# pandas.testing.assert_frame_equal

pandas.testing.assert_frame_equal(left, right, check_dtype=True, check_index_type='equiv', check_column_type='equiv', check_frame_type=True, check_less_precise=NoDefault.no_default, check_names=True, by_blocks=False, check_exact=False, check_datetimelike_compat=False, check_categorical=True, check_like=False, check_freq=True, check_flags=True, rtol=1e-05, atol=1e-08, obj='DataFrame')

  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 `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 since version 1.1.0: Use rtol and atol instead to define relative/absolute tolerance, respectively. Similar to `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.

      New in version 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.

      New in version 1.1.0.

    atol float, default 1e-8

      Absolute tolerance. Only used when check_exact is False.

      New in version 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)