Module

A module is a single Python file containing code, such as functions, classes, or variables.

Example: A file named math_utils.py is a module.

Package

A package is a directory (folder) that contains multiple modules, plus a special file named __init__.py.

This setup tells Python that the folder is a package that can be imported.

Example structure:

utilities/

Simple difference

• Module = single file.
• Package = collection of modules organized in a directory.

Python is both, in a friendly hybrid way.

• Python source code (.py) is first compiled into bytecode (.pyc).
• That bytecode is then interpreted by the Python Virtual Machine (PVM).

In essence, Python behaves like a storyteller who first converts your script into a universal script and then performs it line by line.

Python remains a modern favourite for many reasons:

Benefits

Code feels like conversation rather than cryptography.

Pandas, NumPy, Django, Flask, TensorFlow, PyTorch.

Write once, run almost anywhere.

A huge ecosystem of users and contributors.

Less boilerplate; faster prototyping.

Used for web development, data science, AI, automation, DevOps, scripting.

Python’s attribute protection system relies on naming conventions, not strict enforcement.

Global Attribute

A variable declared outside functions/classes, accessible everywhere.

x = 10 # global

Protected Attribute

Prefix with a single underscore _attribute.

Indicates: “Internal use; handle with care.”

Private Attribute

Prefix with double underscore __attribute.

Python performs name-mangling to restrict direct access.

Accessing it outside:

obj = A()

print(obj._A__secret) # name-mangled access

Yes, Python treats Variable, variable, and VARIABLE as separate identifiers.

Case affects variables, functions, classes, and even module names.

Pandas is a powerful open-source data analysis and manipulation library.

It’s like a Swiss-army spreadsheet for Python.

Key features:

• Fast data structures (Series, DataFrame)
• Data cleaning, filtering, grouping
• CSV/Excel/SQL import-export
• Time-series handling

Example:

df = pd.read_csv('sales.csv')

print(df.head())

Python uses the try–except–else–finally blocks.

try:

except ZeroDivisionError:

else:

finally:

Flow:

• try: risky code
• except: handle errors
• else: executes when try succeeds
• finally: always runs (cleanup, logging)

For Loop

Used when you know the number of iterations or are looping over a sequence.

for x in [1, 2, 3]:

While Loop

Used when iteration depends on a condition.

n = 1

while n < 4:

Essence:

• for = iterate over items
• while = iterate until a condition breaks

Yes, indentation is mandatory.

Python uses indentation to define code blocks instead of braces { }.

It enforces clarity and prevents chaotic code nesting.

Example:

if x > 5:

Without indentation, Python will raise IndentationError.

self represents the instance of the class inside methods.

It lets methods access:

• instance attributes
• other methods
• current object state

Example:

c = Car("Tesla")

c.show()

Here, self.model belongs to that particular car instance.

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Module

A module is a single Python file containing code, such as functions, classes, or variables.

Example: A file named math_utils.py is a module.

Package

A package is a directory (folder) that contains multiple modules, plus a special file named __init__.py.

This setup tells Python that the folder is a package that can be imported.

Example structure:

utilities/

Simple difference

• Module = single file.
• Package = collection of modules organized in a directory.

Python is both, in a friendly hybrid way.

• Python source code (.py) is first compiled into bytecode (.pyc).
• That bytecode is then interpreted by the Python Virtual Machine (PVM).

In essence, Python behaves like a storyteller who first converts your script into a universal script and then performs it line by line.

Python remains a modern favourite for many reasons:

Benefits

Code feels like conversation rather than cryptography.

Pandas, NumPy, Django, Flask, TensorFlow, PyTorch.

Write once, run almost anywhere.

A huge ecosystem of users and contributors.

Less boilerplate; faster prototyping.

Used for web development, data science, AI, automation, DevOps, scripting.

Python’s attribute protection system relies on naming conventions, not strict enforcement.

Global Attribute

A variable declared outside functions/classes, accessible everywhere.

x = 10 # global

Protected Attribute

Prefix with a single underscore _attribute.

Indicates: “Internal use; handle with care.”

Private Attribute

Prefix with double underscore __attribute.

Python performs name-mangling to restrict direct access.

Accessing it outside:

obj = A()

print(obj._A__secret) # name-mangled access

Yes, Python treats Variable, variable, and VARIABLE as separate identifiers.

Case affects variables, functions, classes, and even module names.

Pandas is a powerful open-source data analysis and manipulation library.

It’s like a Swiss-army spreadsheet for Python.

Key features:

• Fast data structures (Series, DataFrame)
• Data cleaning, filtering, grouping
• CSV/Excel/SQL import-export
• Time-series handling

Example:

df = pd.read_csv('sales.csv')

print(df.head())

Python uses the try–except–else–finally blocks.

try:

except ZeroDivisionError:

else:

finally:

Flow:

• try: risky code
• except: handle errors
• else: executes when try succeeds
• finally: always runs (cleanup, logging)

For Loop

Used when you know the number of iterations or are looping over a sequence.

for x in [1, 2, 3]:

While Loop

Used when iteration depends on a condition.

n = 1

while n < 4:

Essence:

• for = iterate over items
• while = iterate until a condition breaks

Yes, indentation is mandatory.

Python uses indentation to define code blocks instead of braces { }.

It enforces clarity and prevents chaotic code nesting.

Example:

if x > 5:

Without indentation, Python will raise IndentationError.

self represents the instance of the class inside methods.

It lets methods access:

• instance attributes
• other methods
• current object state

Example:

c = Car("Tesla")

c.show()

Here, self.model belongs to that particular car instance.

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Python uses a private heap for all objects and data structures. The Python Memory Manager handles allocation and deallocation automatically.

Reference Counting

Every object has a counter that tracks how many references point to it.

Example:

a = [1, 2, 3]

b = a # reference count increases

When the count drops to zero, Python frees the object immediately.

Garbage Collection

Python also has a cyclic garbage collector to clean up reference cycles (e.g., objects referencing each other).

Example:

gc.collect()

It detects unreachable cycles and clears them.

Yes, Python supports multiple inheritance.

Python resolves method conflicts using the MRO (Method Resolution Order), following the C3 linearization algorithm.

Memory management relies on:

• A private heap
• The Python Memory Manager
• Automatic garbage collection
• Reference counting
• Memory pools provided by pymalloc for faster allocations

Programmers don’t manually manage memory.