# pandas.testing.assert_series_equal

`pandas.testing.``assert_series_equal`(left, right, check_dtype=True, check_index_type='equiv', check_series_type=True, check_less_precise=False, check_names=True, check_exact=False, check_datetimelike_compat=False, check_categorical=True, obj='Series')

  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 / string {‘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.

  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.

  obj : str, default ‘Series’

  > Specify object name being compared, internally used to show appropriate assertion message.

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