assert_frame_equal(left, right, check_dtype=True, check_index_type='equiv', check_column_type='equiv', check_frame_type=True, check_less_precise=<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')
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.
First DataFrame to compare.
Second DataFrame to compare.
Whether to check the DataFrame dtype is identical.
Whether to check the Index class, dtype and inferred_type are identical.
Whether to check the columns class, dtype and inferred_type are identical. Is passed as the exact
argument of assert_index_equal
.
Whether to check the DataFrame class is identical.
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.
Use :None:None:`rtol` and :None:None:`atol` instead to define relative/absolute tolerance, respectively. Similar to :None:func:`math.isclose`.
Whether to check that the :None:None:`names` attribute for both the :None:None:`index` and :None:None:`column` attributes of the DataFrame is identical.
Specify how to compare internal data. If False, compare by columns. If True, compare by blocks.
Whether to compare number exactly.
Compare datetime-like which is comparable ignoring dtype.
Whether to compare internal Categorical exactly.
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.
Whether to check the :None:None:`freq` attribute on a DatetimeIndex or TimedeltaIndex.
Whether to check the flags
attribute.
Relative tolerance. Only used when check_exact is False.
Absolute tolerance. Only used when check_exact is False.
Specify object name being compared, internally used to show appropriate assertion message.
Check that left and right DataFrame are equal.
DataFrame.equals
Check DataFrame equality.
assert_series_equal
Equivalent method for asserting Series equality.
This example shows comparing two DataFrames that are equal but with columns of differing dtypes.
This example is valid syntax, but we were not able to check execution>>> 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.
This example is valid syntax, but we were not able to check execution>>> assert_frame_equal(df1, df1)
df1 differs from df2 as column 'b' is of a different type.
This example is valid syntax, but we were not able to check execution>>> 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.
This example is valid syntax, but we were not able to check execution>>> assert_frame_equal(df1, df2, check_dtype=False)See :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas._testing.asserters.assert_frame_equal
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