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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 gMb@?gh㈵>g?rXN) isinstancer?)rrMrMrN_get_tol_from_less_precise4s  requivgh㈵>g:0yE>) check_dtyperrtolatolcKs|tk r$tjdtddt|}}t|tjrNt||fd|||d|nt|tj rxt ||fd|||d|nt|tj rt ||fd|||d|nt|rt |rt |rnBt|rt|rn0t|tjst|tjrd}nd}t|||d 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. 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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)rTz.codes)rrTz.valuesorderedN) rr'r categoriesrrZ sort_valuesrZtaker)rrrcheck_category_orderrTlcrcrMrMrNrs"    rr4cCsTt||tt|j|j||ddt|j|j||ddtd|||ddS)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. 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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)rTcSst|dddk r|jS|S)Nbase)rr)rTrMrMrN _get_base6sz+assert_numpy_array_equal.._get_baseZsamez is not copyz is cs|dkr|j|jkr,td|j|jd}x,t||D]\}}t||ds<|d7}q._raise)rrN) rrrrrrr&rr) rrrrr Z check_samerTrrrZ left_baseZ right_baser!rM)rrTrrNrs$ r)rrc Cs.|tk r$tjdtddt|}}t|ts6tdt|tsHtd|r\td||ddt|t rt|t rt |t |krt t j |jt j |j|d d St j |j}t j |j} t || d |d t j ||jt} t j || jt} |rt | | d|d ntj| | |||d|d d S)ac 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. zThe 'check_less_precise' keyword in testing.assert_*_equal is deprecated and will be removed in a future version. 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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. 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. TzThe '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)rTz, zSeries length are differentz.index)rrrrrrrTrzAttributes of )rrTrzatetimelike_compat=True] z is not equal to .)rrrrTr)rrrz category)rTrN)&rrIrrrrr/rrqrrrrrRr*TimedeltaIndexrrrrrr!rrr=rr"r%r+rrrr rrrr#r)rrrcheck_index_typeZcheck_series_typerrrcheck_datetimelike_compatrr check_freqrrrTrrrZlidxZridxrrMrMrNrsG     rr)cCsd}|tk r(tjdtddt|}}t||t|rJt|t|sJt |j |j krzt ||dt |j t |j | r|j ||}}t|j|j||| | |||dd t|j|j||| | |||dd |rN|j}|j}xttt|jt|jD]:}||ks t ||ks.t t||||||d qWn~x|t|jD]n\}}||kspt |jd d |f}|jd d |f}t||||| || | | |d |d |d ||d qZWd 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. 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) TzThe 'check_less_precise' keyword in testing.assert_*_equal is deprecated and will be removed in a future version. 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TN)rrRr+rr*r%rrr/rr)rr4rr5rr2rr6rr3r#rrrr=NotImplementedErrorr)rrrrrMrMrN assert_equal[s2           r2cCs|tjkrtj|}n|tjkr,tj|}n|tjkrBtj|}n|tjkrftj|j}|r|j}nf|tkrxt|}nT|t krt |}nB|t krt |}n0|t j krt j|}n|t krt |}nt||S)z Helper function to wrap the expected output of a test in a given box_class. Parameters ---------- expected : np.ndarray, Index, Series box_cls : {Index, Series, DataFrame} Returns ------- subclass of box_cls )rRr r+r/r)Zto_frameTr5r7r2r6rrto_arrayr1)expectedZbox_clsZ transposerMrMrN box_expecteds,             r6cCsVt|dd}t|rt|St|s,t|r6tj|St|rHtj|St j |SdS)Nr) rr"r7rrr2Z_from_sequencer$r6rr )rTrrMrMrNr4s   r4cCst||tjjt|j|jt|jtjj j s4t t|jtjj j sJt |j}|j}|j |spt dd||ntd||td||t|j|jdS)z Check that the left and right SparseArray are equal. 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Passed to date_range with `freq`. freq : str or Freq The frequency to use for the DatetimeIndex seed : int, optional The random state seed. * name : object dtype with string names * id : int dtype with * x, y : float dtype Examples -------- >>> _make_timeseries() id name x y timestamp 2000-01-01 982 Frank 0.031261 0.986727 2000-01-02 1025 Edith -0.086358 -0.032920 2000-01-03 982 Edith 0.473177 0.298654 2000-01-04 1009 Sarah 0.534344 -0.750377 2000-01-05 963 Zelda -0.271573 0.054424 ... ... ... ... ... 2000-12-27 980 Ingrid -0.132333 -0.422195 2000-12-28 972 Frank -0.376007 -0.298687 2000-12-29 1009 Ursula -0.865047 -0.503133 2000-12-30 1000 Hannah -0.063757 -0.507336 2000-12-31 972 Tim -0.869120 0.531685 timestamp)rYendrr)rirrj)ridry)rr)Nri)rRZ date_rangerqrr RandomStater_namesZpoissonrr)rPrr.) rYrfrseedrrCstater)dfrMrMrN_make_timeseriesas$   roccs*tttttttg}x|D] }|VqWdS)N)rWr[rZrNrGrDra)make_index_funcsmake_index_funcrMrMrNindex_subclass_makers_generators rrccs(tttg}x|D]}||dVqWdS)z Generator which can be iterated over to get instances of all the classes which represent time-series. 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If idx_type is not None, `idx_nlevels` must be 1. "i"/"f" creates an integer/float index, "s"/"u" creates a string/unicode index "dt" create a datetime index. "td" create a datetime index. if unspecified, string labels will be generated. NrjFTr/rvsurUrtdcsg|]}t|qSrM)r=)rKr/)prefixrMrNrLsz#makeCustomIndex..)r/rvrrrUrrrzI is not a legal value for `idx_type`, use 'i'/'f'/'s'/'u'/'dt'/'p'/'td'.css|]}|dkVqdS)rNrM)rKrrMrMrN Bsz"makeCustomIndex..cSs*ddl}|jdd|jd}dd|DS)Nrz[^\d_]_?r_cSsg|] }t|qSrM)r)rKrFrMrMrNrLKsz4makeCustomIndex..keyfunc..)resubsplit)rrZ numeric_tuplerMrMrNkeyfuncGsz makeCustomIndex..keyfuncZ_lZ_g)key)rcss|]}|dVqdS)rNrM)rKrrMrMrNr_s)r)r/rvrrrUrr)r#rqrrrr=rrIrRr@rArWrZr[rHrrsrextendrrrPelementsappendr*rir+r- from_tuples)Znentriesrrrndupe_lidx_typeZidx_funcidxZtuplesr/rZ div_factorZcntjlabelrrrrM)rrNmakeCustomIndexsf (     rc s|dks t|dkst| dks4| dkr0|d ks4t| dksP| dkrL|d ksPtt|d ||| d } t||d ||| d } dkrddfddt|D}t|| | | dS)a Create a DataFrame using supplied parameters. Parameters ---------- nrows, ncols - number of data rows/cols c_idx_names, idx_names - False/True/list of strings, yields No names , default names or uses the provided names for the levels of the corresponding index. You can provide a single string when c_idx_nlevels ==1. c_idx_nlevels - number of levels in columns index. > 1 will yield MultiIndex r_idx_nlevels - number of levels in rows index. > 1 will yield MultiIndex data_gen_f - a function f(row,col) which return the data value at that position, the default generator used yields values of the form "RxCy" based on position. c_ndupe_l, r_ndupe_l - list of integers, determines the number of duplicates for each label at a given level of the corresponding index. The default `None` value produces a multiplicity of 1 across all levels, i.e. a unique index. Will accept a partial list of length N < idx_nlevels, for just the first N levels. If ndupe doesn't divide nrows/ncol, the last label might have lower multiplicity. dtype - passed to the DataFrame constructor as is, in case you wish to have more control in conjunction with a custom `data_gen_f` r_idx_type, c_idx_type - "i"/"f"/"s"/"u"/"dt"/"td". If idx_type is not None, `idx_nlevels` must be 1. "i"/"f" creates an integer/float index, "s"/"u" creates a string/unicode index "dt" create a datetime index. "td" create a timedelta index. if unspecified, string labels will be generated. Examples -------- # 5 row, 3 columns, default names on both, single index on both axis >> makeCustomDataframe(5,3) # make the data a random int between 1 and 100 >> mkdf(5,3,data_gen_f=lambda r,c:randint(1,100)) # 2-level multiindex on rows with each label duplicated # twice on first level, default names on both axis, single # index on both axis >> a=makeCustomDataframe(5,3,r_idx_nlevels=2,r_ndupe_l=[2]) # DatetimeIndex on row, index with unicode labels on columns # no names on either axis >> a=makeCustomDataframe(5,3,c_idx_names=False,r_idx_names=False, r_idx_type="dt",c_idx_type="u") # 4-level multindex on rows with names provided, 2-level multindex # on columns with default labels and default names. >> a=makeCustomDataframe(5,3,r_idx_nlevels=4, r_idx_names=["FEE","FI","FO","FAM"], c_idx_nlevels=2) >> a=mkdf(5,3,r_idx_nlevels=2,c_idx_nlevels=4) rNr/rvrrrUrrrjr)rrrrrRcSsd|d|S)NrrrM)rrxrMrMrNsz%makeCustomDataframe..cs$g|]fddtDqS)csg|]}|qSrMrM)rKrx) data_gen_frrMrNrLsz2makeCustomDataframe...)r)rK)rncols)rrNrLsz'makeCustomDataframe..)r)r/rvrrrUrr)r/rvrrrUrr)rrrr))nrowsrZ c_idx_namesZ r_idx_namesZ c_idx_nlevelsZ r_idx_nlevelsrZ c_ndupe_lZ r_ndupe_lrZ c_idx_typeZ r_idx_typer)rrrM)rrrNmakeCustomDataframees0H  rc s|dkrtj}n tjj|}ttjd|d}d}t||}fdd}|||}x |jkr|d9}|||}qlWtj|djt} || jt} | j | j fS)NrjrgRQ?cs.|jt|}tjtj|dS)N)rrrrfloor)rngZ _extra_sizeind)rrrrMrN_gen_unique_randsz-_create_missing_idx.._gen_unique_randg?g?) rrrjrrminrrrtolist) rrdensity random_stateZmin_rowsZfacZ extra_sizerrrr/rM)rrrrN_create_missing_idxs   r?cCs0t}t|j||d\}}tj|j||f<|S)N)rr)rrrrnanr)rrrnr/rrMrMrNmakeMissingDataframesrcstfdd}|S)aB allows a decorator to take optional positional and keyword arguments. Assumes that taking a single, callable, positional argument means that it is decorating a function, i.e. something like this:: @my_decorator def function(): pass Calls decorator with decorator(f, *args, **kwargs) csNfdd} o,tdko,td}|rFd}g||S|SdS)Ncs|fS)NrM)rv)r decoratorrrMrNdec sz+optional_args..wrapper..decrjr)rqcallable)rrrZ is_decoratingrv)r)rrrNwrapperszoptional_args..wrapper)r)rrrM)rrN optional_argss  r timed out Server Hangup#HTTP Error 503: Service Unavailable502: Proxy ErrorHTTP Error 502: internal errorHTTP Error 502HTTP Error 503HTTP Error 403HTTP Error 400$Temporary failure in name resolutionName or service not knownConnection refusedcertificate verifyeonh6<cCsddl}t|jjtfS)Nr) http.clientIOErrorclient HTTPException TimeoutError)httprMrMrN_get_default_network_errors4 srcCsD|dkrt}yt|WdQRXWn|k r:dSXdSdS)a@ Try to connect to the given url. True if succeeds, False if IOError raised Parameters ---------- url : basestring The URL to try to connect to Returns ------- connectable : bool Return True if no IOError (unable to connect) or URLError (bad url) was raised NFT)rr9)url error_classesrMrMrN can_connect; s rzhttp://www.google.comc sFddlmdkrtd_tfdd}|S)aZ Label a test as requiring network connection and, if an error is encountered, only raise if it does not find a network connection. In comparison to ``network``, this assumes an added contract to your test: you must assert that, under normal conditions, your test will ONLY fail if it does not have network connectivity. You can call this in 3 ways: as a standard decorator, with keyword arguments, or with a positional argument that is the url to check. Parameters ---------- t : callable The test requiring network connectivity. url : path The url to test via ``pandas.io.common.urlopen`` to check for connectivity. Defaults to 'http://www.google.com'. raise_on_error : bool If True, never catches errors. check_before_test : bool If True, checks connectivity before running the test case. error_classes : tuple or Exception error classes to ignore. If not in ``error_classes``, raises the error. defaults to IOError. Be careful about changing the error classes here. skip_errnos : iterable of int Any exception that has .errno or .reason.erno set to one of these values will be skipped with an appropriate message. _skip_on_messages: iterable of string any exception e for which one of the strings is a substring of str(e) will be skipped with an appropriate message. Intended to suppress errors where an errno isn't available. Notes ----- * ``raise_on_error`` supersedes ``check_before_test`` Returns ------- t : callable The decorated test ``t``, with checks for connectivity errors. Example ------- Tests decorated with @network will fail if it's possible to make a network connection to another URL (defaults to google.com):: >>> from pandas._testing import network >>> from pandas.io.common import urlopen >>> @network ... def test_network(): ... with urlopen("rabbit://bonanza.com"): ... pass Traceback ... URLError: You can specify alternative URLs:: >>> @network("http://www.yahoo.com") ... def test_something_with_yahoo(): ... raise IOError("Failure Message") >>> test_something_with_yahoo() Traceback (most recent call last): ... IOError: Failure Message If you set check_before_test, it will check the url first and not run the test on failure:: >>> @network("failing://url.blaher", check_before_test=True) ... def test_something(): ... print("I ran!") ... raise ValueError("Failure") >>> test_something() Traceback (most recent call last): ... Errors not related to networking will always be raised. r)rNTcsr rtsy ||Stk r}zt|dd}| r`t|dr`t|jdd}|krvd|t|tfddDrd|t|sstrnd|WYdd}~XnXdS)Nerrnoreasonz+Skipping test due to known errno and error c3s|]}|jjkVqdS)N)lower)rKm)e_strrMrNr sz+network..wrapper..z;Skipping test because exception message is known and error z4Skipping test due to lack of connectivity and error )r Exceptionrhasattrrr=anyr)rrerrr)_skip_on_messagescheck_before_testrraise_on_errorr skip_errnostr)rrNr s(      znetwork..wrapper)r]rrnetworkr)rrrrrrrrrM)rrrrrrrrrNrW s \ "rrEc csd}tjdd}d}tj||Vg}x|D]}|rt|j|rd}|rt|jttfrddlm} m } | | dd} d|j d| j d |j } |j | j kst | q2|j |jj|j |j |jfq2W|rd t|j} |st | |o|rt d t|Wd QRXd S) ar Context manager for running code expected to either raise a specific warning, or not raise any warnings. Verifies that the code raises the expected warning, and that it does not raise any other unexpected warnings. It is basically a wrapper around ``warnings.catch_warnings``. Parameters ---------- expected_warning : {Warning, False, None}, default Warning The type of Exception raised. ``exception.Warning`` is the base class for all warnings. To check that no warning is returned, specify ``False`` or ``None``. filter_level : str or None, default "always" Specifies whether warnings are ignored, displayed, or turned into errors. Valid values are: * "error" - turns matching warnings into exceptions * "ignore" - discard the warning * "always" - always emit a warning * "default" - print the warning the first time it is generated from each location * "module" - print the warning the first time it is generated from each module * "once" - print the warning the first time it is generated check_stacklevel : bool, default True If True, displays the line that called the function containing the warning to show were the function is called. Otherwise, the line that implements the function is displayed. raise_on_extra_warnings : bool, default True Whether extra warnings not of the type `expected_warning` should cause the test to fail. Examples -------- >>> import warnings >>> with assert_produces_warning(): ... warnings.warn(UserWarning()) ... >>> with assert_produces_warning(False): ... warnings.warn(RuntimeWarning()) ... Traceback (most recent call last): ... AssertionError: Caused unexpected warning(s): ['RuntimeWarning']. >>> with assert_produces_warning(UserWarning): ... warnings.warn(RuntimeWarning()) Traceback (most recent call last): ... AssertionError: Did not see expected warning of class 'UserWarning'. ..warn:: This is *not* thread-safe. T)recordFr) getframeinfostackrzGWarning not set with correct stacklevel. File where warning is raised: z != z. 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