Are you a C++ developer comparing Python vs C++? Are you looking at Python and wondering what all the fuss is about? Do you wonder how Python compares to the concepts you already know? Or perhaps you have a bet on who would win if you locked C++ and Python in a cage and let them battle it out? Then this article is for you!
In this article, you’ll learn about:
- Differences and similarities when you’re comparing Python vs C++
- Times when Python might be a better choice for a problem and vice versa
- Resources to turn to as you have questions while learning Python
This article is aimed at C++ developers who are learning Python. It assumes a basic knowledge of both languages and will use concepts from Python 3.6 and up, as well as C++11 or later.
Let’s dive into looking at Python vs C++!
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Comparing Languages: Python vs C++
Frequently, you’ll find articles that extoll the virtues of one programming language over another. Quite often, they devolve into efforts to promote one language by degrading the other. This isn’t that type of article.
When you’re comparing Python vs C++, remember that they’re both tools, and they both have uses for different problems. Think about comparing a hammer and a screwdriver. You could use a screwdriver to drive in nails, and you could use a hammer to force in screws, but neither experience will be all that effective.
Using the right tool for the job is important. In this article, you’ll learn about the features of Python and C++ that make each of them the right choice for certain types of problems. So, don’t view the “vs” in Python vs C++ as meaning “against.” Rather, think of it as a comparison.
Compilation vs Virtual Machine
Let’s start with the biggest difference when you’re comparing Python vs C++. In C++, you use a compiler that converts your source code into machine code and produces an executable. The executable is a separate file that can then be run as a stand-alone program:
This process outputs actual machine instructions for the specific processor and operating system it’s built for. In this drawing, it’s a Windows program. This means you’d have to recompile your program separately for Windows, Mac, and Linux:
You’ll likely need to modify your C++ code to run on those different systems as well.
Python, on the other hand, uses a different process. Now, remember that you’ll be looking at CPython which is the standard implementation for the language. Unless you’re doing something special, this is the Python you’re running.
Python runs each time you execute your program. It compiles your source just like the C++ compiler. The difference is that Python compiles to bytecode instead of native machine code. Bytecode is the native instruction code for the Python virtual machine. To speed up subsequent runs of your program, Python stores the bytecode in .pyc files:
If you’re using Python 2, then you’ll find these files next to the .py files. For Python 3, you’ll find them in a __pycache__ directory.
The generated bytecode doesn’t run natively on your processor. Instead, it’s run by the Python virtual machine. This is similar to the Java virtual machine or the .NET Common Runtime Environment. The initial run of your code will result in a compilation step. Then, the bytecode will be interpreted to run on your specific hardware:
As long as the program hasn’t been changed, each subsequent run will skip the compilation step and use the previously compiled bytecode to interpret:
Interpreting code is going to be slower than running native code directly on the hardware. So why does Python work that way? Well, interpreting the code in a virtual machine means that only the virtual machine needs to be compiled for a specific operating system on a specific processor. All of the Python code it runs will run on any machine that has Python.
Note: CPython is written in C, so it can run on most systems that have a C compiler.
Another feature of this cross-platform support is that Python’s extensive standard library is written to work on all operating systems.
Using pathlib, for example, will manage path separators for you whether you’re on Windows, Mac, or Linux. The developers of those libraries spent a lot of time making it portable so you don’t need to worry about it in your Python program!
Before you move on, let’s start keeping track of a Python vs C++ comparison chart. As you cover new comparisons, they’ll be added in italics:
| Feature | Python | C++ |
|---|---|---|
| Faster Execution | x | |
| Cross-Platform Execution | x |
Now that you’ve seen the differences in run time when you’re comparing Python vs C++, let’s dig into the specifics of the languages’ syntax.
Syntax Differences
Python and C++ share many syntactical similarities, but there are a few areas worth discussing:
- Whitespace
- Boolean expressions
- Variables and pointers
- Comprehensions
Let’s start with the most contentious one first: whitespace.
Whitespace
The first thing most developers notice when comparing Python vs C++ is the “whitespace issue.” Python uses leading whitespace to mark scope. This means that the body of an if block or other similar structure is indicated by the level of indentation. C++ uses curly braces ({}) to indicate the same idea.
While the Python lexer will accept any whitespace as long as you’re consistent, PEP8 (the official style guide for Python) specifies 4 spaces for each level of indentation. Most editors can be configured to do this automatically.
There has been an enormous amount of writing, shouting, and ranting about Python’s whitespace rules already, so let’s just jump past that issue and on to other matters.
Instead of relying on a lexical marker like ; to end each statement, Python uses the end of the line. If you need to extend a statement over a single line, then you can use the backslash (\) to indicate that. (Note that if you’re inside a set of parentheses, then the continuation character is not needed.)
There are people who are unhappy on both sides of the whitespace issue. Some Python developers love that you don’t have to type out braces and semicolons. Some C++ developers hate the reliance on formatting. Learning to be comfortable with both is your best bet.
Now that you’ve looked at the whitespace issue, let’s move on to one that’s a bit less contentious: Boolean expressions.
Boolean Expressions
The way you’ll use Boolean expressions changes slightly in Python vs C++. In C++, you can use numeric values to indicate true or false, in addition to the built-in values. Anything that evaluates to 0 is considered false, while every other numeric value is true.
Python has a similar concept but extends it to include other cases. The basics are quite similar. The Python documentation states that the following items evaluate to False:
- Constants defined as false:
NoneFalse
- Zeros of any numeric type:
00.00jDecimal(0)Fraction(0, 1)
- Empty sequences and collections:
''()[]{}