Why Python, Specifically

scores = [78, 92, 65, 88]total = sum(scores)average = total / len(scores) if average >= 90:    print("Grade: A")elif average >= 80:    print("Grade: B")else:    print("Grade: C")

A list of four numbers, a total, an average, and a decision based on it. Nothing in that program is disguised. sum adds the list up. len counts it. The indentation is not decoration — it is how Python marks which lines belong to which decision, instead of wrapping them in curly braces the way many other languages do.

That readability is not an accident. Python was designed, from its first release in 1991, around the idea that code is read far more often than it is written, and that a language should optimise for the reader.

When you run a Python file, nothing translates it directly into the ones and zeros your processor understands. The standard implementation, CPython, compiles your file into an intermediate form called bytecode, then an interpreter reads that bytecode one instruction at a time and carries it out.

That extra step is what makes Python interpreted rather than compiled in the sense C or Rust are. You never wait for a separate build step — you run the file and it goes — but every instruction is costing you a layer of translation that a compiled language paid for once, in advance.

That translation layer has a real cost. A tight numeric loop written in raw Python can run tens of times slower than the same loop written in C. For a program that spends its life adding up sixty numbers, you would never notice. For a program processing billions of numbers, you would.

In practice, most Python code that needs to be fast is not slow, because it calls into libraries — for data, for machine learning, for anything numeric — that are themselves written in C underneath and only driven by Python. You get the reading and writing speed of Python and the execution speed of C, for the parts that actually run millions of times.