Debugging your Python apps with Pytest can make solving problems easier. Here are some simple ways to use Pytest for debugging.
Assertions help check if your code works like it should. Instead of just saying something like assert x == 5
, you can add helpful messages to find problems more easily:
def test_addition():
x = add(2, 3)
assert x == 5, f"Expected 5, but got {x}"
--pdb
OptionIf a test fails, you can enter the Python debugger by using the --pdb
option with Pytest:
pytest --pdb
This lets you look at your variables and see what’s happening right where the test failed.
Fixtures help set up what you need for your tests. They make sure that every test starts the same way, which helps you spot problems:
import pytest
@pytest.fixture
def sample_data():
return [1, 2, 3]
def test_data_processing(sample_data):
assert process_data(sample_data) == expected_result
You can choose to run only certain tests or test files. This makes it easier to find where the issues are:
pytest tests/test_file.py
By using these tips, you can successfully debug your Python applications with Pytest. This will help you develop faster and write stronger code!
Debugging your Python apps with Pytest can make solving problems easier. Here are some simple ways to use Pytest for debugging.
Assertions help check if your code works like it should. Instead of just saying something like assert x == 5
, you can add helpful messages to find problems more easily:
def test_addition():
x = add(2, 3)
assert x == 5, f"Expected 5, but got {x}"
--pdb
OptionIf a test fails, you can enter the Python debugger by using the --pdb
option with Pytest:
pytest --pdb
This lets you look at your variables and see what’s happening right where the test failed.
Fixtures help set up what you need for your tests. They make sure that every test starts the same way, which helps you spot problems:
import pytest
@pytest.fixture
def sample_data():
return [1, 2, 3]
def test_data_processing(sample_data):
assert process_data(sample_data) == expected_result
You can choose to run only certain tests or test files. This makes it easier to find where the issues are:
pytest tests/test_file.py
By using these tips, you can successfully debug your Python applications with Pytest. This will help you develop faster and write stronger code!