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Raises ------ ValueError If the data is not length-1. rvz6can only convert an array of size 1 to a Python scalarN)rxnextiter ValueErrorr>r@r@rAitemKs  zIndexOpsMixin.itemcCs|jjS)zD Return the number of bytes in the underlying data. )rnbytesr>r@r@rAr]szIndexOpsMixin.nbytescCs t|jS)zG Return the number of elements in the underlying data. )rxrr>r@r@rAsizedszIndexOpsMixin.sizer+cCs t|dS)aM The ExtensionArray of the data backing this Series or Index. Returns ------- ExtensionArray An ExtensionArray of the values stored within. For extension types, this is the actual array. For NumPy native types, this is a thin (no copy) wrapper around :class:`numpy.ndarray`. ``.array`` differs ``.values`` which may require converting the data to a different form. See Also -------- Index.to_numpy : Similar method that always returns a NumPy array. Series.to_numpy : Similar method that always returns a NumPy array. Notes ----- This table lays out the different array types for each extension dtype within pandas. ================== ============================= dtype array type ================== ============================= category Categorical period PeriodArray interval IntervalArray IntegerNA IntegerArray string StringArray boolean BooleanArray datetime64[ns, tz] DatetimeArray ================== ============================= For any 3rd-party extension types, the array type will be an ExtensionArray. For all remaining dtypes ``.array`` will be a :class:`arrays.NumpyExtensionArray` wrapping the actual ndarray stored within. If you absolutely need a NumPy array (possibly with copying / coercing data), then use :meth:`Series.to_numpy` instead. Examples -------- For regular NumPy types like int, and float, a PandasArray is returned. >>> pd.Series([1, 2, 3]).array [1, 2, 3] Length: 3, dtype: int64 For extension types, like Categorical, the actual ExtensionArray is returned >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a'])) >>> ser.array ['a', 'b', 'a'] Categories (2, object): ['a', 'b'] Nrr>r@r@rAarrayks?zIndexOpsMixin.arrayNFznpt.DTypeLike | NoneboolrFz np.ndarray)rcopyna_valuerEcKst|jr$|jj|f||d|S|rHt|d}td|dtj|j |d}|sf|t j k r| }|t j k r||t |<|S)a A NumPy ndarray representing the values in this Series or Index. Parameters ---------- dtype : str or numpy.dtype, optional The dtype to pass to :meth:`numpy.asarray`. copy : bool, default False Whether to ensure that the returned value is not a view on another array. Note that ``copy=False`` does not *ensure* that ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that a copy is made, even if not strictly necessary. na_value : Any, optional The value to use for missing values. The default value depends on `dtype` and the type of the array. .. versionadded:: 1.0.0 **kwargs Additional keywords passed through to the ``to_numpy`` method of the underlying array (for extension arrays). .. versionadded:: 1.0.0 Returns ------- numpy.ndarray See Also -------- Series.array : Get the actual data stored within. Index.array : Get the actual data stored within. DataFrame.to_numpy : Similar method for DataFrame. Notes ----- The returned array will be the same up to equality (values equal in `self` will be equal in the returned array; likewise for values that are not equal). When `self` contains an ExtensionArray, the dtype may be different. For example, for a category-dtype Series, ``to_numpy()`` will return a NumPy array and the categorical dtype will be lost. For NumPy dtypes, this will be a reference to the actual data stored in this Series or Index (assuming ``copy=False``). Modifying the result in place will modify the data stored in the Series or Index (not that we recommend doing that). For extension types, ``to_numpy()`` *may* require copying data and coercing the result to a NumPy type (possibly object), which may be expensive. When you need a no-copy reference to the underlying data, :attr:`Series.array` should be used instead. This table lays out the different dtypes and default return types of ``to_numpy()`` for various dtypes within pandas. ================== ================================ dtype array type ================== ================================ category[T] ndarray[T] (same dtype as input) period ndarray[object] (Periods) interval ndarray[object] (Intervals) IntegerNA ndarray[object] datetime64[ns] datetime64[ns] datetime64[ns, tz] ndarray[object] (Timestamps) ================== ================================ Examples -------- >>> ser = pd.Series(pd.Categorical(['a', 'b', 'a'])) >>> ser.to_numpy() array(['a', 'b', 'a'], dtype=object) Specify the `dtype` to control how datetime-aware data is represented. Use ``dtype=object`` to return an ndarray of pandas :class:`Timestamp` objects, each with the correct ``tz``. >>> ser = pd.Series(pd.date_range('2000', periods=2, tz="CET")) >>> ser.to_numpy(dtype=object) array([Timestamp('2000-01-01 00:00:00+0100', tz='CET'), Timestamp('2000-01-02 00:00:00+0100', tz='CET')], dtype=object) Or ``dtype='datetime64[ns]'`` to return an ndarray of native datetime64 values. The values are converted to UTC and the timezone info is dropped. >>> ser.to_numpy(dtype="datetime64[ns]") ... # doctest: +ELLIPSIS array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00...'], dtype='datetime64[ns]') )rrrz/to_numpy() got an unexpected keyword argument 'rd)r)rrrto_numpyrokeys TypeErrorrqasarrayrlib no_defaultrZ asanyarrayr!)r?rrrrrresultr@r@rArsc   zIndexOpsMixin.to_numpycCs|j Srm)rr>r@r@rAemptyszIndexOpsMixin.emptyTskipnacOs&t|t||tj|j|dS)a Return the maximum value of the Index. Parameters ---------- axis : int, optional For compatibility with NumPy. Only 0 or None are allowed. skipna : bool, default True Exclude NA/null values when showing the result. *args, **kwargs Additional arguments and keywords for compatibility with NumPy. Returns ------- scalar Maximum value. See Also -------- Index.min : Return the minimum value in an Index. Series.max : Return the maximum value in a Series. DataFrame.max : Return the maximum values in a DataFrame. Examples -------- >>> idx = pd.Index([3, 2, 1]) >>> idx.max() 3 >>> idx = pd.Index(['c', 'b', 'a']) >>> idx.max() 'c' For a MultiIndex, the maximum is determined lexicographically. >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)]) >>> idx.max() ('b', 2) r)rvalidate_minmax_axisZ validate_maxr$Znanmaxrr?rwrrrr@r@rAmax#s(  zIndexOpsMixin.maxrminlargest)opZopposerg)rrEcOsX|j}t|t|||}t|trF|s<|r>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0, ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}}) >>> s Corn Flakes 100.0 Almond Delight 110.0 Cinnamon Toast Crunch 120.0 Cocoa Puff 110.0 dtype: float64 >>> s.argmax() 2 >>> s.argmin() 0 The maximum cereal calories is the third element and the minimum cereal calories is the first element, since series is zero-indexed. rzrN) rrrZvalidate_argmax_with_skipnarnr+r!anyargmaxr$Z nanargmaxr?rwrrrZdelegater@r@rArOs4   zIndexOpsMixin.argmaxcOs&t|t||tj|j|dS)a Return the minimum value of the Index. Parameters ---------- axis : {None} Dummy argument for consistency with Series. skipna : bool, default True Exclude NA/null values when showing the result. *args, **kwargs Additional arguments and keywords for compatibility with NumPy. Returns ------- scalar Minimum value. See Also -------- Index.max : Return the maximum value of the object. Series.min : Return the minimum value in a Series. DataFrame.min : Return the minimum values in a DataFrame. Examples -------- >>> idx = pd.Index([3, 2, 1]) >>> idx.min() 1 >>> idx = pd.Index(['c', 'b', 'a']) >>> idx.min() 'a' For a MultiIndex, the minimum is determined lexicographically. >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)]) >>> idx.min() ('a', 1) r)rrZ validate_minr$Znanminrrr@r@rArs(  zIndexOpsMixin.minsmallestcOsX|j}t|t|||}t|trF|s<|rr@r@rArszIndexOpsMixin.tolistcCs2t|jtjst|jSt|jjt|jjSdS)a Return an iterator of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period) Returns ------- iterator N) rnrrqrrrmaprrangerr>r@r@rA__iter__s  zIndexOpsMixin.__iter__cCstt|S)zc Return True if there are any NaNs. Enables various performance speedups. )rr!rr>r@r@rAhasnanss zIndexOpsMixin.hasnansznpt.NDArray[np.bool_]cCs t|jSrm)r!rr>r@r@rAr!szIndexOpsMixin.isnar)rwr numeric_only filter_typerCnamec Ks>t||d}|dkr,tt|jd||fd|i|S)zA Perform the reduction type operation if we can. Nz cannot perform the operation r)rSrr=rY) r?rrrwrrrkwdsrr@r@rA_reduces  zIndexOpsMixin._reducec sBt|r9z+IndexOpsMixin._map_values..)Zdtype_if_empty)Nignorez+na_action must either be 'ignore' or None, z was passedrr2rNcSs ||Srm)rvaluesfr@r@rArircSst||t|tjSrm)rZmap_infer_maskr!viewrqZuint8rr@r@rArms)rrndictrKr,rqZfloat64r rindexZnotnarrr rrZ get_indexerr#Ztake_ndrNotImplementedErrorZastyperFrZ map_infer) r?ZmapperZ na_actionmsgcatrZindexerZ new_valuesZmap_fr@rrA _map_valuessJ              zIndexOpsMixin._map_valuesr3) normalizesort ascendingdropnarEcCst||||||dS)a Return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default. Parameters ---------- normalize : bool, default False If True then the object returned will contain the relative frequencies of the unique values. sort : bool, default True Sort by frequencies. ascending : bool, default False Sort in ascending order. bins : int, optional Rather than count values, group them into half-open bins, a convenience for ``pd.cut``, only works with numeric data. dropna : bool, default True Don't include counts of NaN. Returns ------- Series See Also -------- Series.count: Number of non-NA elements in a Series. DataFrame.count: Number of non-NA elements in a DataFrame. DataFrame.value_counts: Equivalent method on DataFrames. Examples -------- >>> index = pd.Index([3, 1, 2, 3, 4, np.nan]) >>> index.value_counts() 3.0 2 1.0 1 2.0 1 4.0 1 dtype: int64 With `normalize` set to `True`, returns the relative frequency by dividing all values by the sum of values. >>> s = pd.Series([3, 1, 2, 3, 4, np.nan]) >>> s.value_counts(normalize=True) 3.0 0.4 1.0 0.2 2.0 0.2 4.0 0.2 dtype: float64 **bins** Bins can be useful for going from a continuous variable to a categorical variable; instead of counting unique apparitions of values, divide the index in the specified number of half-open bins. >>> s.value_counts(bins=3) (0.996, 2.0] 2 (2.0, 3.0] 2 (3.0, 4.0] 1 dtype: int64 **dropna** With `dropna` set to `False` we can also see NaN index values. >>> s.value_counts(dropna=False) 3.0 2 1.0 1 2.0 1 4.0 1 NaN 1 dtype: int64 )rrrbinsr)r))r?rrrrrr@r@rAr)~sVzIndexOpsMixin.value_countscCsV|j}t|tjsJ|}t|jtjrR|jjdkrRt|trRt|}nt |}|S)N)mM) rrnrqrrr8rkindr rr()r?rrr@r@rAr8s    zIndexOpsMixin.unique)rrEcCs|}|rt|}t|S)a Return number of unique elements in the object. Excludes NA values by default. Parameters ---------- dropna : bool, default True Don't include NaN in the count. Returns ------- int See Also -------- DataFrame.nunique: Method nunique for DataFrame. Series.count: Count non-NA/null observations in the Series. Examples -------- >>> s = pd.Series([1, 3, 5, 7, 7]) >>> s 0 1 1 3 2 5 3 7 4 7 dtype: int64 >>> s.nunique() 4 )r8r"rx)r?rZuniqsr@r@rAnuniques"zIndexOpsMixin.nuniquecCs|jddt|kS)zr Return boolean if values in the object are unique. Returns ------- bool F)r)rrxr>r@r@rA is_uniques zIndexOpsMixin.is_uniquecCstjdttd|jS)a$ Return boolean if values in the object are monotonically increasing. .. deprecated:: 1.5.0 is_monotonic is deprecated and will be removed in a future version. Use is_monotonic_increasing instead. Returns ------- bool zhis_monotonic is deprecated and will be removed in a future version. Use is_monotonic_increasing instead.) stacklevel)warningswarn FutureWarningris_monotonic_increasingr>r@r@rA is_monotonics zIndexOpsMixin.is_monotoniccCsddlm}||jS)z Return boolean if values in the object are monotonically increasing. Returns ------- bool rIndex)pandasrrr?rr@r@rAr4s z%IndexOpsMixin.is_monotonic_increasingcCsddlm}||jS)z Return boolean if values in the object are monotonically decreasing. Returns ------- bool rr)rris_monotonic_decreasingrr@r@rArAs z%IndexOpsMixin.is_monotonic_decreasing)rRrEcCsRt|jdr|jj|dS|jj}|rNt|rNtsNttj|j }|t |7}|S)aN Memory usage of the values. Parameters ---------- deep : bool, default False Introspect the data deeply, interrogate `object` dtypes for system-level memory consumption. Returns ------- bytes used See Also -------- numpy.ndarray.nbytes : Total bytes consumed by the elements of the array. Notes ----- Memory usage does not include memory consumed by elements that are not components of the array if deep=False or if used on PyPy rPrQ) rKrrPrrrr rqrrrrZmemory_usage_of_objects)r?rRvrr@r@rA _memory_usageNs zIndexOpsMixin._memory_usager6z sort : bool, default False Sort `uniques` and shuffle `codes` to maintain the relationship. )rorderZ size_hintrzint | lib.NoDefaultzbool | lib.NoDefaultr na_sentineluse_na_sentinelcCstj||||dS)Nr)r# factorize)r?rrrr@r@rArqs zIndexOpsMixin.factorizea Find indices where elements should be inserted to maintain order. Find the indices into a sorted {klass} `self` such that, if the corresponding elements in `value` were inserted before the indices, the order of `self` would be preserved. .. note:: The {klass} *must* be monotonically sorted, otherwise wrong locations will likely be returned. Pandas does *not* check this for you. Parameters ---------- value : array-like or scalar Values to insert into `self`. side : {{'left', 'right'}}, optional If 'left', the index of the first suitable location found is given. If 'right', return the last such index. If there is no suitable index, return either 0 or N (where N is the length of `self`). sorter : 1-D array-like, optional Optional array of integer indices that sort `self` into ascending order. They are typically the result of ``np.argsort``. Returns ------- int or array of int A scalar or array of insertion points with the same shape as `value`. See Also -------- sort_values : Sort by the values along either axis. numpy.searchsorted : Similar method from NumPy. Notes ----- Binary search is used to find the required insertion points. Examples -------- >>> ser = pd.Series([1, 2, 3]) >>> ser 0 1 1 2 2 3 dtype: int64 >>> ser.searchsorted(4) 3 >>> ser.searchsorted([0, 4]) array([0, 3]) >>> ser.searchsorted([1, 3], side='left') array([0, 2]) >>> ser.searchsorted([1, 3], side='right') array([1, 3]) >>> ser = pd.Series(pd.to_datetime(['3/11/2000', '3/12/2000', '3/13/2000'])) >>> ser 0 2000-03-11 1 2000-03-12 2 2000-03-13 dtype: datetime64[ns] >>> ser.searchsorted('3/14/2000') 3 >>> ser = pd.Categorical( ... ['apple', 'bread', 'bread', 'cheese', 'milk'], ordered=True ... ) >>> ser ['apple', 'bread', 'bread', 'cheese', 'milk'] Categories (4, object): ['apple' < 'bread' < 'cheese' < 'milk'] >>> ser.searchsorted('bread') 1 >>> ser.searchsorted(['bread'], side='right') array([3]) If the values are not monotonically sorted, wrong locations may be returned: >>> ser = pd.Series([2, 1, 3]) >>> ser 0 2 1 1 2 3 dtype: int64 >>> ser.searchsorted(1) # doctest: +SKIP 0 # wrong result, correct would be 1 searchsorted.r1zLiteral[('left', 'right')]r/znp.intp)rgsidesorterrEcCsdSrmr@r?rgrrr@r@rArs zIndexOpsMixin.searchsortedznpt.ArrayLike | ExtensionArrayznpt.NDArray[np.intp]cCsdSrmr@rr@r@rArsr)r7leftz$NumpyValueArrayLike | ExtensionArrayznpt.NDArray[np.intp] | np.intpcCs4|j}t|tjs"|j|||dStj||||dS)N)rr)rrnrqrrrr#)r?rgrrrr@r@rArs firstcCs|j|d}||SN)keep) _duplicated)r?rr'r@r@rAdrop_duplicatess zIndexOpsMixin.drop_duplicatesz!Literal[('first', 'last', False)])rrEcCst|j|dSr)r'r)r?rr@r@rArszIndexOpsMixin._duplicatedc Csjt||}|j}t|ddd}t||j}t|}tjddt |||}W5QRX|j ||dS)NT)Z extract_numpyZ extract_ranger)allr) r%Zget_op_result_namerr.Zmaybe_prepare_scalar_for_oprr-rqZerrstateZ arithmetic_op_construct_result)r?otherrZres_nameZlvaluesZrvaluesrr@r@rA _arith_method$s zIndexOpsMixin._arith_methodcCs t|dS)z~ Construct an appropriately-wrapped result from the ArrayLike result of an arithmetic-like operation. 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