Approximate comparisons (pytest.approx) - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of approximate float comparison with pytest.approx

What is the result of running this pytest assertion code snippet?

PyTest

import pytest

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

A Test passes (no assertion error)

B AssertionError: 0.1 + 0.2 is not approximately equal to 0.3

C SyntaxError due to incorrect pytest.approx usage

D TypeError because pytest.approx cannot compare floats

💡 Hint

Remember that floating point arithmetic can cause small precision errors, pytest.approx helps with that.

The sum 0.1 + 0.2 is not exactly 0.3 due to floating point precision, but pytest.approx allows approximate equality, so the assertion passes.

❓ assertion

intermediate

Choosing the correct pytest.approx usage for relative tolerance

Which assertion correctly tests that variable x is approximately 10 with a relative tolerance of 1%?

A assert x == pytest.approx(10, rel=1)

B assert x == pytest.approx(10, rel=0.01)

C assert x == pytest.approx(10, abs=0.01)

D assert x == pytest.approx(10, tol=0.01)

💡 Hint

Check the parameter name for relative tolerance in pytest.approx.

The rel parameter sets relative tolerance. abs sets absolute tolerance. tol is not a valid parameter.

🔧 Debug

advanced

Identify the error in pytest.approx usage

What error will this test raise when run?

PyTest

import pytest

def test_value():
    assert 5 == pytest.approx(5, rel=-0.1)

A Test passes without error

B AssertionError because 5 is not approximately equal to 5 with negative tolerance

C TypeError due to invalid argument type

D ValueError: relative tolerance must be non-negative

💡 Hint

Check the allowed range for relative tolerance values in pytest.approx.

pytest.approx requires rel to be zero or positive. Negative values cause ValueError.

🧠 Conceptual

advanced

Understanding pytest.approx behavior with sequences

Given two lists a = [1.0, 2.0, 3.0] and b = [1.0, 2.01, 3.0], which pytest assertion will pass?

PyTest

import pytest
a = [1.0, 2.0, 3.0]
b = [1.0, 2.01, 3.0]

A assert a == pytest.approx(b, rel=0.005)

B assert a == pytest.approx(b, abs=0.005)

C assert a == pytest.approx(b, abs=0.02)

D assert a == pytest.approx(b, rel=0.01)

💡 Hint

Consider the difference between 2.0 and 2.01 and the tolerance values.

Difference is 0.01. abs=0.02 allows difference up to 0.02, so assertion passes only with option C.

❓ framework

expert

Configuring pytest to globally change default tolerance for approx

How can you globally set pytest.approx to use a relative tolerance of 0.001 for all tests without changing each assertion?

A Override pytest.approx in conftest.py to wrap original approx with rel=0.001

B Use a pytest fixture that monkeypatches pytest.approx to set rel=0.001 by default

C Set an environment variable PYTEST_APPROX_REL=0.001 before running tests

D Create a pytest.ini file with [pytest] and add approx-rel = 0.001

💡 Hint

Think about how to customize pytest behavior programmatically.

pytest does not support config files or env vars for approx tolerance. Overriding approx in conftest.py is the way to globally change behavior.

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