Last Updated: June 01, 2026
Beginner
If you are new to the world of computer programming, choosing a programming language, to begin with, is probably the toughest hurdle. Currently, there are thousands of programming languages with different idiosyncrasies and complexities. On our site, we focus on Python, but there are other languages out there. Before you start your software development journey, choosing a programming language that suits your interests and career goals is important. That said, below are some of the best and in-demand coding languages you should consider.
Python developer and educator with 15+ years building production systems across data engineering, web APIs, and AI tooling. Founder of Python How To Program — 270+ in-depth tutorials covering the modern Python stack.
1. JavaScript
Modern software developers cannot succeed without mastering JavaScript. A 2020 survey done by Stack Overflow found that JavaScript is still the most popular programming language for developers for eight years in a row. More than 70% of study participants reported that they used this language for more than one year.
Together with CSS and HTML, JavaScript is an important coding language for front-end website development. Most websites, including Facebook, Gmail, YouTube, and Twitter, depend on JavaScript to display dynamic content to users for their interactive website pages.
Even though JavaScript is primarily a front-end web development language on browsers, it can be used on the server-side to develop scalable network applications with the help of Node.js. Node.js works with Windows, Linux, Mac OS, and SunOs.
JavaScript is a popular language amongst programming beginners because of its simple learning curve. It is used all through the web, thanks to its speed, and works well with other coding languages, enabling it to be used in various applications. That aside, the demand for JavaScript developers is currently high, with a CareerFoundry study concluding that 72% of businesses need JavaScript developers.
Pros of learning JavaScript
- Fast and can run immediately in browsers
- Provides an enriched and better web interface
- Highly versatile
- It can be used in various applications
- Has multiple add-ons
- Easily integrates with other programming languages.
Cons of learning JavaScript
- Lacks an equivalent or alternate method
- Different web browsers can interpret code lines differently.
2. Python
Python is a general-purpose coding language that is also very learner-friendly; there are even Python classes for children. However, despite being easy to learn, Python is an overly versatile and powerful language, making it suitable for beginners and experts. It is because of this that major companies, including Facebook and Google, use this language.
Python’s popularity is largely attributed to its extensive usage. It has applications in data science, scientific computing, data analytics, animation, database interfacing, web applications, machine learning, and data visualization. This versatility also explains the high demand for experts in this language.
Key features of Python include;
- It has a unique selling point – simple, productive, elegant, and powerful in one package.
- It influences other programming languages, such as Go and Julia
- Best for back-end web development with first-class integration with other programming languages, such as C++ and C.
- It offers many tools that can be applied in computational science, mathematics, statistics, and various libraries and frameworks, such as NumPy, Scikit-Learn, and Pandas.
Pros of learning Python
- Works in various platforms
- Improves developers and programmers productivity
- Has a wide array of support frameworks and libraries
- Powered by object-oriented programming
Cons of learning Python
- Not ideal for mobile computing
- It has a primitive and underdeveloped database

3. Java
Java is another popular coding language commonly used in-app and web development. Despite being an old coding language, Java is still in demand due to its complexity. Unfortunately, it isn’t beginner-friendly. It is a platform-independent language and a popular choice for various organizations, including Google and Airbnb, for its stability.
Key features of Java include;
- It is a multi-paradigm and feature-rich programming language
- Very productive for developers
- Moderate learning curve
- It doesn’t have major changes and updates like Python and Scala
- Has the best runtime
Pros of learning Java
- Has a wide array of open-source libraries
- Automated garbage collection
- Allows for platform independence
- Supports multithreading and distributed computing
- Has multiple APIs that support completion of various tasks, such as database connection, networking, and XML parsing
Cons of learning Java
- Expensive memory management
- Slow compared to other coding languages, such as C and C++
4. C#
C# is an object-oriented programming language developed by Microsoft. It was initially designed as part of the .NET framework for developing windows applications but is currently used in various applications. It is a general-purpose coding language used particularly in back-end development, game creation, mobile app development, and more. Despite being a Windows-specific language, it can also be used in Android, Linux, and iOS platforms.
The language has a legion of libraries and frameworks that have accrued for the last 20 years. Like Java, C# is independent of other platforms, thanks to its Common Language Runtime feature.
Pros of learning C#
- Can work with shared codebases
- Safe compared C++ and C
- Uses similar syntax with C++ and other C-derived languages
- Has rich data types and library
- Has a fast compilation and execution
Cons of learning C#
- Less flexible compared to C++
- You should have good knowledge to solve errors

5. PHP
PHP is another excellent programming language with many applications. While it faces stiff competition from other languages, such as Python and JavaScript, especially for web development, there is still a high demand for PHP professionals in the current job market. PHP is also a general-purpose and dynamic coding language that can be used to develop server-side applications.
Pros of learning PHP
- Easy to learn and use
- Has a wide ecosystem and community support
- Has many frameworks
- Supports object-oriented and functional paradigms
- Supports various automation tools
Cons of PHP
- Builds slow web pages
- Lacks error and security handling features
6. Angular
Angular is a recently updated and improved version of the initial AngularJS framework developed by Google. Compared to other recent coding languages, such as React, Angular has a steep learning curve but offers better practical solutions for front-end development. Developers can also program complicated and scalable applications using Angular, thanks to its great functionality, aesthetic visual designs, and business logic.
Key features of Angular include;
- Features a model-view control architecture that facilitates dynamic modeling
- Uses HTML coding language to develop user interfaces that are simple and easy to understand
- Uses old JavaScript objects, which are self-sufficient and very functional
- Has Angular filters, which filter data before being viewed
Pros of learning Angular
- Requires minimal coding experience to use
- Allows development of high-quality hybrid apps
- Has quick app prototyping
- Has enhanced testing ability
Cons of Angular
- Angular developed apps are dynamic, diminishing their performance
- Complicated pages in apps can cause glitches
- Difficult to learn

7. React
Also called ReactJS, React is a JavaScript framework developed by Facebook that enables programmers to develop user interfaces with dynamic abilities. Sites built using React respond faster, and developers can switch between multiple variable elements seamlessly. The language also enables businesses to build and maintain customer loyalty by providing a great user experience.
Pros of learning React
- Easy to learn and SEO friendly
- Reuses various components, thus saves time
- Has an open-source library
- Supported by a strong online community
- Has plenty of helpful development tools
Cons of React
- Additional SEO hurdle
- Has poor code documentation
The Bottom Line
As you choose your preferred web development language to learn, ensure that you aren’t guided by flashy inclinations and popularity contests. Even though the realm of computer programming keeps changing rapidly, the languages mentioned above can withstand these changes. Learning one or more of these languages will put you in a great position for many years to come. Make use of federal funding to pay for your online programming courses and Bootcamps. Veterans can learn web development languages at a discount using the GI Bill Benefits.
How To Use Python ward for Modern Python Testing
Intermediate
You have written enough test_something functions to last a lifetime. Your test files are full of names like test_user_creation_when_email_is_valid_and_age_is_over_18 — forty characters of snake_case just to describe one behaviour. When a test fails, the output tells you the function name but not what the test was actually checking. You end up reading the function body to figure it out, which defeats the point of a test name entirely.
Python’s ward library takes a different approach. Instead of naming test functions, you describe them — passing a plain English string to the @test decorator. When a test fails, ward prints exactly what it was supposed to do, in the language you wrote it, not a slug-cased identifier. The library also ships with a fixture system built on function arguments, a fluent expect() assertion API, and parameterized tests that read like a table of examples. Install it with pip install ward — no extra dependencies.
This article walks you through everything you need to use ward effectively. We will cover writing your first tests with the @test decorator, organizing shared setup with @fixture, running parameterized test cases, using the expect() assertion chain, and building a real-world test suite for a small utility library. By the end, you will have a working test suite and a clear picture of where ward fits against pytest.
Writing a ward Test: Quick Example
Before diving into the details, here is a minimal ward test file you can run right now. Create a file called test_math.py and paste in the following:
# test_math.py
from ward import test
@test("adding two positive integers returns their sum")
def _():
assert 1 + 2 == 3
@test("dividing by zero raises ZeroDivisionError")
def _():
try:
_ = 10 / 0
assert False, "should have raised"
except ZeroDivisionError:
pass
Run it with:
ward
Output:
PASS test_math adding two positive integers returns their sum
PASS test_math dividing by zero raises ZeroDivisionError
2 passed in 0.02 seconds
Two things stand out immediately. First, every test function is named _ — the name is irrelevant because ward uses the string you pass to @test instead. Second, the output reads like a sentence, not a mangled identifier. Those two changes alone make failing tests much easier to diagnose at a glance.
The sections below explain each ward feature in depth and show you how to apply them to realistic code.
What Is ward and Why Use It?
Ward is a Python testing framework designed to make test code more readable and test output more useful. It was created as an alternative to pytest and unittest, borrowing the best ideas from both while rethinking the parts that have always felt awkward — especially test naming and fixture injection.
The central idea is that a test is a fact you assert about your code, and that fact deserves to be written in human language. In pytest you write def test_cart_total_is_zero_when_no_items_added():. In ward you write @test("cart total is zero when no items are added"). The string is the documentation; the function body is the proof.
Here is how ward compares to pytest on the features most Python developers care about:
| Feature | pytest | ward |
|---|---|---|
| Test description | Function name (snake_case) | Plain English string |
| Fixtures | @pytest.fixture | @fixture with argument injection |
| Parameterization | @pytest.mark.parametrize | @using with each() |
| Assertions | assert (with rewriting) | assert or fluent expect() |
| Output format | Dots, F, E characters + tracebacks | Coloured PASS/FAIL lines with descriptions |
| Installation | pip install pytest | pip install ward |
Ward is not trying to replace pytest in every project — it is a deliberate choice that pays off most when you want test output to function as living documentation. If you share test runs with non-developers or treat CI output as a changelog, ward’s readable output earns its place immediately.
Installing ward
Ward requires Python 3.6 or later. Install it into your project’s virtual environment:
# install.sh
pip install ward
Verify the installation:
# verify_install.py
import ward
print(ward.__version__)
Output:
0.68.0b0
Ward discovers tests automatically. By default it searches for any file matching the pattern test_*.py in the current directory and its subdirectories — the same convention used by pytest. You can override the search path:
# Run all tests in a specific directory
ward --path tests/
# Run tests matching a keyword in their description
ward --search "total"
# Run tests in a single file
ward --path tests/test_cart.py
There is no configuration file required to get started. For larger projects, ward reads from a pyproject.toml [tool.ward] section if one exists.
Writing Tests with @test
The @test decorator is the foundation of every ward test suite. It takes a single string argument that describes what the test is verifying. The decorated function’s name is ignored entirely — the convention is to name every test function _ to make that clear.
# test_string_utils.py
from ward import test
def shout(text):
"""Convert text to uppercase with an exclamation mark."""
return text.upper() + "!"
@test("shout converts text to uppercase")
def _():
assert shout("hello") == "HELLO!"
@test("shout appends an exclamation mark")
def _():
result = shout("ward")
assert result.endswith("!")
@test("shout works on an already uppercase string")
def _():
assert shout("PYTHON") == "PYTHON!"
Output:
PASS test_string_utils shout converts text to uppercase
PASS test_string_utils shout appends an exclamation mark
PASS test_string_utils shout works on an already uppercase string
3 passed in 0.01 seconds
When a test fails, ward shows you the description, the file and line number, and the values that caused the failure. You get the “what failed” and “what were the values” without reading the traceback from the bottom up. That is a significant quality-of-life improvement when you have a large test suite and a CI run with dozens of failures.
Shared Setup with @fixture
Most tests need shared setup — a database connection, a sample data structure, or a configured object. Ward handles this with the @fixture decorator, which works very similarly to pytest fixtures. You declare a fixture function, then inject it into your test by using the fixture function as a default argument.
# test_user.py
from ward import test, fixture
@fixture
def sample_user():
return {
"name": "Alice",
"email": "alice@example.com",
"age": 25,
"active": True,
}
@test("user name is a string")
def _(user=sample_user):
assert isinstance(user["name"], str)
@test("user age is a positive integer")
def _(user=sample_user):
assert user["age"] > 0
@test("inactive users cannot be created with this fixture")
def _(user=sample_user):
assert user["active"] is True
Output:
PASS test_user user name is a string
PASS test_user user age is a positive integer
PASS test_user inactive users cannot be created with this fixture
3 passed in 0.01 seconds
The fixture function runs fresh for each test that uses it — there is no shared state between tests unless you explicitly use a module-level or session-level scope. To add teardown logic, use yield inside the fixture:
# test_file_fixture.py
import os
import tempfile
from ward import test, fixture
@fixture
def temp_file():
# Setup: create a temporary file
fd, path = tempfile.mkstemp(suffix=".txt")
os.close(fd)
with open(path, "w") as f:
f.write("hello ward")
yield path
# Teardown: delete the file after the test
if os.path.exists(path):
os.remove(path)
@test("temp file exists on disk")
def _(path=temp_file):
assert os.path.isfile(path)
@test("temp file contains the expected text")
def _(path=temp_file):
with open(path) as f:
content = f.read()
assert content == "hello ward"
Output:
PASS test_file_fixture temp file exists on disk
PASS test_file_fixture temp file contains the expected text
2 passed in 0.03 seconds
The code after yield runs automatically after each test that uses temp_file. Ward handles the teardown even if the test throws an exception, so you do not need try/finally blocks in your test functions.
Parameterized Tests with @using and each()
Parameterized tests let you run the same logic against multiple inputs without duplicating code. Ward’s approach is cleaner than pytest’s marker syntax — you use the @using decorator combined with the each() helper to define the value sets.
# test_math_ops.py
from ward import test, each, using
def clamp(value, lo, hi):
"""Clamp value to the range [lo, hi]."""
return max(lo, min(hi, value))
@test("clamp({value}, {lo}, {hi}) returns {expected}")
@using(
value=each(5, -3, 100, 50),
lo=each(0, 0, 0, 0),
hi=each(10, 10, 10, 10),
expected=each(5, 0, 10, 10),
)
def _(value, lo, hi, expected):
assert clamp(value, lo, hi) == expected
Output:
PASS test_math_ops clamp(5, 0, 10) returns 5
PASS test_math_ops clamp(-3, 0, 10) returns 0
PASS test_math_ops clamp(100, 0, 10) returns 10
PASS test_math_ops clamp(50, 0, 10) returns 10
4 passed in 0.01 seconds
Notice that ward interpolates the parameter values into the test description for each run. When one case fails, the output tells you exactly which input caused the problem — clamp(100, 0, 10) returns 10 is far more useful than test_clamp_params[2].
The each() values are matched positionally — the first value from every each() forms the first test case, the second values form the second, and so on. All each() calls in one @using decorator must have the same length.
Fluent Assertions with expect()
Ward provides an optional fluent assertion API called expect(). Instead of writing bare assert statements, you chain methods that read like English and produce more specific error messages when they fail. Use whichever style you prefer — both work the same way under the hood.
# test_expect.py
from ward import test, expect
@test("expect: integer is within range")
def _():
expect(42).to_be_greater_than(40)
expect(42).to_be_less_than(50)
@test("expect: string contains a substring")
def _():
expect("pythonhowtoprogram").to_contain("python")
@test("expect: list has expected length")
def _():
items = ["a", "b", "c"]
expect(items).has_length(3)
@test("expect: dictionary contains a key")
def _():
config = {"debug": True, "timeout": 30}
expect(config).to_contain_key("debug")
@test("expect: value is an instance of a type")
def _():
expect(3.14).to_be_instance_of(float)
Output:
PASS test_expect expect: integer is within range
PASS test_expect expect: string contains a substring
PASS test_expect expect: list has expected length
PASS test_expect expect: dictionary contains a key
PASS test_expect expect: value is an instance of a type
5 passed in 0.02 seconds
When an expect() assertion fails, ward reports both the expected condition and the actual value on separate lines. For example, if expect(15).to_be_greater_than(40) fails, the output reads Expected 15 to be greater than 40 — no need to open the test file and read the assert statement.
The expect() API supports chaining, so you can write several assertions about the same value in sequence. This is useful when a single test function validates multiple properties of one object, and you want each failure to be described independently.
Real-Life Example: Testing a Shopping Cart
Let us build a test suite for a simple shopping cart module using everything covered above: fixtures for shared state, parameterized tests for pricing logic, and expect() for readable assertions.
First, the module under test:
# cart.py
class Cart:
"""A simple shopping cart with item management and total calculation."""
def __init__(self):
self._items = {} # name -> {"price": float, "qty": int}
def add(self, name, price, qty=1):
if name in self._items:
self._items[name]["qty"] += qty
else:
self._items[name] = {"price": price, "qty": qty}
def remove(self, name):
self._items.pop(name, None)
def total(self, tax_rate=0.0):
subtotal = sum(v["price"] * v["qty"] for v in self._items.values())
return round(subtotal * (1 + tax_rate), 2)
def item_count(self):
return sum(v["qty"] for v in self._items.values())
def is_empty(self):
return len(self._items) == 0
Now the ward test suite:
# test_cart.py
from ward import test, fixture, expect, each, using
from cart import Cart
# ----- Fixtures -----
@fixture
def empty_cart():
return Cart()
@fixture
def stocked_cart():
c = Cart()
c.add("apple", price=0.99, qty=3)
c.add("bread", price=2.49)
c.add("milk", price=1.75, qty=2)
return c
# ----- Basic behaviour -----
@test("a new cart is empty")
def _(cart=empty_cart):
expect(cart.is_empty()).to_be_truthy()
expect(cart.item_count()).equals(0)
@test("adding an item increases item count")
def _(cart=empty_cart):
cart.add("apple", price=0.99, qty=4)
expect(cart.item_count()).equals(4)
@test("adding the same item twice increases quantity not entry count")
def _(cart=empty_cart):
cart.add("apple", 0.99, qty=1)
cart.add("apple", 0.99, qty=2)
expect(cart.item_count()).equals(3)
@test("removing an item reduces total")
def _(cart=stocked_cart):
before = cart.total()
cart.remove("bread")
expect(cart.total()).to_be_less_than(before)
# ----- Total calculation (parameterized) -----
@test("total with {rate*100:.0f}% tax is {expected}")
@using(
rate=each(0.0, 0.1, 0.2),
expected=each(9.46, 10.41, 11.35),
)
def _(cart=stocked_cart, rate=None, expected=None):
# stocked: 3*0.99 + 1*2.49 + 2*1.75 = 9.46 subtotal
expect(cart.total(tax_rate=rate)).equals(expected)
Output:
PASS test_cart a new cart is empty
PASS test_cart adding an item increases item count
PASS test_cart adding the same item twice increases quantity not entry count
PASS test_cart removing an item reduces total
PASS test_cart total with 0% tax is 9.46
PASS test_cart total with 10% tax is 10.41
PASS test_cart total with 20% tax is 11.35
7 passed in 0.03 seconds
The test suite demonstrates three things at once: the stocked_cart fixture gives each test a fresh, pre-populated cart without any shared mutation; the parameterized tax tests cover three pricing scenarios without duplicated code; and the expect() calls make each assertion readable without a comment explaining what is being checked. To extend the example, try adding tests for negative quantities, zero-price items, or a discount() method — the fixture and parameterization patterns scale without any structural changes.
Frequently Asked Questions
Can ward tests coexist with pytest tests in the same project?
Yes, but they run in separate commands. Ward discovers test_*.py files using the same convention as pytest, but the two runners are independent — running ward only executes tests decorated with @test, and running pytest only executes functions prefixed with test_. If you are migrating gradually, you can keep both frameworks active during the transition. Ward can also use pytest fixtures defined in a conftest.py file, which eases migration considerably.
How do fixture scopes work in ward?
Ward fixtures support four scopes: Scope.Test (default, reruns for every test), Scope.Module (reruns once per test file), Scope.Global (reruns once per entire test run), and Scope.Call (same as Test). Set the scope with @fixture(scope=Scope.Module). Use module or global scope for expensive operations like database connections or API clients that you do not want to recreate thousands of times.
Should I use expect() or plain assert statements?
Both work correctly and produce useful output. The expect() API gives you method names that double as documentation — expect(value).to_be_greater_than(10) is self-explanatory even in a code review. Plain assert is faster to type and feels more natural if you are coming from pytest. A common middle ground: use assert for straightforward equality checks and expect() when comparing ranges, types, or collection membership where the error message matters most.
How do I run a single test or a subset of tests?
Use the --search flag to filter by description substring: ward --search "cart total" will run every test whose description contains “cart total”. You can also use --path tests/test_cart.py to restrict the discovery scope to a single file. For more precise control, ward supports tags via @test("...", tags=["slow"]) combined with ward --tags slow on the command line.
Does ward support async tests?
Yes. Ward automatically detects async def test functions and fixture functions and runs them using asyncio.run(). You do not need any plugin or marker — just write async def _(): and ward handles the event loop. This works the same way for fixtures, so an async fixture can await a database connection and yield it to an async test without any additional setup.
How do I get code coverage with ward?
Run ward under coverage using the standard coverage.py wrapper: coverage run -m ward. After the run completes, generate the report with coverage report or coverage html for a browsable HTML breakdown. Ward does not bundle its own coverage tool — it relies on coverage.py, which is the same approach pytest recommends and integrates cleanly with CI pipelines and codecov.io.
Conclusion
Python ward replaces the test-naming ceremony of traditional frameworks with something more direct: a plain English description that appears in every test report, every CI log, and every team standup where someone asks “what broke?” The @test decorator, @fixture injection, @using parameterization, and the expect() assertion chain all work together to make tests read like specifications and failures read like bug reports.
The shopping cart example in this article covers the core patterns you will reach for in a real project. Try extending it — add a coupon fixture that returns a discount function, write parameterized tests for boundary cases like zero-quantity items, or switch the Cart storage to a database and test the async fixture pattern. Each of those extensions fits naturally into the structure already in place.
For the full API reference, see the official ward documentation at ward.readthedocs.io. The project is also actively maintained on GitHub at github.com/darrenburns/ward, where you can find the changelog and open issues.
Related Articles
Further Reading: For more details, see the official Python tutorial.
Frequently Asked Questions
How does Python compare to JavaScript for web development?
Python excels in backend development with Django and Flask. JavaScript dominates the frontend and runs on the backend with Node.js. Python is preferred for data-heavy backends, while JavaScript enables full-stack development with a single language.
Is Python slower than other web languages?
Python is generally slower in raw execution speed compared to Go, Java, or Node.js. However, for most web apps the bottleneck is I/O, not CPU speed. Python’s developer productivity and rich ecosystem often outweigh the performance difference.
Can Python be used for frontend web development?
Python is primarily a backend language. Tools like Brython, Pyodide, and PyScript allow Python in the browser, but for production frontends JavaScript/TypeScript with React or Vue remains the standard.
What makes Python a good choice for web APIs?
Python offers mature API frameworks (Flask, FastAPI, Django REST Framework), excellent library support for data processing, simple syntax, and strong integration with databases, ML models, and third-party services.
Should I learn Python or JavaScript for web development?
Learn Python if you focus on data science, ML, or backend APIs. Learn JavaScript for full-stack web development. Many developers learn both. Python’s versatility across web, data, and automation makes it a strong choice.
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- FastAPI vs Flask: Which Should You Use?
- How To Create a REST API with Flask
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