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Understand Python’s inner workings step-by-step!
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Python is one of the most popular and beginner-friendly programming languages in the world. It's used in web development, data science, automation, machine learning, and more. But to truly harness its power, you need to understand How Python Works: From Code to Execution – A Deep Dive
This guide explains Python's architecture, interpreter, execution flow, memory management, and more, with simplified diagrams and sketches to help visualize the process.
Python is a high-level, interpreted, and object-oriented language. Unlike C or Java, where the code must be compiled, Python runs your code line by line using an interpreter.
Interpreted: No compilation step is needed.
Dynamic typing: You don’t need to declare variable types.
Automatic memory management: Handled by Python’s garbage collector.
Extensible: Python can integrate with C/C++ code for performance.
When you write a Python script and run it, a series of steps takes place to convert your code into actions the computer can understand.
Source Code (.py)
↓
Python Interpreter
↓
Bytecode (.pyc)
↓
Python Virtual Machine (PVM)
↓
Execution Output
# example.py
a = 5
b = 10
print(a + b)
This code is human-readable and saved with an .py extension.
The Python interpreter (like CPython, the most commonly used version) first parses and compiles the source code into bytecode, an intermediate format.
Bytecode is a low-level, platform-independent representation of your code.
Sketch: Bytecode Compilation
def add():
a = 5
b = 10
return a + b
↓ (Compilation Step)
[ LOAD_CONST 5, LOAD_CONST 10, BINARY_ADD, RETURN_VALUE ]
These bytecode instructions are stored in .pyc files under the __pycache__ folder.
The PVM reads bytecode line by line and executes it. It’s like a runtime engine that interprets and executes Python bytecode.
The PVM handles execution, memory allocation, and garbage collection.
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Compiles .py to .pyc files.
Executes bytecode line-by-line.
Handles object allocation, garbage collection, and memory reuse.
Python provides rich built-in types like int, list, dict, and many standard modules (math, random, etc.).
There are several implementations of the Python interpreter:
| Interpreter | Language | Key Feature |
| CPython | C | Official version, most widely used |
| Jython | Java | Python on the Java platform |
| PyPy | Python | Faster execution using JIT compiler |
| IronPython | C# | Integrates with .NET framework |
Python uses automatic memory management via a built-in Garbage Collector (GC).
+-------------------------+ +--------------------------+
| Stack | | Heap |
| | | |
| - Function frames | | - Objects (lists, dicts) |
| - Local variables | | - Class instances |
+-------------------------+ +--------------------------+
Code Variables → Memory Allocation → Reference Counting → Garbage Collection
Every object in Python has a reference count. When no variables reference the object, it is garbage collected.
a = [1, 2, 3] # reference count = 1
b = a # reference count = 2
del a # reference count = 1
del b # reference count = 0 → object deleted
Python also uses a cyclic garbage collector to find objects that reference each other and are no longer needed.
Memory in Python is divided into two main areas:
| Memory Type | Purpose |
| Stack | Stores function calls and local variables |
| Heap | Stores objects like lists, dictionaries, custom classes |
+-----------------+
| Python Stack | <---- Function calls, local vars
+-----------------+
| Python Heap | <---- Objects, classes, lists, etc.
+-----------------+
Each time you call a function, a new frame is pushed onto the stack.
Everything in Python is an object, including numbers, functions, and even modules.
type(5) # <class 'int'>
type(len) # <class 'builtin_function_or_method'>
type("hello") # <class 'str'>
Each object has:
Type: Defined at creation
ID: Unique memory address
Reference Count: Number of references to it
Let’s walk through an example:
def greet(name):
return "Hello, " + name
greet("Alice")
Python parses and compiles the code to bytecode.
A function object greet is created.
greet("Alice") is called.
The stack frame is created.
"Hello, " + name is executed.
Result returned, stack frame popped.
You can use the dis Module to inspect bytecode:
import dis
def add():
return 1 + 2
dis.dis(add)
Output:
2 0 LOAD_CONST 1 (1)
2 LOAD_CONST 2 (2)
4 BINARY_ADD
6 RETURN_VALUE
This is what the PVM reads and executes.
While CPython is interpreted, PyPy uses Just-In-Time (JIT) compilation to speed up execution.
JIT compiles hot code paths into machine code.
Great for long-running applications (e.g., web servers, scientific computation).
Python’s CPython interpreter has a Global Interpreter Lock (GIL), which prevents multiple threads from executing Python bytecode simultaneously.
Good for I/O-bound tasks.
Poor for CPU-bound tasks in pure Python.
You can use:
multiprocessing for CPU-bound parallelism.
asyncio for asynchronous I/O.
+-------------------------+
| Python Code (.py) |
+-------------------------+
↓
Python Compiler → Bytecode (.pyc)
↓
+---------------------------+
| Python Virtual Machine |
| (Executes Bytecode) |
+---------------------------+
↓ ↓ ↓
Memory Mgmt Function Stack Output
If you want to explore Python deeper, try these tools:
| Tool | Purpose |
| dis | Disassembles bytecode |
| gc | Access the garbage collector |
| pdb | Python debugger |
| objgraph | Visualize memory and object references |
| tracemalloc | Track memory usage |
# email_sender.py
def send_email(to, subject):
print(f"Sending email to {to} with subject: {subject}")
send_email("user@example.com", "Welcome!")
Execution Flow:
Python interpreter starts.
The compiler parses and converts the code into bytecode.
PVM executes each bytecode instruction.
Memory allocated for strings, functions, etc.
Stack frames are managed for function calls.
The output is printed.
Python is interpreted, not compiled.
It compiles code to bytecode and runs it using a Virtual Machine.
Memory management is automatic with reference counting and garbage collection.
Python's performance is limited by the GIL but can be enhanced with multiprocessing or tools like PyPy.
Everything in Python is an object, including functions and numbers.
Understanding how Python works internally gives you a deeper insight into writing better, more optimized code. Whether you're debugging a tricky issue or optimizing for performance, knowing the role of the interpreter, bytecode, memory model, and execution flow is crucial.
If you're building projects, testing performance, or simply learning Python more deeply, revisit this internal architecture often. It’s the foundation of the language.
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