1has_only_ints_or_nan__pyx_fuse_0has_only_ints_or_nanBad call flags for CyFunction__pyx_capi____loader__loader__file__origin__package__parent__path__submodule_search_locations%.200s() needs an argumentkeywords must be strings_cython_3_0_5Missing type objectendunparsable format string'complex double''signed char''unsigned char''short''unsigned short''int''unsigned int''long''unsigned long''long long''unsigned long long''double''complex long double''bool''char''complex float''float'a structPython objecta pointera string'long double'buffer dtypeFunction is not fusedan integer is requiredndarraycannot import name %Sname '%U' is not defined%s (%s:%d)lib.pyxpandas._libs.lib.eq_NA_compatexactlypandas._libs.lib.is_bool_listpandas._libs.lib.__defaults____repr__util.pxdhas_only_ints_or_nanView.MemoryView._errat leastat most__cinit__shapeformatExpected %s, got %.200sView.MemoryView._err_dimView.MemoryView._err_extentsis_f_contigis_c_contigView.MemoryView._unellipsify'NoneType' is not iterableView.MemoryView.Enum.__init____setstate_cython__tuple__reduce_cython__pandas._libs.lib.Seen.saw_intbuiltinscython_runtime__builtins__does not match__debug__int (PyObject *, int)c_is_list_likenumpyflatiterbroadcastgenericnumberunsignedintegerinexactcomplexfloatingflexiblecharacterufuncdatetimedatetimedeltatzinfopandas._libs.missingC_NATypepandas._libs.tslibs.baseABCTimestamppandas._libs.tslibs.offsetsBaseOffset_Timestamp_TSObjectpandas._libs.tslibs.nattype_TimedeltaC_NApandas._libs.tslibs.timezonesPyDateTime_TZInfo *utc_stdlib__pyx_t_5numpy_int64_tNPY_NATPyObject *c_nat_stringsc_NaTis_matching_nachecknullint (PyObject *)is_null_datetime64is_null_timedelta64is_offset_objecttz_compareconvert_to_tsobjectchecknull_with_natpandas._libs.tslibs.periodis_period_objectconvert_to_timedelta64View.MemoryViewdatetime.datetime_CAPInumpy._core._multiarray_umathnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is NULL pointer__init__.cython-30.pxdnumpy.import_arraypandas._pandas_parser_CAPI__orig_bases__init pandas._libs.libpandas._libs.lib.is_np_dtypekindsdtypes_all_equaltypesarrtuples_to_object_arraypandas._libs.lib.map_inferis_datetime64_arrayis_integer_arrayis_datetime_arrayis_date_arrayis_time_arrayis_float_arrayis_bool_arrayis_string_arrayunicodeis_interval_arraypandas._libs.lib.get_itemsizeassignmentmaybe_convert_objectsis_pyarrow_arraypandas._libs.lib.is_list_likepandas._libs.lib.is_periodpandas._libs.lib.is_decimalpandas._libs.lib.infer_dtypemaybe_convert_numericna_valuespandas._libs.lib.is_complexpandas._libs.lib.is_boolis_int_or_nonepandas._libs.lib.is_integerpandas._libs.lib.is_floatis_all_arraylikeconvert_nans_to_NAleftmaybe_booleans_to_slicemaybe_indices_to_sliceindicesndarrayspandas._libs.lib.fast_zipdicts_to_arraydictscolumnsfast_unique_multiple_listlistsitem_from_zerodimpandas._libs.lib.is_scalar__pyx_unpickle_Enumcopy_fortranget_reverse_indexerpandas._libs.lib.has_infsget_level_sortercodescount_level_2dindexpandas._libs.lib.indices_fastkeyssorted_labelsoutfast_multigetmappingView.MemoryView.memview_slicememviewsliceobjcopybase_tz__pyx_fused_cpdeffast_unique_multiple_list_genpandas._libs.lib.is_iteratormin_widthmemory_usage_of_objectsto_object_array_tuplesensure_string_arrayarray_equivalent_objectrightnumpy.PyArray_MultiIterNew2pandas._libs.lib.is_intervalgenerate_slicesBuffer not C contiguous.generate_bins_dt64__doc____signatures____self____reduce____module__func_docfunc_name__name____qualname__func_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__kwdefaults____annotations___is_coroutineCythonUnboundCMethodpandas._libs.lib.memoryviewstridessuboffsetsndimnbytespandas._libs.lib.Enumpandas._libs.lib.arraymemview__getattr__pandas._libs.lib.Validatorpandas._libs.lib.Seennumeric_is_bool_or_na__pyx_fuse_2_map_infer_mask__pyx_fuse_1_map_infer_mask__pyx_fuse_0_map_infer_mask__pyx_fuse_1is_range_indexer__pyx_fuse_0is_range_indexer__pyx_fuse_1has_infs__pyx_fuse_0has_infsconst uint8_tconst int64_tconst intp_tconst doubleconst floatuint64_tdouble complexfloat64_tint32_tpm@m@mpm@m@m@m@m@mm0m@m@m m@m@m m m m@m@m@m@m@m@m@m@m@m@m@m@m@m@m@m@mpmpm m@m0mmm0m@m@m m@m@m@mpm m@mpmԝt&@Zȝޜ͛͛Қޚfr~&@Zƚܝܝܝ:'ܝCCCCCCCCCCCCCCCCCCCCCCCCCCCy˞CyyCCyCCCyyCy\TT\TTTTT TTTTTTTTTTTTTTTTTTTT\\)T< TTTTT\T\֞֞֞;5(ߞߞRߞw7hC;::::[C;);N;>DY,YTPHP>> from pandas.api.types import infer_dtype >>> 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' >>> from decimal import Decimal >>> 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' >>> import 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' Return True if given object is a pyarrow Array or ChunkedArray. 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 Return True if given object is Period. Returns ------- bool Return True if given object is complex. Returns ------- bool Examples -------- >>> pd.api.types.is_complex(1 + 1j) True >>> pd.api.types.is_complex(1) False Return True if given object is boolean. Returns ------- bool Examples -------- >>> pd.api.types.is_bool(True) True >>> pd.api.types.is_bool(1) False Return True if given object is integer or None. Returns ------- bool Return True if given object is integer. Returns ------- bool Examples -------- >>> pd.api.types.is_integer(1) True >>> pd.api.types.is_integer(1.0) False Return True if given object is float. Returns ------- bool Examples -------- >>> pd.api.types.is_float(1.0) True >>> pd.api.types.is_float(1) False Parameters ---------- index : ndarray[intp] 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([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] Int64 (datetime64) version of generic python version in ``groupby.py``. Should we treat these as levels of a MultiIndex, as opposed to Index items? 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. 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. Perform an element by element comparison on 1-d integer arrays, meant for indexer comparisons Perform an element by element comparison on N-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 Parameters ---------- indexer : np.ndarray[np.intp] length : int Returns ------- np.ndarray[np.intp] Notes ----- 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 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 -------- >>> 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 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 the memory usage of an object array in bytes. Does not include the actual bytes of the pointers unable to allocate shape and strides.tuple[np.ndarray, np.ndarray | None]numpy._core.umath failed to importnumpy._core.multiarray failed to importno default __reduce__ due to non-trivial __cinit__is_timedelta_or_timedelta64_arrayis_period is deprecated and will be removed in a future version. Use isinstance(obj, pd.Period) instead.is_interval is deprecated and will be removed in a future version. Use isinstance(obj, pd.Interval) instead.got differing extents in dimension construct_1d_object_array_from_listlikeUnable to convert item to objectTimedeltaValidator.__setstate_cython__TimedeltaValidator.__reduce_cython__Time zones are not available from the C-API.TemporalValidator.__setstate_cython__TemporalValidator.__reduce_cython__StringValidator.__setstate_cython__ Return True if given object is complex. Returns ------- bool Examples -------- >>> pd.api.types.is_complex(1 + 1j) True >>> pd.api.types.is_complex(1) False Return True if given object is boolean. Returns ------- bool Examples -------- >>> pd.api.types.is_bool(True) True >>> pd.api.types.is_bool(1) False Return True if given object is integer. Returns ------- bool Examples -------- >>> pd.api.types.is_integer(1) True >>> pd.api.types.is_integer(1.0) False Return True if given object is float. Returns ------- bool Examples -------- >>> pd.api.types.is_float(1.0) True >>> pd.api.types.is_float(1) False Out of bounds on buffer access (axis Invalid mode, expected 'c' or 'fortran', got Invalid length for values or for binnerIntegerValidator.__setstate_cython__IntegerValidator.__reduce_cython__IntegerNaValidator.__setstate_cython__IntegerNaValidator.__reduce_cython__IntegerFloatValidator.__setstate_cython__IntegerFloatValidator.__reduce_cython__Indirect dimensions not supportedIncompatible checksums (0x%x vs (0x82a3537, 0x6ae9995, 0xb068931) = (name))Function call with ambiguous argument typesFloatValidator.__setstate_cython__Expected at least %d argument%s, got %dEmpty shape tuple for cython.arrayDecimalValidator.__setstate_cython__DecimalValidator.__reduce_cython__DatetimeValidator.__setstate_cython__DatetimeValidator.__reduce_cython__Datetime64Validator.__setstate_cython__Datetime64Validator.__reduce_cython__ComplexValidator.__setstate_cython__ComplexValidator.__reduce_cython__Cannot transpose memoryview with indirect dimensionsCannot create writable memory view from read-only memoryviewCannot assign to read-only memoryviewCan only create a buffer that is contiguous in memory.BytesValidator.__setstate_cython__Buffer view does not expose stridesAnyTimedeltaValidator.__setstate_cython__AnyTimedeltaValidator.__reduce_cython__AnyDatetimeValidator.__reduce_cython__All dimensions preceding dimension %d must be indexed and not slicedis_datetime_with_singletz_array child class must define is_value_typed child class must define is_valid_nullTimeValidator.__setstate_cython__StringValidator.__reduce_cython__ 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 -------- >>> from pandas.api.types import infer_dtype >>> 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' >>> from decimal import Decimal >>> 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' >>> import 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' 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]) DateValidator.__setstate_cython__ 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 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 Cannot specify 'dtype_if_all_nat' without convert_non_numeric=TrueBoolValidator.__setstate_cython__AnyDatetimeValidator.__setstate_cython__all arrays must be same length 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 FloatValidator.__reduce_cython__BytesValidator.__reduce_cython__unable to allocate array data.fast_unique_multiple_list_genValues falls before first binTimeValidator.__reduce_cython__DateValidator.__reduce_cython__BoolValidator.__reduce_cython__/project/pandas/_libs/lib.pyxndarray[Python object,ndim=2]ndarray[Python object,ndim=1]itemsize <= 0 for cython.arrayValues falls after last binValidator.__setstate_cython__Step may not be zero (axis %d)No matching signature foundusing_pyarrow_string_dtypepandas.core.dtypes.missingpandas.core.dtypes.genericpandas._libs.tslibs.perioditem_from_zerodim (line 287)convert_to_masked_nullableIndex out of bounds (axis %d)pandas.core.arrays.string_fast_unique_multiple_listconvert_to_nullable_dtypeValidator.__reduce_cython__Dimension %d is not directcheck_na_tuples_nonequalEmpty string encounteredpandas.util._exceptionspandas.core.dtypes.castmemory_usage_of_objectsmaybe_booleans_to_slicearray_equivalent_objecttuples_to_object_arrayto_object_array_tuplesmaybe_indices_to_sliceis_list_like (line 1201)Seen.__setstate_cython__Cannot index with type 'pandas._libs.intervalmaybe_convert_objectsmaybe_convert_numericinfer_dtype (line 1478)Invalid shape in axis pandas._libs.missingis_iterator (line 254)is_integer (line 1092)is_complex (line 1141)has_only_ints_or_nanconstruct_array_typeSeen.__reduce_cython__OutOfBoundsTimedeltaInteger out of range.pandas._libs.tslibsmixed-integer-floatis_datetime64_arrayget_reverse_indexerensure_string_arrayconvert_non_numericOutOfBoundsDatetimeNotImplementedErrorpandas.core.arraysis_scalar (line 160)is_float (line 1073)generate_bins_dt64convert_nans_to_NAcline_in_tracebackasyncio.coroutines__pyx_unpickle_Enumitem_from_zerodimis_interval_arrayis_datetime_arrayis_bool (line 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