Learn what a test is and how to run the first one of your own with the pytest library! You will get used to the pytest testing framework and the command-line interface. You will also learn how to process specific contexts, like "failed tests" and "skipping the test" with pytest markers.

Introduction to Testing in Python

The first test suite

pytest.raises

Invoking pytest from CLI

Run the test!

Run with the keyword

Applying test markers

Markers use cases

Failed tests with xfail

Conditional skipping

Creating Tests with pytest

Learn what a fixture is and how to simplify your code by using it in tests. You will get familiar with the fixture @pytest.fixture decorator and the fixture tools. You will analyze your code to see the "fixture part" in it. Finally, learn how to use teardowns to prevent software failures.

Introduction to fixtures

Getting familiar with fixtures

Data preparation

Run with a fixture

Chain Fixtures Requests

Chain this out

List with a custom length

Fixtures autouse

autouse statements

Auto add numbers

Fixtures Teardowns

Data with teardown

Read data with teardown

Pytest Fixtures

Learn what the basic testing types are and their features. Learn about test cases and how they help to implement tests. You will get more skilled with creating test functions and running pytest from CLI in IDE exercises. Finally, you will be able to differentiate the different testing types and create tests for each of them.

Unit testing with pytest

Unit testing terms

Cover more test cases

Factorial of number

Run factorial

Feature testing with pytest

Feature or unit testing

Aggregate with sum

Integration testing with pytest

Integration test or not

Read the file

Performance testing with pytest

What is performance testing?

Finding an element

Speed of loops

Basic Testing Types

In this final chapter, you will meet the unittest framework. First, you will learn basic assertion methods, then its CLI interface, and how to use fixtures. Finally, you will put everything together in the practical examples of data pipelines.

Meeting the Unittest

Factorial with unittest

Is prime or not

CLI Interface

import unittest

def func_factorial(number):
    if number < 0:
        raise ValueError('Factorial is not defined for negative values')
    factorial = 1
    while number > 1:
        factorial = factorial * number
        number = number - 1
    return factorial

class TestFactorial(unittest.TestCase):
    def test_positives(self):
        self.assertEqual(func_factorial(5), 120)

    def test_zero(self):
        self.assertEqual(func_factorial(0), 1)

    def test_negatives(self):
        with self.assertRaises(ValueError):
            func_factorial(-1)


Run factorial with unittest

import unittest

def err_func_factorial(number):
    if number < 0:
        raise ValueError('Factorial is not defined for negative values')
    factorial = 1
    while number > 2:
        factorial = factorial * number
        number = number - 1
    return factorial

class TestFactorial(unittest.TestCase):
    def test_err_func_1(self):
        self.assertEqual(err_func_factorial(3), 6)
    def test_err_func_2(self):
        self.assertEqual(err_func_factorial(4), 24)


import unittest

def err_func_factorial(number):
    if number < 0:
        raise ValueError('Factorial is not defined for negative values')
    factorial = 1
    while number > 1:
        factorial = factorial * number
        number = number - 1
    return factorial

class TestFactorial(unittest.TestCase):
    def test_err_func_1(self):
        self.assertEqual(err_func_factorial(3), 6)
    def test_err_func_2(self):
        self.assertEqual(err_func_factorial(4), 24)


Erroneouos factorial

- Run this command to run the testing script with the verbose flag:
  python3 -m unittest -v err_factorial_unittest.py
  
- Run this command to run the testing script with the fail fast flag:
  python3 -m unittest -f err_factorial_unittest.py
  
- Run this command to run the testing script with the keyword flag:
  python3 -m unittest -k "err_func_1" err_factorial_unittest.py


Unittest options

Fixtures in unittest

import unittest

class TestWord(unittest.TestCase):
    # Fixture setup method
    def setUp(self):
        # Initialize the word banana here
        self.word = ____

    # Test method
    def test_the_word(self):
        # Add the tests here
        ____
        ____
        ____
    
    # Fixture teardown method
    def tearDown(self):
        # Delete the word variable here
        ____


import unittest

class TestWord(unittest.TestCase):
    # Fixture setup method
    def setUp(self):
        # Initialize the word banana here
        self.word = 'banana'

    # Test method
    def test_the_word(self):
        # Add the tests here
        self.assertIn('b', self.word)
        self.assertNotIn('B', self.word)
        self.assertNotIn('y', self.word)
    
    # Fixture teardown method
    def tearDown(self):
        # Delete the word variable here
        del self.word


Test the string variable

import unittest

def check_palindrome(string):
    reversed_string = string[::-1]
    return string == reversed_string

def create_data():
    return ['level', 'step', 'peep', 'toot']

class TestPalindrome(unittest.TestCase):
    def setUp(self):
        # Initialize data here
        ____
    
    def test_func(self):
        expected_result = [True, False, True, True]
        data_checked = list(map(check_palindrome, self.data))
        # Verify the checked data here
        ____

    def tearDown(self):
        # Clear the data here
        ____


import unittest

def check_palindrome(string):
    reversed_string = string[::-1]
    return string == reversed_string

def create_data():
    return ['level', 'step', 'peep', 'toot']

class TestPalindrome(unittest.TestCase):
    def setUp(self):
        # Initialize data here
        self.data = create_data()
    
    def test_func(self):
        expected_result = [True, False, True, True]
        data_checked = list(map(check_palindrome, self.data))
        # Verify the checked data here
        self.assertEqual(data_checked, expected_result)

    def tearDown(self):
        # Clear the data here
        self.data.clear()


Palindrome check

Practical examples

import pytest
import pandas as pd

DF_PATH = "/usr/local/share/salaries.csv"
@pytest.fixture
def read_df():
    return pd.read_csv(DF_PATH)

def get_grouped(df):
    return df.groupby('work_year').agg({'salary': 'describe'})['salary']

def test_read_df(read_df):
    # Check the type of the dataframe
    ____
    # Check that read_df contains rows
    ____

def test_grouped(read_df):
    df = read_df
    salary_by_year = get_grouped(df)
    # Check the nulls here
    ____


import pytest
import pandas as pd

DF_PATH = "/usr/local/share/salaries.csv"
@pytest.fixture
def read_df():
    return pd.read_csv(DF_PATH)

def get_grouped(df):
    return df.groupby('work_year').agg({'salary': 'describe'})['salary']

def test_read_df(read_df):
    # Check the type of the dataframe
    assert isinstance(read_df, pd.DataFrame)
    # Check that df contains rows
    assert read_df.shape[0] > 0

def test_grouped(read_df):
    df = read_df
    salary_by_year = get_grouped(df)
    # Check the nulls here
    assert salary_by_year.isna().sum().sum() == 0


Integration and unit tests

import pytest
import pandas as pd

DF_PATH = "/usr/local/share/salaries.csv"
@pytest.fixture
def read_df():
    return pd.read_csv(DF_PATH)

def get_grouped(df):
    return df.groupby('work_year').agg({'salary': 'describe'})['salary']

def test_feature_2022(read_df):
    salary_by_year = get_grouped(read_df)
    salary_2022 = salary_by_year.loc[2022, '50%']
    # Check the median type here
    ____
    # Check the median is greater than zero
    ____

# Use benchmark here
def test_reading_speed(____):
    ____


import pytest
import pandas as pd

DF_PATH = "/usr/local/share/salaries.csv"
@pytest.fixture
def read_df():
    return pd.read_csv(DF_PATH)

def get_grouped(df):
    return df.groupby('work_year').agg({'salary': 'describe'})['salary']

def test_feature_2022(read_df):
    salary_by_year = get_grouped(read_df)
    salary_2022 = salary_by_year.loc[2022, '50%']
    # Check the median type here
    assert isinstance(salary_2022, float)
    # Check the median is greater than zero
    assert 0 < salary_2022

# Use benchmark here
def test_reading_speed(benchmark):
    benchmark(pd.read_csv, DF_PATH)


Feature and performance tests

import unittest
import pandas as pd
DF_PATH = 'https://assets.datacamp.com/production/repositories/6253/datasets/f015ac99df614ada3ef5e011c168054ca369d23b/energy_truncated.csv'

def get_data():
    return pd.read_csv(DF_PATH)

def min_country(df):
    return df['VALUE'].idxmin()

class TestDF(unittest.TestCase):
    def setUp(self):
        self.df = get_data()
        self.df.drop('previousYearToDate', axis=1, inplace=True)
        self.df = self.df.groupby('COUNTRY')\
            .agg({'VALUE': 'sum'})

    def test_NAs(self):
        # Check the number of nulls
        ____

    def test_argmax(self):
        # Check that min_country returns a string
        ____

    def tearDown(self):
        self.df.drop(self.df.index, inplace=True)


import unittest
import pandas as pd
DF_PATH = 'https://assets.datacamp.com/production/repositories/6253/datasets/f015ac99df614ada3ef5e011c168054ca369d23b/energy_truncated.csv'

def get_data():
    return pd.read_csv(DF_PATH)

def min_country(df):
    return df['VALUE'].idxmin()

class TestDF(unittest.TestCase):
    def setUp(self):
        self.df = get_data()
        self.df.drop('previousYearToDate', axis=1, inplace=True)
        self.df = self.df.groupby('COUNTRY')\
            .agg({'VALUE': 'sum'})

    def test_NAs(self):
        # Check the number of nulls
        self.assertEqual(self.df.isna().sum().sum(), 0)

    def test_argmax(self):
        # Check that min_country returns a string
        self.assertIsInstance(min_country(self.df), str)

    def tearDown(self):
        self.df.drop(self.df.index, inplace=True)


Energy pipeline

Congratulations!

Writing tests with unittest

DS_salaries

Energy

Games

Plenty of people write code. Some of them make it work and profitable. But sometimes, even the smartest of the best programmers makes a mistake that can cost millions of dollars. How to decrease the possibility of getting into such a fiasco? How do you ensure that you create a program that does exactly what you want? The very simple answer is: write tests!

During this journey, you will learn the very basics of creating tests in Python. You will meet four types of software testing methods. You will create your own tests to check if the program or a data pipeline works as expected before it goes to production. Whether it is the unexpected null, a typo in your dataset, or mixed-up signs in the equation. You can, and you will catch those cases with the tests.

After the course completion, you will know the types of testing methods, and you will be able to choose the most suitable ones for a specific context. You also will be able to design those tests and implement them in Python using the pytest and the unittest libraries.

<h2>Why tests?</h2>Plenty of people write code. Some of them make it work and profitable. But sometimes, even the smartest of the best programmers makes a mistake that can cost millions of dollars. How to decrease the possibility of getting into such a fiasco? How do you ensure that you create a program that does exactly what you want? The very simple answer is: write tests!<br><br><h2>Python testing basics</h2>During this journey, you will learn the very basics of creating tests in Python. You will meet four types of software testing methods. You will create your own tests to check if the program or a data pipeline works as expected before it goes to production. Whether it is the unexpected null, a typo in your dataset, or mixed-up signs in the equation. You can, and you will catch those cases with the tests.<br><br><h2>Testing with pytest and unittest</h2>After the course completion, you will know the types of testing methods, and you will be able to choose the most suitable ones for a specific context. You also will be able to design those tests and implement them in Python using the `pytest` and the `unittest` libraries.

Writing Functions in Python

Software Engineering Principles in Python

Master Python testing: Learn methods, create checks, and ensure error-free code with pytest and unittest.

Practical examples

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