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N)r) )r� r� r� r� r� � s zNDFrame._constructorc C s | j S )N)rv )r� r� r� r� �_data� s z NDFrame._datar �index� _AXIS_ORDERS)r r� �rowszdict[Axis, int]�_AXIS_TO_AXIS_NUMBER�_AXIS_REVERSED�int�_info_axis_number�_info_axis_name� _AXIS_LENzdict[str, int]c C s" | j d }tjdt|d� ddiS )z.. deprecated:: 1.1.0r� z"_AXIS_NUMBERS has been deprecated.)� stacklevelr� r )�ndim�warnings�warn� FutureWarning)r� �levelr� r� r� r� � s zNDFrame._AXIS_NUMBERSzdict[int, str]c C s" | j d }tjdt|d� ddiS )z.. deprecated:: 1.1.0r� z _AXIS_NAMES has been deprecated.)r� r r� )r� r� r� r� )r� r� r� r� r� r� � s zNDFrame._AXIS_NAMESc s& � fdd�|p� j D �}|�|� |S )z%Return an axes dictionary for myself.c s i | ]}� � |�|�qS r� )� _get_axis)�.0r� )r� r� r� � <dictcomp>� s z0NDFrame._construct_axes_dict.<locals>.<dictcomp>)r� �update)r� rq �kwargs�dr� )r� r� �_construct_axes_dict� s z NDFrame._construct_axes_dict)� require_allc s� t |�}xZ| jD ]P}|� kry|�d�� |< W q tk r^ } z|rNtd�|�W dd}~X Y qX qW � �fdd�| jD �}|� fS )a~ Construct and returns axes if supplied in args/kwargs. If require_all, raise if all axis arguments are not supplied return a tuple of (axes, kwargs). sentinel specifies the default parameter when an axis is not supplied; useful to distinguish when a user explicitly passes None in scenarios where None has special meaning. r z)not enough/duplicate arguments specified!Nc s i | ]}� � |��|�qS r� )�pop)r� r� )r� �sentinelr� r� r� s z:NDFrame._construct_axes_from_arguments.<locals>.<dictcomp>)�listr� r� � IndexError� TypeError)r� �argsr� r� r� r� �errrq r� )r� r� r� � _construct_axes_from_arguments� s z&NDFrame._construct_axes_from_argumentsr )r� r� c C s: y | j | S tk r4 td|� d| j� ���Y nX d S )NzNo axis named z for object type )r� �KeyError� ValueErrorr� )r� r� r� r� r� �_get_axis_number s zNDFrame._get_axis_numberc C s | � |�}| j| S )N)r� r� )r� r� � axis_numberr� r� r� �_get_axis_name& s zNDFrame._get_axis_namerU c C s* | � |�}|dkst�|dkr$| jS | jS )N> r r� r )r� �AssertionErrorr� �columns)r� r� r� r� r� r� r� , s zNDFrame._get_axisc C s& | � |�}| jr"| jd }|| S |S )z'Map the axis to the block_manager axis.r� )r� r� r� )r� r� �mr� r� r� r� 2 s z NDFrame._get_block_manager_axisz dict[str, Series | MultiIndex]c C s� t | |�}i }|d }xZt|j�D ]L\}}|d k r<| }}n|� d|� �}|}|�|�} | �� } || _| ||< q"W t|t�r�|} n|�� } | ||< |S )Nr Zlevel_)�getattr� enumerate�names�get_level_valuesZ to_seriesr� r� rV ) r� r� Z axis_indexr� �prefix�i�name�keyr� Z level_values�sZdindexr� r� r� �_get_axis_resolvers< s" zNDFrame._get_axis_resolversz#dict[Hashable, Series | MultiIndex]c sF ddl m� i }x | jD ]}|�| �|�� qW � fdd�|�� D �S )Nr )�clean_column_namec s$ i | ] \}}t |t�s|� |��qS r� )r� r� )r� �k�v)r� r� r� r� c s z0NDFrame._get_index_resolvers.<locals>.<dictcomp>)� pandas.core.computation.parsingr� r� r� r� r� )r� r� � axis_namer� )r� r� �_get_index_resolvers[ s z NDFrame._get_index_resolverszdict[Hashable, Series]c s: ddl m� t| t�r$� | j�| iS � fdd�| �� D �S )z� Return the special character free column resolvers of a dataframe. Column names with special characters are 'cleaned up' so that they can be referred to by backtick quoting. Used in :meth:`DataFrame.eval`. r )r� c s$ i | ] \}}t |t�s|� |��qS r� )r� r� )r� r� r� )r� r� r� r� s s z9NDFrame._get_cleaned_column_resolvers.<locals>.<dictcomp>)r� r� r� rD r� r� )r� r� )r� r� � _get_cleaned_column_resolverse s z%NDFrame._get_cleaned_column_resolversc C s t | | j�S )N)r� r� )r� r� r� r� � _info_axisw s zNDFrame._info_axisc C s t | | j�S )N)r� �_stat_axis_name)r� r� r� r� � _stat_axis{ s zNDFrame._stat_axisztuple[int, ...]c s t � fdd�� jD ��S )z3 Return a tuple of axis dimensions c 3 s | ]}t � �|��V qd S )N)r� r� )r� r� )r� r� r� � <genexpr>� s z NDFrame.shape.<locals>.<genexpr>)�tupler� )r� r� )r� r� �shape s z NDFrame.shapez list[Index]c s � fdd�� j D �S )z? Return index label(s) of the internal NDFrame c s g | ]}� � |��qS r� )r� )r� r� )r� r� r� � <listcomp>� s z NDFrame.axes.<locals>.<listcomp>)r� )r� r� )r� r� rq � s z NDFrame.axesc C s | j jS )a� Return an int representing the number of axes / array dimensions. Return 1 if Series. Otherwise return 2 if DataFrame. See Also -------- ndarray.ndim : Number of array dimensions. Examples -------- >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3}) >>> s.ndim 1 >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.ndim 2 )rv r� )r� r� r� r� r� � s z NDFrame.ndimc C s t �| j�S )a Return an int representing the number of elements in this object. Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame. See Also -------- ndarray.size : Number of elements in the array. Examples -------- >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3}) >>> s.size 3 >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.size 4 )�np�prodr� )r� r� r� r� �size� s z NDFrame.size.zLiteral[False])r� r� rs r� c C s d S )Nr� )r� �labelsr� rs r� r� r� �set_axis� s zNDFrame.set_axisz Literal[True]c C s d S )Nr� )r� r� r� rs r� r� r� r� � s )r� rs r� c C s d S )Nr� )r� r� rs r� r� r� r� � s zFrameOrSeries | Nonec C s d S )Nr� )r� r� r� rs r� r� r� r� � s )r� rs c C s | � |� | �|||�S )a� Assign desired index to given axis. Indexes for%(extended_summary_sub)s row labels can be changed by assigning a list-like or Index. Parameters ---------- labels : list-like, Index The values for the new index. axis : %(axes_single_arg)s, default 0 The axis to update. The value 0 identifies the rows%(axis_description_sub)s. inplace : bool, default False Whether to return a new %(klass)s instance. Returns ------- renamed : %(klass)s or None An object of type %(klass)s or None if ``inplace=True``. See Also -------- %(klass)s.rename_axis : Alter the name of the index%(see_also_sub)s. )�*_check_inplace_and_allows_duplicate_labels�_set_axis_nocheck)r� r� r� rs r� r� r� r� � s c C s8 |rt | | �|�|� n | �� }|j||dd� |S d S )NT)r� rs )�setattrr� r� r� )r� r� r� rs r� r� r� r� r� � s zNDFrame._set_axis_nocheck)r� r� r� c C s" t |�}| j�||� | �� d S )N)rY rv r� �_clear_item_cache)r� r� r� r� r� r� � _set_axis� s zNDFrame._set_axisc s� �� |�}�� |�}||kr,|r(��� S �S ||||i� � �fdd�t�j�D �}�j�||�}|rl|�� }�j|f|�� j�dd�S )z� Interchange axes and swap values axes appropriately. Returns ------- y : same as input c 3 s | ]}�� � �||��V qd S )N)r� �get)r� r� )�mappingr� r� r� r� s z#NDFrame.swapaxes.<locals>.<genexpr>�swapaxes)�method)r� r� �ranger� r� r r� r� )r� Zaxis1Zaxis2r� r� �jZnew_axes� new_valuesr� )r r� r� r s zNDFrame.swapaxesrr )rr c C s$ | � |�}|�|�}| j||dd�S )a� Return {klass} with requested index / column level(s) removed. Parameters ---------- level : int, str, or list-like If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels. axis : {{0 or 'index', 1 or 'columns'}}, default 0 Axis along which the level(s) is removed: * 0 or 'index': remove level(s) in column. * 1 or 'columns': remove level(s) in row. Returns ------- {klass} {klass} with requested index / column level(s) removed. Examples -------- >>> df = pd.DataFrame([ ... [1, 2, 3, 4], ... [5, 6, 7, 8], ... [9, 10, 11, 12] ... ]).set_index([0, 1]).rename_axis(['a', 'b']) >>> df.columns = pd.MultiIndex.from_tuples([ ... ('c', 'e'), ('d', 'f') ... ], names=['level_1', 'level_2']) >>> df level_1 c d level_2 e f a b 1 2 3 4 5 6 7 8 9 10 11 12 >>> df.droplevel('a') level_1 c d level_2 e f b 2 3 4 6 7 8 10 11 12 >>> df.droplevel('level_2', axis=1) level_1 c d a b 1 2 3 4 5 6 7 8 9 10 11 12 F)r� rs )r� � droplevelr� )r� r� r� r� Z new_labelsr� r� r� r & s ; zNDFrame.droplevelr z Series | Any)�itemr� c C s | | }| |= |S )Nr� )r� r �resultr� r� r� r� e s z NDFrame.popc s@ � dkrt | j�n | �� �f� | jt� fdd�t| j�D �� S )a� Squeeze 1 dimensional axis objects into scalars. Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged. This method is most useful when you don't know if your object is a Series or DataFrame, but you do know it has just a single column. In that case you can safely call `squeeze` to ensure you have a Series. Parameters ---------- axis : {0 or 'index', 1 or 'columns', None}, default None A specific axis to squeeze. By default, all length-1 axes are squeezed. Returns ------- DataFrame, Series, or scalar The projection after squeezing `axis` or all the axes. See Also -------- Series.iloc : Integer-location based indexing for selecting scalars. DataFrame.iloc : Integer-location based indexing for selecting Series. Series.to_frame : Inverse of DataFrame.squeeze for a single-column DataFrame. Examples -------- >>> primes = pd.Series([2, 3, 5, 7]) Slicing might produce a Series with a single value: >>> even_primes = primes[primes % 2 == 0] >>> even_primes 0 2 dtype: int64 >>> even_primes.squeeze() 2 Squeezing objects with more than one value in every axis does nothing: >>> odd_primes = primes[primes % 2 == 1] >>> odd_primes 1 3 2 5 3 7 dtype: int64 >>> odd_primes.squeeze() 1 3 2 5 3 7 dtype: int64 Squeezing is even more effective when used with DataFrames. >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b']) >>> df a b 0 1 2 1 3 4 Slicing a single column will produce a DataFrame with the columns having only one value: >>> df_a = df[['a']] >>> df_a a 0 1 1 3 So the columns can be squeezed down, resulting in a Series: >>> df_a.squeeze('columns') 0 1 1 3 Name: a, dtype: int64 Slicing a single row from a single column will produce a single scalar DataFrame: >>> df_0a = df.loc[df.index < 1, ['a']] >>> df_0a a 0 1 Squeezing the rows produces a single scalar Series: >>> df_0a.squeeze('rows') a 1 Name: 0, dtype: int64 Squeezing all axes will project directly into a scalar: >>> df_0a.squeeze() 1 Nc 3 s2 | ]*\}}|� kr"t |�d kr"dntd�V qdS )r� r N)r� �slice)r� r� r� )r� r� r� r� � s z"NDFrame.squeeze.<locals>.<genexpr>)r r� r� �ilocr� r� rq )r� r� r� )r� r� �squeezek s h zNDFrame.squeeze�ignore)r� r� r� r� rs r� �errorszRenamer | Nonez Axis | Nonez Level | None) r� �mapperr� r� r� r� rs r� r r� c sv |dkr |dkr |dkr t d��|dk s0|dk rT|dk rBt d��qp|dk rpt d��n |rl| �|�dkrl|}n|}| �|� |r�| n | j|d�} x�t||f�D ]�\} } | dkr�q�| �| �} t�| �} |dk r�| �|�}t | ��s(| � | �� |dk�r(t � � dk ��r(� fd d �t| �D �}t |� d ���| � | |�}| j|| d d � | �� q�W |�rd| �| � dS | j| dd�S dS )a� Alter axes input function or functions. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error. Alternatively, change ``Series.name`` with a scalar value (Series only). Parameters ---------- %(axes)s : scalar, list-like, dict-like or function, optional Scalar or list-like will alter the ``Series.name`` attribute, and raise on DataFrame. dict-like or functions are transformations to apply to that axis' values copy : bool, default True Also copy underlying data. inplace : bool, default False Whether to return a new {klass}. If True then value of copy is ignored. level : int or level name, default None In case of a MultiIndex, only rename labels in the specified level. errors : {'ignore', 'raise'}, default 'ignore' If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`, or `columns` contains labels that are not present in the Index being transformed. If 'ignore', existing keys will be renamed and extra keys will be ignored. Returns ------- renamed : {klass} (new object) Raises ------ KeyError If any of the labels is not found in the selected axis and "errors='raise'". See Also -------- NDFrame.rename_axis Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s 0 1 1 2 2 3 dtype: int64 >>> s.rename("my_name") # scalar, changes Series.name 0 1 1 2 2 3 Name: my_name, dtype: int64 >>> s.rename(lambda x: x ** 2) # function, changes labels 0 1 1 2 4 3 dtype: int64 >>> s.rename({1: 3, 2: 5}) # mapping, changes labels 0 1 3 2 5 3 dtype: int64 Since ``DataFrame`` doesn't have a ``.name`` attribute, only mapping-type arguments are allowed. >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) >>> df.rename(2) Traceback (most recent call last): ... TypeError: 'int' object is not callable ``DataFrame.rename`` supports two calling conventions * ``(index=index_mapper, columns=columns_mapper, ...)`` * ``(mapper, axis={'index', 'columns'}, ...)`` We *highly* recommend using keyword arguments to clarify your intent. >>> df.rename(index=str, columns={"A": "a", "B": "c"}) a c 0 1 4 1 2 5 2 3 6 >>> df.rename(index=str, columns={"A": "a", "C": "c"}) a B 0 1 4 1 2 5 2 3 6 Using axis-style parameters >>> df.rename(str.lower, axis='columns') a b 0 1 4 1 2 5 2 3 6 >>> df.rename({1: 2, 2: 4}, axis='index') A B 0 1 4 2 2 5 4 3 6 See the :ref:`user guide <basics.rename>` for more. Nz must pass an index to renamez:Cannot specify both 'axis' and any of 'index' or 'columns'z<Cannot specify both 'mapper' and any of 'index' or 'columns'r� )r� �raise���c s g | ]\}}� | d kr|�qS )r r� )r� r� �label)�indexerr� r� r� ~ s z"NDFrame.rename.<locals>.<listcomp>z not found in axisT)r� rs �rename)r )r� r� r� r� r� r� �com�get_rename_function�_get_level_number�callable�get_indexer_forr� r� Z_transform_indexr� r� �_update_inplacer� )r� r r� r� r� r� rs r� r r Zaxis_noZ replacements�ax�fZmissing_labels� new_indexr� )r r� r � sD { zNDFrame.renamer )r� T)rs Fc sl | j d|tjd�\}}|�dd�}|�dd�}|�dd�}|d k rL| �|�}|rltd t|�� �d � d ���t|d�}|tjk r�t |�p�t |�o�t |� }|r�| j |||d �S t d ��n�|r�| n | j|d�}x�t| j�D ]�}|�| �|��} | tjkr�q�t | ��pt | ��ot | � }|�r"| } n(t�| �� | �|�j} � fdd�| D �} |j | |dd � q�W |�sh|S d S )a9 Set the name of the axis for the index or columns. Parameters ---------- mapper : scalar, list-like, optional Value to set the axis name attribute. index, columns : scalar, list-like, dict-like or function, optional A scalar, list-like, dict-like or functions transformations to apply to that axis' values. Note that the ``columns`` parameter is not allowed if the object is a Series. This parameter only apply for DataFrame type objects. Use either ``mapper`` and ``axis`` to specify the axis to target with ``mapper``, or ``index`` and/or ``columns``. axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to rename. copy : bool, default True Also copy underlying data. inplace : bool, default False Modifies the object directly, instead of creating a new Series or DataFrame. Returns ------- Series, DataFrame, or None The same type as the caller or None if ``inplace=True``. See Also -------- Series.rename : Alter Series index labels or name. DataFrame.rename : Alter DataFrame index labels or name. Index.rename : Set new names on index. Notes ----- ``DataFrame.rename_axis`` supports two calling conventions * ``(index=index_mapper, columns=columns_mapper, ...)`` * ``(mapper, axis={'index', 'columns'}, ...)`` The first calling convention will only modify the names of the index and/or the names of the Index object that is the columns. In this case, the parameter ``copy`` is ignored. The second calling convention will modify the names of the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis *labels*. We *highly* recommend using keyword arguments to clarify your intent. Examples -------- **Series** >>> s = pd.Series(["dog", "cat", "monkey"]) >>> s 0 dog 1 cat 2 monkey dtype: object >>> s.rename_axis("animal") animal 0 dog 1 cat 2 monkey dtype: object **DataFrame** >>> df = pd.DataFrame({"num_legs": [4, 4, 2], ... "num_arms": [0, 0, 2]}, ... ["dog", "cat", "monkey"]) >>> df num_legs num_arms dog 4 0 cat 4 0 monkey 2 2 >>> df = df.rename_axis("animal") >>> df num_legs num_arms animal dog 4 0 cat 4 0 monkey 2 2 >>> df = df.rename_axis("limbs", axis="columns") >>> df limbs num_legs num_arms animal dog 4 0 cat 4 0 monkey 2 2 **MultiIndex** >>> df.index = pd.MultiIndex.from_product([['mammal'], ... ['dog', 'cat', 'monkey']], ... names=['type', 'name']) >>> df limbs num_legs num_arms type name mammal dog 4 0 cat 4 0 monkey 2 2 >>> df.rename_axis(index={'type': 'class'}) limbs num_legs num_arms class name mammal dog 4 0 cat 4 0 monkey 2 2 >>> df.rename_axis(columns=str.upper) LIMBS num_legs num_arms type name mammal dog 4 0 cat 4 0 monkey 2 2 r� )r� r� Trs Fr� r Nz2rename_axis() got an unexpected keyword argument "�")r� rs z,Use `.rename` to alter labels with a mapper.)r� c s g | ] }� |��qS r� r� )r� r� )r r� r� r� 4 s z'NDFrame.rename_axis.<locals>.<listcomp>)r� r � no_defaultr� r� r� r� �keysr. r@ r; r7 �_set_axis_namer� r� r r� r r� r r r� r� ) r� r r� rq r� rs r� Z non_mapperr r� ZnewnamesZcurnamesr� )r r� � rename_axis� s> } zNDFrame.rename_axisc C sP | � |�}| �|��|�}t|d�}|r,| n| �� }|j||dd� |sL|S dS )a4 Set the name(s) of the axis. Parameters ---------- name : str or list of str Name(s) to set. axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to set the label. The value 0 or 'index' specifies index, and the value 1 or 'columns' specifies columns. inplace : bool, default False If `True`, do operation inplace and return None. Returns ------- Series, DataFrame, or None The same type as the caller or `None` if `inplace` is `True`. See Also -------- DataFrame.rename : Alter the axis labels of :class:`DataFrame`. Series.rename : Alter the index labels or set the index name of :class:`Series`. Index.rename : Set the name of :class:`Index` or :class:`MultiIndex`. Examples -------- >>> df = pd.DataFrame({"num_legs": [4, 4, 2]}, ... ["dog", "cat", "monkey"]) >>> df num_legs dog 4 cat 4 monkey 2 >>> df._set_axis_name("animal") num_legs animal dog 4 cat 4 monkey 2 >>> df.index = pd.MultiIndex.from_product( ... [["mammal"], ['dog', 'cat', 'monkey']]) >>> df._set_axis_name(["type", "name"]) num_legs type name mammal dog 4 cat 4 monkey 2 rs T)r� rs N)r� r� Z set_namesr. r� r� )r� r� r� rs �idxZrenamedr� r� r� r" 9 s 3 zNDFrame._set_axis_namec s t � �fdd��jD ��S )Nc 3 s$ | ] }�� |��� � |��V qd S )N)r� �equals)r� r� )�otherr� r� r� r� { s z(NDFrame._indexed_same.<locals>.<genexpr>)�allr� )r� r& r� )r& r� r� � _indexed_samex s zNDFrame._indexed_samer� )r& r� c C s8 t |t| ��s t | t|��s dS tt|�}| j�|j�S )a. Test whether two objects contain the same elements. This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal. The row/column index do not need to have the same type, as long as the values are considered equal. Corresponding columns must be of the same dtype. Parameters ---------- other : Series or DataFrame The other Series or DataFrame to be compared with the first. Returns ------- bool True if all elements are the same in both objects, False otherwise. See Also -------- Series.eq : Compare two Series objects of the same length and return a Series where each element is True if the element in each Series is equal, False otherwise. DataFrame.eq : Compare two DataFrame objects of the same shape and return a DataFrame where each element is True if the respective element in each DataFrame is equal, False otherwise. testing.assert_series_equal : Raises an AssertionError if left and right are not equal. Provides an easy interface to ignore inequality in dtypes, indexes and precision among others. testing.assert_frame_equal : Like assert_series_equal, but targets DataFrames. numpy.array_equal : Return True if two arrays have the same shape and elements, False otherwise. Examples -------- >>> df = pd.DataFrame({1: [10], 2: [20]}) >>> df 1 2 0 10 20 DataFrames df and exactly_equal have the same types and values for their elements and column labels, which will return True. >>> exactly_equal = pd.DataFrame({1: [10], 2: [20]}) >>> exactly_equal 1 2 0 10 20 >>> df.equals(exactly_equal) True DataFrames df and different_column_type have the same element types and values, but have different types for the column labels, which will still return True. >>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]}) >>> different_column_type 1.0 2.0 0 10 20 >>> df.equals(different_column_type) True DataFrames df and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types. >>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]}) >>> different_data_type 1 2 0 10.0 20.0 >>> df.equals(different_data_type) False F)r� �typer ru rv r% )r� r& r� r� r� r% ~ s O zNDFrame.equalsc C sX | j }t|�rt�|�}n4t|�s2t|�s2t|�r>t�|�}ntd|j � ���| � |�S )Nz*Unary negative expects numeric dtype, not ) �_valuesr4 �operator�invr= rA r> �negr� r� �__array_wrap__)r� r� �arrr� r� r� �__neg__� s zNDFrame.__neg__c C sR | j }t|�r|}n4t|�s,t|�s,t|�r8t�|�}ntd|j� ���| � |�S )NzBUnary plus expects bool, numeric, timedelta, or object dtype, not ) r* r4 r= rA r> r+ �posr� r� r. )r� r� r/ r� r� r� �__pos__� s zNDFrame.__pos__c C s, | j s | S | j�tj�}| �|�j| dd�S )N� __invert__)r )r� rv �applyr+ �invertr� r� )r� �new_datar� r� r� r3 � s zNDFrame.__invert__c C s t dt| �j� d���d S )NzThe truth value of a zC is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().)r� r) r� )r� r� r� r� � __nonzero__� s zNDFrame.__nonzero__c C sH | � � }t|ttjf�r t|�S t|�r<tdt| �j� ���| � � dS )a" Return the bool of a single element Series or DataFrame. This must be a boolean scalar value, either True or False. It will raise a ValueError if the Series or DataFrame does not have exactly 1 element, or that element is not boolean (integer values 0 and 1 will also raise an exception). Returns ------- bool The value in the Series or DataFrame. See Also -------- Series.astype : Change the data type of a Series, including to boolean. DataFrame.astype : Change the data type of a DataFrame, including to boolean. numpy.bool_ : NumPy boolean data type, used by pandas for boolean values. Examples -------- The method will only work for single element objects with a boolean value: >>> pd.Series([True]).bool() True >>> pd.Series([False]).bool() False >>> pd.DataFrame({'col': [True]}).bool() True >>> pd.DataFrame({'col': [False]}).bool() False z0bool cannot act on a non-boolean single element N) r r� �boolr� �bool_r@ r� r) r� r7 )r� r� r� r� r� r8 s "z NDFrame.boolc C s | � � S )N)�abs)r� r� r� r� �__abs__5 s zNDFrame.__abs__)r� �decimalsr� c C s | � |�S )N)�round)r� r< r� r� r� � __round__9 s zNDFrame.__round__c C s: | � |�}|dk o8t|�o8|| j| jko8| j||d� S )aw Test whether a key is a level reference for a given axis. To be considered a level reference, `key` must be a string that: - (axis=0): Matches the name of an index level and does NOT match a column label. - (axis=1): Matches the name of a column level and does NOT match an index label. Parameters ---------- key : str Potential level name for the given axis axis : int, default 0 Axis that levels are associated with (0 for index, 1 for columns) Returns ------- is_level : bool N)r� )r� rE rq r� �_is_label_reference)r� r� r� r� r� r� �_is_level_referenceE s zNDFrame._is_level_referencec sJ �� � �� � fdd�t�j�D �}�dk oHt��oHt��fdd�|D ��S )a: Test whether a key is a label reference for a given axis. To be considered a label reference, `key` must be a string that: - (axis=0): Matches a column label - (axis=1): Matches an index label Parameters ---------- key : str Potential label name axis : int, default 0 Axis perpendicular to the axis that labels are associated with (0 means search for column labels, 1 means search for index labels) Returns ------- is_label: bool c 3 s | ]}|� kr|V qd S )Nr� )r� r )r� r� r� r� z s z.NDFrame._is_label_reference.<locals>.<genexpr>Nc 3 s | ]}� �j | kV qd S )N)rq )r� r )r� r� r� r� r� s )r� r r� rE �any)r� r� r� � other_axesr� )r� r� r� r� r? d s zNDFrame._is_label_reference)r� r� r� c C s | j ||d�p| j||d�S )a3 Test whether a key is a label or level reference for a given axis. To be considered either a label or a level reference, `key` must be a string that: - (axis=0): Matches a column label or an index level - (axis=1): Matches an index label or a column level Parameters ---------- key : str Potential label or level name axis : int, default 0 Axis that levels are associated with (0 for index, 1 for columns) Returns ------- bool )r� )r@ r? )r� r� r� r� r� r� � _is_label_or_level_reference� s z$NDFrame._is_label_or_level_referencec s� �� � �� � fdd�t�j�D �}�dk r�t��r���j� jkr�t��fdd�|D ��r�� dkrfdnd\}}� dkrzdnd\}}d�� d |� d |� d |� d |� d � }t|��dS ) a� Check whether `key` is ambiguous. By ambiguous, we mean that it matches both a level of the input `axis` and a label of the other axis. Parameters ---------- key : str or object Label or level name. axis : int, default 0 Axis that levels are associated with (0 for index, 1 for columns). Raises ------ ValueError: `key` is ambiguous c 3 s | ]}|� kr|V qd S )Nr� )r� r )r� r� r� r� � s z:NDFrame._check_label_or_level_ambiguity.<locals>.<genexpr>Nc 3 s | ]}� �j | kV qd S )N)rq )r� r )r� r� r� r� r� � s r )Zanr� )r� �column�'z ' is both � z level and z label, which is ambiguous.)r� r r� rE rq r� rA r� ) r� r� r� rB Z level_articleZ level_typeZ label_articleZ label_type�msgr� )r� r� r� r� � _check_label_or_level_ambiguity� s $z'NDFrame._check_label_or_level_ambiguityz np.ndarrayc s� | � � �� � fdd�t| j�D �}| j|� d�rT| j|� d� | j||d d�j}n*| j|� d�rv| j� � |�j}nt |��|j dkr�|r�t | � |d �t�r�d}nd}� dkr�dnd }td |� d |� d |� ���|S ) a Return a 1-D array of values associated with `key`, a label or level from the given `axis`. Retrieval logic: - (axis=0): Return column values if `key` matches a column label. Otherwise return index level values if `key` matches an index level. - (axis=1): Return row values if `key` matches an index label. Otherwise return column level values if 'key' matches a column level Parameters ---------- key : str Label or level name. axis : int, default 0 Axis that levels are associated with (0 for index, 1 for columns) Returns ------- values : np.ndarray Raises ------ KeyError if `key` matches neither a label nor a level ValueError if `key` matches multiple labels FutureWarning if `key` is ambiguous. This will become an ambiguity error in a future version c s g | ]}|� kr|�qS r� r� )r� r )r� r� r� r� � s z6NDFrame._get_label_or_level_values.<locals>.<listcomp>)r� r r� zX For a multi-index, the label must be a tuple with elements corresponding to each level.� rD r� zThe z label 'z' is not unique.)r� r r� r? rH �xsr* r@ rq r� r� r� r� r� rV r� )r� r� r� rB r� Z multi_messageZlabel_axis_namer� )r� r� �_get_label_or_level_values� s # z"NDFrame._get_label_or_level_values)r� c s� �� � �� t�|�}� �fdd�|D �}|r@td� � d|� ���� �fdd�|D �}� �fdd�|D �}��� }� dkr�|r�|j|ddd � |r�|j|d dd � nB|r�t|jt �r�|j� |�|_nt |jj �|_|r�|j|ddd � |S ) a Drop labels and/or levels for the given `axis`. For each key in `keys`: - (axis=0): If key matches a column label then drop the column. Otherwise if key matches an index level then drop the level. - (axis=1): If key matches an index label then drop the row. Otherwise if key matches a column level then drop the level. Parameters ---------- keys : str or list of str labels or levels to drop axis : int, default 0 Axis that levels are associated with (0 for index, 1 for columns) Returns ------- dropped: DataFrame Raises ------ ValueError if any `keys` match neither a label nor a level c s g | ]}�j |� d �s|�qS ))r� )rC )r� r� )r� r� r� r� r� ( s z2NDFrame._drop_labels_or_levels.<locals>.<listcomp>z;The following keys are not valid labels or levels for axis z: c s g | ]}�j |� d �r|�qS ))r� )r@ )r� r� )r� r� r� r� r� 2 s c s g | ]}�j |� d �s|�qS ))r� )r@ )r� r� )r� r� r� r� r� 4 s r T)�droprs r� )r� rs ) r� r Zmaybe_make_listr� r� Z reset_indexrL r� r� rV r rX r� )r� r! r� Z invalid_keysZlevels_to_dropZlabels_to_dropZdroppedr� )r� r� r� �_drop_labels_or_levels s* z NDFrame._drop_labels_or_levels�__hash__c C s t | j�S )z~ Iterate over info axis. Returns ------- iterator Info axis as iterator. )�iterr� )r� r� r� r� �__iter__\ s zNDFrame.__iter__c C s | j S )z� Get the 'info axis' (see Indexing for more). This is index for Series, columns for DataFrame. Returns ------- Index Info axis. )r� )r� r� r� r� r! h s z NDFrame.keysc c s" x | j D ]}|| | fV qW dS )z� Iterate over (label, values) on info axis This is index for Series and columns for DataFrame. Returns ------- Generator N)r� )r� �hr� r� r� r� u s z NDFrame.itemsc C s | � � S )N)r� )r� r� r� r� � iteritems� s zNDFrame.iteritemsc C s t | j�S )zReturns length of info axis)r� r� )r� r� r� r� �__len__� s zNDFrame.__len__c C s || j kS )z#True if the key is in the info axis)r� )r� r� r� r� r� � __contains__� s zNDFrame.__contains__c s t � fdd�� jD ��S )a� Indicator whether DataFrame is empty. True if DataFrame is entirely empty (no items), meaning any of the axes are of length 0. Returns ------- bool If DataFrame is empty, return True, if not return False. See Also -------- Series.dropna : Return series without null values. DataFrame.dropna : Return DataFrame with labels on given axis omitted where (all or any) data are missing. Notes ----- If DataFrame contains only NaNs, it is still not considered empty. See the example below. Examples -------- An example of an actual empty DataFrame. Notice the index is empty: >>> df_empty = pd.DataFrame({'A' : []}) >>> df_empty Empty DataFrame Columns: [A] Index: [] >>> df_empty.empty True If we only have NaNs in our DataFrame, it is not considered empty! We will need to drop the NaNs to make the DataFrame empty: >>> df = pd.DataFrame({'A' : [np.nan]}) >>> df A 0 NaN >>> df.empty False >>> df.dropna().empty True c 3 s | ]}t � �|��d kV qdS )r N)r� r� )r� r� )r� r� r� r� � s z NDFrame.empty.<locals>.<genexpr>)rA r� )r� r� )r� r� �empty� s 0z NDFrame.emptyi� zNpDtype | None)r� r� c C s t j| j|d�S )N)r� )r� Zasarrayr* )r� r� r� r� r� � __array__� s zNDFrame.__array__z,tuple[Callable, tuple[Any, ...], int] | None)r �contextc C s> t �|�}t|�r|S | j| jdd�}| j|f|�j| dd�S )aZ Gets called after a ufunc and other functions. Parameters ---------- result: np.ndarray The result of the ufunc or other function called on the NumPy array returned by __array__ context: tuple of (func, tuple, int) This parameter is returned by ufuncs as a 3-element tuple: (name of the ufunc, arguments of the ufunc, domain of the ufunc), but is not set by other numpy functions.q Notes ----- Series implements __array_ufunc_ so this not called for ufunc on Series. F)r� r. )r )r Zitem_from_zerodimr@ r� r� r� r� )r� r rW �resr� r� r� r� r. � s zNDFrame.__array_wrap__znp.ufuncr )�ufuncr �inputsr� c O s t j| ||f|�|�S )N)rI Z array_ufunc)r� rY r rZ r� r� r� r� �__array_ufunc__� s zNDFrame.__array_ufunc__zdict[str, Any]c sB � fdd�� j D �}� j� j� j � j� fdd�� jjD �d�|�S )Nc s i | ]}t � |d �|�qS )N)r� )r� r� )r� r� r� r� � s z(NDFrame.__getstate__.<locals>.<dictcomp>c s i | ]}� j | |�qS r� )r� )r� r� )r� r� r� r� s )rv r� r| r� r} )r| rv r� r� r� Z_keys)r� �metar� )r� r� � __getstate__� s zNDFrame.__getstate__c C s2 t |t�r|| _�nt |t��rd|kr>d|kr>|�d�|d< |�d�}|d k �r|�di �}t�| d|� |�dddi�}t�| dt| f|�� t | j | j �}x6t |�D ]*}||kr�|dkr�|| }t�| ||� q�W x4|� � D ] \}}||kr�t�| ||� q�W ntd��nt|�d k�r(td��i | _d S ) Nr� rv r� r� r} r� Tz(Pre-0.12 pickles are no longer supported� )r� r[ rv r� r� r r� r� rR �setr~ r| r� r� r� r� rw )r� �stater� r� r� r\ r� r� r� r� r� � __setstate__ s. zNDFrame.__setstate__c C s. dd� tt| ��� d�}t| �j� d|� d�S )N�[�,�]�(�))�join�maprj r) r� )r� Zpreprr� r� r� �__repr__0 s zNDFrame.__repr__c C s t �d�r| �� S dS dS )z� Returns a LaTeX representation for a particular object. Mainly for use with nbconvert (jupyter notebook conversion to pdf). zdisplay.latex.reprN)r � get_option�to_latex)r� r� r� r� � _repr_latex_6 s zNDFrame._repr_latex_c C sD t �d�r@| �t �d��}|jdd�}tt|�}tj|tj d�S dS )zh Not a real Jupyter special repr method, but we use the same naming convention. zdisplay.html.table_schemazdisplay.max_rows�table)�orient)�object_pairs_hookN) r rj �head�to_jsonr r� �json�loads� collections� OrderedDict)r� r� Zas_jsonr� r� r� �_repr_data_resource_A s z NDFrame._repr_data_resource_�storage_options)rr rw �Sheet1rI �infz str | Noner! )� sheet_name�na_rep� float_formatrw r� c C sX t | t�r| n| �� }ddlm} ||||||||| |d� }|j||| | || |d� dS )a� Write {klass} to an Excel sheet. To write a single {klass} to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an `ExcelWriter` object with a target file name, and specify a sheet in the file to write to. Multiple sheets may be written to by specifying unique `sheet_name`. With all data written to the file it is necessary to save the changes. Note that creating an `ExcelWriter` object with a file name that already exists will result in the contents of the existing file being erased. Parameters ---------- excel_writer : path-like, file-like, or ExcelWriter object File path or existing ExcelWriter. sheet_name : str, default 'Sheet1' Name of sheet which will contain DataFrame. na_rep : str, default '' Missing data representation. float_format : str, optional Format string for floating point numbers. For example ``float_format="%.2f"`` will format 0.1234 to 0.12. columns : sequence or list of str, optional Columns to write. header : bool or list of str, default True Write out the column names. If a list of string is given it is assumed to be aliases for the column names. index : bool, default True Write row names (index). index_label : str or sequence, optional Column label for index column(s) if desired. If not specified, and `header` and `index` are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. startrow : int, default 0 Upper left cell row to dump data frame. startcol : int, default 0 Upper left cell column to dump data frame. engine : str, optional Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this via the options ``io.excel.xlsx.writer``, ``io.excel.xls.writer``, and ``io.excel.xlsm.writer``. .. deprecated:: 1.2.0 As the `xlwt <https://pypi.org/project/xlwt/>`__ package is no longer maintained, the ``xlwt`` engine will be removed in a future version of pandas. merge_cells : bool, default True Write MultiIndex and Hierarchical Rows as merged cells. encoding : str, optional Encoding of the resulting excel file. Only necessary for xlwt, other writers support unicode natively. inf_rep : str, default 'inf' Representation for infinity (there is no native representation for infinity in Excel). verbose : bool, default True Display more information in the error logs. freeze_panes : tuple of int (length 2), optional Specifies the one-based bottommost row and rightmost column that is to be frozen. {storage_options} .. versionadded:: 1.2.0 See Also -------- to_csv : Write DataFrame to a comma-separated values (csv) file. ExcelWriter : Class for writing DataFrame objects into excel sheets. read_excel : Read an Excel file into a pandas DataFrame. read_csv : Read a comma-separated values (csv) file into DataFrame. Notes ----- For compatibility with :meth:`~DataFrame.to_csv`, to_excel serializes lists and dicts to strings before writing. Once a workbook has been saved it is not possible to write further data without rewriting the whole workbook. Examples -------- Create, write to and save a workbook: >>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']], ... index=['row 1', 'row 2'], ... columns=['col 1', 'col 2']) >>> df1.to_excel("output.xlsx") # doctest: +SKIP To specify the sheet name: >>> df1.to_excel("output.xlsx", ... sheet_name='Sheet_name_1') # doctest: +SKIP If you wish to write to more than one sheet in the workbook, it is necessary to specify an ExcelWriter object: >>> df2 = df1.copy() >>> with pd.ExcelWriter('output.xlsx') as writer: # doctest: +SKIP ... df1.to_excel(writer, sheet_name='Sheet_name_1') ... df2.to_excel(writer, sheet_name='Sheet_name_2') ExcelWriter can also be used to append to an existing Excel file: >>> with pd.ExcelWriter('output.xlsx', ... mode='a') as writer: # doctest: +SKIP ... df.to_excel(writer, sheet_name='Sheet_name_3') To set the library that is used to write the Excel file, you can pass the `engine` keyword (the default engine is automatically chosen depending on the file extension): >>> df1.to_excel('output1.xlsx', engine='xlsxwriter') # doctest: +SKIP r )�ExcelFormatter)r{ �cols�headerr| r� � index_label� merge_cells�inf_rep)rz �startrow�startcol� freeze_panes�enginerw N)r� rC �to_frameZpandas.io.formats.excelr} �write)r� Z excel_writerrz r{ r| r� r r� r� r� r� r� r� �encodingr� �verboser� rw r� r} � formatterr� r� r� �to_excelQ s* zNDFrame.to_excel)rw � �ms�inferzFilePathOrBuffer | Nonez(Callable[[Any], JSONSerializable] | Noner z int | None) � path_or_bufrn � date_format�double_precision� force_ascii� date_unit�default_handler�lines� compressionr� �indentrw r� c C sd ddl m} |dkr"|dkr"d}n |dkr.d}t�| � | p>d} | j|| |||||||| | | | d� S )aI Convert the object to a JSON string. Note NaN's and None will be converted to null and datetime objects will be converted to UNIX timestamps. Parameters ---------- path_or_buf : str or file handle, optional File path or object. If not specified, the result is returned as a string. orient : str Indication of expected JSON string format. * Series: - default is 'index' - allowed values are: {{'split', 'records', 'index', 'table'}}. * DataFrame: - default is 'columns' - allowed values are: {{'split', 'records', 'index', 'columns', 'values', 'table'}}. * The format of the JSON string: - 'split' : dict like {{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}} - 'records' : list like [{{column -> value}}, ... , {{column -> value}}] - 'index' : dict like {{index -> {{column -> value}}}} - 'columns' : dict like {{column -> {{index -> value}}}} - 'values' : just the values array - 'table' : dict like {{'schema': {{schema}}, 'data': {{data}}}} Describing the data, where data component is like ``orient='records'``. date_format : {{None, 'epoch', 'iso'}} Type of date conversion. 'epoch' = epoch milliseconds, 'iso' = ISO8601. The default depends on the `orient`. For ``orient='table'``, the default is 'iso'. For all other orients, the default is 'epoch'. double_precision : int, default 10 The number of decimal places to use when encoding floating point values. force_ascii : bool, default True Force encoded string to be ASCII. date_unit : str, default 'ms' (milliseconds) The time unit to encode to, governs timestamp and ISO8601 precision. One of 's', 'ms', 'us', 'ns' for second, millisecond, microsecond, and nanosecond respectively. default_handler : callable, default None Handler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object. lines : bool, default False If 'orient' is 'records' write out line-delimited json format. Will throw ValueError if incorrect 'orient' since others are not list-like. compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}} A string representing the compression to use in the output file, only used when the first argument is a filename. By default, the compression is inferred from the filename. index : bool, default True Whether to include the index values in the JSON string. Not including the index (``index=False``) is only supported when orient is 'split' or 'table'. indent : int, optional Length of whitespace used to indent each record. .. versionadded:: 1.0.0 {storage_options} .. versionadded:: 1.2.0 Returns ------- None or str If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None. See Also -------- read_json : Convert a JSON string to pandas object. Notes ----- The behavior of ``indent=0`` varies from the stdlib, which does not indent the output but does insert newlines. Currently, ``indent=0`` and the default ``indent=None`` are equivalent in pandas, though this may change in a future release. ``orient='table'`` contains a 'pandas_version' field under 'schema'. This stores the version of `pandas` used in the latest revision of the schema. Examples -------- >>> import json >>> df = pd.DataFrame( ... [["a", "b"], ["c", "d"]], ... index=["row 1", "row 2"], ... columns=["col 1", "col 2"], ... ) >>> result = df.to_json(orient="split") >>> parsed = json.loads(result) >>> json.dumps(parsed, indent=4) # doctest: +SKIP {{ "columns": [ "col 1", "col 2" ], "index": [ "row 1", "row 2" ], "data": [ [ "a", "b" ], [ "c", "d" ] ] }} Encoding/decoding a Dataframe using ``'records'`` formatted JSON. Note that index labels are not preserved with this encoding. >>> result = df.to_json(orient="records") >>> parsed = json.loads(result) >>> json.dumps(parsed, indent=4) # doctest: +SKIP [ {{ "col 1": "a", "col 2": "b" }}, {{ "col 1": "c", "col 2": "d" }} ] Encoding/decoding a Dataframe using ``'index'`` formatted JSON: >>> result = df.to_json(orient="index") >>> parsed = json.loads(result) >>> json.dumps(parsed, indent=4) # doctest: +SKIP {{ "row 1": {{ "col 1": "a", "col 2": "b" }}, "row 2": {{ "col 1": "c", "col 2": "d" }} }} Encoding/decoding a Dataframe using ``'columns'`` formatted JSON: >>> result = df.to_json(orient="columns") >>> parsed = json.loads(result) >>> json.dumps(parsed, indent=4) # doctest: +SKIP {{ "col 1": {{ "row 1": "a", "row 2": "c" }}, "col 2": {{ "row 1": "b", "row 2": "d" }} }} Encoding/decoding a Dataframe using ``'values'`` formatted JSON: >>> result = df.to_json(orient="values") >>> parsed = json.loads(result) >>> json.dumps(parsed, indent=4) # doctest: +SKIP [ [ "a", "b" ], [ "c", "d" ] ] Encoding with Table Schema: >>> result = df.to_json(orient="table") >>> parsed = json.loads(result) >>> json.dumps(parsed, indent=4) # doctest: +SKIP {{ "schema": {{ "fields": [ {{ "name": "index", "type": "string" }}, {{ "name": "col 1", "type": "string" }}, {{ "name": "col 2", "type": "string" }} ], "primaryKey": [ "index" ], "pandas_version": "0.20.0" }}, "data": [ {{ "index": "row 1", "col 1": "a", "col 2": "b" }}, {{ "index": "row 2", "col 1": "c", "col 2": "d" }} ] }} r )rr Nrm Ziso�epoch) r� r� rn r� r� r� r� r� r� r� r� r� rw )� pandas.iorr r Zis_nonnegative_intrq )r� r� rn r� r� r� r� r� r� r� r� r� rw rr r� r� r� rq � s. zNDFrame.to_jsonr� �strict�UTF-8zint | dict[str, int] | Nonezbool_t | list[str] | None) r� �mode� complevel�complib�appendrg r� � min_itemsize�dropna� data_columnsr r� r� c C s8 ddl m} |j||| ||||||| | | | | |d� dS )a. Write the contained data to an HDF5 file using HDFStore. Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects. In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key. .. warning:: One can store a subclass of ``DataFrame`` or ``Series`` to HDF5, but the type of the subclass is lost upon storing. For more information see the :ref:`user guide <io.hdf5>`. Parameters ---------- path_or_buf : str or pandas.HDFStore File path or HDFStore object. key : str Identifier for the group in the store. mode : {'a', 'w', 'r+'}, default 'a' Mode to open file: - 'w': write, a new file is created (an existing file with the same name would be deleted). - 'a': append, an existing file is opened for reading and writing, and if the file does not exist it is created. - 'r+': similar to 'a', but the file must already exist. complevel : {0-9}, optional Specifies a compression level for data. A value of 0 disables compression. complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib' Specifies the compression library to be used. As of v0.20.2 these additional compressors for Blosc are supported (default if no compressor specified: 'blosc:blosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError. append : bool, default False For Table formats, append the input data to the existing. format : {'fixed', 'table', None}, default 'fixed' Possible values: - 'fixed': Fixed format. Fast writing/reading. Not-appendable, nor searchable. - 'table': Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data. - If None, pd.get_option('io.hdf.default_format') is checked, followed by fallback to "fixed" errors : str, default 'strict' Specifies how encoding and decoding errors are to be handled. See the errors argument for :func:`open` for a full list of options. encoding : str, default "UTF-8" min_itemsize : dict or int, optional Map column names to minimum string sizes for columns. nan_rep : Any, optional How to represent null values as str. Not allowed with append=True. data_columns : list of columns or True, optional List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See :ref:`io.hdf5-query-data-columns`. Applicable only to format='table'. See Also -------- read_hdf : Read from HDF file. DataFrame.to_parquet : Write a DataFrame to the binary parquet format. DataFrame.to_sql : Write to a SQL table. DataFrame.to_feather : Write out feather-format for DataFrames. DataFrame.to_csv : Write out to a csv file. Examples -------- >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, ... index=['a', 'b', 'c']) >>> df.to_hdf('data.h5', key='df', mode='w') We can add another object to the same file: >>> s = pd.Series([1, 2, 3, 4]) >>> s.to_hdf('data.h5', key='s') Reading from HDF file: >>> pd.read_hdf('data.h5', 'df') A B a 1 4 b 2 5 c 3 6 >>> pd.read_hdf('data.h5', 's') 0 1 1 2 2 3 3 4 dtype: int64 Deleting file with data: >>> import os >>> os.remove('data.h5') r )�pytables) r� r� r� r� rg r� r� �nan_repr� r� r r� N)r� r� �to_hdf)r� r� r� r� r� r� r� rg r� r� r� r� r� r r� r� r� r� r� r� s" zNDFrame.to_hdf�failzDtypeArg | None)r� � if_existsr� r� r� c C s. ddl m} | j| ||||||||| d� dS )a Write records stored in a DataFrame to a SQL database. Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created, appended to, or overwritten. Parameters ---------- name : str Name of SQL table. con : sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection Using SQLAlchemy makes it possible to use any DB supported by that library. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable See `here <https://docs.sqlalchemy.org/en/13/core/connections.html>`_. schema : str, optional Specify the schema (if database flavor supports this). If None, use default schema. if_exists : {'fail', 'replace', 'append'}, default 'fail' How to behave if the table already exists. * fail: Raise a ValueError. * replace: Drop the table before inserting new values. * append: Insert new values to the existing table. index : bool, default True Write DataFrame index as a column. Uses `index_label` as the column name in the table. index_label : str or sequence, default None Column label for index column(s). If None is given (default) and `index` is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. chunksize : int, optional Specify the number of rows in each batch to be written at a time. By default, all rows will be written at once. dtype : dict or scalar, optional Specifying the datatype for columns. If a dictionary is used, the keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns. method : {None, 'multi', callable}, optional Controls the SQL insertion clause used: * None : Uses standard SQL ``INSERT`` clause (one per row). * 'multi': Pass multiple values in a single ``INSERT`` clause. * callable with signature ``(pd_table, conn, keys, data_iter)``. Details and a sample callable implementation can be found in the section :ref:`insert method <io.sql.method>`. Raises ------ ValueError When the table already exists and `if_exists` is 'fail' (the default). See Also -------- read_sql : Read a DataFrame from a table. Notes ----- Timezone aware datetime columns will be written as ``Timestamp with timezone`` type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone. References ---------- .. [1] https://docs.sqlalchemy.org .. [2] https://www.python.org/dev/peps/pep-0249/ Examples -------- Create an in-memory SQLite database. >>> from sqlalchemy import create_engine >>> engine = create_engine('sqlite://', echo=False) Create a table from scratch with 3 rows. >>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']}) >>> df name 0 User 1 1 User 2 2 User 3 >>> df.to_sql('users', con=engine) >>> engine.execute("SELECT * FROM users").fetchall() [(0, 'User 1'), (1, 'User 2'), (2, 'User 3')] An `sqlalchemy.engine.Connection` can also be passed to `con`: >>> with engine.begin() as connection: ... df1 = pd.DataFrame({'name' : ['User 4', 'User 5']}) ... df1.to_sql('users', con=connection, if_exists='append') This is allowed to support operations that require that the same DBAPI connection is used for the entire operation. >>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']}) >>> df2.to_sql('users', con=engine, if_exists='append') >>> engine.execute("SELECT * FROM users").fetchall() [(0, 'User 1'), (1, 'User 2'), (2, 'User 3'), (0, 'User 4'), (1, 'User 5'), (0, 'User 6'), (1, 'User 7')] Overwrite the table with just ``df2``. >>> df2.to_sql('users', con=engine, if_exists='replace', ... index_label='id') >>> engine.execute("SELECT * FROM users").fetchall() [(0, 'User 6'), (1, 'User 7')] Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars. >>> df = pd.DataFrame({"A": [1, None, 2]}) >>> df A 0 1.0 1 NaN 2 2.0 >>> from sqlalchemy.types import Integer >>> df.to_sql('integers', con=engine, index=False, ... dtype={"A": Integer()}) >>> engine.execute("SELECT * FROM integers").fetchall() [(1,), (None,), (2,)] r )�sql)�schemar� r� r� � chunksizer� r N)r� r� �to_sql) r� r� �conr� r� r� r� r� r� r r� r� r� r� r� � s zNDFrame.to_sql)r� �protocolrw r� c C s" ddl m} || ||||d� dS )u� Pickle (serialize) object to file. Parameters ---------- path : str File path where the pickled object will be stored. compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, default 'infer' A string representing the compression to use in the output file. By default, infers from the file extension in specified path. Compression mode may be any of the following possible values: {{‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}}. If compression mode is ‘infer’ and path_or_buf is path-like, then detect compression mode from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’ or ‘.xz’. (otherwise no compression). If dict given and mode is ‘zip’ or inferred as ‘zip’, other entries passed as additional compression options. protocol : int Int which indicates which protocol should be used by the pickler, default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4, 5. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL. .. [1] https://docs.python.org/3/library/pickle.html. {storage_options} .. versionadded:: 1.2.0 See Also -------- read_pickle : Load pickled pandas object (or any object) from file. DataFrame.to_hdf : Write DataFrame to an HDF5 file. DataFrame.to_sql : Write DataFrame to a SQL database. DataFrame.to_parquet : Write a DataFrame to the binary parquet format. Examples -------- >>> original_df = pd.DataFrame({{"foo": range(5), "bar": range(5, 10)}}) >>> original_df foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> original_df.to_pickle("./dummy.pkl") >>> unpickled_df = pd.read_pickle("./dummy.pkl") >>> unpickled_df foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> import os >>> os.remove("./dummy.pkl") r )� to_pickle)r� r� rw N)Zpandas.io.pickler� )r� �pathr� r� rw r� r� r� r� r� E s F zNDFrame.to_pickle)�excel�sepr� c K s( ddl m} |j| f||d�|�� dS )an Copy object to the system clipboard. Write a text representation of object to the system clipboard. This can be pasted into Excel, for example. Parameters ---------- excel : bool, default True Produce output in a csv format for easy pasting into excel. - True, use the provided separator for csv pasting. - False, write a string representation of the object to the clipboard. sep : str, default ``'\t'`` Field delimiter. **kwargs These parameters will be passed to DataFrame.to_csv. See Also -------- DataFrame.to_csv : Write a DataFrame to a comma-separated values (csv) file. read_clipboard : Read text from clipboard and pass to read_table. Notes ----- Requirements for your platform. - Linux : `xclip`, or `xsel` (with `PyQt4` modules) - Windows : none - OS X : none Examples -------- Copy the contents of a DataFrame to the clipboard. >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C']) >>> df.to_clipboard(sep=',') # doctest: +SKIP ... # Wrote the following to the system clipboard: ... # ,A,B,C ... # 0,1,2,3 ... # 1,4,5,6 We can omit the index by passing the keyword `index` and setting it to false. >>> df.to_clipboard(sep=',', index=False) # doctest: +SKIP ... # Wrote the following to the system clipboard: ... # A,B,C ... # 1,2,3 ... # 4,5,6 r )� clipboards)r� r� N)r� r� � to_clipboard)r� r� r� r� r� r� r� r� r� � s : zNDFrame.to_clipboardc C s. t d�}| jdkr |j�| �S |j�| �S dS )a* Return an xarray object from the pandas object. Returns ------- xarray.DataArray or xarray.Dataset Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series. See Also -------- DataFrame.to_hdf : Write DataFrame to an HDF5 file. DataFrame.to_parquet : Write a DataFrame to the binary parquet format. Notes ----- See the `xarray docs <https://xarray.pydata.org/en/stable/>`__ Examples -------- >>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2), ... ('parrot', 'bird', 24.0, 2), ... ('lion', 'mammal', 80.5, 4), ... ('monkey', 'mammal', np.nan, 4)], ... columns=['name', 'class', 'max_speed', ... 'num_legs']) >>> df name class max_speed num_legs 0 falcon bird 389.0 2 1 parrot bird 24.0 2 2 lion mammal 80.5 4 3 monkey mammal NaN 4 >>> df.to_xarray() <xarray.Dataset> Dimensions: (index: 4) Coordinates: * index (index) int64 0 1 2 3 Data variables: name (index) object 'falcon' 'parrot' 'lion' 'monkey' class (index) object 'bird' 'bird' 'mammal' 'mammal' max_speed (index) float64 389.0 24.0 80.5 nan num_legs (index) int64 2 2 4 4 >>> df['max_speed'].to_xarray() <xarray.DataArray 'max_speed' (index: 4)> array([389. , 24. , 80.5, nan]) Coordinates: * index (index) int64 0 1 2 3 >>> dates = pd.to_datetime(['2018-01-01', '2018-01-01', ... '2018-01-02', '2018-01-02']) >>> df_multiindex = pd.DataFrame({'date': dates, ... 'animal': ['falcon', 'parrot', ... 'falcon', 'parrot'], ... 'speed': [350, 18, 361, 15]}) >>> df_multiindex = df_multiindex.set_index(['date', 'animal']) >>> df_multiindex speed date animal 2018-01-01 falcon 350 parrot 18 2018-01-02 falcon 361 parrot 15 >>> df_multiindex.to_xarray() <xarray.Dataset> Dimensions: (animal: 2, date: 2) Coordinates: * date (date) datetime64[ns] 2018-01-01 2018-01-02 * animal (animal) object 'falcon' 'parrot' Data variables: speed (date, animal) int64 350 18 361 15 �xarrayr� N)r' r� Z DataArrayZ from_seriesZDatasetZfrom_dataframe)r� r� r� r� r� � to_xarray� s M zNDFrame.to_xarray)Zreturns�NaN�.c C s� | j dkr| �� } | dkr$t�d�} |dkr6t�d�}|dkrHt�d�}|dkrZt�d�}|dkrlt�d�}td| �} t| |||||||| | | ||d � }t|�j|| | |||||||d � S ) aD Render object to a LaTeX tabular, longtable, or nested table/tabular. Requires ``\usepackage{{booktabs}}``. The output can be copy/pasted into a main LaTeX document or read from an external file with ``\input{{table.tex}}``. .. versionchanged:: 1.0.0 Added caption and label arguments. .. versionchanged:: 1.2.0 Added position argument, changed meaning of caption argument. Parameters ---------- buf : str, Path or StringIO-like, optional, default None Buffer to write to. If None, the output is returned as a string. columns : list of label, optional The subset of columns to write. Writes all columns by default. col_space : int, optional The minimum width of each column. header : bool or list of str, default True Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names. index : bool, default True Write row names (index). na_rep : str, default 'NaN' Missing data representation. formatters : list of functions or dict of {{str: function}}, optional Formatter functions to apply to columns' elements by position or name. The result of each function must be a unicode string. List must be of length equal to the number of columns. float_format : one-parameter function or str, optional, default None Formatter for floating point numbers. For example ``float_format="%.2f"`` and ``float_format="{{:0.2f}}".format`` will both result in 0.1234 being formatted as 0.12. sparsify : bool, optional Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row. By default, the value will be read from the config module. index_names : bool, default True Prints the names of the indexes. bold_rows : bool, default False Make the row labels bold in the output. column_format : str, optional The columns format as specified in `LaTeX table format <https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g. 'rcl' for 3 columns. By default, 'l' will be used for all columns except columns of numbers, which default to 'r'. longtable : bool, optional By default, the value will be read from the pandas config module. Use a longtable environment instead of tabular. Requires adding a \usepackage{{longtable}} to your LaTeX preamble. escape : bool, optional By default, the value will be read from the pandas config module. When set to False prevents from escaping latex special characters in column names. encoding : str, optional A string representing the encoding to use in the output file, defaults to 'utf-8'. decimal : str, default '.' Character recognized as decimal separator, e.g. ',' in Europe. multicolumn : bool, default True Use \multicolumn to enhance MultiIndex columns. The default will be read from the config module. multicolumn_format : str, default 'l' The alignment for multicolumns, similar to `column_format` The default will be read from the config module. multirow : bool, default False Use \multirow to enhance MultiIndex rows. Requires adding a \usepackage{{multirow}} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module. caption : str or tuple, optional Tuple (full_caption, short_caption), which results in ``\caption[short_caption]{{full_caption}}``; if a single string is passed, no short caption will be set. .. versionadded:: 1.0.0 .. versionchanged:: 1.2.0 Optionally allow caption to be a tuple ``(full_caption, short_caption)``. label : str, optional The LaTeX label to be placed inside ``\label{{}}`` in the output. This is used with ``\ref{{}}`` in the main ``.tex`` file. .. versionadded:: 1.0.0 position : str, optional The LaTeX positional argument for tables, to be placed after ``\begin{{}}`` in the output. .. versionadded:: 1.2.0 {returns} See Also -------- DataFrame.to_string : Render a DataFrame to a console-friendly tabular output. DataFrame.to_html : Render a DataFrame as an HTML table. Examples -------- >>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'], ... mask=['red', 'purple'], ... weapon=['sai', 'bo staff'])) >>> print(df.to_latex(index=False)) # doctest: +NORMALIZE_WHITESPACE \begin{{tabular}}{{lll}} \toprule name & mask & weapon \\ \midrule Raphael & red & sai \\ Donatello & purple & bo staff \\ \bottomrule \end{{tabular}} r� Nzdisplay.latex.longtablezdisplay.latex.escapezdisplay.latex.multicolumnz display.latex.multicolumn_formatzdisplay.latex.multirowrm ) r� � col_spacer{ r r� � formattersr| � bold_rows�sparsify� index_names�escape�decimal) �buf� column_format� longtabler� � multicolumn�multicolumn_format�multirow�captionr �position)r� r� r rj r rh ri rk )r� r� r� r� r r� r{ r� r| r� r� r� r� r� r� r� r� r� r� r� r� r r� r� r� r� r� rk ' sN zNDFrame.to_latexrc �wr z FilePathOrBuffer[AnyStr] | NonezSequence[Hashable] | Nonezbool_t | list[str]zIndexLabel | None)r� r� r{ r| r� r r� r� r� r� r� �quoting� quotechar�line_terminatorr� r� � doublequote� escapecharr� r rw r� c C sX t | t�r| n| �� }t||||||d�}t|�j|||| || | ||| || ||||d�S )aD Write object to a comma-separated values (csv) file. Parameters ---------- path_or_buf : str or file handle, default None File path or object, if None is provided the result is returned as a string. If a non-binary file object is passed, it should be opened with `newline=''`, disabling universal newlines. If a binary file object is passed, `mode` might need to contain a `'b'`. .. versionchanged:: 1.2.0 Support for binary file objects was introduced. sep : str, default ',' String of length 1. Field delimiter for the output file. na_rep : str, default '' Missing data representation. float_format : str, default None Format string for floating point numbers. columns : sequence, optional Columns to write. header : bool or list of str, default True Write out the column names. If a list of strings is given it is assumed to be aliases for the column names. index : bool, default True Write row names (index). index_label : str or sequence, or False, default None Column label for index column(s) if desired. If None is given, and `header` and `index` are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R. mode : str Python write mode, default 'w'. encoding : str, optional A string representing the encoding to use in the output file, defaults to 'utf-8'. `encoding` is not supported if `path_or_buf` is a non-binary file object. compression : str or dict, default 'infer' If str, represents compression mode. If dict, value at 'method' is the compression mode. Compression mode may be any of the following possible values: {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}. If compression mode is 'infer' and `path_or_buf` is path-like, then detect compression mode from the following extensions: '.gz', '.bz2', '.zip' or '.xz'. (otherwise no compression). If dict given and mode is one of {{'zip', 'gzip', 'bz2'}}, or inferred as one of the above, other entries passed as additional compression options. .. versionchanged:: 1.0.0 May now be a dict with key 'method' as compression mode and other entries as additional compression options if compression mode is 'zip'. .. versionchanged:: 1.1.0 Passing compression options as keys in dict is supported for compression modes 'gzip' and 'bz2' as well as 'zip'. .. versionchanged:: 1.2.0 Compression is supported for binary file objects. .. versionchanged:: 1.2.0 Previous versions forwarded dict entries for 'gzip' to `gzip.open` instead of `gzip.GzipFile` which prevented setting `mtime`. quoting : optional constant from csv module Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format` then floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric. quotechar : str, default '\"' String of length 1. Character used to quote fields. line_terminator : str, optional The newline character or character sequence to use in the output file. Defaults to `os.linesep`, which depends on the OS in which this method is called ('\\n' for linux, '\\r\\n' for Windows, i.e.). chunksize : int or None Rows to write at a time. date_format : str, default None Format string for datetime objects. doublequote : bool, default True Control quoting of `quotechar` inside a field. escapechar : str, default None String of length 1. Character used to escape `sep` and `quotechar` when appropriate. decimal : str, default '.' Character recognized as decimal separator. E.g. use ',' for European data. errors : str, default 'strict' Specifies how encoding and decoding errors are to be handled. See the errors argument for :func:`open` for a full list of options. .. versionadded:: 1.1.0 {storage_options} .. versionadded:: 1.2.0 Returns ------- None or str If path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None. See Also -------- read_csv : Load a CSV file into a DataFrame. to_excel : Write DataFrame to an Excel file. Examples -------- >>> df = pd.DataFrame({{'name': ['Raphael', 'Donatello'], ... 'mask': ['red', 'purple'], ... 'weapon': ['sai', 'bo staff']}}) >>> df.to_csv(index=False) 'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n' Create 'out.zip' containing 'out.csv' >>> compression_opts = dict(method='zip', ... archive_name='out.csv') # doctest: +SKIP >>> df.to_csv('out.zip', index=False, ... compression=compression_opts) # doctest: +SKIP )�framer r� r{ r| r� )r� r� r� r r� r� r� r� r� r� r� r� r� r� rw )r� rC r� rh ri �to_csv)r� r� r� r{ r| r� r r� r� r� r� r� r� r� r� r� r� r� r� r� r rw r� r� r� r� r� r� � s4 zNDFrame.to_csvc C s t | ��dS )z# Reset the cacher. N)r) )r� r� r� r� � _reset_cacher� s zNDFrame._reset_cacher)�clear�verify_is_copyr� c C s" |r| j ddd� |r | �� dS )a! See if we need to update our parent cacher if clear, then clear our cache. Parameters ---------- clear : bool, default False Clear the item cache. verify_is_copy : bool, default True Provide is_copy checks. � �referent)r� �tN)�_check_setitem_copyr� )r� r� r� r� r� r� �_maybe_update_cacher� s z NDFrame._maybe_update_cacherc C s t | ��d S )N)r) )r� r� r� r� r� � s zNDFrame._clear_item_cache)r� �is_copyr� c K sX |dk rt jdtdd� t�d|� | �� | jj|| �|�dd�}| � |�j | dd �S ) a� Return the elements in the given *positional* indices along an axis. This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object. Parameters ---------- indices : array-like An array of ints indicating which positions to take. axis : {0 or 'index', 1 or 'columns', None}, default 0 The axis on which to select elements. ``0`` means that we are selecting rows, ``1`` means that we are selecting columns. is_copy : bool Before pandas 1.0, ``is_copy=False`` can be specified to ensure that the return value is an actual copy. Starting with pandas 1.0, ``take`` always returns a copy, and the keyword is therefore deprecated. .. deprecated:: 1.0.0 **kwargs For compatibility with :meth:`numpy.take`. Has no effect on the output. Returns ------- taken : same type as caller An array-like containing the elements taken from the object. See Also -------- DataFrame.loc : Select a subset of a DataFrame by labels. DataFrame.iloc : Select a subset of a DataFrame by positions. numpy.take : Take elements from an array along an axis. Examples -------- >>> df = pd.DataFrame([('falcon', 'bird', 389.0), ... ('parrot', 'bird', 24.0), ... ('lion', 'mammal', 80.5), ... ('monkey', 'mammal', np.nan)], ... columns=['name', 'class', 'max_speed'], ... index=[0, 2, 3, 1]) >>> df name class max_speed 0 falcon bird 389.0 2 parrot bird 24.0 3 lion mammal 80.5 1 monkey mammal NaN Take elements at positions 0 and 3 along the axis 0 (default). Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That's because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3. >>> df.take([0, 3]) name class max_speed 0 falcon bird 389.0 1 monkey mammal NaN Take elements at indices 1 and 2 along the axis 1 (column selection). >>> df.take([1, 2], axis=1) class max_speed 0 bird 389.0 2 bird 24.0 3 mammal 80.5 1 mammal NaN We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists. >>> df.take([-1, -2]) name class max_speed 1 monkey mammal NaN 3 lion mammal 80.5 Nz�is_copy is deprecated and will be removed in a future version. 'take' always returns a copy, so there is no need to specify this.r^ )r� r� T)r� �verify�take)r ) r� r� r� �nvZ validate_take�_consolidate_inplacerv r� r� r� r� )r� �indicesr� r� r� r6 r� r� r� r� � s R z NDFrame.takec C s2 | j ||d�}|�|��| �|��s.|�| � |S )a Internal version of the `take` method that sets the `_is_copy` attribute to keep track of the parent dataframe (using in indexing for the SettingWithCopyWarning). See the docstring of `take` for full explanation of the parameters. )r� r� )r� r� r% � _set_is_copy)r� r� r� r r� r� r� �_take_with_is_copy$ s zNDFrame._take_with_is_copy)� drop_levelc C s@ | � |�}| �|�}t|t�r.tjdtdd� |dk r�t|t�sHtd��|j |||d�\}}t d�g| j }|||< t |�} | j | } t| | �|�|� | S |dkr�|r�| | S | j} n| j} | �� t| t��r"y| j|d|d�\}} W n4 tk �r } ztd |� ��| �W dd} ~ X Y nX n^| �|�}t|tj��rn|jtjk�r`|�� \}| j||d �S | j||d �S t|��s�| | } t|��r�|dk�r�| j dk�r�| j | S | j � |�}| j || j| j| |jd �} nVt|��r�| j dd�t ||d �f } n.|dk�r| j dd�|f } n| j | } | | _| j | | j! d � | S ) aL Return cross-section from the Series/DataFrame. This method takes a `key` argument to select data at a particular level of a MultiIndex. Parameters ---------- key : label or tuple of label Label contained in the index, or partially in a MultiIndex. axis : {0 or 'index', 1 or 'columns'}, default 0 Axis to retrieve cross-section on. level : object, defaults to first n levels (n=1 or len(key)) In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be referred by label or position. drop_level : bool, default True If False, returns object with same levels as self. Returns ------- Series or DataFrame Cross-section from the original Series or DataFrame corresponding to the selected index levels. See Also -------- DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array. DataFrame.iloc : Purely integer-location based indexing for selection by position. Notes ----- `xs` can not be used to set values. MultiIndex Slicers is a generic way to get/set values on any level or levels. It is a superset of `xs` functionality, see :ref:`MultiIndex Slicers <advanced.mi_slicers>`. Examples -------- >>> d = {'num_legs': [4, 4, 2, 2], ... 'num_wings': [0, 0, 2, 2], ... 'class': ['mammal', 'mammal', 'mammal', 'bird'], ... 'animal': ['cat', 'dog', 'bat', 'penguin'], ... 'locomotion': ['walks', 'walks', 'flies', 'walks']} >>> df = pd.DataFrame(data=d) >>> df = df.set_index(['class', 'animal', 'locomotion']) >>> df num_legs num_wings class animal locomotion mammal cat walks 4 0 dog walks 4 0 bat flies 2 2 bird penguin walks 2 2 Get values at specified index >>> df.xs('mammal') num_legs num_wings animal locomotion cat walks 4 0 dog walks 4 0 bat flies 2 2 Get values at several indexes >>> df.xs(('mammal', 'dog')) num_legs num_wings locomotion walks 4 0 Get values at specified index and level >>> df.xs('cat', level=1) num_legs num_wings class locomotion mammal walks 4 0 Get values at several indexes and levels >>> df.xs(('bird', 'walks'), ... level=[0, 'locomotion']) num_legs num_wings animal penguin 2 2 Get values at specified column and axis >>> df.xs('num_wings', axis=1) class animal locomotion mammal cat walks 0 dog walks 0 bat flies 2 bird penguin walks 2 Name: num_wings, dtype: int64 zoPassing lists as key for xs is deprecated and will be removed in a future version. Pass key as a tuple instead.r^ )r� NzIndex must be a MultiIndex)r� r� r� r z'Expected label or tuple of labels, got )r� )r� r� r� )r� )"r� r� r� r� r� r� r� rV r� Z get_loc_levelr r� r� r r� r� r� r� r� Z_get_loc_level�get_locr� �ndarrayr� r9 Znonzeror� r@ r* rv Zfast_xs�_constructor_slicedr� �_is_view)r� r� r� r� r� r� �loc�new_axZ_indexerr r r� r �eZindsr r� r� r� rJ 2 sl d $ z NDFrame.xsc C s t | ��d S )N)r) )r� r r� r� r� � __getitem__� s zNDFrame.__getitem__r )r� �slobjr� c C s` t |t�stt|���| �|�}| �| jj||d��}|�| �}|dkpL|j }|j | |d� |S )zp Construct a slice of this container. Slicing with this method is *always* positional. )r� r )r� ) r� r r� r) r� r� rv Z get_slicer� r� r� )r� r� r� r r� r� r� r� �_slice� s zNDFrame._slice)�refr� r� c C s( |s d | _ n|d k st�t�|�| _ d S )N)ry r� �weakrefr� )r� r� r� r� r� r� r� � s zNDFrame._set_is_copyc C s | j r| jddd� dS )aV Check if we are a view, have a cacher, and are of mixed type. If so, then force a setitem_copy check. Should be called just near setting a value Will return a boolean if it we are a view and are cached, but a single-dtype meaning that the cacher should be updated following setting. � r� )r� r� F)ry r� )r� r� r� r� �%_check_is_chained_assignment_possible s z-NDFrame._check_is_chained_assignment_possibler� �settingc C s� |s| j sdS t�d�}|dkr$dS | j dk rjt| j t�sj| � � }t�|�r`|dk rj|j| jkrjd| _ dS t| j t�r~| j }n|dkr�d}nd}|dkr�t� |��n|dkr�t j |tj |d� dS ) ar Parameters ---------- stacklevel : int, default 4 the level to show of the stack when the error is output t : str, the type of setting error force : bool, default False If True, then force showing an error. validate if we are doing a setitem on a chained copy. If you call this function, be sure to set the stacklevel such that the user will see the error *at the level of setting* It is technically possible to figure out that we are setting on a copy even WITH a multi-dtyped pandas object. In other words, some blocks may be views while other are not. Currently _is_view will ALWAYS return False for multi-blocks to avoid having to handle this case. df = DataFrame(np.arange(0,9), columns=['count']) df['group'] = 'b' # This technically need not raise SettingWithCopy if both are view # (which is not # generally guaranteed but is usually True. However, # this is in general not a good practice and we recommend using .loc. df.iloc[0:5]['group'] = 'a' Nzmode.chained_assignmentr� z� A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copya A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copyr r� )r� ) ry r rj r� r� �gcZ get_referentsr� r ZSettingWithCopyErrorr� r� ZSettingWithCopyWarning)r� r� r� �forcer� �rr� r� r� r� s& zNDFrame._check_setitem_copyc C s� d}d}| j dkrDt| jt�rDy|| jjk}W n tk rB Y nX |r�t|t�sX|f}x6| jD ],}t|t�r`|dt|�� |kr`| |= d}q`W |s�| jd � |�}| j � |�| _ y | j |= W n t k r� Y nX dS )z Delete item Fr^ NTr )r� r� r� rV Z_enginer� r� r� rq r� rv Zideleterw r� )r� r� ZdeletedZmaybe_shortcut�colr� r� r� r� � __delitem__a s* zNDFrame.__delitem__c C s |r| j jstd��d S )NzQCannot specify 'inplace=True' when 'self.flags.allows_duplicate_labels' is False.)r� r� r� )r� rs r� r� r� r� � s z2NDFrame._check_inplace_and_allows_duplicate_labelsc C s( y| | S t ttfk r" |S X dS )a Get item from object for given key (ex: DataFrame column). Returns default value if not found. Parameters ---------- key : object Returns ------- value : same type as items contained in object N)r� r� r� )r� r� �defaultr� r� r� r � s z NDFrame.getc C s | j jS )z:Return boolean indicating if self is view of another array)rv Zis_view)r� r� r� r� r� � s zNDFrame._is_view)r� r r� r� c C s" |j | j||||d�}| jf |�S )a Return an object with matching indices as other object. Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False. Parameters ---------- other : Object of the same data type Its row and column indices are used to define the new indices of this object. method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'} Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index. * None (default): don't fill gaps * pad / ffill: propagate last valid observation forward to next valid * backfill / bfill: use next valid observation to fill gap * nearest: use nearest valid observations to fill gap. copy : bool, default True Return a new object, even if the passed indexes are the same. limit : int, default None Maximum number of consecutive labels to fill for inexact matches. tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations must satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type. Returns ------- Series or DataFrame Same type as caller, but with changed indices on each axis. See Also -------- DataFrame.set_index : Set row labels. DataFrame.reset_index : Remove row labels or move them to new columns. DataFrame.reindex : Change to new indices or expand indices. Notes ----- Same as calling ``.reindex(index=other.index, columns=other.columns,...)``. Examples -------- >>> df1 = pd.DataFrame([[24.3, 75.7, 'high'], ... [31, 87.8, 'high'], ... [22, 71.6, 'medium'], ... [35, 95, 'medium']], ... columns=['temp_celsius', 'temp_fahrenheit', ... 'windspeed'], ... index=pd.date_range(start='2014-02-12', ... end='2014-02-15', freq='D')) >>> df1 temp_celsius temp_fahrenheit windspeed 2014-02-12 24.3 75.7 high 2014-02-13 31.0 87.8 high 2014-02-14 22.0 71.6 medium 2014-02-15 35.0 95.0 medium >>> df2 = pd.DataFrame([[28, 'low'], ... [30, 'low'], ... [35.1, 'medium']], ... columns=['temp_celsius', 'windspeed'], ... index=pd.DatetimeIndex(['2014-02-12', '2014-02-13', ... '2014-02-15'])) >>> df2 temp_celsius windspeed 2014-02-12 28.0 low 2014-02-13 30.0 low 2014-02-15 35.1 medium >>> df2.reindex_like(df1) temp_celsius temp_fahrenheit windspeed 2014-02-12 28.0 NaN low 2014-02-13 30.0 NaN low 2014-02-14 NaN NaN NaN 2014-02-15 35.1 NaN medium )rq r r� �limit� tolerance)r� r� �reindex)r� r& r r� r� r� r� r� r� r� � reindex_like� s fzNDFrame.reindex_liker )rs r c C s� t |d�}|d k r>|d k s"|d k r*td��| �|�}||i} n.|d k sN|d k rd| �||fi �\} } ntd��| } x.| �� D ]"\}}|d k rz| j||||d�} qzW |r�| �| � n| S d S )Nrs z2Cannot specify both 'labels' and 'index'/'columns'z>Need to specify at least one of 'labels', 'index' or 'columns')r� r )r. r� r� r� r� � _drop_axisr ) r� r� r� r� r� r� rs r r� rq �_r� r� r� r� rL s" z NDFrame.drop)r� r r� c C s| | � |�}| �|�}| �|�}|jrr|dk rPt|t�s>td��|j|||d�}n|j||d�}| jf ||i�}�nt |�p�t|t �}t t � |��}|dk r�t|t�s�td��|�|��|� } |dkr�| �� r�t|� d���nnt|t��r|jdk�r|�s|�d��|� } n<|�|� } |�|�d k�� } |dk�rL| �rLt|� d���td�g| j } | | | � |�< | jt | � }|S ) a� Drop labels from specified axis. Used in the ``drop`` method internally. Parameters ---------- labels : single label or list-like axis : int or axis name level : int or level name, default None For MultiIndex errors : {'ignore', 'raise'}, default 'raise' If 'ignore', suppress error and existing labels are dropped. Nzaxis must be a MultiIndex)r� r )r r z not found in axisr� r r )r� r� r� Z is_uniquer� rV r� rL r� rF r� r0 r Zindex_labels_to_arrayr� �isinr' r� r� r rA r r� r� ) r� r� r� r� r r� Znew_axisr Zis_tuple_labelsr Zlabels_missing�slicerr� r� r� r� = s< zNDFrame._drop_axis)r� r� c C s( | � � | �� |j| _| j|d� dS )z� Replace self internals with result. Parameters ---------- result : same type as self verify_is_copy : bool, default True Provide is_copy checks. )r� N)Z _reset_cacher� rv r� )r� r r� r� r� r� r s zNDFrame._update_inplace)r� r� r� c C s& t jdj|d�}| j|i}| jf |�S )a� Prefix labels with string `prefix`. For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed. Parameters ---------- prefix : str The string to add before each label. Returns ------- Series or DataFrame New Series or DataFrame with updated labels. See Also -------- Series.add_suffix: Suffix row labels with string `suffix`. DataFrame.add_suffix: Suffix column labels with string `suffix`. Examples -------- >>> s = pd.Series([1, 2, 3, 4]) >>> s 0 1 1 2 2 3 3 4 dtype: int64 >>> s.add_prefix('item_') item_0 1 item_1 2 item_2 3 item_3 4 dtype: int64 >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]}) >>> df A B 0 1 3 1 2 4 2 3 5 3 4 6 >>> df.add_prefix('col_') col_A col_B 0 1 3 1 2 4 2 3 5 3 4 6 z {prefix}{})r� )� functools�partialrg r� r )r� r� r r r� r� r� � add_prefix� s 7 zNDFrame.add_prefix)r� �suffixr� c C s& t jdj|d�}| j|i}| jf |�S )a� Suffix labels with string `suffix`. For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed. Parameters ---------- suffix : str The string to add after each label. Returns ------- Series or DataFrame New Series or DataFrame with updated labels. See Also -------- Series.add_prefix: Prefix row labels with string `prefix`. DataFrame.add_prefix: Prefix column labels with string `prefix`. Examples -------- >>> s = pd.Series([1, 2, 3, 4]) >>> s 0 1 1 2 2 3 3 4 dtype: int64 >>> s.add_suffix('_item') 0_item 1 1_item 2 2_item 3 3_item 4 dtype: int64 >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]}) >>> df A B 0 1 3 1 2 4 2 3 5 3 4 6 >>> df.add_suffix('_col') A_col B_col 0 1 3 1 2 4 2 3 5 3 4 6 z {}{suffix})r )r� r� rg r� r )r� r r r r� r� r� � add_suffix� s 7 zNDFrame.add_suffix� quicksort�lastr% )rs r� � na_position� ignore_indexr� c C s t | ��dS )u� Sort by the values along either axis. Parameters ----------%(optional_by)s axis : %(axes_single_arg)s, default 0 Axis to be sorted. ascending : bool or list of bool, default True Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by. inplace : bool, default False If True, perform operation in-place. kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort' Choice of sorting algorithm. See also :func:`numpy.sort` for more information. `mergesort` and `stable` are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label. na_position : {'first', 'last'}, default 'last' Puts NaNs at the beginning if `first`; `last` puts NaNs at the end. ignore_index : bool, default False If True, the resulting axis will be labeled 0, 1, …, n - 1. .. versionadded:: 1.0.0 key : callable, optional Apply the key function to the values before sorting. This is similar to the `key` argument in the builtin :meth:`sorted` function, with the notable difference that this `key` function should be *vectorized*. It should expect a ``Series`` and return a Series with the same shape as the input. It will be applied to each column in `by` independently. .. versionadded:: 1.1.0 Returns ------- DataFrame or None DataFrame with sorted values or None if ``inplace=True``. See Also -------- DataFrame.sort_index : Sort a DataFrame by the index. Series.sort_values : Similar method for a Series. Examples -------- >>> df = pd.DataFrame({ ... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'], ... 'col2': [2, 1, 9, 8, 7, 4], ... 'col3': [0, 1, 9, 4, 2, 3], ... 'col4': ['a', 'B', 'c', 'D', 'e', 'F'] ... }) >>> df col1 col2 col3 col4 0 A 2 0 a 1 A 1 1 B 2 B 9 9 c 3 NaN 8 4 D 4 D 7 2 e 5 C 4 3 F Sort by col1 >>> df.sort_values(by=['col1']) col1 col2 col3 col4 0 A 2 0 a 1 A 1 1 B 2 B 9 9 c 5 C 4 3 F 4 D 7 2 e 3 NaN 8 4 D Sort by multiple columns >>> df.sort_values(by=['col1', 'col2']) col1 col2 col3 col4 1 A 1 1 B 0 A 2 0 a 2 B 9 9 c 5 C 4 3 F 4 D 7 2 e 3 NaN 8 4 D Sort Descending >>> df.sort_values(by='col1', ascending=False) col1 col2 col3 col4 4 D 7 2 e 5 C 4 3 F 2 B 9 9 c 0 A 2 0 a 1 A 1 1 B 3 NaN 8 4 D Putting NAs first >>> df.sort_values(by='col1', ascending=False, na_position='first') col1 col2 col3 col4 3 NaN 8 4 D 4 D 7 2 e 5 C 4 3 F 2 B 9 9 c 0 A 2 0 a 1 A 1 1 B Sorting with a key function >>> df.sort_values(by='col4', key=lambda col: col.str.lower()) col1 col2 col3 col4 0 A 2 0 a 1 A 1 1 B 2 B 9 9 c 3 NaN 8 4 D 4 D 7 2 e 5 C 4 3 F Natural sort with the key argument, using the `natsort <https://github.com/SethMMorton/natsort>` package. >>> df = pd.DataFrame({ ... "time": ['0hr', '128hr', '72hr', '48hr', '96hr'], ... "value": [10, 20, 30, 40, 50] ... }) >>> df time value 0 0hr 10 1 128hr 20 2 72hr 30 3 48hr 40 4 96hr 50 >>> from natsort import index_natsorted >>> df.sort_values( ... by="time", ... key=lambda x: np.argsort(index_natsorted(df["time"])) ... ) time value 0 0hr 10 3 48hr 40 2 72hr 30 4 96hr 50 1 128hr 20 N)r) )r� r� � ascendingrs r� r r r� r� r� r� � sort_values s zNDFrame.sort_valuesz%bool_t | int | Sequence[bool_t | int]r )r rs r� r �sort_remainingr r� c C s� t |d�}| �|�}t|�}| �|�} t| |||||| �} | d krR|rJd S | �� S | �|�} | jj| | dd�} | � | | j | � � � |r�t | t �r�dnd}| � |t�t| ��� | �| �}|r�| �|�S |j| dd�S d S )Nrs F)r� r� r� r � sort_index)r )r. r� r- r� rb r� r� rv r� r� rq Z_sort_levels_monotonicr� rC �ibase� default_indexr� r� r r� )r� r� r� r rs r� r r r r� �targetr �baxisr6 r r� r� r� r � s( zNDFrame.sort_indexrq )rr rq Zoptional_labelsZ optional_axisc s� � � ||�\}}t�|�dd��}|�dd�}|�dd�}|�dd�}|�dd�}|�dd�} |�d d� |r�td t|�� �d � d ���� �� t� fd d�|� � D ��r�|r�� � � S � S � � |||�r؈ � ||| �S � � |||||| |�j� dd�S )a� Conform {klass} to new index with optional filling logic. Places NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and ``copy=False``. Parameters ---------- {optional_labels} {axes} : array-like, optional New labels / index to conform to, should be specified using keywords. Preferably an Index object to avoid duplicating data. {optional_axis} method : {{None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}} Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index. * None (default): don't fill gaps * pad / ffill: Propagate last valid observation forward to next valid. * backfill / bfill: Use next valid observation to fill gap. * nearest: Use nearest valid observations to fill gap. copy : bool, default True Return a new object, even if the passed indexes are the same. level : int or name Broadcast across a level, matching Index values on the passed MultiIndex level. fill_value : scalar, default np.NaN Value to use for missing values. Defaults to NaN, but can be any "compatible" value. limit : int, default None Maximum number of consecutive elements to forward or backward fill. tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type. Returns ------- {klass} with changed index. See Also -------- DataFrame.set_index : Set row labels. DataFrame.reset_index : Remove row labels or move them to new columns. DataFrame.reindex_like : Change to same indices as other DataFrame. Examples -------- ``DataFrame.reindex`` supports two calling conventions * ``(index=index_labels, columns=column_labels, ...)`` * ``(labels, axis={{'index', 'columns'}}, ...)`` We *highly* recommend using keyword arguments to clarify your intent. Create a dataframe with some fictional data. >>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror'] >>> df = pd.DataFrame({{'http_status': [200, 200, 404, 404, 301], ... 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]}}, ... index=index) >>> df http_status response_time Firefox 200 0.04 Chrome 200 0.02 Safari 404 0.07 IE10 404 0.08 Konqueror 301 1.00 Create a new index and reindex the dataframe. By default values in the new index that do not have corresponding records in the dataframe are assigned ``NaN``. >>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10', ... 'Chrome'] >>> df.reindex(new_index) http_status response_time Safari 404.0 0.07 Iceweasel NaN NaN Comodo Dragon NaN NaN IE10 404.0 0.08 Chrome 200.0 0.02 We can fill in the missing values by passing a value to the keyword ``fill_value``. Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keyword ``method`` to fill the ``NaN`` values. >>> df.reindex(new_index, fill_value=0) http_status response_time Safari 404 0.07 Iceweasel 0 0.00 Comodo Dragon 0 0.00 IE10 404 0.08 Chrome 200 0.02 >>> df.reindex(new_index, fill_value='missing') http_status response_time Safari 404 0.07 Iceweasel missing missing Comodo Dragon missing missing IE10 404 0.08 Chrome 200 0.02 We can also reindex the columns. >>> df.reindex(columns=['http_status', 'user_agent']) http_status user_agent Firefox 200 NaN Chrome 200 NaN Safari 404 NaN IE10 404 NaN Konqueror 301 NaN Or we can use "axis-style" keyword arguments >>> df.reindex(['http_status', 'user_agent'], axis="columns") http_status user_agent Firefox 200 NaN Chrome 200 NaN Safari 404 NaN IE10 404 NaN Konqueror 301 NaN To further illustrate the filling functionality in ``reindex``, we will create a dataframe with a monotonically increasing index (for example, a sequence of dates). >>> date_index = pd.date_range('1/1/2010', periods=6, freq='D') >>> df2 = pd.DataFrame({{"prices": [100, 101, np.nan, 100, 89, 88]}}, ... index=date_index) >>> df2 prices 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0 Suppose we decide to expand the dataframe to cover a wider date range. >>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D') >>> df2.reindex(date_index2) prices 2009-12-29 NaN 2009-12-30 NaN 2009-12-31 NaN 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0 2010-01-07 NaN The index entries that did not have a value in the original data frame (for example, '2009-12-29') are by default filled with ``NaN``. If desired, we can fill in the missing values using one of several options. For example, to back-propagate the last valid value to fill the ``NaN`` values, pass ``bfill`` as an argument to the ``method`` keyword. >>> df2.reindex(date_index2, method='bfill') prices 2009-12-29 100.0 2009-12-30 100.0 2009-12-31 100.0 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0 2010-01-07 NaN Please note that the ``NaN`` value present in the original dataframe (at index value 2010-01-03) will not be filled by any of the value propagation schemes. This is because filling while reindexing does not look at dataframe values, but only compares the original and desired indexes. If you do want to fill in the ``NaN`` values present in the original dataframe, use the ``fillna()`` method. See the :ref:`user guide <basics.reindexing>` for more. r Nr� r� Tr� r� � fill_valuer� z.reindex() got an unexpected keyword argument "r r c 3 s* | ]"\}}|d k r� � |��|�V qd S )N)r� Z identical)r� r� r )r� r� r� r� � s z"NDFrame.reindex.<locals>.<genexpr>r� )r )r� rK Zclean_reindex_fill_methodr� r� r� r! r� r' r� r� �_needs_reindex_multi�_reindex_multi� _reindex_axesr� ) r� r� r� rq r r� r� r� r� r r� )r� r� r� � s0 S zNDFrame.reindexc C sp | }xf| j D ]\} || } | dkr"q | �| �} | j| ||||d�\} } | �| �}|j|| | gi||dd�}q W |S )z%Perform the reindex for all the axes.N)r� r� r� r F)r r� � allow_dups)r� r� r� r� �_reindex_with_indexers)r� rq r� r� r� r r r� r� r� r� r r r r� r� r� r� r � s zNDFrame._reindex_axesc C s, t j|�� � | jko*|dko*|dko*| j S )z$Check if we do need a multi reindex.N)r �count_not_noner� r� �_is_mixed_type)r� rq r r� r� r� r� r � s z NDFrame._needs_reindex_multic C s t | ��d S )N)r) )r� rq r� r r� r� r� r � s zNDFrame._reindex_multi)r� r� r r� c C s� | j }xft|�� �D ]V}|| \}}| �|�} |dkr8qt|�}|dk rPt|�}|j||| |||d�}d}qW |r�|| j kr�|�� }| �|�� | �S )z*allow_dups indicates an internal call hereN)r� r r r� F) rv �sortedr! r� rY r1 �reindex_indexerr� r� r� ) r� � reindexersr r� r r6 r� r� r r r� r� r� r � s( z NDFrame._reindex_with_indexers)r� �like�regexr� c s� t �|�|�}|dkr td��|dkr,| j}| �|�� |dk rf| �|�}| jf |� fdd�|D �i�S �r�dd��fdd � }� �|�}| j|d �| S |r�dd��fd d � }t � |��� �|�}| j|d �| S td ��dS ) u Subset the dataframe rows or columns according to the specified index labels. Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index. Parameters ---------- items : list-like Keep labels from axis which are in items. like : str Keep labels from axis for which "like in label == True". regex : str (regular expression) Keep labels from axis for which re.search(regex, label) == True. axis : {0 or ‘index’, 1 or ‘columns’, None}, default None The axis to filter on, expressed either as an index (int) or axis name (str). By default this is the info axis, 'index' for Series, 'columns' for DataFrame. Returns ------- same type as input object See Also -------- DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array. Notes ----- The ``items``, ``like``, and ``regex`` parameters are enforced to be mutually exclusive. ``axis`` defaults to the info axis that is used when indexing with ``[]``. Examples -------- >>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])), ... index=['mouse', 'rabbit'], ... columns=['one', 'two', 'three']) >>> df one two three mouse 1 2 3 rabbit 4 5 6 >>> # select columns by name >>> df.filter(items=['one', 'three']) one three mouse 1 3 rabbit 4 6 >>> # select columns by regular expression >>> df.filter(regex='e$', axis=1) one three mouse 1 3 rabbit 4 6 >>> # select rows containing 'bbi' >>> df.filter(like='bbi', axis=0) one two three rabbit 4 5 6 r� zDKeyword arguments `items`, `like`, or `regex` are mutually exclusiveNc s g | ]}|� kr|�qS r� r� )r� r� )r� r� r� r� v s z"NDFrame.filter.<locals>.<listcomp>r� )r� c s � d k s t �� t| �kS )N)r� r2 )�x)r r� r� r y s zNDFrame.filter.<locals>.f)r� c s � � t| ��d k S )N)�searchr2 )r )�matcherr� r� r � s z,Must pass either `items`, `like`, or `regex`) r r r� r� r� r� r� rh r� �re�compile) r� r� r r r� Znkwr� r r� r� )r� r r r� �filter# s( F zNDFrame.filterr� )r� �nr� c C s | j d|� S )ae Return the first `n` rows. This function returns the first `n` rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it. For negative values of `n`, this function returns all rows except the last `n` rows, equivalent to ``df[:-n]``. Parameters ---------- n : int, default 5 Number of rows to select. Returns ------- same type as caller The first `n` rows of the caller object. See Also -------- DataFrame.tail: Returns the last `n` rows. Examples -------- >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion', ... 'monkey', 'parrot', 'shark', 'whale', 'zebra']}) >>> df animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey 5 parrot 6 shark 7 whale 8 zebra Viewing the first 5 lines >>> df.head() animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey Viewing the first `n` lines (three in this case) >>> df.head(3) animal 0 alligator 1 bee 2 falcon For negative values of `n` >>> df.head(-3) animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey 5 parrot N)r )r� r" r� r� r� rp � s Gz NDFrame.headc C s& |dkr| j dd� S | j | d� S )a5 Return the last `n` rows. This function returns last `n` rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows. For negative values of `n`, this function returns all rows except the first `n` rows, equivalent to ``df[n:]``. Parameters ---------- n : int, default 5 Number of rows to select. Returns ------- type of caller The last `n` rows of the caller object. See Also -------- DataFrame.head : The first `n` rows of the caller object. Examples -------- >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion', ... 'monkey', 'parrot', 'shark', 'whale', 'zebra']}) >>> df animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey 5 parrot 6 shark 7 whale 8 zebra Viewing the last 5 lines >>> df.tail() animal 4 monkey 5 parrot 6 shark 7 whale 8 zebra Viewing the last `n` lines (three in this case) >>> df.tail(3) animal 6 shark 7 whale 8 zebra For negative values of `n` >>> df.tail(-3) animal 3 lion 4 monkey 5 parrot 6 shark 7 whale 8 zebra r N)r )r� r" r� r� r� �tail� s Gz NDFrame.tailz float | None)r� �frac�replacer� r r� c C sT |dkr| j }| �|�}| j| }t�|�} |dk �rtt|t�rP|�| j| �}t|t �r�t| t �r�|dkr�y | | }W q� t k r� } zt d�| �W dd} ~ X Y q�X q�t d��nt d��t| t�r�| j } n| j} | |dd�}t|�|kr�t d��|tjk�� �s|tj k�� �r t d ��|dk �� �r2t d ��|�d�}|�� d k�rn|�� dk�rf||�� }nt d ��|j}|dk�r�|dk�r�d }nz|dk �r�|d k�r�|�s�t d ��nV|dk�r�|d dk�r�t d��n4|dk�r�|dk �r�t|| �}n|dk �rt d��|dk �rt d��| j||||d�} | j| |d�} |�rPt�t| ��| _| S )u� Return a random sample of items from an axis of object. You can use `random_state` for reproducibility. Parameters ---------- n : int, optional Number of items from axis to return. Cannot be used with `frac`. Default = 1 if `frac` = None. frac : float, optional Fraction of axis items to return. Cannot be used with `n`. replace : bool, default False Allow or disallow sampling of the same row more than once. weights : str or ndarray-like, optional Default 'None' results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed. random_state : int, array-like, BitGenerator, np.random.RandomState, optional If int, array-like, or BitGenerator (NumPy>=1.17), seed for random number generator If np.random.RandomState, use as numpy RandomState object. .. versionchanged:: 1.1.0 array-like and BitGenerator (for NumPy>=1.17) object now passed to np.random.RandomState() as seed axis : {0 or ‘index’, 1 or ‘columns’, None}, default None Axis to sample. Accepts axis number or name. Default is stat axis for given data type (0 for Series and DataFrames). ignore_index : bool, default False If True, the resulting index will be labeled 0, 1, …, n - 1. .. versionadded:: 1.3.0 Returns ------- Series or DataFrame A new object of same type as caller containing `n` items randomly sampled from the caller object. See Also -------- DataFrameGroupBy.sample: Generates random samples from each group of a DataFrame object. SeriesGroupBy.sample: Generates random samples from each group of a Series object. numpy.random.choice: Generates a random sample from a given 1-D numpy array. Notes ----- If `frac` > 1, `replacement` should be set to `True`. Examples -------- >>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0], ... 'num_wings': [2, 0, 0, 0], ... 'num_specimen_seen': [10, 2, 1, 8]}, ... index=['falcon', 'dog', 'spider', 'fish']) >>> df num_legs num_wings num_specimen_seen falcon 2 2 10 dog 4 0 2 spider 8 0 1 fish 0 0 8 Extract 3 random elements from the ``Series`` ``df['num_legs']``: Note that we use `random_state` to ensure the reproducibility of the examples. >>> df['num_legs'].sample(n=3, random_state=1) fish 0 spider 8 falcon 2 Name: num_legs, dtype: int64 A random 50% sample of the ``DataFrame`` with replacement: >>> df.sample(frac=0.5, replace=True, random_state=1) num_legs num_wings num_specimen_seen dog 4 0 2 fish 0 0 8 An upsample sample of the ``DataFrame`` with replacement: Note that `replace` parameter has to be `True` for `frac` parameter > 1. >>> df.sample(frac=2, replace=True, random_state=1) num_legs num_wings num_specimen_seen dog 4 0 2 fish 0 0 8 falcon 2 2 10 falcon 2 2 10 fish 0 0 8 dog 4 0 2 fish 0 0 8 dog 4 0 2 Using a DataFrame column as weights. Rows with larger value in the `num_specimen_seen` column are more likely to be sampled. >>> df.sample(n=2, weights='num_specimen_seen', random_state=1) num_legs num_wings num_specimen_seen falcon 2 2 10 fish 0 0 8 Nr z+String passed to weights not a valid columnzLStrings can only be passed to weights when sampling from rows on a DataFramez@Strings cannot be passed as weights when sampling from a Series.�float64)r� z5Weights and axis to be sampled must be of same lengthz*weight vector may not include `inf` valuesz.weight vector many not include negative valuesr� z$Invalid weights: weights sum to zerozJReplace has to be set to `True` when upsampling the population `frac` > 1.z$Only integers accepted as `n` valuesz0Please enter a value for `frac` OR `n`, not bothzCA negative number of rows requested. Please provide positive value.)r� r% �p)r� ) �_stat_axis_numberr� r� r � random_stater� rD r� rq r� rC r� r� r� r� r� r� ry rA �fillna�sumr* r= �choicer� r r r� )r� r" r$ r% �weightsr) r� r Z axis_length�rsr� �func�locsr r� r� r� �sample sr " zNDFrame.samplez/Callable[..., T] | tuple[Callable[..., T], str]r" )r/ r� c O s t j| |f|�|�S )a� Apply func(self, \*args, \*\*kwargs). Parameters ---------- func : function Function to apply to the {klass}. ``args``, and ``kwargs`` are passed into ``func``. Alternatively a ``(callable, data_keyword)`` tuple where ``data_keyword`` is a string indicating the keyword of ``callable`` that expects the {klass}. args : iterable, optional Positional arguments passed into ``func``. kwargs : mapping, optional A dictionary of keyword arguments passed into ``func``. Returns ------- object : the return type of ``func``. See Also -------- DataFrame.apply : Apply a function along input axis of DataFrame. DataFrame.applymap : Apply a function elementwise on a whole DataFrame. Series.map : Apply a mapping correspondence on a :class:`~pandas.Series`. Notes ----- Use ``.pipe`` when chaining together functions that expect Series, DataFrames or GroupBy objects. Instead of writing >>> func(g(h(df), arg1=a), arg2=b, arg3=c) # doctest: +SKIP You can write >>> (df.pipe(h) ... .pipe(g, arg1=a) ... .pipe(func, arg2=b, arg3=c) ... ) # doctest: +SKIP If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose ``f`` takes its data as ``arg2``: >>> (df.pipe(h) ... .pipe(g, arg1=a) ... .pipe((func, 'arg2'), arg1=a, arg3=c) ... ) # doctest: +SKIP )r �pipe)r� r/ r� r� r� r� r� r2 � s :z NDFrame.pipe)r� r r� c K s� t |t�rxx |jD ]}|j| | j|< qW |jj| j_x@t| j�t|j�@ D ](}t |t�s^t�t � | |t ||d�� qLW |dkr�t dd� |j D ��}|| j_| S )a� Propagate metadata from other to self. Parameters ---------- other : the object from which to get the attributes that we are going to propagate method : str, optional A passed method name providing context on where ``__finalize__`` was called. .. warning:: The value passed as `method` are not currently considered stable across pandas releases. Nr` c s s | ]}|j jV qd S )N)r� r� )r� r r� r� r� r� [ s z'NDFrame.__finalize__.<locals>.<genexpr>)r� ru r� r� r� r_ r| r� r� r� r� r� r' Zobjs)r� r& r r� r� r� r� r� r� r� ; s zNDFrame.__finalize__)r� c C s> || j kr2|| jkr2|| jkr2| j�|�r2| | S t�| |�S )z� After regular attribute access, try looking up the name This allows simpler access to columns for interactive use. )r r| r� r� Z$_can_hold_identifiers_and_holds_namer� �__getattribute__)r� r� r� r� r� � __getattr__a s zNDFrame.__getattr__)r� r� c C s� yt �| |� t �| ||�S tk r. Y nX || jkrJt �| ||� n�|| jkrdt �| ||� n�yJt| |�}t|t�r�t �| ||� n"|| j kr�|| |< nt �| ||� W nF tt fk r� t| t �r�t |�r�t jddd� t �| ||� Y nX dS )z� After regular attribute access, try setting the name This allows simpler access to columns for interactive use. z�Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-accessr^ )r� N)r� r3 r� �AttributeErrorr r| r� r� rU r� r� rC r; r� r� )r� r� r� �existingr� r� r� r� q s, zNDFrame.__setattr__c s$ t � �� }| jjr |�| jj� |S )z� add the string-like attributes from the info_axis. If info_axis is a MultiIndex, its first level values are used. )�super�_dir_additionsr� Z_can_hold_stringsr� Z_dir_additions_for_owner)r� � additions)� __class__r� r� r8 � s zNDFrame._dir_additionsc C sD t | jttf�r|� S t| jj�}|� }t| jj�|kr@| �� |S )z^ Consolidate _mgr -- if the blocks have changed, then clear the cache )r� rv rZ r\ r� r� r� )r� r Z blocks_beforer r� r� r� �_protect_consolidate� s z NDFrame._protect_consolidatec s � fdd�}� � |� dS )z)Consolidate data in place and return Nonec s � j �� � _ d S )N)rv � consolidater� )r� r� r� r � s z'NDFrame._consolidate_inplace.<locals>.fN)r; )r� r r� )r� r� r� � s z NDFrame._consolidate_inplacec s& � fdd�}� � |�}� �|��� �S )z� Compute NDFrame with "consolidated" internals (data of each dtype grouped together in a single ndarray). Returns ------- consolidated : same type as caller c s � j �� S )N)rv r< r� )r� r� r� �<lambda>� � z&NDFrame._consolidate.<locals>.<lambda>)r; r� r� )r� r Z cons_datar� )r� r� � _consolidate� s zNDFrame._consolidatec C s& | j jr dS | j jrdS | j�� dkS )NFTr� )rv �is_single_blockZany_extension_types�dtypesZnunique)r� r� r� r� r � s zNDFrame._is_mixed_typec C s0 | j r,| jjs,t|�r$t�|�r$dS td��dS )z?check whether we allow in-place setting with this type of valueTzHCannot do inplace boolean setting on mixed-types with a non np.nan value)r rv Zis_numeric_mixed_typer: r� �isnanr� )r� r� r� r� r� �_check_inplace_setting� s z NDFrame._check_inplace_settingc C s | � | j�� ��| �S )N)r� rv Zget_numeric_datar� )r� r� r� r� �_get_numeric_data� s zNDFrame._get_numeric_datac C s | � | j�� ��| �S )N)r� rv Z get_bool_datar� )r� r� r� r� �_get_bool_data� s zNDFrame._get_bool_datac C s t | ��d S )N)r) )r� r� r� r� r� � s zNDFrame.valuesc C s t | ��dS )zinternal implementationN)r) )r� r� r� r� r* � s zNDFrame._valuesc C s | j �� }| j|| jtjd�S )aJ Return the dtypes in the DataFrame. This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types are stored with the ``object`` dtype. See :ref:`the User Guide <basics.dtypes>` for more. Returns ------- pandas.Series The data type of each column. Examples -------- >>> df = pd.DataFrame({'float': [1.0], ... 'int': [1], ... 'datetime': [pd.Timestamp('20180310')], ... 'string': ['foo']}) >>> df.dtypes float float64 int int64 datetime datetime64[ns] string object dtype: object )r� r� )rv Z get_dtypesr� r� r� Zobject_)r� r� r� r� r� rA � s zNDFrame.dtypes)r� r� r r� c sB t ��r��jdkrHt��dks(�j�kr0td����j }��|� |�S x ��� D ]}|�krRtd��qRW g }x���� D ]B\}}|�kr�|�|j�| � |d�� qx|�� r�|� � n|� qxW nZt ��r�jdkr� ��fdd�t t�j ��D �}n&�j j�� |d�}��|�j�dd�S |�s(�� � S t|dd d �} �j | _ | S ) aE Cast a pandas object to a specified dtype ``dtype``. Parameters ---------- dtype : data type, or dict of column name -> data type Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame's columns to column-specific types. copy : bool, default True Return a copy when ``copy=True`` (be very careful setting ``copy=False`` as changes to values then may propagate to other pandas objects). errors : {'raise', 'ignore'}, default 'raise' Control raising of exceptions on invalid data for provided dtype. - ``raise`` : allow exceptions to be raised - ``ignore`` : suppress exceptions. On error return original object. Returns ------- casted : same type as caller See Also -------- to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type. numpy.ndarray.astype : Cast a numpy array to a specified type. Notes ----- .. deprecated:: 1.3.0 Using ``astype`` to convert from timezone-naive dtype to timezone-aware dtype is deprecated and will raise in a future version. Use :meth:`Series.dt.tz_localize` instead. Examples -------- Create a DataFrame: >>> d = {'col1': [1, 2], 'col2': [3, 4]} >>> df = pd.DataFrame(data=d) >>> df.dtypes col1 int64 col2 int64 dtype: object Cast all columns to int32: >>> df.astype('int32').dtypes col1 int32 col2 int32 dtype: object Cast col1 to int32 using a dictionary: >>> df.astype({'col1': 'int32'}).dtypes col1 int32 col2 int64 dtype: object Create a series: >>> ser = pd.Series([1, 2], dtype='int32') >>> ser 0 1 1 2 dtype: int32 >>> ser.astype('int64') 0 1 1 2 dtype: int64 Convert to categorical type: >>> ser.astype('category') 0 1 1 2 dtype: category Categories (2, int64): [1, 2] Convert to ordered categorical type with custom ordering: >>> from pandas.api.types import CategoricalDtype >>> cat_dtype = CategoricalDtype( ... categories=[2, 1], ordered=True) >>> ser.astype(cat_dtype) 0 1 1 2 dtype: category Categories (2, int64): [2 < 1] Note that using ``copy=False`` and changing data on a new pandas object may propagate changes: >>> s1 = pd.Series([1, 2]) >>> s2 = s1.astype('int64', copy=False) >>> s2[0] = 10 >>> s1 # note that s1[0] has changed too 0 10 1 2 dtype: int64 Create a series of dates: >>> ser_date = pd.Series(pd.date_range('20200101', periods=3)) >>> ser_date 0 2020-01-01 1 2020-01-02 2 2020-01-03 dtype: datetime64[ns] r� zFOnly the Series name can be used for the key in Series dtype mappings.zHOnly a column name can be used for the key in a dtype mappings argument.)r� r� r c s( g | ] }�j d d �|f j�� d��qS )N)r� )r r� )r� r� )r� r� r� r� r� r� � s z"NDFrame.astype.<locals>.<listcomp>r� )r F)r� r� )r7 r� r� r� r� r� r! r� r� r� r9 r r� rv r� r� r` ) r� r� r� r �new_type�col_name�resultsr� r6 r r� )r� r� r� r� r� s6 v zNDFrame.astype)r� r� r� c C s* | j j|d�}| �� | �|�j| dd�S )a2 Make a copy of this object's indices and data. When ``deep=True`` (default), a new object will be created with a copy of the calling object's data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below). When ``deep=False``, a new object will be created without copying the calling object's data or index (only references to the data and index are copied). Any changes to the data of the original will be reflected in the shallow copy (and vice versa). Parameters ---------- deep : bool, default True Make a deep copy, including a copy of the data and the indices. With ``deep=False`` neither the indices nor the data are copied. Returns ------- copy : Series or DataFrame Object type matches caller. Notes ----- When ``deep=True``, data is copied but actual Python objects will not be copied recursively, only the reference to the object. This is in contrast to `copy.deepcopy` in the Standard Library, which recursively copies object data (see examples below). While ``Index`` objects are copied when ``deep=True``, the underlying numpy array is not copied for performance reasons. Since ``Index`` is immutable, the underlying data can be safely shared and a copy is not needed. Examples -------- >>> s = pd.Series([1, 2], index=["a", "b"]) >>> s a 1 b 2 dtype: int64 >>> s_copy = s.copy() >>> s_copy a 1 b 2 dtype: int64 **Shallow copy versus default (deep) copy:** >>> s = pd.Series([1, 2], index=["a", "b"]) >>> deep = s.copy() >>> shallow = s.copy(deep=False) Shallow copy shares data and index with original. >>> s is shallow False >>> s.values is shallow.values and s.index is shallow.index True Deep copy has own copy of data and index. >>> s is deep False >>> s.values is deep.values or s.index is deep.index False Updates to the data shared by shallow copy and original is reflected in both; deep copy remains unchanged. >>> s[0] = 3 >>> shallow[1] = 4 >>> s a 3 b 4 dtype: int64 >>> shallow a 3 b 4 dtype: int64 >>> deep a 1 b 2 dtype: int64 Note that when copying an object containing Python objects, a deep copy will copy the data, but will not do so recursively. Updating a nested data object will be reflected in the deep copy. >>> s = pd.Series([[1, 2], [3, 4]]) >>> deep = s.copy() >>> s[0][0] = 10 >>> s 0 [10, 2] 1 [3, 4] dtype: object >>> deep 0 [10, 2] 1 [3, 4] dtype: object )r� r� )r )rv r� r� r� r� )r� r� r� r� r� r� r� � s jz NDFrame.copyc C s | j |d�S )N)r� )r� )r� r� r� r� r� �__copy__1 s zNDFrame.__copy__c C s | j dd�S )zq Parameters ---------- memo, default None Standard signature. Unused T)r� )r� )r� �memor� r� r� � __deepcopy__5 s zNDFrame.__deepcopy__)r� �datetime�numericr r� c C s> t |d� t |d� t |d� | �| jj|||dd���| �S )a Attempt to infer better dtype for object columns Parameters ---------- datetime : bool, default False If True, convert to date where possible. numeric : bool, default False If True, attempt to convert to numbers (including strings), with unconvertible values becoming NaN. timedelta : bool, default False If True, convert to timedelta where possible. Returns ------- converted : same as input object rL rM r T)rL rM r r� )r. r� rv �convertr� )r� rL rM r r� r� r� �_convert? s zNDFrame._convertc C s$ | � | jjddddd��j| dd�S )a� Attempt to infer better dtypes for object columns. Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction. Returns ------- converted : same type as input object See Also -------- to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to numeric type. convert_dtypes : Convert argument to best possible dtype. Examples -------- >>> df = pd.DataFrame({"A": ["a", 1, 2, 3]}) >>> df = df.iloc[1:] >>> df A 1 1 2 2 3 3 >>> df.dtypes A object dtype: object >>> df.infer_objects().dtypes A int64 dtype: object TF)rL rM r r� � infer_objects)r )r� rv rN r� )r� r� r� r� rP c s *zNDFrame.infer_objects)r� rP �convert_string�convert_integer�convert_boolean�convert_floatingr� c s` | j dkr | ����� ��S � ����fdd�| �� D �}t|�dkrTt|ddd�S | �� S dS )a� Convert columns to best possible dtypes using dtypes supporting ``pd.NA``. .. versionadded:: 1.0.0 Parameters ---------- infer_objects : bool, default True Whether object dtypes should be converted to the best possible types. convert_string : bool, default True Whether object dtypes should be converted to ``StringDtype()``. convert_integer : bool, default True Whether, if possible, conversion can be done to integer extension types. convert_boolean : bool, defaults True Whether object dtypes should be converted to ``BooleanDtypes()``. convert_floating : bool, defaults True Whether, if possible, conversion can be done to floating extension types. If `convert_integer` is also True, preference will be give to integer dtypes if the floats can be faithfully casted to integers. .. versionadded:: 1.2.0 Returns ------- Series or DataFrame Copy of input object with new dtype. See Also -------- infer_objects : Infer dtypes of objects. to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type. Notes ----- By default, ``convert_dtypes`` will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support ``pd.NA``. By using the options ``convert_string``, ``convert_integer``, ``convert_boolean`` and ``convert_boolean``, it is possible to turn off individual conversions to ``StringDtype``, the integer extension types, ``BooleanDtype`` or floating extension types, respectively. For object-dtyped columns, if ``infer_objects`` is ``True``, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to ``StringDtype``, ``BooleanDtype`` or an appropriate integer or floating extension type, otherwise leave as ``object``. If the dtype is integer, convert to an appropriate integer extension type. If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. Otherwise, convert to an appropriate floating extension type. .. versionchanged:: 1.2 Starting with pandas 1.2, this method also converts float columns to the nullable floating extension type. In the future, as new dtypes are added that support ``pd.NA``, the results of this method will change to support those new dtypes. Examples -------- >>> df = pd.DataFrame( ... { ... "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")), ... "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")), ... "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")), ... "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")), ... "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")), ... "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")), ... } ... ) Start with a DataFrame with default dtypes. >>> df a b c d e f 0 1 x True h 10.0 NaN 1 2 y False i NaN 100.5 2 3 z NaN NaN 20.0 200.0 >>> df.dtypes a int32 b object c object d object e float64 f float64 dtype: object Convert the DataFrame to use best possible dtypes. >>> dfn = df.convert_dtypes() >>> dfn a b c d e f 0 1 x True h 10 <NA> 1 2 y False i <NA> 100.5 2 3 z <NA> <NA> 20 200.0 >>> dfn.dtypes a Int32 b string c boolean d string e Int64 f Float64 dtype: object Start with a Series of strings and missing data represented by ``np.nan``. >>> s = pd.Series(["a", "b", np.nan]) >>> s 0 a 1 b 2 NaN dtype: object Obtain a Series with dtype ``StringDtype``. >>> s.convert_dtypes() 0 a 1 b 2 <NA> dtype: string r� c s" g | ]\}}|� ���� ���qS r� )�_convert_dtypes)r� rG r� )rS rT rR rQ rP r� r� r� " s z*NDFrame.convert_dtypes.<locals>.<listcomp>r F)r� r� N)r� rU r� r� r` r� )r� rP rQ rR rS rT rH r� )rS rT rR rQ rP r� �convert_dtypes� s zNDFrame.convert_dtypesc C s6 t |d�}t||�\}}| �� |dkr,d}| �|�}|dkr�| jjsz|dkrz|rXt� �| jj||d�j}|j� � |_|S | jj ||||d|d�}�nt| j dk�r t |t tf�r�t|td�}|j| jd d �}|j}n t|�s�ntd t|�j� d ���| jj||||d �}� n�t |t tf��r�|dk�r.td��|�r8| n| �� }t |t �} xR|�� D ]F\} } | |k�rj�qT|| } | �s||n|�| �} | j| |d| d� �qTW |�s�|S dS t|��s�| jj||||d �}n>t |t��r�| j dk�r�| � | � � |�j }nt dt|�� ���| � |�}|�r$| �!|�S |j"| dd�S dS )aA Fill NA/NaN values using the specified method. Parameters ---------- value : scalar, dict, Series, or DataFrame Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list. method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None Method to use for filling holes in reindexed Series pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use next valid observation to fill gap. axis : {axes_single_arg} Axis along which to fill missing values. inplace : bool, default False If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame). limit : int, default None If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None. downcast : dict, default is None A dict of item->dtype of what to downcast if possible, or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible). Returns ------- {klass} or None Object with missing values filled or None if ``inplace=True``. See Also -------- interpolate : Fill NaN values using interpolation. reindex : Conform object to new index. asfreq : Convert TimeSeries to specified frequency. Examples -------- >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0], ... [3, 4, np.nan, 1], ... [np.nan, np.nan, np.nan, 5], ... [np.nan, 3, np.nan, 4]], ... columns=list("ABCD")) >>> df A B C D 0 NaN 2.0 NaN 0 1 3.0 4.0 NaN 1 2 NaN NaN NaN 5 3 NaN 3.0 NaN 4 Replace all NaN elements with 0s. >>> df.fillna(0) A B C D 0 0.0 2.0 0.0 0 1 3.0 4.0 0.0 1 2 0.0 0.0 0.0 5 3 0.0 3.0 0.0 4 We can also propagate non-null values forward or backward. >>> df.fillna(method="ffill") A B C D 0 NaN 2.0 NaN 0 1 3.0 4.0 NaN 1 2 3.0 4.0 NaN 5 3 3.0 3.0 NaN 4 Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1, 2, and 3 respectively. >>> values = {{"A": 0, "B": 1, "C": 2, "D": 3}} >>> df.fillna(value=values) A B C D 0 0.0 2.0 2.0 0 1 3.0 4.0 2.0 1 2 0.0 1.0 2.0 5 3 0.0 3.0 2.0 4 Only replace the first NaN element. >>> df.fillna(value=values, limit=1) A B C D 0 0.0 2.0 2.0 0 1 3.0 4.0 NaN 1 2 NaN 1.0 NaN 5 3 NaN 3.0 NaN 4 When filling using a DataFrame, replacement happens along the same column names and same indices >>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE")) >>> df.fillna(df2) A B C D 0 0.0 2.0 0.0 0 1 3.0 4.0 0.0 1 2 0.0 0.0 0.0 5 3 0.0 3.0 0.0 4 rs Nr r� )r r� T)r r� r� rs Zcoerce�downcast)Zdtype_if_emptyF)r� zF"value" parameter must be a scalar, dict or Series, but you passed a "r )r� r� rs rW z9Currently only can fill with dict/Series column by column)r� rs rW r^ zinvalid fill value with a r* )r )#r. r/ r� r� rv r@ r� r" r* rW � interpolater� r� r� rD rO r� r� r� r* r; r� r) r� r� r� r rC �whererH r� r� r� r r� )r� r� r r� rs r� rW r r6 Zis_dictr� r� r� Z downcast_kr� r� r� r* 3 sn u zNDFrame.fillnaz None | Axisz None | int)r� r� rs r� r� c C s | j d||||d�S )z� Synonym for :meth:`DataFrame.fillna` with ``method='ffill'``. 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Z_replace_columnwiser� r� rv Z replace_listr� r r� )r� r] r� rs r� r r r� r! r� Z are_mappingsZ to_rep_dictZ value_dictr� r� r r6 r r� )r� r] r� r� r% ( s� zNDFrame.replace�linear) r� r r� r� rs �limit_direction� limit_arearW r� c K s� t |d�}| �|�}ddddg} |dko.|| k} | r:| jn| } | jrL| �� S || krZ| j}t| jt�rv|dkrvt d��|d kr�|d kr�d nd }n@|d kr�|d kr�t d|� d���|d kr�|d kr�t d|� d���| j dkr�t � | j t �d�k�r�td��|dk�r t �t| j��} t| �} nT| j} ddddh} t| j��pNt| j��pNt| j�}|| k�rp|�spt d|� d���t| ��� �r�td��| jjf ||| |||||d�|��}| �|�}| �r�|j}|�r�| � |�S |j | d d �S d S ) a� Fill NaN values using an interpolation method. Please note that only ``method='linear'`` is supported for DataFrame/Series with a MultiIndex. Parameters ---------- method : str, default 'linear' Interpolation technique to use. One of: * 'linear': Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. * 'time': Works on daily and higher resolution data to interpolate given length of interval. * 'index', 'values': use the actual numerical values of the index. * 'pad': Fill in NaNs using existing values. * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'spline', 'barycentric', 'polynomial': Passed to `scipy.interpolate.interp1d`. These methods use the numerical values of the index. Both 'polynomial' and 'spline' require that you also specify an `order` (int), e.g. ``df.interpolate(method='polynomial', order=5)``. * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima', 'cubicspline': Wrappers around the SciPy interpolation methods of similar names. See `Notes`. * 'from_derivatives': Refers to `scipy.interpolate.BPoly.from_derivatives` which replaces 'piecewise_polynomial' interpolation method in scipy 0.18. axis : {{0 or 'index', 1 or 'columns', None}}, default None Axis to interpolate along. limit : int, optional Maximum number of consecutive NaNs to fill. Must be greater than 0. inplace : bool, default False Update the data in place if possible. limit_direction : {{'forward', 'backward', 'both'}}, Optional Consecutive NaNs will be filled in this direction. If limit is specified: * If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'. * If 'method' is 'backfill' or 'bfill', 'limit_direction' must be 'backwards'. If 'limit' is not specified: * If 'method' is 'backfill' or 'bfill', the default is 'backward' * else the default is 'forward' .. versionchanged:: 1.1.0 raises ValueError if `limit_direction` is 'forward' or 'both' and method is 'backfill' or 'bfill'. raises ValueError if `limit_direction` is 'backward' or 'both' and method is 'pad' or 'ffill'. limit_area : {{`None`, 'inside', 'outside'}}, default None If limit is specified, consecutive NaNs will be filled with this restriction. * ``None``: No fill restriction. * 'inside': Only fill NaNs surrounded by valid values (interpolate). * 'outside': Only fill NaNs outside valid values (extrapolate). downcast : optional, 'infer' or None, defaults to None Downcast dtypes if possible. ``**kwargs`` : optional Keyword arguments to pass on to the interpolating function. Returns ------- Series or DataFrame or None Returns the same object type as the caller, interpolated at some or all ``NaN`` values or None if ``inplace=True``. See Also -------- fillna : Fill missing values using different methods. scipy.interpolate.Akima1DInterpolator : Piecewise cubic polynomials (Akima interpolator). scipy.interpolate.BPoly.from_derivatives : Piecewise polynomial in the Bernstein basis. scipy.interpolate.interp1d : Interpolate a 1-D function. scipy.interpolate.KroghInterpolator : Interpolate polynomial (Krogh interpolator). scipy.interpolate.PchipInterpolator : PCHIP 1-d monotonic cubic interpolation. scipy.interpolate.CubicSpline : Cubic spline data interpolator. Notes ----- The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima' methods are wrappers around the respective SciPy implementations of similar names. These use the actual numerical values of the index. For more information on their behavior, see the `SciPy documentation <https://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__ and `SciPy tutorial <https://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html>`__. Examples -------- Filling in ``NaN`` in a :class:`~pandas.Series` via linear interpolation. >>> s = pd.Series([0, 1, np.nan, 3]) >>> s 0 0.0 1 1.0 2 NaN 3 3.0 dtype: float64 >>> s.interpolate() 0 0.0 1 1.0 2 2.0 3 3.0 dtype: float64 Filling in ``NaN`` in a Series by padding, but filling at most two consecutive ``NaN`` at a time. >>> s = pd.Series([np.nan, "single_one", np.nan, ... "fill_two_more", np.nan, np.nan, np.nan, ... 4.71, np.nan]) >>> s 0 NaN 1 single_one 2 NaN 3 fill_two_more 4 NaN 5 NaN 6 NaN 7 4.71 8 NaN dtype: object >>> s.interpolate(method='pad', limit=2) 0 NaN 1 single_one 2 single_one 3 fill_two_more 4 fill_two_more 5 fill_two_more 6 NaN 7 4.71 8 4.71 dtype: object Filling in ``NaN`` in a Series via polynomial interpolation or splines: Both 'polynomial' and 'spline' methods require that you also specify an ``order`` (int). >>> s = pd.Series([0, 2, np.nan, 8]) >>> s.interpolate(method='polynomial', order=2) 0 0.000000 1 2.000000 2 4.666667 3 8.000000 dtype: float64 Fill the DataFrame forward (that is, going down) along each column using linear interpolation. Note how the last entry in column 'a' is interpolated differently, because there is no entry after it to use for interpolation. Note how the first entry in column 'b' remains ``NaN``, because there is no entry before it to use for interpolation. >>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0), ... (np.nan, 2.0, np.nan, np.nan), ... (2.0, 3.0, np.nan, 9.0), ... (np.nan, 4.0, -4.0, 16.0)], ... columns=list('abcd')) >>> df a b c d 0 0.0 NaN -1.0 1.0 1 NaN 2.0 NaN NaN 2 2.0 3.0 NaN 9.0 3 NaN 4.0 -4.0 16.0 >>> df.interpolate(method='linear', limit_direction='forward', axis=0) a b c d 0 0.0 NaN -1.0 1.0 1 1.0 2.0 -2.0 5.0 2 2.0 3.0 -3.0 9.0 3 2.0 4.0 -4.0 16.0 Using polynomial interpolation. >>> df['d'].interpolate(method='polynomial', order=2) 0 1.0 1 4.0 2 9.0 3 16.0 Name: d, dtype: float64 rs rZ r[ r\ �backfillr� ra z@Only `method=linear` interpolation is supported on MultiIndexes.N)rd r[ Zbackward�forward)r\ rZ z0`limit_direction` must be 'forward' for method `�`z1`limit_direction` must be 'backward' for method `r^ r� zvCannot interpolate with all object-dtype columns in the DataFrame. Try setting at least one column to a numeric dtype.r� r� Znearest�timez9Index column must be numeric or datetime type when using z_ method other than linear. Try setting a numeric or datetime index column before interpolating.zkInterpolation with NaNs in the index has not been implemented. Try filling those NaNs before interpolating.)r r� r� r� rb rc rs rW rX )r ) r. r� r" rU r� r� r� r� rV r� r� r� r' rA r� r� �aranger� rU r= r5 rA rG rA r� rv rX r� r r� )r� r r� r� rs rb rc rW r� Zfillna_methodsZshould_transposer� r� �methodsZis_numeric_or_datetimer6 r r� r� r� rX � sl P zNDFrame.interpolatec C s t |t�rt|�}| jjs"td��t | t�}|rB|dk r^td��n |dkrP| j}t|�s^|g}t|�}|�s| jd }t | jt �r�t || jj d�}||k r�|s�| j | j|t jd�S t jS |�r| jj|dd�}|dkr�|d 8 }| j}x$|dk�rt|| ��r|d 8 }q�W || S t |t��s6|�r,t|�nt|g�}|�rD| �� n| | �� �d �}|�� �r�|�r�td | �} | jt j|| jd �S |�r�td | �} | jt j|| jd �S td | �} | j t j| j|d d �S | j�||j �} | dk} | �| �} || _t j| j| < |�r | S | j d S )a� Return the last row(s) without any NaNs before `where`. The last row (for each element in `where`, if list) without any NaN is taken. In case of a :class:`~pandas.DataFrame`, the last row without NaN considering only the subset of columns (if not `None`) If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame Parameters ---------- where : date or array-like of dates Date(s) before which the last row(s) are returned. subset : str or array-like of str, default `None` For DataFrame, if not `None`, only use these columns to check for NaNs. Returns ------- scalar, Series, or DataFrame The return can be: * scalar : when `self` is a Series and `where` is a scalar * Series: when `self` is a Series and `where` is an array-like, or when `self` is a DataFrame and `where` is a scalar * DataFrame : when `self` is a DataFrame and `where` is an array-like Return scalar, Series, or DataFrame. See Also -------- merge_asof : Perform an asof merge. Similar to left join. Notes ----- Dates are assumed to be sorted. Raises if this is not the case. Examples -------- A Series and a scalar `where`. >>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40]) >>> s 10 1.0 20 2.0 30 NaN 40 4.0 dtype: float64 >>> s.asof(20) 2.0 For a sequence `where`, a Series is returned. The first value is NaN, because the first element of `where` is before the first index value. >>> s.asof([5, 20]) 5 NaN 20 2.0 dtype: float64 Missing values are not considered. The following is ``2.0``, not NaN, even though NaN is at the index location for ``30``. >>> s.asof(30) 2.0 Take all columns into consideration >>> df = pd.DataFrame({'a': [10, 20, 30, 40, 50], ... 'b': [None, None, None, None, 500]}, ... index=pd.DatetimeIndex(['2018-02-27 09:01:00', ... '2018-02-27 09:02:00', ... '2018-02-27 09:03:00', ... '2018-02-27 09:04:00', ... '2018-02-27 09:05:00'])) >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30', ... '2018-02-27 09:04:30'])) a b 2018-02-27 09:03:30 NaN NaN 2018-02-27 09:04:30 NaN NaN Take a single column into consideration >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30', ... '2018-02-27 09:04:30']), ... subset=['a']) a b 2018-02-27 09:03:30 30.0 NaN 2018-02-27 09:04:30 40.0 NaN z asof requires a sorted indexNz subset is not valid for Seriesr )�freq)r� r� r� �right)�sider� ro )r� r� rm )r� r� r ) r� r� r r� Z is_monotonicr� rD r� r; rW r rj r� r� r& �nan� searchsortedr* rG rU rA r' r r� r� Z asof_locsr� r� r ) r� rY �subset� is_seriesZis_list�startr� r� Znullsr0 rK r� r� r� r� �asof sb a z NDFrame.asofc C s t | �j| dd�S )aX Detect missing values. Return a boolean same-sized object indicating if the values are NA. NA values, such as None or :attr:`numpy.NaN`, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings ``''`` or :attr:`numpy.inf` are not considered NA values (unless you set ``pandas.options.mode.use_inf_as_na = True``). Returns ------- {klass} Mask of bool values for each element in {klass} that indicates whether an element is an NA value. See Also -------- {klass}.isnull : Alias of isna. {klass}.notna : Boolean inverse of isna. {klass}.dropna : Omit axes labels with missing values. isna : Top-level isna. Examples -------- Show which entries in a DataFrame are NA. >>> df = pd.DataFrame(dict(age=[5, 6, np.NaN], ... born=[pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... name=['Alfred', 'Batman', ''], ... toy=[None, 'Batmobile', 'Joker'])) >>> df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker >>> df.isna() age born name toy 0 False True False True 1 False False False False 2 True False False False Show which entries in a Series are NA. >>> ser = pd.Series([5, 6, np.NaN]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64 >>> ser.isna() 0 False 1 False 2 True dtype: bool rG )r )rG r� )r� r� r� r� rG � s =z NDFrame.isnac C s t | �j| dd�S )N�isnull)r )rG r� )r� r� r� r� rs � s zNDFrame.isnullc C s t | �j| dd�S )as Detect existing (non-missing) values. Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings ``''`` or :attr:`numpy.inf` are not considered NA values (unless you set ``pandas.options.mode.use_inf_as_na = True``). NA values, such as None or :attr:`numpy.NaN`, get mapped to False values. Returns ------- {klass} Mask of bool values for each element in {klass} that indicates whether an element is not an NA value. See Also -------- {klass}.notnull : Alias of notna. {klass}.isna : Boolean inverse of notna. {klass}.dropna : Omit axes labels with missing values. notna : Top-level notna. Examples -------- Show which entries in a DataFrame are not NA. >>> df = pd.DataFrame(dict(age=[5, 6, np.NaN], ... born=[pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... name=['Alfred', 'Batman', ''], ... toy=[None, 'Batmobile', 'Joker'])) >>> df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker >>> df.notna() age born name toy 0 True False True False 1 True True True True 2 False True True True Show which entries in a Series are not NA. >>> ser = pd.Series([5, 6, np.NaN]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64 >>> ser.notna() 0 True 1 True 2 False dtype: bool rH )r )rH r� )r� r� r� r� rH � s =z NDFrame.notnac C s t | �j| dd�S )N�notnull)r )rH r� )r� r� r� r� rt 7 s zNDFrame.notnull)rs c C s� |d k rt �t|��s,|d k r4t �t|��r4td��| }t| j�}t jdd��J |d k rr| |k}|j||d dd�}|d k r�| |k}|j||d dd�}W d Q R X t �|�r�t j||< |r�| �|�S |S d S )Nz*Cannot use an NA value as a clip thresholdr )r' F)r� rs ) r� rA rG r� r* ZerrstaterY rm r )r� �lower�upperrs r �maskro r� r� r� �_clip_with_scalar; s" zNDFrame._clip_with_scalarc C s� |d k r| � |�}t|�rLt|�rL|jdkr<| jd ||d�S | j|d |d�S t|t�s�t|�r�t| t�rz| j|| j d�}nt | ||d d�d }t|�r�|jdkr�t j nt j }|� |�}n|}|||d�t| �B }| j||||d�S )N�le)rs )r� )Zflexr� )r� )r� rs )r� r@ r< r� rx r� rD r; r� r� r_ r� ry r* rG rY )r� � thresholdr r� rs r Z threshold_infro r� r� r� �_clip_with_one_boundU s z NDFrame._clip_with_one_boundc O sJ t |d�}t�|||�}|dk r*| �|�}t|�}t|�sJt�|�rXd}nt�|�rXd}t|�}t|�sxt�|�r�d}nt�|�r�d}|dk r�|dk r�t |�r�t |�r�t ||�t ||� }}|dks�t |�r�t |�r�|dks�t |�r�t |�r�| j |||d�S | } |dk �r | j|| j||d�} |dk �rF|�r2| } | j|| j||d�} | S )a] Trim values at input threshold(s). Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis. Parameters ---------- lower : float or array-like, default None Minimum threshold value. All values below this threshold will be set to it. A missing threshold (e.g `NA`) will not clip the value. upper : float or array-like, default None Maximum threshold value. All values above this threshold will be set to it. A missing threshold (e.g `NA`) will not clip the value. axis : int or str axis name, optional Align object with lower and upper along the given axis. inplace : bool, default False Whether to perform the operation in place on the data. *args, **kwargs Additional keywords have no effect but might be accepted for compatibility with numpy. Returns ------- Series or DataFrame or None Same type as calling object with the values outside the clip boundaries replaced or None if ``inplace=True``. See Also -------- Series.clip : Trim values at input threshold in series. DataFrame.clip : Trim values at input threshold in dataframe. numpy.clip : Clip (limit) the values in an array. Examples -------- >>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]} >>> df = pd.DataFrame(data) >>> df col_0 col_1 0 9 -2 1 -3 -7 2 0 6 3 -1 8 4 5 -5 Clips per column using lower and upper thresholds: >>> df.clip(-4, 6) col_0 col_1 0 6 -2 1 -3 -4 2 0 6 3 -1 6 4 5 -4 Clips using specific lower and upper thresholds per column element: >>> t = pd.Series([2, -4, -1, 6, 3]) >>> t 0 2 1 -4 2 -1 3 6 4 3 dtype: int64 >>> df.clip(t, t + 4, axis=0) col_0 col_1 0 6 2 1 -3 -4 2 0 3 3 6 8 4 5 3 Clips using specific lower threshold per column element, with missing values: >>> t = pd.Series([2, -4, np.NaN, 6, 3]) >>> t 0 2.0 1 -4.0 2 NaN 3 6.0 4 3.0 dtype: float64 >>> df.clip(t, axis=0) col_0 col_1 0 9 2 1 -3 -4 2 0 6 3 6 8 4 5 3 rs N)rs )r r� rs )r. r� Zvalidate_clip_with_axisr� rG r; r� rA r' r@ �min�maxr< rx r{ �gery ) r� ru rv r� rs r� r� Z isna_lowerZ isna_upperr r� r� r� �clipw sD j z NDFrame.clip)r� �how� normalizer� c C s ddl m} || |||||d�S )a� Convert time series to specified frequency. Returns the original data conformed to a new index with the specified frequency. If the index of this {klass} is a :class:`~pandas.PeriodIndex`, the new index is the result of transforming the original index with :meth:`PeriodIndex.asfreq <pandas.PeriodIndex.asfreq>` (so the original index will map one-to-one to the new index). Otherwise, the new index will be equivalent to ``pd.date_range(start, end, freq=freq)`` where ``start`` and ``end`` are, respectively, the first and last entries in the original index (see :func:`pandas.date_range`). The values corresponding to any timesteps in the new index which were not present in the original index will be null (``NaN``), unless a method for filling such unknowns is provided (see the ``method`` parameter below). The :meth:`resample` method is more appropriate if an operation on each group of timesteps (such as an aggregate) is necessary to represent the data at the new frequency. Parameters ---------- freq : DateOffset or str Frequency DateOffset or string. method : {{'backfill'/'bfill', 'pad'/'ffill'}}, default None Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present): * 'pad' / 'ffill': propagate last valid observation forward to next valid * 'backfill' / 'bfill': use NEXT valid observation to fill. how : {{'start', 'end'}}, default end For PeriodIndex only (see PeriodIndex.asfreq). normalize : bool, default False Whether to reset output index to midnight. fill_value : scalar, optional Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present). Returns ------- {klass} {klass} object reindexed to the specified frequency. See Also -------- reindex : Conform DataFrame to new index with optional filling logic. Notes ----- To learn more about the frequency strings, please see `this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__. Examples -------- Start by creating a series with 4 one minute timestamps. >>> index = pd.date_range('1/1/2000', periods=4, freq='T') >>> series = pd.Series([0.0, None, 2.0, 3.0], index=index) >>> df = pd.DataFrame({{'s': series}}) >>> df s 2000-01-01 00:00:00 0.0 2000-01-01 00:01:00 NaN 2000-01-01 00:02:00 2.0 2000-01-01 00:03:00 3.0 Upsample the series into 30 second bins. >>> df.asfreq(freq='30S') s 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 NaN 2000-01-01 00:01:30 NaN 2000-01-01 00:02:00 2.0 2000-01-01 00:02:30 NaN 2000-01-01 00:03:00 3.0 Upsample again, providing a ``fill value``. >>> df.asfreq(freq='30S', fill_value=9.0) s 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 9.0 2000-01-01 00:01:00 NaN 2000-01-01 00:01:30 9.0 2000-01-01 00:02:00 2.0 2000-01-01 00:02:30 9.0 2000-01-01 00:03:00 3.0 Upsample again, providing a ``method``. >>> df.asfreq(freq='30S', method='bfill') s 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 NaN 2000-01-01 00:01:30 2.0 2000-01-01 00:02:00 2.0 2000-01-01 00:02:30 3.0 2000-01-01 00:03:00 3.0 r )�asfreq)r r� r� r )�pandas.core.resampler� )r� rj r r� r� r r� r� r� r� r� s r zNDFrame.asfreq)r� rr r� c C sP |dkr| j }| �|�}| �|�}t|t�s4td��|j||d�}| j||d�S )a� Select values at particular time of day (e.g., 9:30AM). Parameters ---------- time : datetime.time or str axis : {0 or 'index', 1 or 'columns'}, default 0 Returns ------- Series or DataFrame Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- between_time : Select values between particular times of the day. first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_at_time : Get just the index locations for values at particular time of the day. Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='12H') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 00:00:00 1 2018-04-09 12:00:00 2 2018-04-10 00:00:00 3 2018-04-10 12:00:00 4 >>> ts.at_time('12:00') A 2018-04-09 12:00:00 2 2018-04-10 12:00:00 4 NzIndex must be DatetimeIndex)rr )r� )r( r� r� r� rT r� Zindexer_at_timer� )r� rg rr r� r� r r� r� r� �at_time� s - zNDFrame.at_time)r� � include_start� include_endr� c C sT |dkr| j }| �|�}| �|�}t|t�s4td��|j||||d�}| j||d�S )ab Select values between particular times of the day (e.g., 9:00-9:30 AM). By setting ``start_time`` to be later than ``end_time``, you can get the times that are *not* between the two times. Parameters ---------- start_time : datetime.time or str Initial time as a time filter limit. end_time : datetime.time or str End time as a time filter limit. include_start : bool, default True Whether the start time needs to be included in the result. include_end : bool, default True Whether the end time needs to be included in the result. axis : {0 or 'index', 1 or 'columns'}, default 0 Determine range time on index or columns value. Returns ------- Series or DataFrame Data from the original object filtered to the specified dates range. Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- at_time : Select values at a particular time of the day. first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_between_time : Get just the index locations for values between particular times of the day. Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 00:00:00 1 2018-04-10 00:20:00 2 2018-04-11 00:40:00 3 2018-04-12 01:00:00 4 >>> ts.between_time('0:15', '0:45') A 2018-04-10 00:20:00 2 2018-04-11 00:40:00 3 You get the times that are *not* between two times by setting ``start_time`` later than ``end_time``: >>> ts.between_time('0:45', '0:15') A 2018-04-09 00:00:00 1 2018-04-12 01:00:00 4 NzIndex must be DatetimeIndex)r� r� )r� )r( r� r� r� rT r� Zindexer_between_timer� )r� � start_time�end_timer� r� r� r� r r� r� r� � between_time� s F zNDFrame.between_timerq � start_dayz str | TimestampConvertibleTypesz TimedeltaConvertibleTypes | Nonern )�closedr � conventionr� rS �origin�offsetr� c C s8 ddl m} | �|�}| | ||||||||| | | | d� S )a�: Resample time-series data. Convenience method for frequency conversion and resampling of time series. The object must have a datetime-like index (`DatetimeIndex`, `PeriodIndex`, or `TimedeltaIndex`), or the caller must pass the label of a datetime-like series/index to the ``on``/``level`` keyword parameter. Parameters ---------- rule : DateOffset, Timedelta or str The offset string or object representing target conversion. axis : {{0 or 'index', 1 or 'columns'}}, default 0 Which axis to use for up- or down-sampling. For `Series` this will default to 0, i.e. along the rows. Must be `DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`. closed : {{'right', 'left'}}, default None Which side of bin interval is closed. The default is 'left' for all frequency offsets except for 'M', 'A', 'Q', 'BM', 'BA', 'BQ', and 'W' which all have a default of 'right'. label : {{'right', 'left'}}, default None Which bin edge label to label bucket with. The default is 'left' for all frequency offsets except for 'M', 'A', 'Q', 'BM', 'BA', 'BQ', and 'W' which all have a default of 'right'. convention : {{'start', 'end', 's', 'e'}}, default 'start' For `PeriodIndex` only, controls whether to use the start or end of `rule`. kind : {{'timestamp', 'period'}}, optional, default None Pass 'timestamp' to convert the resulting index to a `DateTimeIndex` or 'period' to convert it to a `PeriodIndex`. By default the input representation is retained. loffset : timedelta, default None Adjust the resampled time labels. .. deprecated:: 1.1.0 You should add the loffset to the `df.index` after the resample. See below. base : int, default 0 For frequencies that evenly subdivide 1 day, the "origin" of the aggregated intervals. For example, for '5min' frequency, base could range from 0 through 4. Defaults to 0. .. deprecated:: 1.1.0 The new arguments that you should use are 'offset' or 'origin'. on : str, optional For a DataFrame, column to use instead of index for resampling. Column must be datetime-like. level : str or int, optional For a MultiIndex, level (name or number) to use for resampling. `level` must be datetime-like. origin : {{'epoch', 'start', 'start_day', 'end', 'end_day'}}, Timestamp or str, default 'start_day' The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If a timestamp is not used, these values are also supported: - 'epoch': `origin` is 1970-01-01 - 'start': `origin` is the first value of the timeseries - 'start_day': `origin` is the first day at midnight of the timeseries .. versionadded:: 1.1.0 - 'end': `origin` is the last value of the timeseries - 'end_day': `origin` is the ceiling midnight of the last day .. versionadded:: 1.3.0 offset : Timedelta or str, default is None An offset timedelta added to the origin. .. versionadded:: 1.1.0 Returns ------- pandas.core.Resampler :class:`~pandas.core.Resampler` object. See Also -------- Series.resample : Resample a Series. DataFrame.resample : Resample a DataFrame. groupby : Group {klass} by mapping, function, label, or list of labels. asfreq : Reindex a {klass} with the given frequency without grouping. Notes ----- See the `user guide <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling>`__ for more. To learn more about the offset strings, please see `this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>`__. Examples -------- Start by creating a series with 9 one minute timestamps. >>> index = pd.date_range('1/1/2000', periods=9, freq='T') >>> series = pd.Series(range(9), index=index) >>> series 2000-01-01 00:00:00 0 2000-01-01 00:01:00 1 2000-01-01 00:02:00 2 2000-01-01 00:03:00 3 2000-01-01 00:04:00 4 2000-01-01 00:05:00 5 2000-01-01 00:06:00 6 2000-01-01 00:07:00 7 2000-01-01 00:08:00 8 Freq: T, dtype: int64 Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin. >>> series.resample('3T').sum() 2000-01-01 00:00:00 3 2000-01-01 00:03:00 12 2000-01-01 00:06:00 21 Freq: 3T, dtype: int64 Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed value in the resampled bucket with the label ``2000-01-01 00:03:00`` does not include 3 (if it did, the summed value would be 6, not 3). To include this value close the right side of the bin interval as illustrated in the example below this one. >>> series.resample('3T', label='right').sum() 2000-01-01 00:03:00 3 2000-01-01 00:06:00 12 2000-01-01 00:09:00 21 Freq: 3T, dtype: int64 Downsample the series into 3 minute bins as above, but close the right side of the bin interval. >>> series.resample('3T', label='right', closed='right').sum() 2000-01-01 00:00:00 0 2000-01-01 00:03:00 6 2000-01-01 00:06:00 15 2000-01-01 00:09:00 15 Freq: 3T, dtype: int64 Upsample the series into 30 second bins. >>> series.resample('30S').asfreq()[0:5] # Select first 5 rows 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 1.0 2000-01-01 00:01:30 NaN 2000-01-01 00:02:00 2.0 Freq: 30S, dtype: float64 Upsample the series into 30 second bins and fill the ``NaN`` values using the ``pad`` method. >>> series.resample('30S').pad()[0:5] 2000-01-01 00:00:00 0 2000-01-01 00:00:30 0 2000-01-01 00:01:00 1 2000-01-01 00:01:30 1 2000-01-01 00:02:00 2 Freq: 30S, dtype: int64 Upsample the series into 30 second bins and fill the ``NaN`` values using the ``bfill`` method. >>> series.resample('30S').bfill()[0:5] 2000-01-01 00:00:00 0 2000-01-01 00:00:30 1 2000-01-01 00:01:00 1 2000-01-01 00:01:30 2 2000-01-01 00:02:00 2 Freq: 30S, dtype: int64 Pass a custom function via ``apply`` >>> def custom_resampler(arraylike): ... return np.sum(arraylike) + 5 ... >>> series.resample('3T').apply(custom_resampler) 2000-01-01 00:00:00 8 2000-01-01 00:03:00 17 2000-01-01 00:06:00 26 Freq: 3T, dtype: int64 For a Series with a PeriodIndex, the keyword `convention` can be used to control whether to use the start or end of `rule`. Resample a year by quarter using 'start' `convention`. Values are assigned to the first quarter of the period. >>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01', ... freq='A', ... periods=2)) >>> s 2012 1 2013 2 Freq: A-DEC, dtype: int64 >>> s.resample('Q', convention='start').asfreq() 2012Q1 1.0 2012Q2 NaN 2012Q3 NaN 2012Q4 NaN 2013Q1 2.0 2013Q2 NaN 2013Q3 NaN 2013Q4 NaN Freq: Q-DEC, dtype: float64 Resample quarters by month using 'end' `convention`. Values are assigned to the last month of the period. >>> q = pd.Series([1, 2, 3, 4], index=pd.period_range('2018-01-01', ... freq='Q', ... periods=4)) >>> q 2018Q1 1 2018Q2 2 2018Q3 3 2018Q4 4 Freq: Q-DEC, dtype: int64 >>> q.resample('M', convention='end').asfreq() 2018-03 1.0 2018-04 NaN 2018-05 NaN 2018-06 2.0 2018-07 NaN 2018-08 NaN 2018-09 3.0 2018-10 NaN 2018-11 NaN 2018-12 4.0 Freq: M, dtype: float64 For DataFrame objects, the keyword `on` can be used to specify the column instead of the index for resampling. >>> d = {{'price': [10, 11, 9, 13, 14, 18, 17, 19], ... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}} >>> df = pd.DataFrame(d) >>> df['week_starting'] = pd.date_range('01/01/2018', ... periods=8, ... freq='W') >>> df price volume week_starting 0 10 50 2018-01-07 1 11 60 2018-01-14 2 9 40 2018-01-21 3 13 100 2018-01-28 4 14 50 2018-02-04 5 18 100 2018-02-11 6 17 40 2018-02-18 7 19 50 2018-02-25 >>> df.resample('M', on='week_starting').mean() price volume week_starting 2018-01-31 10.75 62.5 2018-02-28 17.00 60.0 For a DataFrame with MultiIndex, the keyword `level` can be used to specify on which level the resampling needs to take place. >>> days = pd.date_range('1/1/2000', periods=4, freq='D') >>> d2 = {{'price': [10, 11, 9, 13, 14, 18, 17, 19], ... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}} >>> df2 = pd.DataFrame( ... d2, ... index=pd.MultiIndex.from_product( ... [days, ['morning', 'afternoon']] ... ) ... ) >>> df2 price volume 2000-01-01 morning 10 50 afternoon 11 60 2000-01-02 morning 9 40 afternoon 13 100 2000-01-03 morning 14 50 afternoon 18 100 2000-01-04 morning 17 40 afternoon 19 50 >>> df2.resample('D', level=0).sum() price volume 2000-01-01 21 110 2000-01-02 22 140 2000-01-03 32 150 2000-01-04 36 90 If you want to adjust the start of the bins based on a fixed timestamp: >>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00' >>> rng = pd.date_range(start, end, freq='7min') >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng) >>> ts 2000-10-01 23:30:00 0 2000-10-01 23:37:00 3 2000-10-01 23:44:00 6 2000-10-01 23:51:00 9 2000-10-01 23:58:00 12 2000-10-02 00:05:00 15 2000-10-02 00:12:00 18 2000-10-02 00:19:00 21 2000-10-02 00:26:00 24 Freq: 7T, dtype: int64 >>> ts.resample('17min').sum() 2000-10-01 23:14:00 0 2000-10-01 23:31:00 9 2000-10-01 23:48:00 21 2000-10-02 00:05:00 54 2000-10-02 00:22:00 24 Freq: 17T, dtype: int64 >>> ts.resample('17min', origin='epoch').sum() 2000-10-01 23:18:00 0 2000-10-01 23:35:00 18 2000-10-01 23:52:00 27 2000-10-02 00:09:00 39 2000-10-02 00:26:00 24 Freq: 17T, dtype: int64 >>> ts.resample('17min', origin='2000-01-01').sum() 2000-10-01 23:24:00 3 2000-10-01 23:41:00 15 2000-10-01 23:58:00 45 2000-10-02 00:15:00 45 Freq: 17T, dtype: int64 If you want to adjust the start of the bins with an `offset` Timedelta, the two following lines are equivalent: >>> ts.resample('17min', origin='start').sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17T, dtype: int64 >>> ts.resample('17min', offset='23h30min').sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17T, dtype: int64 If you want to take the largest Timestamp as the end of the bins: >>> ts.resample('17min', origin='end').sum() 2000-10-01 23:35:00 0 2000-10-01 23:52:00 18 2000-10-02 00:09:00 27 2000-10-02 00:26:00 63 Freq: 17T, dtype: int64 In contrast with the `start_day`, you can use `end_day` to take the ceiling midnight of the largest Timestamp as the end of the bins and drop the bins not containing data: >>> ts.resample('17min', origin='end_day').sum() 2000-10-01 23:38:00 3 2000-10-01 23:55:00 15 2000-10-02 00:12:00 45 2000-10-02 00:29:00 45 Freq: 17T, dtype: int64 To replace the use of the deprecated `base` argument, you can now use `offset`, in this example it is equivalent to have `base=2`: >>> ts.resample('17min', offset='2min').sum() 2000-10-01 23:16:00 0 2000-10-01 23:33:00 9 2000-10-01 23:50:00 36 2000-10-02 00:07:00 39 2000-10-02 00:24:00 24 Freq: 17T, dtype: int64 To replace the use of the deprecated `loffset` argument: >>> from pandas.tseries.frequencies import to_offset >>> loffset = '19min' >>> ts_out = ts.resample('17min').sum() >>> ts_out.index = ts_out.index + to_offset(loffset) >>> ts_out 2000-10-01 23:33:00 0 2000-10-01 23:50:00 9 2000-10-02 00:07:00 21 2000-10-02 00:24:00 54 2000-10-02 00:41:00 24 Freq: 17T, dtype: int64 r )� get_resampler) rj r r� r� r� �loffsetr� rS r� r� r� r� )r� r� r� )r� �ruler� r� r r� r� r� rS �onr� r� r� r� r� r� r� �resample s& zNDFrame.resamplec C s� t | jt�std��t| j�dkr&| S t|�}t |t�sb|�| jd �rb| jd |j | }}n| jd | }}t |t�r�|| jkr�| jj |dd�}| j d|� S | j d|� S )a� Select initial periods of time series data based on a date offset. When having a DataFrame with dates as index, this function can select the first few rows based on a date offset. Parameters ---------- offset : str, DateOffset or dateutil.relativedelta The offset length of the data that will be selected. For instance, '1M' will display all the rows having their index within the first month. Returns ------- Series or DataFrame A subset of the caller. Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- last : Select final periods of time series based on a date offset. at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day. Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='2D') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 1 2018-04-11 2 2018-04-13 3 2018-04-15 4 Get the rows for the first 3 days: >>> ts.first('3D') A 2018-04-09 1 2018-04-11 2 Notice the data for 3 first calendar days were returned, not the first 3 days observed in the dataset, and therefore data for 2018-04-13 was not returned. z+'first' only supports a DatetimeIndex indexr �left)rl N) r� r� rT r� r� r r Z is_on_offsetrS rn r r� )r� r� �end_date�endr� r� r� �first� s 4 z NDFrame.firstc C sZ t | jt�std��t| j�dkr&| S t|�}| jd | }| jj|dd�}| j|d� S )a� Select final periods of time series data based on a date offset. For a DataFrame with a sorted DatetimeIndex, this function selects the last few rows based on a date offset. Parameters ---------- offset : str, DateOffset, dateutil.relativedelta The offset length of the data that will be selected. For instance, '3D' will display all the rows having their index within the last 3 days. Returns ------- Series or DataFrame A subset of the caller. Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- first : Select initial periods of time series based on a date offset. at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day. Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='2D') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 1 2018-04-11 2 2018-04-13 3 2018-04-15 4 Get the rows for the last 3 days: >>> ts.last('3D') A 2018-04-13 3 2018-04-15 4 Notice the data for 3 last calendar days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned. z*'last' only supports a DatetimeIndex indexr r rk )rl N)r� r� rT r� r� r rn r )r� r� Z start_daterq r� r� r� r s 4 z NDFrame.last�average�keep)r� r � numeric_only� na_optionr �pctr� c sx �� ����dkr d}t|��� �����fdd�}|dkr^y|��S tk r\ d}Y nX |rl��� } n�} || �S )a� Compute numerical data ranks (1 through n) along axis. By default, equal values are assigned a rank that is the average of the ranks of those values. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 Index to direct ranking. method : {'average', 'min', 'max', 'first', 'dense'}, default 'average' How to rank the group of records that have the same value (i.e. ties): * average: average rank of the group * min: lowest rank in the group * max: highest rank in the group * first: ranks assigned in order they appear in the array * dense: like 'min', but rank always increases by 1 between groups. numeric_only : bool, optional For DataFrame objects, rank only numeric columns if set to True. na_option : {'keep', 'top', 'bottom'}, default 'keep' How to rank NaN values: * keep: assign NaN rank to NaN values * top: assign lowest rank to NaN values * bottom: assign highest rank to NaN values ascending : bool, default True Whether or not the elements should be ranked in ascending order. pct : bool, default False Whether or not to display the returned rankings in percentile form. Returns ------- same type as caller Return a Series or DataFrame with data ranks as values. See Also -------- core.groupby.GroupBy.rank : Rank of values within each group. Examples -------- >>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog', ... 'spider', 'snake'], ... 'Number_legs': [4, 2, 4, 8, np.nan]}) >>> df Animal Number_legs 0 cat 4.0 1 penguin 2.0 2 dog 4.0 3 spider 8.0 4 snake NaN The following example shows how the method behaves with the above parameters: * default_rank: this is the default behaviour obtained without using any parameter. * max_rank: setting ``method = 'max'`` the records that have the same values are ranked using the highest rank (e.g.: since 'cat' and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.) * NA_bottom: choosing ``na_option = 'bottom'``, if there are records with NaN values they are placed at the bottom of the ranking. * pct_rank: when setting ``pct = True``, the ranking is expressed as percentile rank. >>> df['default_rank'] = df['Number_legs'].rank() >>> df['max_rank'] = df['Number_legs'].rank(method='max') >>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom') >>> df['pct_rank'] = df['Number_legs'].rank(pct=True) >>> df Animal Number_legs default_rank max_rank NA_bottom pct_rank 0 cat 4.0 2.5 3.0 2.5 0.625 1 penguin 2.0 1.0 1.0 1.0 0.250 2 dog 4.0 2.5 3.0 2.5 0.625 3 spider 8.0 4.0 4.0 4.0 1.000 4 snake NaN NaN NaN 5.0 NaN > �bottom�topr� z3na_option must be one of 'keep', 'top', or 'bottom'c s8 t j| j��� ��d�}�j|f| �� �}|j�dd�S )N)r� r r r� r� �rank)r )�algosr� r� r� r� r� )r� ZranksZ ranks_obj)r r� r r� r� r� r� r� �ranker� s z NDFrame.rank.<locals>.rankerNT)r� r� r� rD ) r� r� r r� r� r r� rG r� r� r� )r r� r r� r� r� r� r� X s [ z NDFrame.rank�comparer� )� align_axis� keep_shape� keep_equalc C s� ddl m} t| �t|�k rNt| �jt|�j }}td|� d|� d|� d���| |k| �� |�� @ B }ddg} |s�| �|�} |�|�}|s�t| t�r�|� � } |j d d �} | j | | f } |j | | f }n| | } || }|d kr�d } n | � |�} || |g| | d �} | | j k�r| S | � | �}t�|j�}t�t|��|_ttd |j��dg }t| t��rj| j|| d �} n | �|�} || | j | d �_t�| j| ��d | j| d g�j�� }| j|| d �} | S )Nr )r` zcan only compare 'z' (not 'z ') with 'rE r� r& r� )r� )r� r� )r� r! r^ ) �pandas.core.reshape.concatr` r) r� r� rG rY r� rC rA r� r� r� r� r� �arrayr� rh r� r� r ZnlevelsZreorder_levelsr� �reshaper" �flattenr� )r� r& r� r� r� r` Zcls_selfZ cls_otherrw r! ZcmaskZrmaskr� �diffr Zax_names�orderr� r� r� r� r� � sF *zNDFrame.compare�outerc s, t �|�}| dkr��j� jkr�t�t�rj�j} | �fdd�� jD �f� �� �} | j� |||||||| d� S t� t�r�� j} | � fdd��jD �f��� �} �j| |||||||| d� S |dk rȈ� |�}t� t �r�j� |||||||| d� S t� t��r�j � |||||||| d� S t dt � �� ���dS )a� Align two objects on their axes with the specified join method. Join method is specified for each axis Index. Parameters ---------- other : DataFrame or Series join : {{'outer', 'inner', 'left', 'right'}}, default 'outer' axis : allowed axis of the other object, default None Align on index (0), columns (1), or both (None). level : int or level name, default None Broadcast across a level, matching Index values on the passed MultiIndex level. copy : bool, default True Always returns new objects. If copy=False and no reindexing is required then original objects are returned. fill_value : scalar, default np.NaN Value to use for missing values. Defaults to NaN, but can be any "compatible" value. method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None Method to use for filling holes in reindexed Series: - pad / ffill: propagate last valid observation forward to next valid. - backfill / bfill: use NEXT valid observation to fill gap. limit : int, default None If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None. fill_axis : {axes_single_arg}, default 0 Filling axis, method and limit. broadcast_axis : {axes_single_arg}, default None Broadcast values along this axis, if aligning two objects of different dimensions. 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Used in __setitem__. rs Nrk r� F)rg r� r� r� z,Array conditional must be same shape as selfz5Boolean array expected for the condition, not {dtype})r� )r� r� r� )rg r� r� r r� r )r r� )r� r z.cannot align with a higher dimensional NDFrameT)Z extract_numpyz4other must be the same shape as self when an ndarrayr� )rw �newr� )r& �condr� r ),r. r� r �apply_if_callabler� ru r� �hasattrr� Z asanyarrayr� r� r� r� r8 r* rU rC r4 rg r� rA r� r� r� r� r� r( r* r* r� Z broadcast_tor� rV rP r� rM r� rC rv Zputmaskr rY r� )r� r� r& rs r� r� r r� r rG �dtr� r6 r r� r� r� �_where(" s� zNDFrame._where�True�FalserY rw )rr r� Zcond_revr� Z name_otherc C s< t �|| �}|tjk r&tjdtdd� | j||||||d�S )a� Replace values where the condition is {cond_rev}. Parameters ---------- cond : bool {klass}, array-like, or callable Where `cond` is {cond}, keep the original value. Where {cond_rev}, replace with corresponding value from `other`. If `cond` is callable, it is computed on the {klass} and should return boolean {klass} or array. The callable must not change input {klass} (though pandas doesn't check it). other : scalar, {klass}, or callable Entries where `cond` is {cond_rev} are replaced with corresponding value from `other`. If other is callable, it is computed on the {klass} and should return scalar or {klass}. The callable must not change input {klass} (though pandas doesn't check it). inplace : bool, default False Whether to perform the operation in place on the data. axis : int, default None Alignment axis if needed. level : int, default None Alignment level if needed. errors : str, {{'raise', 'ignore'}}, default 'raise' Note that currently this parameter won't affect the results and will always coerce to a suitable dtype. - 'raise' : allow exceptions to be raised. - 'ignore' : suppress exceptions. On error return original object. try_cast : bool, default None Try to cast the result back to the input type (if possible). .. deprecated:: 1.3.0 Manually cast back if necessary. Returns ------- Same type as caller or None if ``inplace=True``. See Also -------- :func:`DataFrame.{name_other}` : Return an object of same shape as self. Notes ----- The {name} method is an application of the if-then idiom. For each element in the calling DataFrame, if ``cond`` is ``{cond}`` the element is used; otherwise the corresponding element from the DataFrame ``other`` is used. The signature for :func:`DataFrame.where` differs from :func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to ``np.where(m, df1, df2)``. For further details and examples see the ``{name}`` documentation in :ref:`indexing <indexing.where_mask>`. Examples -------- >>> s = pd.Series(range(5)) >>> s.where(s > 0) 0 NaN 1 1.0 2 2.0 3 3.0 4 4.0 dtype: float64 >>> s.mask(s > 0) 0 0.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 >>> s.where(s > 1, 10) 0 10 1 10 2 2 3 3 4 4 dtype: int64 >>> s.mask(s > 1, 10) 0 0 1 1 2 10 3 10 4 10 dtype: int64 >>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B']) >>> df A B 0 0 1 1 2 3 2 4 5 3 6 7 4 8 9 >>> m = df % 3 == 0 >>> df.where(m, -df) A B 0 0 -1 1 -2 3 2 -4 -5 3 6 -7 4 -8 9 >>> df.where(m, -df) == np.where(m, df, -df) A B 0 True True 1 True True 2 True True 3 True True 4 True True >>> df.where(m, -df) == df.mask(~m, -df) A B 0 True True 1 True True 2 True True 3 True True 4 True True zFtry_cast keyword is deprecated and will be removed in a future versionr� )r� )r )r r� r r r� r� r� r� )r� r� r& rs r� r� r �try_castr� r� r� rY �" s z NDFrame.wherec C s\ t |d�}t�|| �}|tjk r0tjdtdd� t|d�sDt � |�}| j | |||||d�S )Nrs zFtry_cast keyword is deprecated and will be removed in a future versionr� )r� r3 )r& rs r� r� r ) r. r r� r r r� r� r� r� r� r� rY )r� r� r& rs r� r� r r� r� r� r� rw J# s z NDFrame.maskc C s |dkr| � � S |dkrH| �|�}| jj|||d�}| �|�j| dd�S | �|�}|dkr�t|dd�}|dkrzt|dd�}|dkr�d }t|��nt |t �r�t |�}t |t �r�t |j �}||kr�|dk s�t�td |j� d |j� ���|�|�} n |�||�} | j| |d �} | j| dd�S ) a� Shift index by desired number of periods with an optional time `freq`. When `freq` is not passed, shift the index without realigning the data. If `freq` is passed (in this case, the index must be date or datetime, or it will raise a `NotImplementedError`), the index will be increased using the periods and the `freq`. `freq` can be inferred when specified as "infer" as long as either freq or inferred_freq attribute is set in the index. Parameters ---------- periods : int Number of periods to shift. Can be positive or negative. freq : DateOffset, tseries.offsets, timedelta, or str, optional Offset to use from the tseries module or time rule (e.g. 'EOM'). If `freq` is specified then the index values are shifted but the data is not realigned. That is, use `freq` if you would like to extend the index when shifting and preserve the original data. If `freq` is specified as "infer" then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown. axis : {{0 or 'index', 1 or 'columns', None}}, default None Shift direction. fill_value : object, optional The scalar value to use for newly introduced missing values. the default depends on the dtype of `self`. For numeric data, ``np.nan`` is used. For datetime, timedelta, or period data, etc. :attr:`NaT` is used. For extension dtypes, ``self.dtype.na_value`` is used. .. versionchanged:: 1.1.0 Returns ------- {klass} Copy of input object, shifted. See Also -------- Index.shift : Shift values of Index. DatetimeIndex.shift : Shift values of DatetimeIndex. PeriodIndex.shift : Shift values of PeriodIndex. tshift : Shift the time index, using the index's frequency if available. Examples -------- >>> df = pd.DataFrame({{"Col1": [10, 20, 15, 30, 45], ... "Col2": [13, 23, 18, 33, 48], ... "Col3": [17, 27, 22, 37, 52]}}, ... index=pd.date_range("2020-01-01", "2020-01-05")) >>> df Col1 Col2 Col3 2020-01-01 10 13 17 2020-01-02 20 23 27 2020-01-03 15 18 22 2020-01-04 30 33 37 2020-01-05 45 48 52 >>> df.shift(periods=3) Col1 Col2 Col3 2020-01-01 NaN NaN NaN 2020-01-02 NaN NaN NaN 2020-01-03 NaN NaN NaN 2020-01-04 10.0 13.0 17.0 2020-01-05 20.0 23.0 27.0 >>> df.shift(periods=1, axis="columns") Col1 Col2 Col3 2020-01-01 NaN 10 13 2020-01-02 NaN 20 23 2020-01-03 NaN 15 18 2020-01-04 NaN 30 33 2020-01-05 NaN 45 48 >>> df.shift(periods=3, fill_value=0) Col1 Col2 Col3 2020-01-01 0 0 0 2020-01-02 0 0 0 2020-01-03 0 0 0 2020-01-04 10 13 17 2020-01-05 20 23 27 >>> df.shift(periods=3, freq="D") Col1 Col2 Col3 2020-01-04 10 13 17 2020-01-05 20 23 27 2020-01-06 15 18 22 2020-01-07 30 33 37 2020-01-08 45 48 52 >>> df.shift(periods=3, freq="infer") Col1 Col2 Col3 2020-01-04 10 13 17 2020-01-05 20 23 27 2020-01-06 15 18 22 2020-01-07 30 33 37 2020-01-08 45 48 52 r N)�periodsr� r �shift)r r� rj Z inferred_freqz6Freq was not set in the index hence cannot be inferredz Given freq z! does not match PeriodIndex freq )r� )r� r� rv r� r� r� r� r� r� r� r� r rW rj r� Z rule_coder� ) r� r� rj r� r r6 r� rG Z orig_freqr� r r� r� r� r� v# s6 h z NDFrame.shift)r� r� r� c C s� d}t j|tdd� |dkr | S |dkr@td| �}t|d�}nt| d�}td|�}| j||d�}| �|�| }|j||dd� |j| d d �S ) a� Equivalent to `shift` without copying data. The shifted data will not include the dropped periods and the shifted axis will be smaller than the original. .. deprecated:: 1.2.0 slice_shift is deprecated, use DataFrame/Series.shift instead. Parameters ---------- periods : int Number of periods to move, can be positive or negative. Returns ------- shifted : same type as caller Notes ----- While the `slice_shift` is faster than `shift`, you may pay for it later during alignment. zzThe 'slice_shift' method is deprecated and will be removed in a future version. You can use DataFrame/Series.shift insteadr^ )r� r N)r� T)r� rs � slice_shift)r )r� r� r� r r� r� r� r� )r� r� r� rG ZvslicerZislicerZnew_objZ shifted_axisr� r� r� r� $ s zNDFrame.slice_shift)r� r� r� r� c C s* t jdtdd� |dkr d}| �|||�S )uW Shift the time index, using the index's frequency if available. .. deprecated:: 1.1.0 Use `shift` instead. Parameters ---------- periods : int Number of periods to move, can be positive or negative. freq : DateOffset, timedelta, or str, default None Increment to use from the tseries module or time rule expressed as a string (e.g. 'EOM'). axis : {0 or ‘index’, 1 or ‘columns’, None}, default 0 Corresponds to the axis that contains the Index. Returns ------- shifted : Series/DataFrame Notes ----- If freq is not specified then tries to use the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown zWtshift is deprecated and will be removed in a future version. Please use shift instead.r^ )r� Nr� )r� r� r� r� )r� r� rj r� r� r� r� r� 9$ s zNDFrame.tshift)r� r� r� c C s |dkr| j }| �|�}| �|�}|js6|js6td��|jrXddlm} ||�}||�}|dk r�|dk r�||kr�td|� d|� ���t |�dkr�|jr�|| }}t dd�g| j }t ||�||< | j t |� }t|t�r�t|| �|�|�||�� |�r |�� }|S )a Truncate a Series or DataFrame before and after some index value. This is a useful shorthand for boolean indexing based on index values above or below certain thresholds. Parameters ---------- before : date, str, int Truncate all rows before this index value. after : date, str, int Truncate all rows after this index value. axis : {0 or 'index', 1 or 'columns'}, optional Axis to truncate. Truncates the index (rows) by default. copy : bool, default is True, Return a copy of the truncated section. Returns ------- type of caller The truncated Series or DataFrame. See Also -------- DataFrame.loc : Select a subset of a DataFrame by label. DataFrame.iloc : Select a subset of a DataFrame by position. Notes ----- If the index being truncated contains only datetime values, `before` and `after` may be specified as strings instead of Timestamps. Examples -------- >>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'], ... 'B': ['f', 'g', 'h', 'i', 'j'], ... 'C': ['k', 'l', 'm', 'n', 'o']}, ... index=[1, 2, 3, 4, 5]) >>> df A B C 1 a f k 2 b g l 3 c h m 4 d i n 5 e j o >>> df.truncate(before=2, after=4) A B C 2 b g l 3 c h m 4 d i n The columns of a DataFrame can be truncated. >>> df.truncate(before="A", after="B", axis="columns") A B 1 a f 2 b g 3 c h 4 d i 5 e j For Series, only rows can be truncated. >>> df['A'].truncate(before=2, after=4) 2 b 3 c 4 d Name: A, dtype: object The index values in ``truncate`` can be datetimes or string dates. >>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s') >>> df = pd.DataFrame(index=dates, data={'A': 1}) >>> df.tail() A 2016-01-31 23:59:56 1 2016-01-31 23:59:57 1 2016-01-31 23:59:58 1 2016-01-31 23:59:59 1 2016-02-01 00:00:00 1 >>> df.truncate(before=pd.Timestamp('2016-01-05'), ... after=pd.Timestamp('2016-01-10')).tail() A 2016-01-09 23:59:56 1 2016-01-09 23:59:57 1 2016-01-09 23:59:58 1 2016-01-09 23:59:59 1 2016-01-10 00:00:00 1 Because the index is a DatetimeIndex containing only dates, we can specify `before` and `after` as strings. They will be coerced to Timestamps before truncation. >>> df.truncate('2016-01-05', '2016-01-10').tail() A 2016-01-09 23:59:56 1 2016-01-09 23:59:57 1 2016-01-09 23:59:58 1 2016-01-09 23:59:59 1 2016-01-10 00:00:00 1 Note that ``truncate`` assumes a 0 value for any unspecified time component (midnight). This differs from partial string slicing, which returns any partially matching dates. >>> df.loc['2016-01-05':'2016-01-10', :].tail() A 2016-01-10 23:59:55 1 2016-01-10 23:59:56 1 2016-01-10 23:59:57 1 2016-01-10 23:59:58 1 2016-01-10 23:59:59 1 Nz truncate requires a sorted indexr )� to_datetimez Truncate: z must be after r� )r( r� r� Zis_monotonic_increasingZis_monotonic_decreasingr� Z _is_all_datesZpandas.core.tools.datetimesr� r� r r� r� r� r� rV r� r� �truncater� ) r� �before�afterr� r� r r� r� r r� r� r� r� e$ s, x zNDFrame.truncatec s� �� � �� ��� �}� �fdd�}t|t�rV|�|�}||j| |�}|j||d�}n*|dd|jfkrvtd|� d���|||�}�j |d�}|j |� d d �}|j �d d �S ) aB Convert tz-aware axis to target time zone. Parameters ---------- tz : str or tzinfo object axis : the axis to convert level : int, str, default None If axis is a MultiIndex, convert a specific level. Otherwise must be None. copy : bool, default True Also make a copy of the underlying data. Returns ------- {klass} Object with time zone converted axis. Raises ------ TypeError If the axis is tz-naive. c sL t | d�s>t| �dkr0��� �}t|� d���qHtg |d�} n | �|�} | S )N� tz_convertr z, is not a valid DatetimeIndex or PeriodIndex)�tz)r� r� r� r� rT r� )r r� �ax_name)r� r� r� r� � _tz_convert % s z'NDFrame.tz_convert.<locals>._tz_convert)r� Nr z The level z is not valid)r� F)r� rs r� )r ) r� r� r� rV r �levels� set_levelsr� r� r� r� r� ) r� r� r� r� r� r r� � new_levelr r� )r� r� r� r� % s zNDFrame.tz_convert)r� r� � nonexistentr� c s� d}||kr t |t�s td����� �� ��� �}� �fdd�} t |t�rx|�|�}| |j| |||�} |j| |d�}n.|dd|j fkr�td|� d ���| ||||�}�j |d �} | j |� d d �} | j �d d�S )a Localize tz-naive index of a Series or DataFrame to target time zone. This operation localizes the Index. To localize the values in a timezone-naive Series, use :meth:`Series.dt.tz_localize`. Parameters ---------- tz : str or tzinfo axis : the axis to localize level : int, str, default None If axis ia a MultiIndex, localize a specific level. Otherwise must be None. copy : bool, default True Also make a copy of the underlying data. ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the `ambiguous` parameter dictates how ambiguous times should be handled. - 'infer' will attempt to infer fall dst-transition hours based on order - bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times) - 'NaT' will return NaT where there are ambiguous times - 'raise' will raise an AmbiguousTimeError if there are ambiguous times. nonexistent : str, default 'raise' A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. Valid values are: - 'shift_forward' will shift the nonexistent time forward to the closest existing time - 'shift_backward' will shift the nonexistent time backward to the closest existing time - 'NaT' will return NaT where there are nonexistent times - timedelta objects will shift nonexistent times by the timedelta - 'raise' will raise an NonExistentTimeError if there are nonexistent times. Returns ------- Series or DataFrame Same type as the input. Raises ------ TypeError If the TimeSeries is tz-aware and tz is not None. Examples -------- Localize local times: >>> s = pd.Series([1], ... index=pd.DatetimeIndex(['2018-09-15 01:30:00'])) >>> s.tz_localize('CET') 2018-09-15 01:30:00+02:00 1 dtype: int64 Be careful with DST changes. When there is sequential data, pandas can infer the DST time: >>> s = pd.Series(range(7), ... index=pd.DatetimeIndex(['2018-10-28 01:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 03:00:00', ... '2018-10-28 03:30:00'])) >>> s.tz_localize('CET', ambiguous='infer') 2018-10-28 01:30:00+02:00 0 2018-10-28 02:00:00+02:00 1 2018-10-28 02:30:00+02:00 2 2018-10-28 02:00:00+01:00 3 2018-10-28 02:30:00+01:00 4 2018-10-28 03:00:00+01:00 5 2018-10-28 03:30:00+01:00 6 dtype: int64 In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly >>> s = pd.Series(range(3), ... index=pd.DatetimeIndex(['2018-10-28 01:20:00', ... '2018-10-28 02:36:00', ... '2018-10-28 03:46:00'])) >>> s.tz_localize('CET', ambiguous=np.array([True, True, False])) 2018-10-28 01:20:00+02:00 0 2018-10-28 02:36:00+02:00 1 2018-10-28 03:46:00+01:00 2 dtype: int64 If the DST transition causes nonexistent times, you can shift these dates forward or backward with a timedelta object or `'shift_forward'` or `'shift_backward'`. >>> s = pd.Series(range(2), ... index=pd.DatetimeIndex(['2015-03-29 02:30:00', ... '2015-03-29 03:30:00'])) >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward') 2015-03-29 03:00:00+02:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward') 2015-03-29 01:59:59.999999999+01:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 >>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H')) 2015-03-29 03:30:00+02:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 )r ZNaTZ shift_forwardZshift_backwardzoThe nonexistent argument must be one of 'raise', 'NaT', 'shift_forward', 'shift_backward' or a timedelta objectc sR t | d�s>t| �dkr0��� �}t|� d���qNtg |d�} n| j|||d�} | S )N� tz_localizer z, is not a valid DatetimeIndex or PeriodIndex)r� )� ambiguousr� )r� r� r� r� rT r� )r r� r� r� r� )r� r� r� r� � _tz_localize�% s z)NDFrame.tz_localize.<locals>._tz_localize)r� Nr z The level z is not valid)r� F)r� rs r� )r ) r� r r� r� r� rV r r� r� r� r� r� r� ) r� r� r� r� r� r� r� Znonexistent_optionsr r� r� r r� )r� r� r� r� ;% s& zNDFrame.tz_localizec C s t �| �S )a� Return a Series/DataFrame with absolute numeric value of each element. This function only applies to elements that are all numeric. Returns ------- abs Series/DataFrame containing the absolute value of each element. See Also -------- numpy.absolute : Calculate the absolute value element-wise. Notes ----- For ``complex`` inputs, ``1.2 + 1j``, the absolute value is :math:`\sqrt{ a^2 + b^2 }`. Examples -------- Absolute numeric values in a Series. >>> s = pd.Series([-1.10, 2, -3.33, 4]) >>> s.abs() 0 1.10 1 2.00 2 3.33 3 4.00 dtype: float64 Absolute numeric values in a Series with complex numbers. >>> s = pd.Series([1.2 + 1j]) >>> s.abs() 0 1.56205 dtype: float64 Absolute numeric values in a Series with a Timedelta element. >>> s = pd.Series([pd.Timedelta('1 days')]) >>> s.abs() 0 1 days dtype: timedelta64[ns] Select rows with data closest to certain value using argsort (from `StackOverflow <https://stackoverflow.com/a/17758115>`__). >>> df = pd.DataFrame({ ... 'a': [4, 5, 6, 7], ... 'b': [10, 20, 30, 40], ... 'c': [100, 50, -30, -50] ... }) >>> df a b c 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50 >>> df.loc[(df.c - 43).abs().argsort()] a b c 1 5 20 50 0 4 10 100 2 6 30 -30 3 7 40 -50 )r� r: )r� r� r� r� r: �% s Fz NDFrame.absc C s t | ||||d�S )a�" Generate descriptive statistics. Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding ``NaN`` values. Analyzes both numeric and object series, as well as ``DataFrame`` column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail. Parameters ---------- percentiles : list-like of numbers, optional The percentiles to include in the output. All should fall between 0 and 1. The default is ``[.25, .5, .75]``, which returns the 25th, 50th, and 75th percentiles. include : 'all', list-like of dtypes or None (default), optional A white list of data types to include in the result. Ignored for ``Series``. Here are the options: - 'all' : All columns of the input will be included in the output. - A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit ``numpy.number``. To limit it instead to object columns submit the ``numpy.object`` data type. Strings can also be used in the style of ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To select pandas categorical columns, use ``'category'`` - None (default) : The result will include all numeric columns. exclude : list-like of dtypes or None (default), optional, A black list of data types to omit from the result. Ignored for ``Series``. Here are the options: - A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit ``numpy.number``. To exclude object columns submit the data type ``numpy.object``. Strings can also be used in the style of ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To exclude pandas categorical columns, use ``'category'`` - None (default) : The result will exclude nothing. datetime_is_numeric : bool, default False Whether to treat datetime dtypes as numeric. This affects statistics calculated for the column. For DataFrame input, this also controls whether datetime columns are included by default. .. versionadded:: 1.1.0 Returns ------- Series or DataFrame Summary statistics of the Series or Dataframe provided. See Also -------- DataFrame.count: Count number of non-NA/null observations. DataFrame.max: Maximum of the values in the object. DataFrame.min: Minimum of the values in the object. DataFrame.mean: Mean of the values. DataFrame.std: Standard deviation of the observations. DataFrame.select_dtypes: Subset of a DataFrame including/excluding columns based on their dtype. Notes ----- For numeric data, the result's index will include ``count``, ``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and upper percentiles. By default the lower percentile is ``25`` and the upper percentile is ``75``. The ``50`` percentile is the same as the median. For object data (e.g. strings or timestamps), the result's index will include ``count``, ``unique``, ``top``, and ``freq``. The ``top`` is the most common value. The ``freq`` is the most common value's frequency. Timestamps also include the ``first`` and ``last`` items. If multiple object values have the highest count, then the ``count`` and ``top`` results will be arbitrarily chosen from among those with the highest count. For mixed data types provided via a ``DataFrame``, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If ``include='all'`` is provided as an option, the result will include a union of attributes of each type. The `include` and `exclude` parameters can be used to limit which columns in a ``DataFrame`` are analyzed for the output. The parameters are ignored when analyzing a ``Series``. Examples -------- Describing a numeric ``Series``. >>> s = pd.Series([1, 2, 3]) >>> s.describe() count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 dtype: float64 Describing a categorical ``Series``. >>> s = pd.Series(['a', 'a', 'b', 'c']) >>> s.describe() count 4 unique 3 top a freq 2 dtype: object Describing a timestamp ``Series``. >>> s = pd.Series([ ... np.datetime64("2000-01-01"), ... np.datetime64("2010-01-01"), ... np.datetime64("2010-01-01") ... ]) >>> s.describe(datetime_is_numeric=True) count 3 mean 2006-09-01 08:00:00 min 2000-01-01 00:00:00 25% 2004-12-31 12:00:00 50% 2010-01-01 00:00:00 75% 2010-01-01 00:00:00 max 2010-01-01 00:00:00 dtype: object Describing a ``DataFrame``. By default only numeric fields are returned. >>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']), ... 'numeric': [1, 2, 3], ... 'object': ['a', 'b', 'c'] ... }) >>> df.describe() numeric count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Describing all columns of a ``DataFrame`` regardless of data type. >>> df.describe(include='all') # doctest: +SKIP categorical numeric object count 3 3.0 3 unique 3 NaN 3 top f NaN a freq 1 NaN 1 mean NaN 2.0 NaN std NaN 1.0 NaN min NaN 1.0 NaN 25% NaN 1.5 NaN 50% NaN 2.0 NaN 75% NaN 2.5 NaN max NaN 3.0 NaN Describing a column from a ``DataFrame`` by accessing it as an attribute. >>> df.numeric.describe() count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Name: numeric, dtype: float64 Including only numeric columns in a ``DataFrame`` description. >>> df.describe(include=[np.number]) numeric count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Including only string columns in a ``DataFrame`` description. >>> df.describe(include=[object]) # doctest: +SKIP object count 3 unique 3 top a freq 1 Including only categorical columns from a ``DataFrame`` description. >>> df.describe(include=['category']) categorical count 3 unique 3 top d freq 1 Excluding numeric columns from a ``DataFrame`` description. >>> df.describe(exclude=[np.number]) # doctest: +SKIP categorical object count 3 3 unique 3 3 top f a freq 1 1 Excluding object columns from a ``DataFrame`` description. >>> df.describe(exclude=[object]) # doctest: +SKIP categorical numeric count 3 3.0 unique 3 NaN top f NaN freq 1 NaN mean NaN 2.0 std NaN 1.0 min NaN 1.0 25% NaN 1.5 50% NaN 2.0 75% NaN 2.5 max NaN 3.0 )r� �include�exclude�datetime_is_numeric� percentiles)rQ )r� r� r� r� r� r� r� r� �describe/& s zzNDFrame.describec K s� | � |�d| j��}|dkr"| }n | j|||d�}|dk s>t�|}|jf |||d�|��} || d } |dk r�| j| j�� } | � |�} | S )a Percentage change between the current and a prior element. Computes the percentage change from the immediately previous row by default. This is useful in comparing the percentage of change in a time series of elements. Parameters ---------- periods : int, default 1 Periods to shift for forming percent change. fill_method : str, default 'pad' How to handle NAs before computing percent changes. limit : int, default None The number of consecutive NAs to fill before stopping. freq : DateOffset, timedelta, or str, optional Increment to use from time series API (e.g. 'M' or BDay()). **kwargs Additional keyword arguments are passed into `DataFrame.shift` or `Series.shift`. Returns ------- chg : Series or DataFrame The same type as the calling object. See Also -------- Series.diff : Compute the difference of two elements in a Series. DataFrame.diff : Compute the difference of two elements in a DataFrame. Series.shift : Shift the index by some number of periods. DataFrame.shift : Shift the index by some number of periods. Examples -------- **Series** >>> s = pd.Series([90, 91, 85]) >>> s 0 90 1 91 2 85 dtype: int64 >>> s.pct_change() 0 NaN 1 0.011111 2 -0.065934 dtype: float64 >>> s.pct_change(periods=2) 0 NaN 1 NaN 2 -0.055556 dtype: float64 See the percentage change in a Series where filling NAs with last valid observation forward to next valid. >>> s = pd.Series([90, 91, None, 85]) >>> s 0 90.0 1 91.0 2 NaN 3 85.0 dtype: float64 >>> s.pct_change(fill_method='ffill') 0 NaN 1 0.011111 2 0.000000 3 -0.065934 dtype: float64 **DataFrame** Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01. >>> df = pd.DataFrame({ ... 'FR': [4.0405, 4.0963, 4.3149], ... 'GR': [1.7246, 1.7482, 1.8519], ... 'IT': [804.74, 810.01, 860.13]}, ... index=['1980-01-01', '1980-02-01', '1980-03-01']) >>> df FR GR IT 1980-01-01 4.0405 1.7246 804.74 1980-02-01 4.0963 1.7482 810.01 1980-03-01 4.3149 1.8519 860.13 >>> df.pct_change() FR GR IT 1980-01-01 NaN NaN NaN 1980-02-01 0.013810 0.013684 0.006549 1980-03-01 0.053365 0.059318 0.061876 Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns. >>> df = pd.DataFrame({ ... '2016': [1769950, 30586265], ... '2015': [1500923, 40912316], ... '2014': [1371819, 41403351]}, ... index=['GOOG', 'APPL']) >>> df 2016 2015 2014 GOOG 1769950 1500923 1371819 APPL 30586265 40912316 41403351 >>> df.pct_change(axis='columns', periods=-1) 2016 2015 2014 GOOG 0.179241 0.094112 NaN APPL -0.252395 -0.011860 NaN r� N)r r� r� )r� rj r� r� ) r� r� r� r* r� r� r� r� Z duplicatedr� ) r� r� Z fill_methodr� rj r� r� r� r� Zshiftedr. r� r� r� � pct_change0' s { zNDFrame.pct_changec sr � d krt d��| j|� dd�}t||�r>�r>t||�f ��S | �� �� tt| �|��� ���fdd�}|�|�S )Nz.Must specify 'axis' when aggregating by level.F)r� r� �sortc s �| f� �d����S )N)r� �skipnar� )r )r� r� r r� r� r� r= �' r> z'NDFrame._agg_by_level.<locals>.<lambda>)r� �groupbyr� r� r� r) Z aggregate)r� r� r� r� r� r� �groupedZapplyfr� )r� r� r r� r� � _agg_by_level�' s zNDFrame._agg_by_levelc K s� t jd||d� |d k rJtjdtdd� |d k r8td��| j||||d�S | jdkr�|d kr�| j||fd ||d �|��}|j||fd |i|��S | j |||||d d �S )Nr� )�fnamez�Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. 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This is the equivalent of the ``numpy.ndarray`` method ``argmin``.c [ s t j| ||||f|�S )N)ru r| )r� r� r� r� r� r� r� r� r� r| G* s z,NDFrame._add_numeric_operations.<locals>.min)r NTN)r NTN)NNN)NNNr� N)NNNr� N)NNNr� N)NT)NT)NT)NT)NNNNr )NNNNr )NNNN)NNNN)NNNN)NNNN)NNNN)NNNN) � _doc_paramsr+ � _bool_doc� _any_desc� _any_see_also� _any_examplesr� � _all_desc� _all_see_also� _all_examplesru r� �__doc__� _num_ddof_doc� _cnum_doc�_cummin_examples�_cummax_examples�_cumsum_examples�_cumprod_examples�_num_doc�_min_count_stub�_stat_func_see_also� _sum_examples�_prod_examplesr �kurtosis� _max_examples� _min_examples)r� r� r� r� rA r' r� r� r� r� r� r� r� r� r+ r� r� r� r� r� r} r| r� r� r� �_add_numeric_operations) s� z NDFrame._add_numeric_operations�singlez*int | timedelta | BaseOffset | BaseIndexer)�window� min_periods�center�win_typer� r� r� r c C sF | � |�}|d k r,t| ||||||||d� S t| ||||||||d� S )N)r r r r r� r� r� r )r� rf re ) r� r r r r r� r� r� r r� r� r� �rollingX* s, zNDFrame.rollingrc )r r r� r r� c C s: | � |�}|d k r$tjdtdd� nd}t| ||||d�S )NzBThe `center` argument on `expanding` will be removed in the futurer^ )r� F)r r r� r )r� r� r� r� rc )r� r r r� r r� r� r� � expanding�* s zNDFrame.expandingz(float | TimedeltaConvertibleTypes | Nonez'str | np.ndarray | FrameOrSeries | Nonerd ) r �span�halflife�alphar �adjust� ignore_nar� �timesr� c C s& | � |�}t| ||||||||| d� S )N) r r! r" r# r r$ r% r� r& )r� rd ) r� r r! r" r# r r$ r% r� r& r� r� r� �ewm�* s z NDFrame.ewmc C sd || |�}| j dkr@|�| �r@t|j| j�r@|j| jdd�< | S | �� | j|j| dd�dd� | S )z< Wrap arithmetic method to operate inplace. r� NF)r� )r� )r� r( r8 r� r* r� r r� )r� r& �opr r� r� r� �_inplace_method�* s zNDFrame._inplace_methodc C s | � |t| �j�S )N)r) r) �__add__)r� r& r� r� r� �__iadd__�* s zNDFrame.__iadd__c C s | � |t| �j�S )N)r) r) �__sub__)r� r& r� r� r� �__isub__�* s zNDFrame.__isub__c C s | � |t| �j�S )N)r) r) �__mul__)r� r& r� r� r� �__imul__�* s zNDFrame.__imul__c C s | � |t| �j�S )N)r) r) � __truediv__)r� r& r� r� r� � __itruediv__�* s zNDFrame.__itruediv__c C s | � |t| �j�S )N)r) r) � __floordiv__)r� r& r� r� r� � __ifloordiv__�* s zNDFrame.__ifloordiv__c C s | � |t| �j�S )N)r) r) �__mod__)r� r& r� r� r� �__imod__�* s zNDFrame.__imod__c C s | � |t| �j�S )N)r) r) �__pow__)r� r& r� r� r� �__ipow__�* s zNDFrame.__ipow__c C s | � |t| �j�S )N)r) r) �__and__)r� r& r� r� r� �__iand__�* s zNDFrame.__iand__c C s | � |t| �j�S )N)r) r) �__or__)r� r& r� r� r� �__ior__�* s zNDFrame.__ior__c C s | � |t| �j�S )N)r) r) �__xor__)r� r& r� r� r� �__ixor__�* s zNDFrame.__ixor__zHashable | None)r� r� c C s$ t | j|d�}|dkrdS | j| S )a Retrieves the index of the first valid value. Parameters ---------- how : {'first', 'last'} Use this parameter to change between the first or last valid index. Returns ------- idx_first_valid : type of index )r� N)r^ r* r� )r� r� Zidxposr� r� r� �_find_valid_index+ s zNDFrame._find_valid_indexr� )r� rr c C s | j dd�S )a Return index for {position} non-NA value or None, if no NA value is found. Returns ------- scalar : type of index Notes ----- If all elements are non-NA/null, returns None. Also returns None for empty {klass}. r� )r� )r> )r� r� r� r� �first_valid_index+ s zNDFrame.first_valid_indexc C s | j dd�S )Nr )r� )r> )r� r� r� r� �last_valid_index%+ s zNDFrame.last_valid_index)FN)NF)T)N)FN)..)..)r F)T)r )N)N)r F)r )r )r )r )r )r )r )N)N)rx rI NNTTNr r NTNry TNN) NNNr� Tr� NFr� TNN) r� NNFNTNNNNr� r� )Nr� TNNNN)TN)NNNTTr� NNNTFNNNNr� NNNNNN)Nrc rI NNTTNr� Nr� Nr NNNTNr� r� N)FT)r N)r )r NT)r )T)r� r� F)N)NTNN)Nr NNNFr )Nr )T)r TFr r FN) r NTFr r TFN)NFF)NNNN)r� )r� )NNFNNNF)N)Tr )T)T)N)FFF)TTTTT)NNNFNN)NFNN)NFNN)NNFNFr\ )ra r NFNNN)N)F)NNNF)NNFN)FN)TTN) r NNrq NNNNNr� N)r r� Nr� TF)r� FF) r� NNTNNNr N)r� NNTNNNr )r� NNTNNNr )r� Nr N)r� r )r� Nr )NNNT)r NT)r NTr r )NNNF)r� r\ NN)r r T)r NTN)r NTN)r NTN)NT)NT)NT)NT)NT)NNNr� N)NNNr� N)NNNr� N)NNNr� N)NNNN)NNNN)NNNN)NNNN)NNNN)NNNN)NNNN)NNNNr )NNNNr )NNNNr )NNN)NFNNr Nr )r� Nr r ) NNNNr TFr N)�r� � __module__� __qualname__r r~ �__annotations__r_ r r� � frozensetr� r| ry r� � classmethodr� r� r� �propertyr� �setterr& r� r� r� r� r� r( r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� r� rq r� r� r r� r� r r r+ �_shared_doc_kwargsr r� r r r, r r r# r" r( r% r0 r2 r3 r7 �__bool__r8 r; r> r@ r? rC rH rK rM rP r! r� rR rS rT rU Z__array_priority__rV r. r[ r] ra ri rl rv ra r� rq r� r� �pickle�HIGHEST_PROTOCOLr� r� r� �fmtZreturn_docstringrk r� r� r� r� r� r� rJ r� r� r� r� r� r� r� r r� r� rL r� r r r r r r� r r r r r! rp r# r1 r2 r� r4 r� r8 r; r� r? r rC rD rE r� r* rA r� r� rI rK rO rP rV r* rZ r\ r[ rd r% rX rr rG rs rH rt rx r{ r r� r� r� r� r� r r� r� r� r� r� r� rm r� rY rw r� r� r� r� r� r� r: r� r� r� r� rA r' r� r� r� r� r� r� r� r� r� r� r| r} r� r� r� r� r r� r+ r� r r� r re r rc r rd r' r) r+ r- r/ r1 r3 r5 r7 r9 r; r= r> r? r@ � __classcell__r� r� )r: r� ru � s8 ( . $ =t& ' +>W - +@Q 7 ' ( D < $ &I <T " b ( b 7 L& "k @ @ B " ($ x # "b H J & V*> $) & m " . & B & & - 0 % +"?$"?$$ "v 7 L 6 $IA &x ? t 5 @ 0 + 8 %H | ! ( ) I 4 & < *ru c C sH dd� dd� t| j�D ��� d�}| jdkr4| jjnd}| j}|||fS )z!Return a tuple of the doc params.�{z, c s s" | ]\}}|� d |� d�V qdS )z (rf Nr� )r� r� r� r� r� r� r� .+ s z _doc_params.<locals>.<genexpr>�}r� Zscalar)rg r� r� r� r� r� )r� r� r� r� r� r� r� r ++ s "r a� {desc} Parameters ---------- axis : {axis_descr} Axis for the function to be applied on. skipna : bool, default True Exclude NA/null values when computing the result. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a {name1}. numeric_only : bool, default None Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series. {min_count}**kwargs Additional keyword arguments to be passed to the function. Returns ------- {name1} or {name2} (if level specified){see_also}{examples} a" {desc} Parameters ---------- axis : {axis_descr} skipna : bool, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a {name1}. ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. numeric_only : bool, default None Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series. Returns ------- {name1} or {name2} (if level specified) Notes ----- To have the same behaviour as `numpy.std`, use `ddof=0` (instead of the default `ddof=1`) a� {desc} Parameters ---------- axis : {{0 or 'index', 1 or 'columns', None}}, default 0 Indicate which axis or axes should be reduced. * 0 / 'index' : reduce the index, return a Series whose index is the original column labels. * 1 / 'columns' : reduce the columns, return a Series whose index is the original index. * None : reduce all axes, return a scalar. bool_only : bool, default None Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series. skipna : bool, default True Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be {empty_value}, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a {name1}. **kwargs : any, default None Additional keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- {name1} or {name2} If level is specified, then, {name2} is returned; otherwise, {name1} is returned. {see_also} {examples}z�Return whether all elements are True, potentially over an axis. Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).a� Examples -------- **Series** >>> pd.Series([True, True]).all() True >>> pd.Series([True, False]).all() False >>> pd.Series([], dtype="float64").all() True >>> pd.Series([np.nan]).all() True >>> pd.Series([np.nan]).all(skipna=False) True **DataFrames** Create a dataframe from a dictionary. >>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]}) >>> df col1 col2 0 True True 1 True False Default behaviour checks if column-wise values all return True. >>> df.all() col1 True col2 False dtype: bool Specify ``axis='columns'`` to check if row-wise values all return True. >>> df.all(axis='columns') 0 True 1 False dtype: bool Or ``axis=None`` for whether every value is True. >>> df.all(axis=None) False z�See Also -------- Series.all : Return True if all elements are True. DataFrame.any : Return True if one (or more) elements are True. a Return cumulative {desc} over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative {desc}. Parameters ---------- axis : {{0 or 'index', 1 or 'columns'}}, default 0 The index or the name of the axis. 0 is equivalent to None or 'index'. skipna : bool, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA. *args, **kwargs Additional keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- {name1} or {name2} Return cumulative {desc} of {name1} or {name2}. See Also -------- core.window.Expanding.{accum_func_name} : Similar functionality but ignores ``NaN`` values. {name2}.{accum_func_name} : Return the {desc} over {name2} axis. {name2}.cummax : Return cumulative maximum over {name2} axis. {name2}.cummin : Return cumulative minimum over {name2} axis. {name2}.cumsum : Return cumulative sum over {name2} axis. {name2}.cumprod : Return cumulative product over {name2} axis. {examples}a� Examples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cummin() 0 2.0 1 NaN 2 2.0 3 -1.0 4 -1.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cummin(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the minimum in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cummin() A B 0 2.0 1.0 1 2.0 NaN 2 1.0 0.0 To iterate over columns and find the minimum in each row, use ``axis=1`` >>> df.cummin(axis=1) A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 a� Examples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cumsum() 0 2.0 1 NaN 2 7.0 3 6.0 4 6.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cumsum(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the sum in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cumsum() A B 0 2.0 1.0 1 5.0 NaN 2 6.0 1.0 To iterate over columns and find the sum in each row, use ``axis=1`` >>> df.cumsum(axis=1) A B 0 2.0 3.0 1 3.0 NaN 2 1.0 1.0 a� Examples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cumprod() 0 2.0 1 NaN 2 10.0 3 -10.0 4 -0.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cumprod(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the product in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cumprod() A B 0 2.0 1.0 1 6.0 NaN 2 6.0 0.0 To iterate over columns and find the product in each row, use ``axis=1`` >>> df.cumprod(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 0.0 a� Examples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cummax() 0 2.0 1 NaN 2 5.0 3 5.0 4 5.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cummax(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the maximum in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cummax() A B 0 2.0 1.0 1 3.0 NaN 2 3.0 1.0 To iterate over columns and find the maximum in each row, use ``axis=1`` >>> df.cummax(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 1.0 a2 See Also -------- numpy.any : Numpy version of this method. Series.any : Return whether any element is True. Series.all : Return whether all elements are True. DataFrame.any : Return whether any element is True over requested axis. DataFrame.all : Return whether all elements are True over requested axis. z�Return whether any element is True, potentially over an axis. Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).a\ Examples -------- **Series** For Series input, the output is a scalar indicating whether any element is True. >>> pd.Series([False, False]).any() False >>> pd.Series([True, False]).any() True >>> pd.Series([], dtype="float64").any() False >>> pd.Series([np.nan]).any() False >>> pd.Series([np.nan]).any(skipna=False) True **DataFrame** Whether each column contains at least one True element (the default). >>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]}) >>> df A B C 0 1 0 0 1 2 2 0 >>> df.any() A True B True C False dtype: bool Aggregating over the columns. >>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]}) >>> df A B 0 True 1 1 False 2 >>> df.any(axis='columns') 0 True 1 True dtype: bool >>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]}) >>> df A B 0 True 1 1 False 0 >>> df.any(axis='columns') 0 True 1 False dtype: bool Aggregating over the entire DataFrame with ``axis=None``. >>> df.any(axis=None) True `any` for an empty DataFrame is an empty Series. >>> pd.DataFrame([]).any() Series([], dtype: bool) a� Examples -------- >>> idx = pd.MultiIndex.from_arrays([ ... ['warm', 'warm', 'cold', 'cold'], ... ['dog', 'falcon', 'fish', 'spider']], ... names=['blooded', 'animal']) >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64 >>> s.{stat_func}() {default_output}Zstat_func_exampler+ ZSum� � � )� stat_func�verbZdefault_outputZlevel_output_0Zlevel_output_1a By default, the sum of an empty or all-NA Series is ``0``. >>> pd.Series([], dtype="float64").sum() # min_count=0 is the default 0.0 This can be controlled with the ``min_count`` parameter. For example, if you'd like the sum of an empty series to be NaN, pass ``min_count=1``. >>> pd.Series([], dtype="float64").sum(min_count=1) nan Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and empty series identically. >>> pd.Series([np.nan]).sum() 0.0 >>> pd.Series([np.nan]).sum(min_count=1) nanr} ZMaxr� r| ZMinr^ a See Also -------- Series.sum : Return the sum. Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.a� Examples -------- By default, the product of an empty or all-NA Series is ``1`` >>> pd.Series([], dtype="float64").prod() 1.0 This can be controlled with the ``min_count`` parameter >>> pd.Series([], dtype="float64").prod(min_count=1) nan Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and empty series identically. >>> pd.Series([np.nan]).prod() 1.0 >>> pd.Series([np.nan]).prod(min_count=1) nanz�min_count : int, default 0 The required number of valid values to perform the operation. If fewer than ``min_count`` non-NA values are present the result will be NA. r z Index | Nonez#tuple[FrameOrSeries, FrameOrSeries])r� rk r� r� c C sH t | jj�r@| jj|jjkr@|dk r@| �� } |�� }|| _||_| |fS )zt If we are aligning timezone-aware DatetimeIndexes and the timezones do not match, convert both to UTC. N)r6 r� r� r� r� )r� rk r� r� r� r� r� �- s r� )�� __future__r rt rL r r� r� rr r+ rJ r �typingr r r r r r r r r r� r� �numpyr� Zpandas._configr Z pandas._libsr Zpandas._libs.tslibsr r r r Zpandas._typingr r r r r r r r r r r r r r r! r" r# r$ r% r& Zpandas.compat._optionalr' Zpandas.compat.numpyr( r� Z pandas.errorsr) r* Zpandas.util._decoratorsr+ r, Zpandas.util._validatorsr- r. r/ Zpandas.core.dtypes.commonr0 r1 r2 r3 r4 r5 r6 r7 r8 r9 r: r; r< r= r> r? r@ rA rB Zpandas.core.dtypes.genericrC rD Z pandas.core.dtypes.inferencerE rF Zpandas.core.dtypes.missingrG rH Z pandas.corerI rJ rK rL Zpandas.core.algorithms�coreZ algorithmsr� Zpandas.core.arraysrM Zpandas.core.baserN Zpandas.core.common�commonr Zpandas.core.constructionrO rP Zpandas.core.describerQ Zpandas.core.flagsrR Zpandas.core.indexesrS r Zpandas.core.indexes.apirT rU rV rW rX rY Zpandas.core.internalsrZ r[ r\ Z"pandas.core.internals.constructionr] Zpandas.core.missingr^ Zpandas.core.opsr_ r� r` Zpandas.core.shared_docsra Zpandas.core.sortingrb Zpandas.core.windowrc rd re rf Zpandas.io.formatsrg rL Zpandas.io.formats.formatrh ri Zpandas.io.formats.printingrj rk rl Zpandas.core.framerm r� rn Zpandas.core.seriesro Zpandas.core.window.indexersrp rH r8 r� Z IndexingMixinru r r r r r r r r r r r r r r r r r r r r r r� r� r� r� r� �<module> s` , X T A" &.#AAAA F
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