Testing¶
snoop : Smart Print to Debug Your Python Function¶
!pip install snoop
If you want to figure out what is happening in your code without adding many print statements, try snoop.
To use snoop, simply add the @snoop decorator to a function you want to understand.
import snoop
@snoop
def factorial(x: int):
if x == 1:
return 1
else:
return (x * factorial(x-1))
num = 2
print(f'The factorial of {num} is {factorial(num)}')
12:46:29.30 >>> Call to factorial in File "/tmp/ipykernel_72663/20705592.py", line 4
12:46:29.30 ...... x = 2
12:46:29.30 4 | def factorial(x: int):
12:46:29.30 5 | if x == 1:
12:46:29.30 8 | return (x * factorial(x-1))
12:46:29.30 >>> Call to factorial in File "/tmp/ipykernel_72663/20705592.py", line 4
12:46:29.30 ...... x = 1
12:46:29.30 4 | def factorial(x: int):
12:46:29.31 5 | if x == 1:
12:46:29.31 6 | return 1
12:46:29.31 <<< Return value from factorial: 1
12:46:29.31 8 | return (x * factorial(x-1))
12:46:29.31 <<< Return value from factorial: 2
The factorial of 2 is 2
pytest benchmark: A Pytest Fixture to Benchmark Your Code¶
!pip install pytest-benchmark
If you want to benchmark your code while testing with pytest, try pytest-benchmark.
To understand how pytest-benchmark works, add the code below to the file named pytest_benchmark_example.py. Note that we add benchmark to the test function that we want to benchmark.
# pytest_benchmark_example.py
def list_comprehension(len_list=5):
return [i for i in range(len_list)]
def test_concat(benchmark):
res = benchmark(list_comprehension)
assert res == [0, 1, 2, 3, 4]
On your terminal, type:
$ pytest pytest_benchmark_example.py
Now you should see the statistics of the time it takes to excecute the test functions on your terminal:
!pytest pytest_benchmark_example.py
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-6.2.5, py-1.10.0, pluggy-0.13.1
benchmark: 3.4.1 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
rootdir: /home/khuyen/book/book/Chapter4
plugins: hydra-core-1.1.1, Faker-8.12.1, benchmark-3.4.1, repeat-0.9.1, anyio-3.3.0
collected 1 item
pytest_benchmark_example.py . [100%]
----------------------------------------------------- benchmark: 1 tests ----------------------------------------------------
Name (time in ns) Min Max Mean StdDev Median IQR Outliers OPS (Mops/s) Rounds Iterations
-----------------------------------------------------------------------------------------------------------------------------
test_concat 286.4501 4,745.5498 309.3872 106.6583 297.5001 5.3500 2686;5843 3.2322 162101 20
-----------------------------------------------------------------------------------------------------------------------------
Legend:
Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.
OPS: Operations Per Second, computed as 1 / Mean
============================== 1 passed in 2.47s ===============================
pytest.mark.parametrize: Test Your Functions with Multiple Inputs¶
!pip install pytest
If you want to test your function with different examples, use pytest.mark.parametrize decorator.
To understand how pytest.mark.parametrize works, start with adding the code below to pytest_parametrize.py. @pytest.mark.parametrize is added to the test function test_text_contain_word to tell pytest to experiment with different pairs of inputs and expected outputs specified in its arguments.
# pytest_parametrize.py
import pytest
def text_contain_word(word: str, text: str):
'''Find whether the text contains a particular word'''
return word in text
test = [
('There is a duck in this text',True),
('There is nothing here', False)
]
@pytest.mark.parametrize('sample, expected', test)
def test_text_contain_word(sample, expected):
word = 'duck'
assert text_contain_word(word, sample) == expected
In the code above, I expect the first sentence to contain the word “duck” and expect the second sentence not to contain that word. Let’s see if my expectations are correct by running:
$ pytest pytest_parametrize.py
!pytest pytest_parametrize.py
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-6.2.5, py-1.10.0, pluggy-1.0.0
benchmark: 3.4.1 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
rootdir: /home/khuyen/book/book/Chapter4
plugins: benchmark-3.4.1, anyio-3.3.0
collecting ...
collected 2 items
pytest_parametrize.py .. [100%]
============================== 2 passed in 0.01s ===============================
Sweet! 2 tests passed when running pytest.
Pytest Fixtures: Use The Same Data for Different Tests¶
!pip install pytest
Sometimes, you might want to use the same data to test different functions. If you are using pytest, use pytest fixtures to provide data to different test functions instead.
To understand how to use pytest fixtures, start with adding the code below to the file named pytest_fixture.py. We use the decorator @pytest.fixture to create the data we want to use many times.
# pytest_fixture.py
import pytest
from textblob import TextBlob
def extract_sentiment(text: str):
"""Extract sentimetn using textblob. Polarity is within range [-1, 1]"""
text = TextBlob(text)
return text.sentiment.polarity
@pytest.fixture
def example_data():
return 'Today I found a duck and I am happy'
def test_extract_sentiment(example_data):
sentiment = extract_sentiment(example_data)
assert sentiment > 0
On your terminal, type:
$ pytest pytest_fixture.py
Output:
!pytest pytest_fixture.py
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-6.2.5, py-1.10.0, pluggy-1.0.0
benchmark: 3.4.1 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
rootdir: /home/khuyen/book/book/Chapter4
plugins: benchmark-3.4.1, anyio-3.3.0
collected 1 item
pytest_fixture.py . [100%]
============================== 1 passed in 0.53s ===============================
Pytest repeat¶
!pip install pytest-repeat
It is a good practice to test your functions to make sure they work as expected, but sometimes you need to test 100 times until you found the rare cases when the test fails. That is when pytest-repeat comes in handy.
To understand how pytest-repeat works, start with putting the code below to the file named pytest_repeat_example.py. The @pytest.mark.repeat(100) decorator tells pytest to repeat the experiment 100 times.
# pytest_repeat_example.py
import pytest
import random
def generate_numbers():
return random.randint(1, 100)
@pytest.mark.repeat(100)
def test_generate_numbers():
assert generate_numbers() > 1 and generate_numbers() < 100
On your terminal, type:
pytest pytest_repeat_example.py
We can see that 100 experiments are executed and passed:
!pytest pytest_repeat_example.py
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-6.2.5, py-1.10.0, pluggy-1.0.0
benchmark: 3.4.1 (defaults: timer=time.perf_counter disable_gc=False min_rounds=5 min_time=0.000005 max_time=1.0 calibration_precision=10 warmup=False warmup_iterations=100000)
rootdir: /home/khuyen/book/book/Chapter4
plugins: benchmark-3.4.1, repeat-0.9.1, anyio-3.3.0
collected 100 items
pytest_repeat_example.py ............................................... [ 47%]
..................................................... [100%]
============================= 100 passed in 0.07s ==============================
Pandera: a Python Library to Validate Your Pandas DataFrame¶
!pip install pandera
The outputs of your pandas DataFrame might not be like what you expected either due to the error in your code or the change in the data format. Using data that is different from what you expected can cause errors or lead to decrease performance.
Thus, it is important to validate your data before using it. A good tool to validate pandas DataFrame is pandera. Pandera is easy to read and use.
import pandera as pa
from pandera import check_input
import pandas as pd
df = pd.DataFrame({"col1": [5.0, 8.0, 10.0], "col2": ["text_1", "text_2", "text_3"]})
schema = pa.DataFrameSchema(
{
"col1": pa.Column(float, pa.Check(lambda minute: 5 <= minute)),
"col2": pa.Column(str, pa.Check.str_startswith("text_")),
}
)
validated_df = schema(df)
validated_df
| col1 | col2 | |
|---|---|---|
| 0 | 5.0 | text_1 |
| 1 | 8.0 | text_2 |
| 2 | 10.0 | text_3 |
You can also use the pandera’s decorator check_inputto validates input pandas DataFrame before entering the function.
@check_input(schema)
def plus_three(df):
df["col1_plus_3"] = df["col1"] + 3
return df
plus_three(df)
| col1 | col2 | col1_plus_3 | |
|---|---|---|---|
| 0 | 5.0 | text_1 | 8.0 |
| 1 | 8.0 | text_2 | 11.0 |
| 2 | 10.0 | text_3 | 13.0 |