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B �5�g>� � @ s� d dl mZ d dlmZ d dlZd dlZd dlmZm Z d dl m Z d dl m mZ d dlmZmZmZmZmZmZmZ d dlmZ d dlmZ d dl Z d d l m Z m Z m Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z& d d l'm(Z(m)Z) d d l*m+Z+m,Z,m-Z-m.Z.m/Z/ d d l0m1Z1 d d l2m3Z3 d dl4m5Z5 de ddfddddd�dd�Z6ddd�dd�Z7d d � Z8d_d d �d!d"�Z9dd e d d d ddd#f d#d#dd dd d d ddd$d%d&� d'd(�Z:d`dd*�d+d,�Z;dad$d$d.�d/d0�Z<d1d2� Z=d3d4� Z>dbd6d7�Z?dcd9d:�Z@ddd<d=�ZAded?d@�ZBdfdBdC�ZCdgdDdE�ZDdhdHdI�ZEd de dFddfdddJ�dKdL�ZFd dd e d dFdFd d d d dddMfd dN�dOdP�ZGd ddd e d dFdFdFd dFd d dddQfdRdS�ZHdTdU� ZIdVdW� ZJdXdY� ZKdZd[� ZLd d\�d]d^�ZMdS )i� )� annotations)�castN)� NoDefault� no_default)�is_matching_na)�is_bool�is_categorical_dtype�is_extension_array_dtype�is_interval_dtype� is_number�is_numeric_dtype�needs_i8_conversion)� PandasDtype)�array_equivalent) � Categorical� DataFrame� DatetimeIndex�Index� IntervalIndex� MultiIndex� PeriodIndex�Series�TimedeltaIndex)� safe_sort�take_nd)� DatetimeArray�ExtensionArray� IntervalArray� PeriodArray�TimedeltaArray)�DatetimeLikeArrayMixin)� StringDtype)� pprint_thing�equivg�h㈵��>g:�0�yE>z bool | strzbool | int | NoDefault�float)� check_dtype�check_less_precise�rtol�atolc K s |t k r$tjdtdd� t|� }}t| t�rLt| |fd|||d�|�� n�t| t�rtt | |fd|||d�|�� n�t| t �r�t | |fd|||d�|�� n||r�t | �r�t |�r�nBt | �r�t |�r�n0t| tj�s�t|tj�r�d}nd}t| ||d � t|�}tj| |f|||d �|�� d S ) a� Check that the left and right objects are approximately equal. By approximately equal, we refer to objects that are numbers or that contain numbers which may be equivalent to specific levels of precision. Parameters ---------- left : object right : object check_dtype : bool or {'equiv'}, default 'equiv' Check dtype if both a and b are the same type. If 'equiv' is passed in, then `RangeIndex` and `Int64Index` are also considered equivalent when doing type checking. check_less_precise : bool or int, default False Specify comparison precision. 5 digits (False) or 3 digits (True) after decimal points are compared. If int, then specify the number of digits to compare. When comparing two numbers, if the first number has magnitude less than 1e-5, we compare the two numbers directly and check whether they are equivalent within the specified precision. Otherwise, we compare the **ratio** of the second number to the first number and check whether it is equivalent to 1 within the specified precision. .. deprecated:: 1.1.0 Use `rtol` and `atol` instead to define relative/absolute tolerance, respectively. Similar to :func:`math.isclose`. rtol : float, default 1e-5 Relative tolerance. .. versionadded:: 1.1.0 atol : float, default 1e-8 Absolute tolerance. .. versionadded:: 1.1.0 z�The 'check_less_precise' keyword in testing.assert_*_equal is deprecated and will be removed in a future version. You can stop passing 'check_less_precise' to silence this warning.� )� stacklevelF)� check_exact�exactr' r( )r+ r% r' r( z numpy array�Input)�obj)r% r' r( N)r �warnings�warn� FutureWarning�_get_tol_from_less_precise� isinstancer �assert_index_equalr �assert_series_equalr �assert_frame_equalr r �np�ndarray�assert_class_equal�bool�_testing�assert_almost_equal)�left�rightr% r& r' r( �kwargsr. � r@ �O/home/digitalm-up/venv/lib/python3.7/site-packages/pandas/_testing/asserters.pyr< 8 s\ . r< z bool | int)r& �returnc C s* t | t�r| rdS dS ndd| S dS )a| Return the tolerance equivalent to the deprecated `check_less_precise` parameter. Parameters ---------- check_less_precise : bool or int Returns ------- float Tolerance to be used as relative/absolute tolerance. Examples -------- >>> # Using check_less_precise as a bool: >>> _get_tol_from_less_precise(False) 0.5e-5 >>> _get_tol_from_less_precise(True) 0.5e-3 >>> # Using check_less_precise as an int representing the decimal >>> # tolerance intended: >>> _get_tol_from_less_precise(2) 0.5e-2 >>> _get_tol_from_less_precise(8) 0.5e-8 g����Mb@?g�h㈵��>g �?� N)r3 r: )r& r@ r@ rA r2 � s r2 c C sZ |j }t| |�s.t|� d|� dt| �� d���t||�sVt|� d|� dt|�� d���dS )a� Helper method for our assert_* methods that ensures that the two objects being compared have the right type before proceeding with the comparison. Parameters ---------- left : The first object being compared. right : The second object being compared. cls : The class type to check against. Raises ------ AssertionError : Either `left` or `right` is not an instance of `cls`. z Expected type z, found z insteadN)�__name__r3 �AssertionError�type)r= r> �cls�cls_namer@ r@ rA �_check_isinstance� s rI Tr: )� compare_keysc C s t | |t� tj| ||d� d S )N)rJ )rI �dictr; �assert_dict_equal)r= r> rJ r@ r@ rA rL � s rL r �str�None) r= r> r, � check_namesr& r+ �check_categorical� check_orderr' r( r. rB c s� d} d dd�� �fdd� } dd� } |t k rFtjd td d � t|� }} t| |t� | | || d � | j|jkr�| � d �}| j� d| � �}|j� d|� �}t| |||� t | �t |�kr�| � d�}t | �� d| � �}t |�� d|� �}t| |||� |�stt | ��} tt |��}| jdk�r�t t | �} t t |�}xft | j�D ]X}| | |�}| ||�}d|� d�}t||�|||| |d� | | j| |j| | d � �q:W |�r�� �r�| �|��s t�| j|jk�t��d t | � }| � dt�|d�� d�}t| || |� n&t��}tj| j|j|| || | |d� |�r6td| || d � t | t ��sNt |t ��r^td| || d � t | t ��svt |t ��r�t | j|j� � �r�t | j!��s�t |j!��r�t"| j|j| � d�d � d S ) at Check that left and right Index are equal. Parameters ---------- left : Index right : Index exact : bool or {'equiv'}, default 'equiv' Whether to check the Index class, dtype and inferred_type are identical. If 'equiv', then RangeIndex can be substituted for Int64Index as well. check_names : bool, default True Whether to check the names attribute. check_less_precise : bool or int, default False Specify comparison precision. Only used when check_exact is False. 5 digits (False) or 3 digits (True) after decimal points are compared. If int, then specify the digits to compare. .. deprecated:: 1.1.0 Use `rtol` and `atol` instead to define relative/absolute tolerance, respectively. Similar to :func:`math.isclose`. check_exact : bool, default True Whether to compare number exactly. check_categorical : bool, default True Whether to compare internal Categorical exactly. check_order : bool, default True Whether to compare the order of index entries as well as their values. If True, both indexes must contain the same elements, in the same order. If False, both indexes must contain the same elements, but in any order. .. versionadded:: 1.2.0 rtol : float, default 1e-5 Relative tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 atol : float, default 1e-8 Absolute tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 obj : str, default 'Index' Specify object name being compared, internally used to show appropriate assertion message. Examples -------- >>> from pandas.testing import assert_index_equal >>> a = pd.Index([1, 2, 3]) >>> b = pd.Index([1, 2, 3]) >>> assert_index_equal(a, b) Tr rN )rB c s� �sd S t | |�|d� � rRtd| ||d� t| j�rRt|j�rRt| j|j�d� | jdkrl|jdks|t�ntd| ||d� d S )N)r, r. �dtype)r. )r, �string� inferred_type)r9 �assert_attr_equalr rR r4 � categoriesrT rE )r= r> r. )rP r, r@ rA � _check_types8 s z(assert_index_equal.<locals>._check_typesc S s: | j | }| j| }t|j||jd�}|j|| j| d�S )N)� fill_value)�name)�levels�codesr �_valuesZ _na_valueZ _shallow_copy�names)�index�level�uniqueZ level_codesZfilledr@ r@ rA �_get_ilevel_valuesJ s z.assert_index_equal.<locals>._get_ilevel_valuesz�The 'check_less_precise' keyword in testing.assert_*_equal is deprecated and will be removed in a future version. You can stop passing 'check_less_precise' to silence this warning.r) )r* )r. z levels are differentz, z length are different� zMultiIndex level [�])r, rO r+ r' r( r. g Y@z values are different (� z %))r' r( r% r. �lobjZrobjr] �freqz categoryN)r )#r r/ r0 r1 r2 rI r Znlevels�raise_assert_detail�lenr r r �ranger4 rZ �equalsr7 �sumr\ �astype�int�roundr: r; r< �valuesrU r3 r r �assert_interval_array_equalr rR �assert_categorical_equal)r= r> r, rO r& r+ rP rQ r' r( r. �__tracebackhide__rW ra �msg1�msg2Zmsg3r_ ZllevelZrlevelre �diff�msgZ exact_boolr@ )rP r, rA r4 � s� ? $r4 r- )r, c C s� d}dd� }|dkrjt | �t |�kr�t | �jt |�jh}t|ddh �r�|� d�}t|||| �||�� n4|r�t | �t |�kr�|� d�}t|||| �||�� d S ) z# Checks classes are equal. Tc S s t | t�r| S t| �jS )N)r3 r rF rD )�xr@ r@ rA � repr_class� s z&assert_class_equal.<locals>.repr_classr# Z Int64IndexZ RangeIndexz classes are not equivalentz classes are differentN)rF rD rh rg )r= r> r, r. rr rx �typesrv r@ r@ rA r9 � s r9 � Attributes)�attrr. c C s� d}t || �}t || �}||kr$dS t||�r2dS y ||k}W n tk rV d}Y nX |tjk|tjkA rrd}nt|t�s�|�� }|r�dS d| � d�}t||||� dS )aO Check attributes are equal. Both objects must have attribute. Parameters ---------- attr : str Attribute name being compared. left : object right : object obj : str, default 'Attributes' Specify object name being compared, internally used to show appropriate assertion message TFz Attribute "z" are differentN) �getattrr � TypeError�pdZNAr3 r: �allrg ) r{ r= r> r. rr Z left_attrZ right_attr�resultrv r@ r@ rA rU � s& rU c C s� dd l m} t| ttjf�r\xl| �� D ]0}dtt|�j �� �}t||j t f�s&t |��q&W n.dtt| �j �� �}t| |j tt f�s�t |��d S )Nr zBone of 'objs' is not a matplotlib Axes instance, type encountered zoobjs is neither an ndarray of Artist instances nor a single ArtistArtist instance, tuple, or dict, 'objs' is a )Zmatplotlib.pyplotZpyplotr3 r r7 r8 Zravel�reprrF rD ZAxesrK rE ZArtist�tuple)ZobjsZplt�elrv r@ r@ rA �"assert_is_valid_plot_return_object� s r� c C s. t | ttf�r| j} t| t�t�| ��� dS )z#Assert that the sequence is sorted.N)r3 r r ro �assert_numpy_array_equalr7 �sort�array)�seqr@ r@ rA �assert_is_sorted s r� r c C s� t | |t� |rDt| j|j|� d�d� t| j|j||� d�d� nxy| j�� }|j�� }W n" tk r~ | j|j }}Y nX t|||� d�d� t| j�| j�|j�|j�|� d�d� t d| ||d� dS )a{ Test that Categoricals are equivalent. Parameters ---------- left : Categorical right : Categorical check_dtype : bool, default True Check that integer dtype of the codes are the same check_category_order : bool, default True Whether the order of the categories should be compared, which implies identical integer codes. If False, only the resulting values are compared. The ordered attribute is checked regardless. obj : str, default 'Categorical' Specify object name being compared, internally used to show appropriate assertion message z .categories)r. z.codes)r% r. z.valuesZorderedN) rI r r4 rV r� r[ Z sort_valuesr} ZtakerU )r= r> r% �check_category_orderr. �lc�rcr@ r@ rA rq s rq r c C sz t | |t� i }| jjjdkr&d|d< t| j|jfd|� d�i|�� t| j|jfd|� d�i|�� td| ||d� dS ) a Test that two IntervalArrays are equivalent. Parameters ---------- left, right : IntervalArray The IntervalArrays to compare. exact : bool or {'equiv'}, default 'equiv' Whether to check the Index class, dtype and inferred_type are identical. If 'equiv', then RangeIndex can be substituted for Int64Index as well. obj : str, default 'IntervalArray' Specify object name being compared, internally used to show appropriate assertion message )�m�MF� check_freqr. z.left�closed)r. N)rI r �_leftrR �kind� assert_equal�_rightrU )r= r> r, r. r? r@ r@ rA rp @ s rp r c C s8 t | |t� t| j|j|� d�d� td| ||d� d S )Nz._data)r. rf )rI r r� �_datarU )r= r> r. r@ r@ rA �assert_period_array_equal] s r� r c C sP d}t | |t� t| j|j|� d�d� |r<td| ||d� td| ||d� d S )NTz._data)r. rf �tz)rI r r� r� rU )r= r> r. r� rr r@ r@ rA �assert_datetime_array_equald s r� r c C s@ d}t | |t� t| j|j|� d�d� |r<td| ||d� d S )NTz._data)r. rf )rI r r� r� rU )r= r> r. r� rr r@ r@ rA � assert_timedelta_array_equaln s r� c C s� d}| � d|� �}t |tj�r0|dt|�� �7 }t |tj�rFt|�}n$t|�sbt |t�sbt |t�rjt|�}t |tj�r�t|�}n$t|�s�t |t�s�t |t�r�t|�}|d|� d|� �7 }|d k r�|d|� �7 }t|��d S )NTz are different z [index]: z [left]: z [right]: z [diff]: ) r3 r7 r8 r" r r r! r� rE )r. �messager= r> ru � index_valuesrr rv r@ r@ rA rg v s* rg F� numpy arrayc s� d}t | |�d� t| |tj� dd� } | | �} | |�} |dkrd| | k r�tt| �� dt| �� ���n*|dkr�| | kr�tt| �� dt| �� ���� ��fd d �} t| |�d �s�| | ||� |r�t| tj�r�t|tj�r�td | |�d� d S )a+ Check that 'np.ndarray' is equivalent. Parameters ---------- left, right : numpy.ndarray or iterable The two arrays to be compared. strict_nan : bool, default False If True, consider NaN and None to be different. check_dtype : bool, default True Check dtype if both a and b are np.ndarray. err_msg : str, default None If provided, used as assertion message. check_same : None|'copy'|'same', default None Ensure left and right refer/do not refer to the same memory area. obj : str, default 'numpy array' Specify object name being compared, internally used to show appropriate assertion message. index_values : numpy.ndarray, default None optional index (shared by both left and right), used in output. T)r. c S s t | dd �d k r| jS | S )N�base)r| r� )r. r@ r@ rA � _get_base� s z+assert_numpy_array_equal.<locals>._get_baseZsamez is not �copyz is c s� |d kr�| j |j kr,t��� d�| j |j � d}x,t| |�D ] \}}t||�d�s<|d7 }q<W |d | j }�� dt�|d�� d�}t�|| |� d � t|��d S ) Nz shapes are differentr )� strict_nanrb g Y@z values are different (rd z %))r� )�shaperg �zipr �sizer7 rn rE )r= r> �err_msgru Zleft_arrZ right_arrrv )r� r. r� r@ rA �_raise� s z(assert_numpy_array_equal.<locals>._raise)r� rR N) r9 rI r7 r8 rE r� r r3 rU ) r= r> r� r% r� Z check_samer. r� rr r� Z left_baseZ right_baser� r@ )r� r. r� rA r� � s$ r� )r' r( c C s. |t k r$tjdtdd� t|� }}t| t�s6td��t|t�sHtd��|r\td| |dd� t| t �r�t|t �r�t |�t | �kr�t t � | j�t � |j�|d � d S t � | �� �}t � |�� �} t || d |d � t � | | �t��} t � || �t��} |�rt | | d|d � ntj| | |||d|d � d S )a- Check that left and right ExtensionArrays are equal. Parameters ---------- left, right : ExtensionArray The two arrays to compare. check_dtype : bool, default True Whether to check if the ExtensionArray dtypes are identical. index_values : numpy.ndarray, default None Optional index (shared by both left and right), used in output. check_less_precise : bool or int, default False Specify comparison precision. Only used when check_exact is False. 5 digits (False) or 3 digits (True) after decimal points are compared. If int, then specify the digits to compare. .. deprecated:: 1.1.0 Use `rtol` and `atol` instead to define relative/absolute tolerance, respectively. Similar to :func:`math.isclose`. check_exact : bool, default False Whether to compare number exactly. rtol : float, default 1e-5 Relative tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 atol : float, default 1e-8 Absolute tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 Notes ----- Missing values are checked separately from valid values. A mask of missing values is computed for each and checked to match. The remaining all-valid values are cast to object dtype and checked. Examples -------- >>> from pandas.testing import assert_extension_array_equal >>> a = pd.Series([1, 2, 3, 4]) >>> b, c = a.array, a.array >>> assert_extension_array_equal(b, c) z�The 'check_less_precise' keyword in testing.assert_*_equal is deprecated and will be removed in a future version. You can stop passing 'check_less_precise' to silence this warning.r) )r* z left is not an ExtensionArrayz right is not an ExtensionArrayrR r )r. )r� NzExtensionArray NA mask)r. r� )r% r' r( r. r� )r r/ r0 r1 r2 r3 r rE rU r rF r� r7 �asarrayZasi8Zisnarl �objectr; r< ) r= r> r% r� r& r+ r' r( Zleft_naZright_naZ left_validZ right_validr@ r@ rA � assert_extension_array_equal� sB 5 r� r )� check_indexc C s� d}|t k r(tjdtdd� t|� } }t| |t� |rFt| ||d� t| �t|�kr�t| �� d| j � �}t|�� d|j � �}t |d||� | r�| j |j ks�t t | j �� dt |j �� ���|r�t| j |j |||| | ||� d �d � | �r$t| j ttf��r$| j }|j }|j|jk�s$t |j|jf��|�r`t| j��rJt|j��rJ| �sJntd | |d |� �d� |�r�t| j��r�t|j��r�| j}|j}t|t��r�t|t��r�t|||t�| j �d � n t |||t |�t�| j �d� �n�|�r6t | j��s�t |j��r6t | j�� t |j���std| j� d|j� d�}t |���n>t!| j��r`t!|j��r`t"| j#|j#� �nt| j��sxt|j��r�t$j%| j|j| ||t |�t�| j �d� n�t&| j��r�t&|j��r�t| j|j|t�| j �d � n�t'| j|j��s�t'|j| j��rt| j|j|t�| j �d � n^t | j��rLt |j��rLt| j|j|t�| j �d � n(t$j%| j|j| ||t |�t�| j �d� |�r�td| ||d� | �r�t| j��s�t|j��r�t(| j|j|� d�| d� dS )a� Check that left and right Series are equal. Parameters ---------- left : Series right : Series check_dtype : bool, default True Whether to check the Series dtype is identical. check_index_type : bool or {'equiv'}, default 'equiv' Whether to check the Index class, dtype and inferred_type are identical. check_series_type : bool, default True Whether to check the Series class is identical. check_less_precise : bool or int, default False Specify comparison precision. Only used when check_exact is False. 5 digits (False) or 3 digits (True) after decimal points are compared. If int, then specify the digits to compare. When comparing two numbers, if the first number has magnitude less than 1e-5, we compare the two numbers directly and check whether they are equivalent within the specified precision. Otherwise, we compare the **ratio** of the second number to the first number and check whether it is equivalent to 1 within the specified precision. .. deprecated:: 1.1.0 Use `rtol` and `atol` instead to define relative/absolute tolerance, respectively. Similar to :func:`math.isclose`. check_names : bool, default True Whether to check the Series and Index names attribute. check_exact : bool, default False Whether to compare number exactly. check_datetimelike_compat : bool, default False Compare datetime-like which is comparable ignoring dtype. check_categorical : bool, default True Whether to compare internal Categorical exactly. check_category_order : bool, default True Whether to compare category order of internal Categoricals. .. versionadded:: 1.0.2 check_freq : bool, default True Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex. .. versionadded:: 1.1.0 check_flags : bool, default True Whether to check the `flags` attribute. .. versionadded:: 1.2.0 rtol : float, default 1e-5 Relative tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 atol : float, default 1e-8 Absolute tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 obj : str, default 'Series' Specify object name being compared, internally used to show appropriate assertion message. check_index : bool, default True Whether to check index equivalence. If False, then compare only values. .. versionadded:: 1.3.0 Examples -------- >>> from pandas.testing import assert_series_equal >>> a = pd.Series([1, 2, 3, 4]) >>> b = pd.Series([1, 2, 3, 4]) >>> assert_series_equal(a, b) Tz�The 'check_less_precise' keyword in testing.assert_*_equal is deprecated and will be removed in a future version. You can stop passing 'check_less_precise' to silence this warning.r) )r* )r. z, zSeries length are differentz != z.index)r, rO r+ rP r' r( r. rR zAttributes of )r% r� )r% r. r� z[datetimelike_compat=True] z is not equal to �.)r' r( r% r. r� rY z category)r. r� N))r r/ r0 r1 r2 rI r r9 rh r^ rg �flagsrE r� r4 r3 r r rf r rR rU r r\ r r� r7 r� r� rM r r rj r rp r� r; r< r �0is_extension_array_dtype_and_needs_i8_conversionrq )r= r> r% �check_index_typeZcheck_series_typer& rO r+ �check_datetimelike_compatrP r� r� � check_flagsr' r( r. r� rr rs rt ZlidxZridxZ left_valuesZ right_valuesrv r@ r@ rA r5 U s� \ * r5 r c C s d}|t k r(tjdtdd� t|� }}t| |t� |rJt| t|��sJt �| j |j krzt ||� d�t | j �� t |j �� � |r�| j |j ks�t t | j �� dt |j �� ���t| j|j||| | | |||� d�d� t| j|j||| | | |||� d �d� | �r| �|�| } }|�r�|�� }| �� }x�ttt|�� �t|�� � ��D ]:}||k�sXt �||k�sft �t|| || ||d � �qFW n�x~t| j�D ]p\}}||k�s�t �| jd d �|f }|jd d �|f }t||||| || | | |� d |� d |� d�||dd� �q�W d S )a Check that left and right DataFrame are equal. This function is intended to compare two DataFrames and output any differences. Is is mostly intended for use in unit tests. Additional parameters allow varying the strictness of the equality checks performed. Parameters ---------- left : DataFrame First DataFrame to compare. right : DataFrame Second DataFrame to compare. check_dtype : bool, default True Whether to check the DataFrame dtype is identical. check_index_type : bool or {'equiv'}, default 'equiv' Whether to check the Index class, dtype and inferred_type are identical. check_column_type : bool or {'equiv'}, default 'equiv' Whether to check the columns class, dtype and inferred_type are identical. Is passed as the ``exact`` argument of :func:`assert_index_equal`. check_frame_type : bool, default True Whether to check the DataFrame class is identical. check_less_precise : bool or int, default False Specify comparison precision. Only used when check_exact is False. 5 digits (False) or 3 digits (True) after decimal points are compared. If int, then specify the digits to compare. When comparing two numbers, if the first number has magnitude less than 1e-5, we compare the two numbers directly and check whether they are equivalent within the specified precision. Otherwise, we compare the **ratio** of the second number to the first number and check whether it is equivalent to 1 within the specified precision. .. deprecated:: 1.1.0 Use `rtol` and `atol` instead to define relative/absolute tolerance, respectively. Similar to :func:`math.isclose`. check_names : bool, default True Whether to check that the `names` attribute for both the `index` and `column` attributes of the DataFrame is identical. by_blocks : bool, default False Specify how to compare internal data. If False, compare by columns. If True, compare by blocks. check_exact : bool, default False Whether to compare number exactly. check_datetimelike_compat : bool, default False Compare datetime-like which is comparable ignoring dtype. check_categorical : bool, default True Whether to compare internal Categorical exactly. check_like : bool, default False If True, ignore the order of index & columns. Note: index labels must match their respective rows (same as in columns) - same labels must be with the same data. check_freq : bool, default True Whether to check the `freq` attribute on a DatetimeIndex or TimedeltaIndex. .. versionadded:: 1.1.0 check_flags : bool, default True Whether to check the `flags` attribute. rtol : float, default 1e-5 Relative tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 atol : float, default 1e-8 Absolute tolerance. Only used when check_exact is False. .. versionadded:: 1.1.0 obj : str, default 'DataFrame' Specify object name being compared, internally used to show appropriate assertion message. See Also -------- assert_series_equal : Equivalent method for asserting Series equality. DataFrame.equals : Check DataFrame equality. Examples -------- This example shows comparing two DataFrames that are equal but with columns of differing dtypes. >>> from pandas._testing import assert_frame_equal >>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]}) >>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]}) df1 equals itself. >>> assert_frame_equal(df1, df1) df1 differs from df2 as column 'b' is of a different type. >>> assert_frame_equal(df1, df2) Traceback (most recent call last): ... AssertionError: Attributes of DataFrame.iloc[:, 1] (column name="b") are different Attribute "dtype" are different [left]: int64 [right]: float64 Ignore differing dtypes in columns with check_dtype. >>> assert_frame_equal(df1, df2, check_dtype=False) Tz�The 'check_less_precise' keyword in testing.assert_*_equal is deprecated and will be removed in a future version. You can stop passing 'check_less_precise' to silence this warning.r) )r* z shape mismatchz != z.index)r, rO r+ rP rQ r' r( r. z.columns)r% r. Nz .iloc[:, z] (column name="z")F) r% r� r+ rO r� rP r� r. r' r( r� )r r/ r0 r1 r2 rI r r3 rF rE r� rg r� r� r4 r^ �columnsZ reindex_likeZ_to_dict_of_blocks�list�set�keysr6 � enumerateZilocr5 )r= r> r% r� Zcheck_column_typeZcheck_frame_typer& rO Z by_blocksr+ r� rP Z check_liker� r� r' r( r. rr ZrblocksZlblocksrR �i�colZlcolZrcolr@ r@ rA r6 N s� ~ "*& r6 c K sZ d}t | t�rJt| |f|� t | ttf�rF| j|jksFt| j|jf���n t | t�rdt| |f|� n�t | t �r~t | |f|� n�t | t �r�t | |f|� n�t | t �r�t| |f|� n�t | t�r�t| |f|� n�t | t�r�t| |f|� npt | t��rt| |f|� nTt | tj��r t| |f|� n6t | t��rJ|i k�s:t�| |k�sVt�n tt| ���dS )aC Wrapper for tm.assert_*_equal to dispatch to the appropriate test function. Parameters ---------- left, right : Index, Series, DataFrame, ExtensionArray, or np.ndarray The two items to be compared. **kwargs All keyword arguments are passed through to the underlying assert method. TN)r3 r r4 r r rf rE r r5 r r6 r rp r r� r r� r r� r r� r7 r8 r� rM �NotImplementedErrorrF )r= r> r? rr r@ r@ rA r� - s2 r� c C s� t | |tjj� t| j|j� t| jtjj j �s4t �t|jtjj j �sJt �| j}|j}|� |�spt dd||� n td| |� td| |� t| �� |�� � dS )z� Check that the left and right SparseArray are equal. Parameters ---------- left : SparseArray right : SparseArray zSparseArray.indexzindex are not equalrX rR N)rI r~ ZarraysZ SparseArrayr� Z sp_valuesr3 Zsp_index�_libs�sparseZ SparseIndexrE rj rg rU Zto_dense)r= r> Z left_indexZ right_indexr@ r@ rA �assert_sp_array_equalU s r� c C s, x&| D ] }||kst dt|�� ���qW d S )NzDid not contain item: )rE r� )�iterableZdic�kr@ r@ rA �assert_contains_allv s r� c K s\ xVt | |�D ]H\}}t||f|� dtt|��� dtt|��� d�}||k s t|��q W dS )z� iter1, iter2: iterables that produce elements comparable with assert_almost_equal Checks that the elements are equal, but not the same object. (Does not check that items in sequences are also not the same object) zExpected object z and object z8 to be different objects, but they were the same object.N)r� r< r� rF rE )Ziter1Ziter2Z eql_kwargsZelem1Zelem2rv r@ r@ rA � assert_copy{ s "r� )rB c C s t | �ot|�S )z� Checks that we have the combination of an ExtensionArraydtype and a dtype that should be converted to int64 Returns ------- bool Related to issue #37609 )r r )Z left_dtypeZ right_dtyper@ r@ rA r� � s r� )T)Tr- )rz )TTr )r# r )r )r T)r T)NN)FTNNr� N)N� __future__r �typingr r/ �numpyr7 Zpandas._libs.libr r Zpandas._libs.missingr Zpandas._libs.testingr� �testingr; Zpandas.core.dtypes.commonr r r r r r r Zpandas.core.dtypes.dtypesr Zpandas.core.dtypes.missingr Zpandasr~ r r r r r r r r r Zpandas.core.algorithmsr r Zpandas.core.arraysr r r r r Z pandas.core.arrays.datetimeliker Zpandas.core.arrays.string_r! 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