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Base and utility classes for pandas objects. � )� annotationsN)� TYPE_CHECKING�Any�Generic�Hashable�TypeVar�cast)� ArrayLike�Dtype�DtypeObj� FrameOrSeries� IndexLabel�Shape�final)�PYPY)�function)�AbstractMethodError)�cache_readonly�doc)�is_categorical_dtype� is_dict_like�is_extension_array_dtype�is_object_dtype� is_scalar)� ABCDataFrame�ABCIndex� ABCSeries)�isna�remove_na_arraylike)� algorithms)� DirNamesMixin)� duplicated�unique1d� value_counts)�OpsMixin)�ExtensionArray)�!create_series_with_explicit_dtype)�Literal)� Categoricalzdict[str, str]� _shared_docs� IndexOpsMixin� )�klassZinplace�uniquer! �_T)�boundc s\ e Zd ZU dZded< edd� �Zdd�dd �Zdd d d �dd�Zdd�� fdd� Z � Z S )� PandasObjectz/ Baseclass for various pandas objects. zdict[str, Any]�_cachec C s t | �S )zJ Class constructor (for this class it's just `__class__`. )�type)�self� r4 �F/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/core/base.py� _constructorZ s zPandasObject._constructor�str)�returnc C s t �| �S )zI Return a string representation for a particular object. )�object�__repr__)r3 r4 r4 r5 r: a s zPandasObject.__repr__Nz str | None�None)�keyr8 c C s4 t | d�sdS |dkr"| j�� n| j�|d� dS )zV Reset cached properties. If ``key`` is passed, only clears that key. r1 N)�hasattrr1 �clear�pop)r3 r< r4 r4 r5 � _reset_cacheh s zPandasObject._reset_cache�intc s< t | dd�}|r2|dd�}tt|�r(|n|�� �S t� �� S )zx Generates the total memory usage for an object that returns either a value or Series of values � memory_usageNT)�deep)�getattrrA r �sum�super� __sizeof__)r3 rB Zmem)� __class__r4 r5 rG s s zPandasObject.__sizeof__)N) �__name__� __module__� __qualname__�__doc__�__annotations__�propertyr6 r: r@ rG � __classcell__r4 r4 )rH r5 r0 R s r0 c @ s& e Zd ZdZdd� Zdd�dd�ZdS ) �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)`. c C s t �| dd� dS )z9 Prevents setting additional attributes. �__frozenTN)r9 � __setattr__)r3 r4 r4 r5 �_freeze� s z NoNewAttributesMixin._freezer7 )r< c C sT t | dd�rB|dksB|t| �jksBt | |d �d k sBtd|� d���t�| ||� d S )NrQ Fr1 z"You cannot add any new attribute '�')rD r2 �__dict__�AttributeErrorr9 rR )r3 r< �valuer4 r4 r5 rR � s z NoNewAttributesMixin.__setattr__N)rI rJ rK rL rS rR r4 r4 r4 r5 rP � s rP c @ s e Zd ZdS )� DataErrorN)rI rJ rK r4 r4 r4 r5 rX � s rX c @ s e Zd ZdS )�SpecificationErrorN)rI rJ rK r4 r4 r4 r5 rY � s rY c @ s� e Zd ZU dZded< dZded< ded< d d gZee�Ze e d d � ��Z e d d� �Z e e dd�dd���Ze e dd� ��Zdd� Zd dd�dd�Zdd� ZeZdS ) �SelectionMixinz� mixin implementing the selection & aggregation interface on a group-like object sub-classes need to define: obj, exclusions r �objNzIndexLabel | None� _selectionzfrozenset[Hashable]� exclusionsr1 � __setstate__c C s& t | jtttttjf�s | jgS | jS )N)� isinstancer\ �list�tupler r �np�ndarray)r3 r4 r4 r5 �_selection_list� s z SelectionMixin._selection_listc C s, | j d kst| jt�r | jS | j| j S d S )N)r\ r_ r[ r )r3 r4 r4 r5 � _selected_obj� s z SelectionMixin._selected_objrA )r8 c C s | j jS )N)re �ndim)r3 r4 r4 r5 rf � s zSelectionMixin.ndimc C sL | j d k r"t| jt�r"| j| j S t| j�dkrB| jj| jdd�S | jS d S )Nr � )�axis)r\ r_ r[ r rd �lenr] Zdrop)r3 r4 r4 r5 �_obj_with_exclusions� s z#SelectionMixin._obj_with_exclusionsc C s� | j d k r td| j � d���t|tttttjf�r�t | j j � |��t |�kr|tt |��| j j ��}tdt|�dd� � ���| jt|�dd�S t| dd �s�|| j j kr�td |� ���| j|dd�S || j kr�td |� ���| j | }|j}| j|||d �S d S ) Nz Column(s) z already selectedzColumns not found: rg ���� )rf Zas_indexFzColumn not found: )rf �subset)r\ � IndexErrorr_ r` ra r r rb rc ri r[ �columns� intersection�set� difference�KeyErrorr7 �_gotitemrD rf )r3 r< �bad_keysrm rf r4 r4 r5 � __getitem__� s zSelectionMixin.__getitem__)rf c C s t | ��dS )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 N)r )r3 r< rf rm r4 r4 r5 rt � s zSelectionMixin._gotitemc O s t | ��d S )N)r )r3 �func�args�kwargsr4 r4 r5 � aggregate� s zSelectionMixin.aggregate)N)rI rJ rK rL rM r\ Z_internal_namesrq Z_internal_names_setr rN rd r re rf rj rv rt rz Zaggr4 r4 r4 r5 rZ � s" rZ c @ s� e Zd ZU dZdZedg�Zded< edd�dd ��Z ed d�d d ��Z d d d�dd�Z ee dd�Z edd�dd��Z dd�dd�Zedd�dd��Zdd � Zedd�d d ��Zedd�d d ��Zed!d�d"d#��Zd$d%ejfd&d'd(d)�d*d+�Zed'd�d,d-��Zdwd'd/�d0d1�Zed2d3d4d5�dxd'dd6�d7d8��Zdyd'd/�d9d:�Zeed3d2d;d5�dzdd�d<d=��Z d>d?� Z e Z d@dA� Z e d'd�dBdC��Z!dDdE� Z"dFd.d$d$dG�dHdI�dJdK�Z#d{dLdM�Z$d|d'd'd'd'dN�dOdP�Z%dQdR� Z&d}d'ddS�dTdU�Z'ed'd�dVdW��Z(ed'd�dXdY��Z)ed'd�dZd[��Z*ed'd�d\d]��Z+d~d'dd^�d_d`�Z,ee-j.dadadae/�0db�dc�dd'dedf�dgdh��Z.die1dj< ee1dj dkdl�d�d(d�dndo��Z2d�dqdr�Z3e4d�dsd(dt�dudv��Z5d$S )�r* zS Common ops mixin to support a unified interface / docs for Series / Index i� �tolistzfrozenset[str]� _hidden_attrsr )r8 c C s t | ��d S )N)r )r3 r4 r4 r5 �dtype s zIndexOpsMixin.dtypezExtensionArray | np.ndarrayc C s t | ��d S )N)r )r3 r4 r4 r5 �_values s zIndexOpsMixin._valuesr. )r3 r8 c O s t �||� | S )zw Return the transpose, which is by definition self. Returns ------- %(klass)s )�nvZvalidate_transpose)r3 rx ry r4 r4 r5 � transpose s zIndexOpsMixin.transposezD Return the transpose, which is by definition self. )r r c C s | j jS )zE Return a tuple of the shape of the underlying data. )r~ �shape)r3 r4 r4 r5 r� ) s zIndexOpsMixin.shaperA c C s t | ��d S )N)r )r3 r4 r4 r5 �__len__0 s zIndexOpsMixin.__len__c C s dS )zO Number of dimensions of the underlying data, by definition 1. rg r4 )r3 r4 r4 r5 rf 4 s zIndexOpsMixin.ndimc C s$ t | �dkrtt| ��S td��dS )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. rg z6can only convert an array of size 1 to a Python scalarN)ri �next�iter� ValueError)r3 r4 r4 r5 �item; s zIndexOpsMixin.itemc C s | j jS )zD Return the number of bytes in the underlying data. )r~ �nbytes)r3 r4 r4 r5 r� M s zIndexOpsMixin.nbytesc C s t | j�S )zG Return the number of elements in the underlying data. )ri r~ )r3 r4 r4 r5 �sizeT s zIndexOpsMixin.sizer% c C s 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 <PandasArray> [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'] N)r )r3 r4 r4 r5 �array[ s ?zIndexOpsMixin.arrayNFz Dtype | None�boolz np.ndarray)r} �copyr8 c K s� t | j�r$| jj|f||d�|��S |rHt|�� �d }td|� d���tj| j |d�}|sf|t j k r�|� � }|t j k r�||| � � < |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]') )r� �na_valuer z/to_numpy() got an unexpected keyword argument 'rT )r} )r r} r� �to_numpyr` �keys� TypeErrorrb �asarrayr~ �lib� no_defaultr� r )r3 r} r� r� ry ru �resultr4 r4 r5 r� � s c zIndexOpsMixin.to_numpyc C s | j S )N)r� )r3 r4 r4 r5 �empty s zIndexOpsMixin.emptyT)�skipnac O s& t �|� t �||� tj| j|d�S )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� )r �validate_minmax_axisZ validate_max�nanopsZnanmaxr~ )r3 rh r� rx ry r4 r4 r5 �max s ( zIndexOpsMixin.maxr� �min�largest)�opZopposerW )r� r8 c O sX | j }t�|� t�|||�}t|t�rF|s<|�� �� r<dS |�� S nt j ||d�S dS )aS 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}} 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 ------- 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. rk )r� N) r~ r r� Zvalidate_argmax_with_skipnar_ r% r �any�argmaxr� Z nanargmax)r3 rh r� rx ry �delegater4 r4 r5 r� G s 4 zIndexOpsMixin.argmaxc O s& t �|� t �||� tj| j|d�S )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� )r r� Z validate_minr� Znanminr~ )r3 rh r� rx ry r4 r4 r5 r� � s ( zIndexOpsMixin.min�smallestc O sX | j }t�|� t�|||�}t|t�rF|s<|�� �� r<dS |�� S nt j ||d�S d S )Nrk )r� ) r~ r r� Zvalidate_argmin_with_skipnar_ r% r r� �argminr� Z nanargmin)r3 rh r� rx ry r� r4 r4 r5 r� � s zIndexOpsMixin.argminc C s" t | jtj�st| j�S | j�� S )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. )r_ r~ rb rc r` r{ )r3 r4 r4 r5 r{ � s zIndexOpsMixin.tolistc C s2 t | jtj�st| j�S t| jjt| jj��S dS )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) r_ r~ rb rc r� �mapr� �ranger� )r3 r4 r4 r5 �__iter__� s zIndexOpsMixin.__iter__c C s t t| ��� �S )zK Return if I have any nans; enables various perf speedups. )r� r r� )r3 r4 r4 r5 �hasnans� s zIndexOpsMixin.hasnansc C s t | j�S )N)r r~ )r3 r4 r4 r5 r � s zIndexOpsMixin.isnar )rh r� � numeric_only� filter_typer7 )�namec K s> t | |d�}|dkr,tt| �j� d|� ���|f d|i|��S )zA Perform the reduction type operation if we can. Nz cannot perform the operation r� )rD r� r2 rI ) r3 r� r� rh r� r� r� �kwdsrw r4 r4 r5 �_reduce� s zIndexOpsMixin._reducec s t |�r<t|t�r.t|d�r.|� � fdd�}nt|tjd�}t|t�r�t| j �rft d| j �}|� |�S | j }|j �|�}t�|j |�}|S t| j �r�t| j d�r�| j }|dk r�t�dd� }nB| j �t�}|d kr�d d� }n$|dkr�tj}nd |� 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__c s � | S )Nr4 )�x)�dict_with_defaultr4 r5 �<lambda>/ � z+IndexOpsMixin._map_values.<locals>.<lambda>)Zdtype_if_emptyr( r� Nc S s | � |�S )N)r� )�values�fr4 r4 r5 r� U r� �ignorec S s t �| |t| ��tj��S )N)r� Zmap_infer_maskr �viewrb Zuint8)r� r� r4 r4 r5 r� Y s z+na_action must either be 'ignore' or None, z was passed)r r_ �dictr= r& rb Zfloat64r r r} r r~ r� �indexZ get_indexerr Ztake_ndr �NotImplementedErrorZastyper9 r� Z map_inferr� ) r3 ZmapperZ na_action�catr� Zindexer� new_valuesZmap_f�msgr4 )r� r5 � _map_values s8 zIndexOpsMixin._map_values)� normalize�sort� ascending�dropnac C s t | |||||d�S )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 )r� r� r� �binsr� )r# )r3 r� r� r� r� r� r4 r4 r5 r# j s VzIndexOpsMixin.value_countsc C sZ | j }t|tj�sN|�� }| jjdkrVt| t�rVt| jdd �d krVt� |�}nt |�}|S )N)�m�M�tz) r~ r_ rb rc r- r} �kindr rD r� r" )r3 r� r� r4 r4 r5 r- � s zIndexOpsMixin.unique)r� r8 c C s | � � }|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 )r- r ri )r3 r� Zuniqsr4 r4 r5 �nunique� s "zIndexOpsMixin.nuniquec C s | j dd�t| �kS )zr Return boolean if values in the object are unique. Returns ------- bool F)r� )r� ri )r3 r4 r4 r5 � is_unique� s zIndexOpsMixin.is_uniquec C s ddl m} || �jS )z� Return boolean if values in the object are monotonic_increasing. Returns ------- bool r )�Index)�pandasr� � is_monotonic)r3 r� r4 r4 r5 r� s zIndexOpsMixin.is_monotonicc C s | j S )z) Alias for is_monotonic. )r� )r3 r4 r4 r5 �is_monotonic_increasing s z%IndexOpsMixin.is_monotonic_increasingc C s ddl m} || �jS )z� Return boolean if values in the object are monotonic_decreasing. Returns ------- bool r )r� )r� r� �is_monotonic_decreasing)r3 r� r4 r4 r5 r� s z%IndexOpsMixin.is_monotonic_decreasing)rC r8 c C sR t | jd�r| jj|d�S | 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 rB )rC ) r= r� rB r� r r r rb rc r~ r� Zmemory_usage_of_objects)r3 rC �vr� r4 r4 r5 � _memory_usage- s zIndexOpsMixin._memory_usager+ z� sort : bool, default False Sort `uniques` and shuffle `codes` to maintain the relationship. )r� �orderZ size_hintr� rk z int | None)r� � na_sentinelc C s t j| ||d�S )N)r� r� )r � factorize)r3 r� r� r4 r4 r5 r� O s 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 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 � searchsortedr� )r, �leftc C s t j| j|||d�S )N)�side�sorter)r r� r~ )r3 rW r� r� r4 r4 r5 r� � s zIndexOpsMixin.searchsorted�firstc C s | j |d�}| | S )N)�keep)� _duplicated)r3 r� r! r4 r4 r5 �drop_duplicates� s z IndexOpsMixin.drop_duplicatesz!Literal[('first', 'last', False)])r� r8 c C s t | j|d�S )N)r� )r! r~ )r3 r� r4 r4 r5 r� � s zIndexOpsMixin._duplicated)NT)NT)NT)NT)N)FTFNT)T)F)Frk )r� N)r� )r� )6rI rJ rK rL Z__array_priority__� frozensetr| rM rN r} r~ r� �Tr� r� rf r� r� r� r� r� r� r� r� r� r r� r� r� r{ Zto_listr� r r� r r� r� r# r- r� r� r� r� r� r� r r� �textwrap�dedentr) r� r� r r� r4 r4 r4 r5 r* s~ Cw, C, Y Y' "e )NrL � __future__r r� �typingr r r r r r �numpyrb Zpandas._libs.libZ_libsr� Zpandas._typingr r r r r r r Z pandas.compatr Zpandas.compat.numpyr r Z pandas.errorsr Zpandas.util._decoratorsr r Zpandas.core.dtypes.commonr r r r r Zpandas.core.dtypes.genericr r r Zpandas.core.dtypes.missingr r Z pandas.corer Zpandas.core.accessorr Zpandas.core.algorithmsr! r" r# Zpandas.core.arrayliker$ Zpandas.core.arraysr% Zpandas.core.constructionr& Zpandas.core.nanops�corer� r' r� r( r) rM Z_indexops_doc_kwargsr. r0 rP � ExceptionrX rY rZ r* r4 r4 r4 r5 �<module> sF $ /"W
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