Approximate comparisons (pytest.approx) - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to assert that 0.1 + 0.2 is approximately equal to 0.3 using pytest.approx.

PyTest

import pytest

def test_sum():
    assert 0.1 + 0.2 == [1]

Drag options to blanks, or click blank then click option'

Apytest.approx(0.3)

B0.3

Capprox(0.3)

D0.30000000000000004

Attempts:

3 left

💡 Hint

Common Mistakes

Using direct equality (==) with floating points causes test failures due to precision errors.

Using a plain float instead of pytest.approx for approximate comparison.

pytest.approx allows approximate comparison of floating point numbers, handling small rounding errors.

2fill in blank

medium

Complete the code to assert that the list [1.0, 2.0, 3.0] is approximately equal to [1.0, 2.0001, 3.0] using pytest.approx.

PyTest

import pytest

def test_list_approx():
    result = [1.0, 2.0, 3.0]
    expected = [1.0, 2.0001, 3.0]
    assert result == [1]

Drag options to blanks, or click blank then click option'

Apytest.approx(expected)

Bexpected

Capprox(expected)

Dexpected.approx()

Attempts:

3 left

💡 Hint

Common Mistakes

Comparing lists directly without pytest.approx causes failures due to small differences.

Trying to call approx() without importing pytest.

pytest.approx works with lists to compare each element approximately.

3fill in blank

hard

Fix the error in the test by completing the code to compare floating point numbers approximately with a relative tolerance of 1e-3.

PyTest

import pytest

def test_relative_tolerance():
    value = 100.0
    expected = 100.1
    assert value == pytest.approx(expected, [1]=1e-3)

Drag options to blanks, or click blank then click option'

Aabs

Brelative

Crel

Drel_tol

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'abs' instead of 'rel' for relative tolerance.

Using 'rel_tol' or 'relative' which are not valid pytest.approx parameters.

The correct keyword argument for relative tolerance in pytest.approx is rel.

4fill in blank

hard

Fill both blanks to assert that 0.123456 is approximately equal to 0.123 with an absolute tolerance of 1e-3.

PyTest

import pytest

def test_absolute_tolerance():
    assert 0.123456 == pytest.approx(0.123, [1]=[2])

Drag options to blanks, or click blank then click option'

Aabs

Brel

C1e-3

D1e-4

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'rel' instead of 'abs' for absolute tolerance.

Setting tolerance too small to allow the difference.

Use abs for absolute tolerance and set it to 1e-3 to allow small absolute differences.

5fill in blank

hard

Fill all three blanks to assert that the dictionary {'a': 1.0, 'b': 2.0} is approximately equal to {'a': 1.001, 'b': 2.002} with relative tolerance 1e-2 and absolute tolerance 1e-4.

PyTest

import pytest

def test_dict_approx():
    result = {'a': 1.0, 'b': 2.0}
    expected = {'a': 1.001, 'b': 2.002}
    assert result == pytest.approx(expected, [1]=[2], [3]=[4])

Drag options to blanks, or click blank then click option'

Arel

Babs

C1e-2

D1e-4

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping 'rel' and 'abs' keywords.

Using incorrect tolerance values causing test failures.

pytest.approx accepts both rel for relative tolerance and abs for absolute tolerance. Here, rel=1e-2 and abs=1e-4 allow approximate comparison of dictionary values.

Practice

1. What is the main purpose of using pytest.approx in tests?

easy

A. To compare floating-point numbers allowing small differences

B. To check if two strings are exactly the same

C. To test if a function raises an exception

D. To verify if a list contains a specific element

Solution

1. Step 1: Understand floating-point comparison issues

   Floating-point numbers can have tiny differences due to rounding, so exact equality often fails.

2. Step 2: Role of pytest.approx

   pytest.approx allows tests to pass if numbers are close within a tolerance, avoiding false failures.

3. Final Answer:

   To compare floating-point numbers allowing small differences -> Option A

4. Quick Check:

   Approximate comparison = floating-point tolerance [OK]

Hint: Use approx for floats, not exact matches [OK]

Common Mistakes:

• Using == for floats causing flaky tests
• Confusing approx with exception testing
• Using approx for string comparisons

2. Which of the following is the correct syntax to assert that result is approximately equal to 3.14 using pytest.approx?

easy

A. assert result == pytest.approx[3.14]

B. assert result == approx(3.14)

C. assert result = pytest.approx(3.14)

D. assert result == pytest.approx(3.14)

Solution

1. Step 1: Identify correct usage of pytest.approx

   The correct syntax uses the function call form: pytest.approx(value).

2. Step 2: Check assertion syntax

   Assertions use '==' to compare, '=' is assignment and brackets [] are invalid here.

3. Final Answer:

   assert result == pytest.approx(3.14) -> Option D

4. Quick Check:

   Use '==' and parentheses with pytest.approx [OK]

Hint: Use '==' and parentheses with pytest.approx [OK]

Common Mistakes:

• Using '=' instead of '==' in assert
• Calling approx without pytest prefix if not imported
• Using square brackets instead of parentheses

3. What will be the result of this test code?

def test_value():
    value = 0.1 + 0.2
    assert value == pytest.approx(0.3)

medium

A. Test passes because 0.1 + 0.2 is exactly 0.3

B. Test fails due to floating-point precision error

C. Test passes because pytest.approx allows small differences

D. SyntaxError because pytest.approx is used incorrectly

Solution

1. Step 1: Understand floating-point addition

   0.1 + 0.2 is not exactly 0.3 due to floating-point precision limits.

2. Step 2: Effect of pytest.approx in assertion

   pytest.approx allows a small tolerance, so the assertion passes despite tiny difference.

3. Final Answer:

   Test passes because pytest.approx allows small differences -> Option C

4. Quick Check:

   Floating-point sum ≈ 0.3 passes with approx [OK]

Hint: Use approx to handle float rounding errors [OK]

Common Mistakes:

• Assuming 0.1 + 0.2 equals exactly 0.3
• Expecting test to fail without approx
• Thinking pytest.approx causes syntax errors

4. Identify the error in this test code snippet:

def test_approx():
    result = 1.0001
    assert result == pytest.approx(1.0, rel=0.00001)

medium

A. The relative tolerance is too small, causing test to fail

B. pytest.approx does not accept rel parameter

C. The assertion syntax is incorrect

D. The variable result should be an integer

Solution

1. Step 1: Check relative tolerance meaning

   rel=0.00001 means allowed difference is 0.00001 * 1.0 = 0.00001.

2. Step 2: Compare actual difference with tolerance

   Difference is 0.0001, which is greater than 0.00001, so assertion fails.

3. Final Answer:

   The relative tolerance is too small, causing test to fail -> Option A

4. Quick Check:

   Difference > rel tolerance causes failure [OK]

Hint: Check if difference fits within rel tolerance [OK]

Common Mistakes:

• Assuming rel parameter is invalid
• Ignoring difference size vs tolerance
• Confusing syntax error with logic error

5. You want to test that a function returns a value close to 100, but it may vary by up to 0.5 units. Which is the best way to write this assertion using pytest.approx?

hard

A. assert result == pytest.approx(100, rel=0.5)

B. assert result == pytest.approx(100, abs=0.5)

C. assert result == pytest.approx(100, abs=0.05)

D. assert result == pytest.approx(100, rel=0.005)

Solution

1. Step 1: Understand absolute vs relative tolerance

   Absolute tolerance (abs) allows fixed difference; relative (rel) allows percentage difference.

2. Step 2: Match tolerance to requirement

   Variation up to 0.5 units means absolute tolerance of 0.5 is correct.

3. Final Answer:

   assert result == pytest.approx(100, abs=0.5) -> Option B

4. Quick Check:

   Use abs=0.5 for fixed unit tolerance [OK]

Hint: Use abs for fixed tolerance, rel for percentage [OK]

Common Mistakes:

• Using rel when fixed tolerance needed
• Setting abs too small to cover variation
• Confusing abs and rel parameters