Understanding Python Code Execution: From Storage to RAM
Ever wondered how Python executes your code? Understanding how Python processes your script—from loading it into RAM to executing each function—is essential for writing efficient programs. Let's break it down step by step.
🚀 1. Code Execution: From Storage to RAM
When you execute a Python script, here’s what happens:
The program file is stored in the hard disk.
When executed, it loads into RAM.
The code becomes a process, and the CPU executes statements one by one.
A process in Python is divided into three main parts:
1️⃣ Code Section - Stores the entire Python script, including all functions. 2️⃣ Stack Memory - Stores function calls and local variables. 3️⃣ Heap Memory - Stores dynamically allocated objects and variables.
🧠 2. Code Sections in RAM
🔹 The Code Section holds the entire script. Every statement should be inside a function; otherwise, Python won't execute it properly.
🔹 Even if you don’t define a main function, Python internally creates one when the script starts.
🔍 Visual Representation:
Hard Disk ---> RAM (Code Section)
|
|---> Stack Memory (Function Calls)
|---> Heap Memory (Objects & Variables)
🎯 3. The Role of main Function
Python automatically calls the main function at execution. The __name__ variable helps identify the currently executing function.
print(__name__) # Output: '__main__' (when running directly)
Avoid explicitly defining another main function, as Python won’t auto-call it.
🔄 4. Stack Memory and Function Execution
When a function is called, it gets loaded into Stack Memory. The CPU executes statements sequentially.
Example:
def greet():
print("Hello, Python!")
greet()
Execution Flow: 1️⃣ The greet() function is loaded into stack memory. 2️⃣ CPU picks each statement and executes it. 3️⃣ Once completed, the function is removed from the stack.
📝 Visual Representation:
Code Section ---> Stack Memory
|
|---> greet() (Function Call)
| |
| |---> print("Hello, Python!")
|
|---> Function Removed (Execution Complete)
🔄 5. Process Lifecycle & Execution Order
The life of a process = execution time of __main__ function
import time
time.sleep(30) # Delay execution but process is still alive
x = 5
print(x)
Here, time.sleep(30) delays execution, but the process remains active until all statements are executed.
🔃 6. Function Calls and Stack Frames
Each function call creates a new stack frame. The CPU shifts execution focus from main to the function’s stack frame.
Example:
def add(a, b):
return a + b
result = add(2, 3)
print(result)
📝 Visual Representation:
Stack Memory:
1️⃣ main() [Starts Execution]
2️⃣ add() [Function Call - Creates New Stack Frame]
3️⃣ add() [Returns Result - Stack Frame Removed]
4️⃣ main() [Resumes Execution, Prints Result]
🔁 7. The Importance of return
The return statement immediately stops function execution and sends output back to the caller.
def multiply(x, y):
return x * y
print("This line will never execute!")
print(multiply(4, 5)) # Output: 20
🚨 Any statement after return is ignored!
🔄 8. Data Sharing Between Functions
Each function has its own stack frame. They cannot access each other’s variables directly.
To share data: ✅ Pass values as arguments. ✅ Use return to send results back.
def square(num):
return num * num
result = square(6) # Function passes value back
print(result) # Output: 36
✅ Multiple values can be returned using lists or tuples:
def get_info():
return ["Alice", 25, "Engineer"]
data = get_info()
print(data) # Output: ['Alice', 25, 'Engineer']
🎯 Conclusion
✅ Python loads scripts into RAM, dividing memory into Code Section, Stack, and Heap. ✅ The Stack Memory stores function calls in stack frames. ✅ Functions return data to the caller, and execution resumes from where it left off. ✅ Use parameters and return values for efficient data sharing between functions.
🚀 Ready to optimize your Python code? Share your thoughts in the comments!