Top 6 BEST Python Testing Frameworks [Updated 2025 List]

This Tutorial Explains How Python can be used for Test Programming and Lists the Features and Comparison of the Top Python Testing Frameworks:

With the widespread application of Artificial Intelligence, Python has become a popular programming language.

This tutorial will cover how Python can be used for test programming along with some Python-based testing frameworks.

Let’s start!!

=> Check ALL Python Tutorials Here.

What Is Python?

According to the traditional definition, Python is an interpreted, high-level, general programming language that helps programmers write manageable and logical code for small as well as large-scale projects.

Some of the benefits of Pythons are:

• No compilation causes the fast execution of the Edit-Test-Debug cycle.
• Easy debugging
• Extensive support library
• Easy to learn data-structure
• High productivity
• Team collaboration

Working In Python

• The interpreter reads the Python code from the source file and examines it for a syntax error.
• If the code is error-free then the interpreter converts the code to its equivalent ‘Byte code’.
• This byte code is then transmitted to the Python Virtual Machine (PVM) where the Byte code is again compiled for error if any.

What Is Python Testing?

• Automated testing is a well-known context in the world of testing. It is where the test plans are being executed using a script instead of a human.
• Python comes with the tools and libraries that support automated testing for your system.
• Python Test cases are comparatively easy to write. With the increased use of Python, Python-based test automation frameworks are also becoming popular.

List Of Python Testing Frameworks

Enlisted below are some Python Testing frameworks that you should know.

1. Robot
2. PyTest
3. Unittest
4. DocTest
5. Nose2
6. Testify

Comparison of Python Testing Tools

Let’s quickly summarize these frameworks in a short comparison table:

(Abbreviations: MIT = Massachusetts Institute of Technology (1980), BSD = Berkeley Software Distribution (1988), ASF = Apache Software Foundation(2004))

Let’s start!!

#1) Robot

• The most popular Robot Framework is an open-source Automation Testing framework based on Python.
• This framework is entirely developed in Python and is used for Acceptance Testing and Test-driven development. Keyword style is being used to write test cases in the Robot framework.
• The Robot is capable of running Java and .Net and also supports automation testing on cross-platform like Windows, Mac OS, and Linux for desktop applications, mobile applications, web applications, etc.
• Along with Acceptance Testing, the Robot is also used for Robotic Process Automation (RPA).
• Pip (Package Installer for Python) is highly recommended for Robot installation.
• The use of tabular data syntax, keyword-driven testing, rich libraries & toolsets, and parallel testing are some of the strong features of Robot that make it popular among testers.

Example:

*** Settings ***
Library           SeleniumLibrary

*** Variables ***
${SERVER}         localhost:7272
${BROWSER}        Firefox
${DELAY}        0
${VALID USER}     demo
${VALID PASSWORD}    mode
${LOGIN URL}      http://${SERVER}/
${WELCOME URL}    http://${SERVER}/welcome.html
${ERROR URL}      http://${SERVER}/error.html

*** Keywords ***
Open Browser To Login Page
    Open Browser    ${LOGIN URL}    ${BROWSER}
    Maximize Browser Window
    Set Selenium Speed    ${DELAY}
Login Page Should Be Open
    Title Should Be    Login Page

Go To Login Page
    Go To    ${LOGIN URL}
    Login Page Should Be Open

Input Username
    [Arguments]    ${username}
    Input Text    username_field    ${username}

Input Password
    [Arguments]    ${password}
    Input Text    password_field    ${password}

Submit Credentials
    Click Button    login_button

Welcome Page Should Be Open
    Location Should Be    ${WELCOME URL}
    Title Should Be    Welcome Page

Here is a sample of Failed Test Execution.

Here is a sample of Successful Test Execution.

Packages/Methods:

Link to API: Robot Framework User Guide
Download Link: Robot

---

#2) PyTest

• PyTest is an open-source Python-based testing framework that is generally all-purpose but especially for Functional and API testing.
• Pip (Package Installer for Python) is required for PyTest installation.
• It supports simple or complex text code to test API, databases, and UIs.
• Simple syntax is helpful for easy test execution.
• Rich plugins and can run tests in parallel.
• Can run any specific subset of tests.

Example:

import pytest                                //Import unittest module//
def test_file1_method():               //Function inside class//
      x=5
      y=6
      assert x+1 == y,"test failed"

To run the test use the py.test command.

Screenshot for Reference:

[image source]

Packages/Methods:

If you want to access a test written in a specific file we use the below command.

py.test <filename>

Pytest Fixture: Pytest Fixture is used to run code before executing the test method to avoid code repetition. This is basically used to initialize database connection.

You can define PyTest fixture as shown below.

@pytest.fixture

Assertion: Assertion is the condition that returns true or false. Test execution stops when the assertion fails.

Given below is an Example:

def test_string_equal():
assert double(55) == 62
assert 25 == 62
+  where 25 = double(55)

Link to API: Pytest API
Download Link: Pytest

---

#3) Unittest

• Unittest is the very first Python-based automated unit test framework that was designed to work with the Python standard library.
• Supports the reuse of test suits and test organization.
• It was inspired by JUnit and supports test automation including test collections, test independence, setup code for tests, etc.
• It is also called as PyUnit.
• Unittest2 is a backport of additional new features added to the Unittest.

Standard workflow of Unittest:

• Import the Unittest module in the program code.
• You can define your class.
• Create functions inside the Class that you have defined.
• Place unittest.main() which is the main method at the bottom of the code to run the test case.

Example:

import unittest                                 //Import unittest module//
def add(x,y):
   return x + y

class Test(unittest.TestCase):          //Define your class with testcase//

   def addition(self):
      self.assertEquals(add(4,5),9)<strong>//Function inside class//

if __name__ == '__main__':
   unittest.main()<strong>//Insert main() method//

Screenshot for Reference:

[image source]

Packages/Methods:

(Note: unittest.mock() is a library for Python testing that allows replacing system parts with mock objects. The core mock class helps to create a test suite easily.)

Link to API: Unittest API
Download Link: Unittest

---

#4) DocTest

• Doctest is a module that is included in Python’s standard distribution and is used for White-box Unit Testing.
• It searches for interactive Python sessions to check if they are working exactly as required.
• It makes use of selective Python capabilities such as docstrings, The Python interactive shell, and Python introspection (determining properties of objects at runtime).
• Core Functions:
  • Updating docstring
  • Performing Regression Testing
• The functions testfile() and testmod() are used to provide the basic interface.

Example:

def test(n):
import math
    if not n >= 0:
        raise ValueError("n must be >= 0") //number should be 0 or greater than 0
    if math.floor(n) != n:
               raise ValueError("n must be exact integer")
                                                                     //Error when number is not an integer
  if n+1 == n:
        raise OverflowError("n too large") //Error when number is too large
    r = 1
    f = 2
    while f <= n:                                      //Calculate factorial
        r *= f
        f += 1
    return r

if __name__ == "__main__":
     import doctest                        //Import doctest
    doctest.testmod()                    //Calling the testmod method

Screenshot for Reference:

[image source]

Packages/Functions:

Note: For checking interactive examples in the text file we can use the testfile() function;

doctest.testfile (“example.txt”)

You can directly run the test from the command line with;

python factorial.py

Link to API: DocTest API
Download Link: Doctest

---

#5) Nose2

• Nose2 is the successor of Nose and it is a Python-based Unit Testing framework that can run Doctests and UnitTests.
• Nose2 is based on unittest hence it is referred to as extend unittest or unittest with the plugin that was designed to make testing simple and easier.
• Nose uses collective tests from unittest.testcase and supports multiple functions for writing tests and exceptions.
• Nose supports package fixtures, classes, modules, and complex initialization to be defined at a single time instead of writing frequently.

Example:

from mynum import *
import nose

def test_add_integers():
    assert add(5, 3) == 8

def test_add_floats():
    assert add(1.5, 2.5) == 4

def test_add_strings():
    nose.tools.assert_raises(AssertionError, add, 'paul', 'carol')
// To throw one of the expected exception to pass

if __name__ == '__main__':
    nose.run()

Screenshot for Reference:

Packages/Methods:

Link to API: Plugins for Nose2
Download Link: Nose2

---

#6) Testify

• Testify was designed to replace unittest and nose. Testify has more advanced features than unittest.
• Testify is popular as a Java implementation of semantic testing (Easy to learn and implement software testing specification).
• Performing Automated unit, Integration, and System Testing is easier to Testify.

Features

• Simple syntax to fixture method.
• Improvised test discovery.
• Class-level setup and teardown fixture method.
• Extensible plugin system.
• Easy to handle testing utilities.

Example:

from testify import *

class AdditionTestCase(TestCase):

    @class_setup
    def init_the_variable(self):
        self.variable = 0

    @setup
    def increment_the_variable(self):
        self.variable += 1

    def test_the_variable(self):
        assert_equal(self.variable, 1)

    @suite('disabled', reason='ticket #123, not equal to 2 places')
    def test_broken(self):
        # raises 'AssertionError: 1 !~= 1.01'
        assert_almost_equal(1, 1.01, threshold=2)

    @teardown
    def decrement_the_variable(self):
        self.variable -= 1

    @class_teardown
    def get_rid_of_the_variable(self):
        self.variable = None

if __name__ == "__main__":
run()

Screenshot for Reference:

[image source]

Packages/Methods:

Link to API: Package files of Testify
Download Link: Testify

---

Additional Python Testing Framework

So far we have reviewed the most popular Python Testing Framework. There are a few more names on this list that might become popular in the future.

#7) Behave

• Behave is referred to as Bthe DD (Behavior Development) test framework that is also used for Black box testing. Behave uses the natural language for writing tests and works with Unicode Strings.
• Behave directory contains feature files that have a plain text format that looks like natural language and Python step implementations.

Link to API: Behave User Guide
Download Link: Behave

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#8) Lettuce

• Lettuce is useful for Behavior Driven Development Testing. It makes the testing process easy and scalable.
• Lettuce includes steps such as:
  • Describing behavior
  • Steps definition in Python.
  • Running the code
  • Modifying code to pass the test.
  • Running the modified code.
• These steps are being followed 3 – 4 times to make the software error-free and thereby enhance its quality.

Link to API: Lettuce Documentation
Download Link: Lettuce

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Frequently Asked Questions And Answers

Let’s have a look at some of the most common FAQs on this topic:

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Conclusion

Subunit, Trial, Test resources, Sancho, and Testtools are some more names added to the list of Python Testing Framework. However, there are only a few tools that have been popularized so far as Python testing is a comparatively new concept that is introduced in the testing world.

Companies are working on making these tools better so that they are easy to understand and perform testing. With the rich and precise class fixtures, plugins, and packages these tools can become well-versed and preferable for performing Python Testing.

Meanwhile, frameworks mentioned above from unit test to Testify are providing much necessary support and service to achieve the intended system performance.