Python Interview Questions — Flashcards | inGrade

Question 1

What are Python's basic data types?

Accepted Answer

Python includes several basic data types: int (integers), float (floating-point numbers), str (strings), bool (booleans), and more complex types such as list, tuple, dict, and set. Python is dynamically typed, meaning you don't need to declare variable types explicitly.

Question 2

What is the difference between a list and a tuple?

Accepted Answer

A List is mutable (can be changed after creation), defined with square brackets ([]). A Tuple is immutable (cannot be changed after creation), defined with parentheses (()). Tuples are generally faster and used for data that should not change, while lists are used for collections that need modification.

Question 3

What are classes and objects in Python?

Accepted Answer

A class is a blueprint for creating objects (instances). Objects are instances of classes, which contain attributes (data) and methods (functions). Python supports OOP concepts like inheritance (inheriting methods from another class), polymorphism (methods with different behaviors), and encapsulation (bundling data and methods).

Question 4

What is a lambda function in Python?

Accepted Answer

A lambda function is an anonymous, short function defined using the lambda keyword. Example: add = lambda x, y: x + y. Lambda functions are useful for short, throwaway functions, especially as arguments to higher-order functions like map(), filter(), and sorted().

Question 5

What is NumPy and what are its benefits?

Accepted Answer

NumPy is a library used for numerical computing in Python. It provides support for multi-dimensional arrays, matrices, and mathematical functions. It is faster than native Python lists due to vectorization and efficient memory management. Vectorized operations allow computation on entire arrays without explicit loops.

Question 6

What is Pandas in Python?

Accepted Answer

Pandas is a data manipulation and analysis library. It provides two main data structures: Series (1D) and DataFrame (2D), which are flexible and easy to use for handling data from various sources like CSV, Excel, SQL, etc. Key operations include filtering, grouping, merging, and handling missing data with .isna(), .dropna(), .fillna().

Question 7

What is the difference between loc[] and iloc[] in Pandas?

Accepted Answer

loc[] is label-based indexing, where you access rows/columns by their labels (e.g., df.loc['row_name', 'col_name']). iloc[] is integer-location based indexing, where you access by row/column number (e.g., df.iloc[0, 1]). loc[] is inclusive of the end label, while iloc[] follows standard Python slicing (exclusive of end).

Question 8

How does Python handle exceptions?

Accepted Answer

Python uses try, except, else, and finally blocks for exception handling. The try block tests code for exceptions, except catches specific exceptions (e.g., except ValueError), else runs if no exception occurred, and finally ensures certain code runs regardless of exceptions. This allows graceful error handling without crashing the program.

Question 9

What are list comprehensions in Python?

Accepted Answer

List comprehensions provide a concise way to create lists in one line. Example: squares = [x**2 for x in range(5)] produces [0, 1, 4, 9, 16]. They can include conditions: evens = [x for x in range(10) if x % 2 == 0]. List comprehensions are generally faster and more readable than equivalent for loops.

Question 10

What is the purpose of the with statement in Python file handling?

Accepted Answer

The with statement ensures that a file is properly opened and closed after use. It automatically takes care of closing the file, even if an exception occurs within the block. Example: with open('myfile.txt', 'r') as file: content = file.read(). This is the recommended way to handle files in Python.

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