🧠 Python DeepCuts — 💡 Dissecting Python Bytecode

Python feels like a high-level language, but beneath the surface CPython runs a stack-based virtual machine that executes bytecode instructions. These instructions are the compiled form of your Python source, and they dictate how expressions, loops, and conditionals execute. The dis module gives us a direct look into these operations.

Understanding bytecode helps you:

• reason about Python performance
• debug tricky control flow
• see how Python evaluates expressions
• understand how the PVM works internally

Let’s peel back the abstraction.

🧩 How Python Turns Code Into Bytecode

Whenever Python executes a function, CPython compiles it into a sequence of instructions such as:

• LOAD_FAST
• BINARY_ADD
• RETURN_VALUE

Here’s a simple example:

def add(a, b):
    return a + b

dis.dis(add)

Expected output:

LOAD_FAST        0 (a)
LOAD_FAST        1 (b)
BINARY_ADD
RETURN_VALUE

Each instruction uses the VM’s internal stack to pass values around.

🔍 Bytecode for Conditionals

Control flow like if becomes conditional jumps in bytecode.

def check(x):
    if x > 10:
        return "big"
    return "small"

dis.dis(check)

You will see opcodes like:

• COMPARE_OP
• POP_JUMP_IF_FALSE
• LOAD_CONST
• RETURN_VALUE

Bytecode reveals the precise branching behavior.

🔄 Loops Become Jump Instructions

A for loop isn’t a looping construct inside Python’s VM — it is a series of stack operations and jumps.

def loop(n):
    total = 0
    for i in range(n):
        total += i
    return total

dis.dis(loop)

You will see:

• GET_ITER
• FOR_ITER
• INPLACE_ADD
• JUMP_ABSOLUTE

This makes the mechanics of iteration crystal clear.

🧠 Python’s Virtual Machine Is Stack-Based

Every operation pushes or pops values from a stack.

Example:

def expr(x, y, z):
    return (x + y) * z

dis.dis(expr)

You will observe:

LOAD_FAST
LOAD_FAST
BINARY_ADD
LOAD_FAST
BINARY_MULTIPLY
RETURN_VALUE

Python never stores intermediate results in registers — everything happens on the VM stack.

🧬 Inspecting Code Objects

Every function has a code object that stores low-level metadata:

def sample(a, b):
    c = a * b
    return c + 10

code = sample.__code__

print(code.co_varnames)
print(code.co_consts)
print(code.co_names)
print(code.co_stacksize)
dis.dis(sample)

You can see:

• local variable names
• constants
• required stack depth
• referenced names

This is the raw structure CPython uses to execute your function.

🔧 Programmatic Inspection with dis.Bytecode

dis.Bytecode provides an iterable form of instructions — perfect for static analysis tools.

bytecode = dis.Bytecode(sample)

for instr in bytecode:
    print(instr.opname, "→", instr.argval)

This level of inspection is how linters, optimizers, and compilers analyze Python code.

✅ Key Points

• Python source code is compiled into bytecode.
• CPython executes bytecode using a stack-based virtual machine.
• Conditionals and loops become jump instructions.
• Bytecode helps explain performance characteristics.
• You can inspect functions at multiple levels: instructions, stack size, constants.

Understanding bytecode opens the door to deeper insights into Python’s behavior and optimization.