Quark’s Outlines: Python Code Objects
Overview, Historical Timeline, Problems & Solutions
An Overview of Python Code Objects
What is a Python code object?
When you write a Python function or module, Python first turns your code into a special internal object. This is called a code object. A Python code object holds the compiled version of your code before it runs. It includes bytecode, variable names, line numbers, and more.
You do not create a code object by hand. Python makes one when you define a function or compile a block of code. This object does not run by itself. It needs a frame or a function to run it.
Python stores compiled bytecode in a code object.
def add(x, y):
return x + y
print(add.__code__.co_code)
This prints the compiled bytecode instructions for the add function.
What does a Python code object contain?
A code object contains details about the function’s arguments, local variables, constants, and instructions. It also stores metadata like the file name, line numbers, and the original source string. These parts help Python run the code correctly.
The code object does not store global variables or default values. Those are held by the function object itself. A code object is fixed. You cannot change it after Python makes it.
Python uses code objects to store compiled instructions and metadata.
def greet(name):
return f"Hello, {name}"
print(greet.__code__.co_varnames) # ('name',)
print(greet.__code__.co_consts) # ('Hello, ', None)
The code object tells you which variables and constants the function uses.
Problem: How do you access a Python code object in Python?
When you define a function, Python adds a __code__ attribute to it. This attribute points to the function’s code object. You can inspect the object’s parts by using attributes like co_argcount, co_code, or co_names.
Python gives you this access to support tools like debuggers, disassemblers, or linters. Most programs never need to read a code object, but tools that study Python behavior often do.
Python gives every function a code object in __code__.
def f(x): return x * 2
print(f.__code__.co_argcount) # 1
print(f.__code__.co_names) # ()
You can explore how Python sees your function behind the scenes.
Why are Python code objects important?
Python uses code objects to run your code. A code object gives Python all the parts it needs to execute a function or a module. It keeps the logic fixed and stored in bytecode.
Tools that inspect, analyze, or modify Python code often use code objects. They let Python work efficiently. They also help when you need to trace errors or debug how something runs.
Python depends on code objects to run your code step by step.
def square(n): return n * n
print(square(4)) # 16
Behind this line, Python is using the code object to run bytecode that multiplies the value.
A Historical Timeline of Python Code Objects
Where do Python code objects come from?
Python code objects follow from early efforts to separate code from data. These objects hold compiled instructions and support tools that analyze, trace, or transform code. This timeline shows how Python shaped the idea of code objects over time.
People separated compiled code from runtime state.
1958 — Early bytecode formats, Lisp and FORTRAN separated compiled code from runtime data.
1970 — Stack frame models, early VMs introduced stack frames to store function execution states.
People designed Python’s first code object format.
1991 — Function and code split, Python 0.9.0 separated function logic into immutable code objects.
1994 — Dis module introduced, Python added tools to inspect and disassemble code objects.
People expanded code object metadata and control.
2001 — Metadata introspection, Python 2.2 added flags and structure to describe argument kinds.
2010 — Bytecode trace tools, Python 3.x improved support for debugging using code object internals.
2019 — Precise line tracking, Python 3.8 added better line number support and instruction offsets.
Problems & Solutions with Python Code Objects
How do you use Python code objects the right way?
Python code objects are internal, but you still use them when you write functions or inspect behavior. These problems show how understanding code objects helps you explore and understand how Python runs your code.
Problem: How do you find out how many arguments a function takes in Python?
You are reading a function and want to know how many arguments it requires. The function is used many times in your program. You want to avoid errors from calling it with the wrong number of arguments.
Problem: How do you check the number of required arguments without reading all the source code?
Solution: Python stores this number in the function’s code object using the co_argcount attribute.
Python shows function argument counts using code objects.
def divide(x, y): return x / y
print(divide.__code__.co_argcount) # 2
This prints 2, showing the function expects two arguments.
Problem: How do you inspect what names a function uses in Python?
You are working with a helper function from another file. You want to see which variables it uses so you can reuse it safely in a new context. You do not want to miss any required inputs.
Problem: How do you list the variable names used in the function body?
Solution: Python keeps variable names in co_varnames and external names in co_names.
Python lists variable and symbol names inside code objects.
def calc(a, b): return a + b + pi
print(calc.__code__.co_varnames) # ('a', 'b')
print(calc.__code__.co_names) # ('pi',)
This shows that a and b are local, while pi is expected from the global scope.
Problem: How do you debug line numbers in an error in Python?
You run a program that fails inside a function. The traceback says there is an error on a line, but you are not sure which line the function started on. You want to know where to look.
Problem: How do you find the first line number in the original source?
Solution: Python stores this in the co_firstlineno attribute of the code object.
Python stores line numbers to help locate function code.
def square(x):
return x * x
print(square.__code__.co_firstlineno) # prints the line number
This tells you where in the file the function starts, useful when scanning tracebacks.
Problem: How do you compare two functions' bytecode in Python?
You are optimizing two functions. They do similar things, but you want to know if Python compiles them to the same instructions. You want to see the exact bytecode each one uses.
Problem: How do you view and compare the compiled bytecode?
Solution: Python stores bytecode in the co_code attribute as a sequence of bytes.
Python gives you raw bytecode using the code object.
def f(x): return x + 1
def g(x): return x + 2
print(f.__code__.co_code)
print(g.__code__.co_code)
The bytecode will differ based on the constants and operations. This lets you compare how Python compiles logic.
Problem: How do you find out what constants a function uses in Python?
You are trying to extract all hardcoded values from your code. You want to scan each function and find the constant strings, numbers, or None values it uses.
Problem: How do you get the list of constants a function includes?
Solution: Python stores all literals in the co_consts tuple inside the code object.
Python lists all constant values inside code objects.
def hello(): return "Hi", None
print(hello.__code__.co_consts) # ('Hi', None)
This shows every literal in the function. You can use this to search for fixed values.
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Mike Vincent is an American software engineer and app developer from Los Angeles, California. More about Mike Vincent
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