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_names=True, by_blocks=False, check_exact=_NoDefault.no_default, check_datetimelike_compat=False, check_categorical=True, check_like=False, check_freq=True, check_flags=True, rtol=_NoDefault.no_default, atol=_NoDefault.no_default, obj='DataFrame')[source]#

Check that left and right DataFrame are equal.

This function is intended to compare two DataFrames and output any differences. It is mostly intended for use in unit tests. Additional parameters allow varying the strictness of the equality checks performed.

Parameters:

leftDataFrame: First DataFrame to compare.
rightDataFrame: Second DataFrame to compare.
check_dtypebool, default True: Whether to check the DataFrame dtype is identical.
check_index_typebool or {‘equiv’}, default ‘equiv’: Whether to check the Index class, dtype and inferred_type are identical.
check_column_typebool 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_typebool, default True: Whether to check the DataFrame class is identical.
check_namesbool, default True: Whether to check that the names attribute for both the index and column attributes of the DataFrame is identical.
by_blocksbool, default False: Specify how to compare internal data. If False, compare by columns. If True, compare by blocks.
check_exactbool, default False: Whether to compare number exactly.

Changed in version 2.2.0: Defaults to True for integer dtypes if none of check_exact, rtol and atol are specified.
check_datetimelike_compatbool, default False: Compare datetime-like which is comparable ignoring dtype.
check_categoricalbool, default True: Whether to compare internal Categorical exactly.
check_likebool, 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_freqbool, default True: Whether to check the freq attribute on a DatetimeIndex or TimedeltaIndex.
check_flagsbool, default True: Whether to check the flags attribute.
rtolfloat, default 1e-5: Relative tolerance. Only used when check_exact is False.
atolfloat, default 1e-8: Absolute tolerance. Only used when check_exact is False.
objstr, 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)