dos: add python interview questions 2026

Author: Tanu-N-Prabhu

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+# Python Programming Interview Questions (2026)
+
+## Advanced Questions That Test Real Engineering Thinking
+
+![alt text](Gemini_Generated_Image_eb3gqneb3gqneb3g.png)
+
+
+<p align = "right"><b>Written by</b>: <i>Tanu Nanda Prabhu</i></p> 
+
+Most Python interview guides focus on syntax and beginner tricks. However, modern interviews in **2026** evaluate something deeper, **how well you understand Python's internals, performance trade-offs, and real-world design decisions.**
+
+Below are **advanced Python interview questions with concise explanations and code examples** that reflect the type of discussions happening in serious engineering interviews today.
+
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+
+
+
+
+# 1. What actually happens when Python executes a function call?
+
+Python does more than simply "run a function". Internally it creates a **new stack frame**, manages local variables, and pushes execution to the **Python Virtual Machine (PVM)**.
+
+### Key Concepts
+- Python compiles `.py` code into **bytecode**
+- Bytecode runs on the **Python Virtual Machine**
+- Each function call creates a **frame object**
+
+### Example
+
+```python
+def add(a, b):
+    return a + b
+
+add(3, 4)
+```
+
+Internally Python performs steps similar to:
+
+1. Create stack frame
+2. Bind `a=3`, `b=4`
+3. Execute bytecode
+4. Return result
+5. Destroy frame
+
+You can inspect the bytecode using:
+
+```python
+import dis
+
+def add(a, b):
+    return a + b
+
+dis.dis(add)
+```
+
+Understanding this helps explain recursion depth limits and function call overhead.
+
+---
+
+<br>
+<br>
+<br>
+<br>
+
+# 2. What is the difference between `__new__` and `__init__`?
+
+Many developers know `__init__`, but advanced interviews expect knowledge of **object creation mechanics**.
+
+### Concept
+
+| Method     | Responsibility                 |
+| :-------- | :------------------------------ |
+| `__new__`  | Creates the object instance    |
+| `__init__` | Initializes the created object |
+
+### Example
+
+```python
+class Demo:
+
+    def __new__(cls):
+        print("Creating instance")
+        return super().__new__(cls)
+
+    def __init__(self):
+        print("Initializing instance")
+
+obj = Demo()
+```
+
+### Output
+
+```text
+Creating instance
+Initializing instance
+```
+
+### When is `__new__` useful?
+- Implementing **Singleton pattern**
+- Subclassing **immutable types**
+- Custom memory allocation
+
+---
+
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+
+# 3. Explain Python's Global Interpreter Lock (GIL)
+
+The GIL ensures that only one thread executes Python bytecode at a time within a process.
+
+### Why it exists
+
+- Simplifies memory management
+- Protects reference counting
+- Prevents race conditions inside the interpreter
+
+
+
+### Example showing limitation
+
+```python
+import threading
+
+def task():
+    for _ in range(10_000_000):
+        pass
+
+t1 = threading.Thread(target=task)
+t2 = threading.Thread(target=task)
+
+t1.start()
+t2.start()
+
+t1.join()
+t2.join()
+```
+
+Even with two threads, CPU-heavy tasks do not run truly in parallel.
+
+### Real-world solutions
+
+- `multiprocessing`
+- `asyncio`
+- Native extensions (C/C++)
+
+---
+
+# 4. What are Python descriptors?
+
+Descriptors power many advanced Python features such as:
+
+- properties
+- methods
+- class attributes
+- ORM frameworks
+
+<br>
+<br>
+
+A descriptor implements any of:
+
+- `__get__`
+- `__set__`
+- `__delete__`
+
+## Example
+
+```python
+class PositiveNumber:
+
+    def __get__(self, instance, owner):
+        return instance._value
+
+    def __set__(self, instance, value):
+        if value < 0:
+            raise ValueError("Value must be positive")
+        instance._value = value
+
+
+class Account:
+    balance = PositiveNumber()
+
+acc = Account()
+acc.balance = 100
+```
+
+This allows **fine-grained control over attribute access**.
+
+---
+
+# 5. How does Python manage memory?
+
+Python uses two main mechanisms:
+
+### 1. Reference Counting
+
+Each object tracks how many references point to it.
+
+```python
+import sys
+
+a = []
+print(sys.getrefcount(a))
+```
+
+<br>
+<br>
+<br>
+<br>
+<br>
+
+
+### 2. Garbage Collector
+
+Handles cyclic references that reference counting cannot clean.
+
+```python
+import gc
+gc.collect()
+```
+
+### Important interview insight
+
+Memory leaks can occur when:
+
+- objects participate in reference cycles
+- `__del__` methods are used improperly
+
+---
+
+# 6. What is the difference between generators and iterators?
+Iterator
+
+An object implementing:
+
+- `__iter__`
+- `__next__`
+  
+### Generator
+
+A simpler way to create iterators using yield.
+
+### Example
+
+```json
+def count_up_to(n):
+    i = 1
+    while i <= n:
+        yield i
+        i += 1
+
+for number in count_up_to(3):
+    print(number)
+```
+
+Advantages of generators:
+
+- Lazy evaluation
+- Lower memory usage
+- Ideal for large datasets and streaming
+
+---
+
+# 7. What are metaclasses in Python?
+
+**A metaclass defines how classes themselves are created.**
+
+If objects are instances of classes,
+then **classes are instances of metaclasses.**
+
+### Example
+
+```python
+class Meta(type):
+
+    def __new__(cls, name, bases, attrs):
+        attrs['version'] = "1.0"
+        return super().__new__(cls, name, bases, attrs)
+
+
+class App(metaclass=Meta):
+    pass
+
+
+print(App.version)
+```
+
+Metaclasses are commonly used in:
+
+- ORMs
+- frameworks
+- API validation systems
+
+---
+
+# Final Thoughts
+
+Modern Python interviews are no longer about remembering syntax. They focus on:
+
+- **Understanding Python internals**
+- **Performance trade-offs**
+- **Writing scalable and maintainable systems**
+
+If you can confidently explain topics like:
+
+- descriptors
+- metaclasses
+- the GIL
+- memory management
+- generators
+
+then you are already operating at a **senior Python engineering level**.
+
+> Keep building. Keep experimenting. Python rewards those who explore its depths.