Testing with Polars

Note

To ensure you are using compatible versions, install with the testfixtures[polars] extra.

When polars is installed, a comparer for polars.DataFrame is automatically registered with ignore_eq=True. It hands off to polars.testing.assert_frame_equal(), so the diff output and tolerance semantics are exactly those of polars’ own test helper. Passing strict=True to compare() switches the underlying call to check_exact=True.

Equal frames compare equal:

>>> import polars as pl
>>> from testfixtures import compare
>>> df1 = pl.DataFrame({'a': [1]})
>>> df2 = pl.DataFrame({'a': [1]})
>>> compare(df1, expected=df2)

When frames differ inside a larger structure, compare()’s breadcrumbs still point at the location of the difference:

>>> compare({'foo': df1}, expected={'foo': pl.DataFrame({'a': [2]})})
Traceback (most recent call last):
 ...
AssertionError: dict not as expected:

values differ:
'foo': shape: (1, 1)
...
While comparing ['foo']: DataFrames are different (value mismatch for column "a")
[left]: shape: (1,)
Series: 'a' [i64]
[
        2
]
[right]: shape: (1,)
Series: 'a' [i64]
[
        1
]

Float comparisons use polars’ default rel_tol and abs_tol, so small differences compare equal:

>>> fa = pl.DataFrame({'x': [1.0]})
>>> fb = pl.DataFrame({'x': [1.0 + 1e-9]})
>>> compare(fa, expected=fb)

Pass strict=True to require bitwise equality instead:

>>> compare(fa, expected=fb, strict=True)
Traceback (most recent call last):
 ...
AssertionError: DataFrames are different (value mismatch for column "x")
[left]: shape: (1,)
Series: 'x' [f64]
[
        1.0
]
[right]: shape: (1,)
Series: 'x' [f64]
[
        1.0
]