Python (ME-506) - Important Questions

Explain Python built-in data types (numeric, sequence, mapping, set, boolean) and demonstrate type conversion methods with examples. Include examples showing implicit and explicit type conversion between $\left\{\text{int, float, str, list, tuple, dict, set}\right\}$.

Write a Python function to sort a list using merge sort. Provide the complete implementation and derive the time complexity $\left\text{in Big-}O\right$ notation.

Compare Python lists and tuples in terms of mutability, typical use-cases, and time complexity of common operations such as append, pop and indexing. State the complexity using Big-O notation, e.g., $O\left(1\right)$ where appropriate.

Explain Python function definitions, positional and keyword arguments, default arguments, variable-length arguments ($*args$, $**kwargs$), lambda expressions, and closures. Provide concise code examples for each.

Explain OOP concepts in Python: class, object, inheritance, method overriding, polymorphism, and encapsulation. Implement a Python class for a simple Matrix with methods for addition and multiplication and overload the $+$ and $*$ operators. State the element-wise addition formula $\left(A + B\right)_{ij} = A_{ij} + B_{ij}$ used in your implementation.

Write a Python program to read a CSV file containing numerical data, compute the column-wise mean values, and handle missing or malformed lines using exception handling (demonstrate try-except-finally). Explain how resources are safely managed.

Using NumPy, demonstrate creation of 1-D and 2-D arrays, broadcasting rules, and vectorized operations. Compute the dot product of two vectors and state the formula $\mathbf{a}\cdot\mathbf{b}=\sum_{i=1}^{n} a_i b_i$ in your explanation.

Using Pandas, show how to load a CSV into a DataFrame, perform grouping and aggregation (groupby and aggregate), create a pivot table, and demonstrate methods to detect and handle missing data (dropna, fillna). Provide short code examples.

Write a Python script using Matplotlib to plot stress vs. strain data from arrays, label the axes, add a title and legend, set axis limits, and save the figure to a PNG file.

Explain Python generators and decorators. Implement a generator that yields Fibonacci numbers and write a decorator that measures and prints the execution time of any function it wraps.