o i@sUdZddlmZddlZddlmZmZmZmZm Z m Z m Z m Z m Z ddlZddlZddlmmZddlmZmZmZmZmZmZddlmZddlmZddl m!Z!dd l"m#Z#m$Z$dd l%m&Z&dd l'm(Z(m)Z)m*Z*m+Z+m,Z,dd l-m.Z.m/Z/m0Z0dd l1m2Z2m3Z3ddl4m5Z5m6Z6m7Z7ddl8m9Z9ddl:m;Z;mm?Z?ddl@mAZAddlBmCZCmDZDmEZEerddlmFZFmGZGmHZHddlImJZJmKZKiZLdeMd<dddddZNe dddZOGddde9ZPGdd d ZQGd!d"d"eeZRGd#dde?ZSdS)$z. Base and utility classes for pandas objects. ) annotationsN) TYPE_CHECKINGAnyGenericHashableLiteralTypeVarcastfinaloverload) ArrayLikeDtypeObj IndexLabelNDFrameTShapenpt)PYPY)functionAbstractMethodError)cache_readonlydoc)find_stack_level)is_categorical_dtype is_dict_likeis_extension_array_dtypeis_object_dtype is_scalar) ABCDataFrameABCIndex ABCSeries)isnaremove_na_arraylike) algorithmsnanopsops) DirNamesMixin) duplicatedunique1d value_counts)OpsMixin)ExtensionArray)!create_series_with_explicit_dtypeensure_wrapped_if_datetimelike extract_array) NumpySorterNumpyValueArrayLike ScalarLike_co) CategoricalSerieszdict[str, str] _shared_docs IndexOpsMixin)klassinplaceuniquer'_T)boundcsNeZdZUdZded<eddZddd ZddddZdfdd Z Z S) PandasObjectz/ Baseclass for various pandas objects. zdict[str, Any]_cachecCst|S)zJ Class constructor (for this class it's just `__class__`. )typeselfrAK/var/www/edux/Edux_v2/venv/lib/python3.10/site-packages/pandas/core/base.py _constructornzPandasObject._constructorreturnstrcCs t|S)zI Return a string representation for a particular object. )object__repr__r?rArArBrHu zPandasObject.__repr__Nkey str | NoneNonecCs6t|dsdS|dur|jdS|j|ddS)zV Reset cached properties. If ``key`` is passed, only clears that key. r=N)hasattrr=clearpop)r@rJrArArB _reset_cache|s zPandasObject._reset_cacheintcs>t|dd}|r|dd}tt|r|S|StS)zx Generates the total memory usage for an object that returns either a value or Series of values memory_usageNTdeep)getattrrQrsumsuper __sizeof__)r@rRmem __class__rArBrXs   zPandasObject.__sizeof__)rErFN)rJrKrErLrErQ) __name__ __module__ __qualname____doc____annotations__propertyrCrHrPrX __classcell__rArArZrBr<fs     r<c@s"eZdZdZddZd dd Zd S) NoNewAttributesMixina Mixin which prevents adding new attributes. Prevents additional attributes via xxx.attribute = "something" after a call to `self.__freeze()`. Mainly used to prevent the user from using wrong attributes on an accessor (`Series.cat/.str/.dt`). If you really want to add a new attribute at a later time, you need to use `object.__setattr__(self, key, value)`. cCst|dddS)z9 Prevents setting additional attributes. __frozenTN)rG __setattr__r?rArArB_freezeszNoNewAttributesMixin._freezerJrFrErLcCsTt|ddr!|dks!|t|jvs!t||ddus!td|dt|||dS)NrfFr=z"You cannot add any new attribute '')rUr>__dict__AttributeErrorrGrg)r@rJvaluerArArBrgs z NoNewAttributesMixin.__setattr__N)rJrFrErL)r^r_r`rarhrgrArArArBres rec@seZdZUdZded<dZded<ded<d d gZeeZe e d d Z e d dZ e e dddZe e ddZddZddddZddZeZdS)SelectionMixinz mixin implementing the selection & aggregation interface on a group-like object sub-classes need to define: obj, exclusions robjNzIndexLabel | None _selectionzfrozenset[Hashable] exclusionsr= __setstate__cCs&t|jtttttjfs|jgS|jSr\) isinstancerolisttupler rnpndarrayr?rArArB_selection_lists zSelectionMixin._selection_listcCs(|jdus t|jtr|jS|j|jSr\)rorrrnr r?rArArB _selected_objs zSelectionMixin._selected_objrErQcC|jjSr\)rxndimr?rArArBrzzSelectionMixin.ndimcCsJ|jdurt|jtr|j|jSt|jdkr"|jj|jdddS|jS)NrT)axis only_slice)rorrrnrrwlenrp _drop_axisr?rArArB_obj_with_exclusionss  z#SelectionMixin._obj_with_exclusionscCs|jdurtd|jdt|tttttjfrIt |j j |t t |kr@tt ||j j }tdt|dd|jt|ddSt|dd sc||j j vr\td ||j|ddS||j vrotd ||j |}|j}|j|||d S) Nz Column(s) z already selectedzColumns not found: r|)rzas_indexFzColumn not found: )rzsubset)ro IndexErrorrrrsrtr rrurvrrncolumns intersectionset differenceKeyErrorrF_gotitemrUrz)r@rJbad_keysrrzrArArB __getitem__s     zSelectionMixin.__getitem__rzcCt|)a sub-classes to define return a sliced object Parameters ---------- key : str / list of selections ndim : {1, 2} requested ndim of result subset : object, default None subset to act on r)r@rJrzrrArArBrs zSelectionMixin._gotitemcOrr\r)r@funcargskwargsrArArB aggregate szSelectionMixin.aggregater]r\)rzrQ)r^r_r`rarbro_internal_namesr_internal_names_setr rcrwrrxrzrrrraggrArArArBrms,      rmc@seZdZUdZdZedgZded<eddd Z edd d Z dddZ ee ddZ edddZ dddZedddZddZedddZedd d!Zedd#d$Zd%d&ejfdd.d/Zedd0d1Zddd4d5Zed6d7d8d9ddd:d;Zdddd9ddd?d@ZdAdBZeZdCdDZe ddEdFZ!ddHdIZ"dJd2d%d%dKddNdOZ#e$ddPdQZ% & 2 & % 2dddWdXZ&dYdZZ'ddd[d\Z(edd]d^Z)edd_d`Z*eddadbZ+eddcddZ,dddfdgZ-ee.j/dhdhdhe01didjd&ejejfddodpZ/dqe2dr<e3 s sddd{d|Z4e3 s sdddd|Z4ee2drdd %dddd|Z4dddZ5e$ ddddZ6ddZ7ddZ8d%S)r5zS Common ops mixin to support a unified interface / docs for Series / Index itolistzfrozenset[str] _hidden_attrsrEr cCrr\rr?rArArBdtyper{zIndexOpsMixin.dtypeExtensionArray | np.ndarraycCrr\rr?rArArB_values"r{zIndexOpsMixin._valuesr@r:cOst|||S)zw Return the transpose, which is by definition self. Returns ------- %(klass)s )nvvalidate_transpose)r@rrrArArB transpose's zIndexOpsMixin.transposezD Return the transpose, which is by definition self. )rrcCry)zE Return a tuple of the shape of the underlying data. )rshaper?rArArBr9rDzIndexOpsMixin.shaperQcCrr\rr?rArArB__len__@zIndexOpsMixin.__len__ Literal[1]cCsdS)zO Number of dimensions of the underlying data, by definition 1. r|rAr?rArArBrzDszIndexOpsMixin.ndimcCs t|dkr tt|Std)a  Return the first element of the underlying data as a Python scalar. Returns ------- scalar The first element of %(klass)s. Raises ------ ValueError If the data is not length-1. r|z6can only convert an array of size 1 to a Python scalar)rnextiter ValueErrorr?rArArBitemKs  zIndexOpsMixin.itemcCry)zD Return the number of bytes in the underlying data. )rnbytesr?rArArBr]rDzIndexOpsMixin.nbytescC t|jS)zG Return the number of elements in the underlying data. )rrr?rArArBsizedrIzIndexOpsMixin.sizer+cCr)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'] rr?rArArBarrayks?zIndexOpsMixin.arrayNFrnpt.DTypeLike | Nonecopyboolna_valuerG np.ndarraycKst|jr|jj|f||d|S|r$t|d}td|dtj|j |d}|s3|t j urE| }|t j urE||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 'ri)r)rrrto_numpyrskeys TypeErrorruasarrayrlib no_defaultr asanyarrayr!)r@rrrrrresultrArArBrs c  zIndexOpsMixin.to_numpycCs|j Sr\)rr?rArArBemptyrzIndexOpsMixin.emptyTskipnacO&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_axis validate_maxr$nanmaxrr@r}rrrrArArBmax# ( zIndexOpsMixin.maxrminlargest)opopposerlcOR|j}t|t|||}t|tr"|s|rdS|St j ||dS)ab Return int position of the {value} value in the Series. If the {op}imum is achieved in multiple locations, the first row position is returned. Parameters ---------- axis : {{None}} Unused. Parameter needed for compatibility with DataFrame. skipna : bool, default True Exclude NA/null values when showing the result. *args, **kwargs Additional arguments and keywords for compatibility with NumPy. Returns ------- int Row position of the {op}imum value. See Also -------- Series.arg{op} : Return position of the {op}imum value. Series.arg{oppose} : Return position of the {oppose}imum value. numpy.ndarray.arg{op} : Equivalent method for numpy arrays. Series.idxmax : Return index label of the maximum values. Series.idxmin : Return index label of the minimum values. Examples -------- Consider dataset containing cereal calories >>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0, ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}}) >>> s Corn Flakes 100.0 Almond Delight 110.0 Cinnamon Toast Crunch 120.0 Cocoa Puff 110.0 dtype: float64 >>> s.argmax() 2 >>> s.argmin() 0 The maximum cereal calories is the third element and the minimum cereal calories is the first element, since series is zero-indexed. rr) rrrvalidate_argmax_with_skipnarrr+r!anyargmaxr$ nanargmaxr@r}rrrdelegaterArArBrOs4  zIndexOpsMixin.argmaxcOr)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)rr validate_minr$nanminrrrArArBrrzIndexOpsMixin.minsmallestcOr)Nrr) rrrvalidate_argmin_with_skipnarrr+r!rargminr$ nanargminrrArArBrs  zIndexOpsMixin.argmincCs |jS)a Return a list 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 ------- list See Also -------- numpy.ndarray.tolist : Return the array as an a.ndim-levels deep nested list of Python scalars. )rrr?rArArBrs zIndexOpsMixin.tolistcCs.t|jtjs t|jSt|jjt|jjS)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 ) rrrrurvrmaprrangerr?rArArB__iter__s zIndexOpsMixin.__iter__cCstt|S)zc Return True if there are any NaNs. Enables various performance speedups. )rr!rr?rArArBhasnanss zIndexOpsMixin.hasnansnpt.NDArray[np.bool_]cCrr\)r!rr?rArArBr!s zIndexOpsMixin.isnar)r}r numeric_only filter_typenamerFc Ks>t||d}|durtt|jd||dd|i|S)zA Perform the reduction type operation if we can. Nz cannot perform the operation rrA)rUrr>r^) r@rrr}rrrkwdsrrArArB_reduces zIndexOpsMixin._reducec s>t|rt|trt|dr|fdd}nt|tjd}t|tr^|dvr1d|d}t||dkr<||j }t |j rLt d |j}||S|j}|j |}t|j|}|St|j rwt|jd rw|j}|d urrtd d}n!|jt}|dkrd d}n|d urtj}n d|d}t||||}|S)a An internal function that maps values using the input correspondence (which can be a dict, Series, or function). Parameters ---------- mapper : function, dict, or Series The input correspondence object na_action : {None, 'ignore'} If 'ignore', propagate NA values, without passing them to the mapping function Returns ------- Union[Index, MultiIndex], inferred The output of the mapping function applied to the index. If the function returns a tuple with more than one element a MultiIndex will be returned. __missing__cs|Sr\rA)xdict_with_defaultrArB9sz+IndexOpsMixin._map_values..)dtype_if_empty)Nignorez+na_action must either be 'ignore' or None, z was passedrr2rNcSs ||Sr\)rvaluesfrArArBris cSst||t|tjSr\)rmap_infer_maskr!viewruuint8rrArArBrms)rrrdictrMr,rufloat64r rindexnotnarrr rr get_indexerr#take_ndrNotImplementedErrorastyperGr map_infer) r@mapper na_actionmsgcatrindexer new_valuesmap_frArrB _map_valuessP          zIndexOpsMixin._map_values normalizesort ascendingdropnar3cCst||||||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@rrrrrrArArBr)~sVzIndexOpsMixin.value_countscCsX|j}t|tjs&|}t|jtjr$|jjdvr$t|tr$t|}|St |}|S)N)mM) rrrrurvr9rkindr rr()r@rrrArArBr9s    zIndexOpsMixin.uniquecCs|}|r t|}t|S)a Return number of unique elements in the object. Excludes NA values by default. Parameters ---------- dropna : bool, default True Don't include NaN in the count. Returns ------- int See Also -------- DataFrame.nunique: Method nunique for DataFrame. Series.count: Count non-NA/null observations in the Series. Examples -------- >>> s = pd.Series([1, 3, 5, 7, 7]) >>> s 0 1 1 3 2 5 3 7 4 7 dtype: int64 >>> s.nunique() 4 )r9r"r)r@runiqsrArArBnuniques"zIndexOpsMixin.nuniquecCs|jddt|kS)zr Return boolean if values in the object are unique. Returns ------- bool F)r)rrr?rArArB is_uniques zIndexOpsMixin.is_uniquecCstjdttd|jS)a$ 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?rArArB is_monotonics  zIndexOpsMixin.is_monotoniccCddlm}||jS)z Return boolean if values in the object are monotonically increasing. Returns ------- bool rIndex)pandasrr r@rrArArBr 4 z%IndexOpsMixin.is_monotonic_increasingcCr )z Return boolean if values in the object are monotonically decreasing. Returns ------- bool rr )rris_monotonic_decreasingrrArArBrArz%IndexOpsMixin.is_monotonic_decreasingrTcCsRt|jdr |jj|dS|jj}|r't|r'ts'ttj|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 rRrS) rMrrRrrrr rurvrrmemory_usage_of_objects)r@rTvrrArArB _memory_usageNs zIndexOpsMixin._memory_usager6z sort : bool, default False Sort `uniques` and shuffle `codes` to maintain the relationship. )rorder size_hintr na_sentinelint | lib.NoDefaultuse_na_sentinelbool | lib.NoDefaultcCstj||||dS)N)rrr)r# factorize)r@rrrrArArBrqszIndexOpsMixin.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.rlr1sideLiteral['left', 'right']sorterr/np.intpcCdSr\rAr@rlrr rArArBrs zIndexOpsMixin.searchsortednpt.ArrayLike | ExtensionArraynpt.NDArray[np.intp]cCr"r\rAr#rArArBrsr)r7left$NumpyValueArrayLike | ExtensionArraynpt.NDArray[np.intp] | np.intpcCs4|j}t|tjs|j|||dStj||||dS)N)rr )rrrrurvrr#)r@rlrr rrArArBrs firstcCs|j|d}||SN)keep) _duplicated)r@r+r'rArArBdrop_duplicatess  zIndexOpsMixin.drop_duplicatesr+Literal['first', 'last', False]cCst|j|dSr*)r'r)r@r+rArArBr,szIndexOpsMixin._duplicatedcCs~t||}|j}t|ddd}t||j}t|}tjddt |||}Wdn1s3wY|j ||dS)NT) extract_numpy extract_ranger)all)r) r%get_op_result_namerr.maybe_prepare_scalar_for_oprr-ruerrstate arithmetic_op_construct_result)r@otherrres_namelvaluesrvaluesrrArArB _arith_method$s zIndexOpsMixin._arith_methodcCr)z~ Construct an appropriately-wrapped result from the ArrayLike result of an arithmetic-like operation. r)r@rrrArArBr61rDzIndexOpsMixin._construct_result)rEr )rEr)r@r:rEr:)rErr])rEr)rEr+)rrrrrrGrEr)rEr)NT)rr)rrrErQ)rEr)rrFr\)FTFNT) rrrrrrrrrEr3)T)rrrErQ)F)rTrrErQ)rrrrrr)..)rlr1rrr r/rEr!)rlr$rrr r/rEr%)r&N)rlr'rrr r/rEr()r))r+r.rEr)9r^r_r`ra__array_priority__ frozensetrrbrcrrrTrrrzrrrrrrrrrrrrrrto_listrrrr!rr rr)r9rrr r rrr#rtextwrapdedentr4r rr-r,r;r6rArArArBr5s            B s   , C,   c _ '     # g  )Tra __future__rr@typingrrrrrrr r r rnumpyrupandas._libs.lib_libsrpandas._typingr r rrrr pandas.compatrpandas.compat.numpyrr pandas.errorsrpandas.util._decoratorsrrpandas.util._exceptionsrpandas.core.dtypes.commonrrrrrpandas.core.dtypes.genericrrr pandas.core.dtypes.missingr!r" pandas.corer#r$r%pandas.core.accessorr&pandas.core.algorithmsr'r(r)pandas.core.arrayliker*pandas.core.arraysr+pandas.core.constructionr,r-r.r/r0r1rr2r3r4rb_indexops_doc_kwargsr:r<rermr5rArArArBsH ,           /"[