When you run a Python script, the code does not start working immediately. Python follows a clear internal process. This process decides how your code is read, checked, stored, and executed. Every Python program goes through the same path. Understanding this internal flow is now part of advanced learning programs like the Python Course in Delhi where the focus is on understanding how Python works behind the scenes, not just writing code.
"Python" relies upon an interpreter for controlling the execution. All the processes from checking the code to allocating memory and then executing the statements are managed by the "interpreter." Failure to comprehend it might make the runtime issues challenging to resolve.
How Python Reads and Understands Code?
Python first opens the script file. It only sees text at this stage. It does not understand any logic yet.
The first step is tokenizing, which means breaking the code into tokens. Tokens represent the smallest units in code: keywords, operators, variable names, literals, and indentation marks.
Key points about token processing:
● Tokenizes code into keywords, names, numbers, and symbols
● Detects indentation levels and structure
● Stops execution if syntax rules are broken
Indentation in Python is treated as a strict rule. Even a small spacing error stops the code here.
After processing the tokens, Python constructs an Abstract Syntax Tree. AST shows the relationship of all lines of code. It then checks for loops, conditions, functions, and classes.
Why AST matters:
● Ensures that code follows Python grammar
● Applies decorators before execution
● Checks the structure of code, without actually running.
● Errors in decorators, syntax, or code structure are detected here.
How Python Converts Code into Instructions?
Once the AST is ready, Python converts it into bytecode. Bytecode is a low-level set of instructions designed for Python. It is not machine code and not human-readable.
Why bytecode is important:
● Enables Python programs to run on multiple platforms
● Reduces redundant parsing for faster execution
● Controls execution efficiency
Even the shortest line of Python code can be translated to several bytecode instructions. Hence, the runtime is more dependent on the instructions rather than the lines of code.
Python retains this code in memory. Python sometimes retains it for later execution as well. This speeds up subsequent execution of this program.
How the Python Virtual Machine Executes Code?
The Python Virtual Machine N python manage, or virtual machine, executes these bytecode instructions one at a time. Internally, it operates by means of a kind of loop that repeatedly reads in and then executes an instruction.
Python uses a stack-based execution model. Values are pushed onto a stack, and popped when operations are performed.
What happens during execution:
● The instructions are loaded and then executed one after the other.
● Operations are done on the stack
● Results are returned or stored
Each call to a function creates a new execution frame. A frame consists of:
● Local Variables
● References to global variables
● Current position of the directive
● Stack of execution
Frames consume memory. Too many nested function invocations can result in slower execution. Deep recursion may result in stack overflow errors or crashes.
Runtime errors such as type mismatch, division by zero, or invalid operations occur at this stage. Advanced learning courses like the Python Course with Placement teaches reading bytecode and performing frame analysis to debug and optimize code for runtime performance.
How Python Manages Memory and Objects?
In the Python language, everything that can exist is an object. The number one, a string, a list, and a function can each
Variables do not store values. Instead, they store the address of the object in memory.
How memory works:
● A new object is created.
● Reference count is attributed
● Variable points to the object
● Several variables can refer to the same object
Python uses the mechanism of reference counting. When there are no more references to an object in Python, Python automatically removes the object.
Some objects may refer to each other; this makes cycles. Cycles cannot be removed by reference counting. Python contains a garbage collector that removes cycles automatically.
Python employs memory pools as well. Smaller objects are recycled, never destroyed, which increases execution speed but may cause misleading memory tracking.
How Imports Work in Python?
Imports are not simple file loads. Python imports are handled in a strict manner.
Import process:
● Checks whether the module is already loaded in the cache
● If found, Python reuses it
● If not, Python searches defined paths
● Run the module code just once.
● Stores the module in memory cache
Module code executes directly upon import. Top-level code within modules executes without functions having to be called. Import order can impact execution. Import errors will terminate a program prematurely.
Advanced systems make use of lazy imports, deferring the loading of modules. This cuts down on memory usage and reduces the time taken at startup. The concept of imports is taught in Python Coaching in Gurgaon and Python Course with Placement programs.
Python Execution Flow Table
|
Stage |
What Python Does |
Why It Matters |
|
File Reading |
Reads code as text |
No logic runs yet |
|
Token Processing |
Breaks code into parts |
Catches syntax errors |
|
AST Creation |
Checks code structure |
Applies decorators |
|
Bytecode Generation |
Converts AST to instructions |
Controls execution |
|
PVM Execution |
Runs bytecode step by step |
Actual logic is performed |
|
Frame Creation |
Stores local variables and stack |
Affects memory and speed |
|
Memory Management |
Tracks references |
Prevents memory leaks |
|
Garbage Collection |
Removes circular references |
Keeps memory clean |
Sum up,
Executing a Python script involves several things that happen behind the scenes. First and foremost, Python reads the file, tokenizes it, builds an AST, generates bytecode, and executes that in the Python Virtual Machine. Memory management is done by object references, reference counting, and garbage collection.
Imports are cached and ordered. These systems define the manner of operation of Python under load, on errors, and in large applications. Understanding this flow will help developers to develop more efficient and reliable code. Knowledge of Python internals is essentially required for advanced learning courses like Python Course with Placement and Python Coaching in Gurgaon.