Top 25 Python Interview Questions for Data Analysts

Python's ease of use, robust library, and vibrant community make it one of the most popular programming languages for data analysis. Understanding fundamental Python ideas is crucial, whether you're trying to improve your Python skills or are getting ready for a data analyst job interview.

The top 25 Python interview questions that are frequently asked during data analyst interviews are covered in this guide. These queries cover everything from fundamental data structures and syntax to more complex subjects like data visualization, Pandas, and NumPy. A thorough explanation is provided after each question to ensure you fully grasp the ideas.

1.      What is Python, and why is it commonly used in data analytics?

Python is a high-level programming language that is renowned for being easy to understand and use. Because of its robust ecosystem of libraries, including Pandas, NumPy, and Matplotlib, which facilitate data manipulation, analysis, and visualization, it is extensively utilized in data analytics.

2.      What are Python’s built-in data types?

Python has several built-in data types:

• Numeric types: int (whole numbers), float (decimal numbers), and complex (numbers with j for imaginary parts).

• Sequence types: list (mutable), tuple (immutable), and range (for loops).

• Text type: str (strings).

• Boolean type: bool (True or False).

• Set types: set and frozenset (collections of unique items).

• Dictionary type: dict (stores key-value pairs).

Learn more in the official Python docs on data types.

3.      What is the difference between a list and a tuple?

A list is mutable, meaning its elements can be changed, while a tuple is immutable, meaning it cannot be modified after creation. Lists are slower compared to tuples but more flexible.

4.      How do you handle missing values in Pandas?

Missing values are common in real-world datasets and can affect data analysis. Pandas provides multiple ways to handle missing values:

• Drop missing values: df.dropna() removes rows with missing values. 

• Fill missing values: df.fillna(value) replaces missing values with a specified value.

• Replace with the column mean: df.fillna(df.mean()) replaces missing values with the column’s mean, which is useful in numerical datasets.

Handling missing data correctly ensures accuracy in analysis and prevents errors in machine learning models.

More on this in the Pandas missing data handling guide.

5.      Explain the difference between a DataFrame and a Series in Pandas.

A DataFrame is a 2-dimensional labeled data structure with potentially multiple sorts of columns. It can be compared to a table that has columns and rows. In contrast, a series is a labeled array that is one dimension and can contain any kind of data.

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

The loc[] and iloc[] functions are used for selecting rows and columns in a DataFrame:

• loc[] is label-based: It selects rows and columns based on their labels (column names).

• iloc[] is index-based: It selects rows and columns based on numerical index positions.

7.      What is a string in Python?

In Python, a string is a collection of characters encapsulated in single, double, or triple quotations. Strings cannot be altered after they are created because they are immutable. Python has a number of built-in techniques for working with strings, including breaking them into smaller pieces, changing specific characters, and changing the string's capitalization. Strings can be cut, indexed, and looped through to extract certain characters or substrings because they are iterable.

8.      What are loops in Python?

Loops are used in Python to execute a block of code repeatedly. The two main types of loops are for loops and while loops. A for loop is used when the number of iterations is known, such as iterating over a list or string. A while loop runs as long as a specific condition remains true. Loops are essential in automating repetitive tasks, such as processing large datasets, filtering records, or performing calculations on multiple elements.

9.      What are conditional statements in Python?

Conditional statements allow the program to make decisions based on specific conditions. Python uses if, elif, and else statements to execute different blocks of code depending on whether a condition evaluates to true or false. These statements help in implementing decision-making logic, such as validating user input, filtering data, or controlling the flow of execution in a program.

10.  What is a function in Python?

A function is a block of reusable code that performs a specific task. Functions help break a large program into smaller, manageable parts, improving readability and reducing redundancy. Functions take input values, known as parameters, and return output values based on the operations performed. By defining functions, programmers can reuse code across different parts of a project, making maintenance easier.

11.  What is a recursive function?

A recursive function is a function that calls itself within its own definition. Recursion is used to solve problems that can be broken down into smaller subproblems, such as calculating a factorial, generating Fibonacci sequences, or traversing hierarchical structures like trees. Recursion must always have a base case, which stops the function from calling itself indefinitely and prevents a program crash due to excessive memory usage.

12.  What is the difference between return and print in Python?

The return statement is used inside a function to send back a value to the caller, whereas the print function simply displays output on the screen. When a function uses return, the returned value can be stored in a variable and used later in calculations or further processing. On the other hand, print only outputs data but does not allow the program to retain that value for future use.

13.  What is list comprehension in Python?

List comprehension is a concise and efficient way to create lists in Python. List comprehension enables defining the list in a single line by applying a transformation or filtering condition, as opposed to building the list using loops. It enhances readability and speed of execution, which makes it helpful for data manipulation tasks like changing values in a dataset or extracting elements that satisfy specific criteria.

14.  What is a lambda function in Python?

A lambda function is a small, anonymous function that is used for simple operations. Unlike conventional functions declared using the def keyword, lambda functions do not have a name and have a single expression. They are commonly employed in instances where a function is needed momentarily, such as filtering lists or sorting data. Lambda functions reduce the number of needless function definitions for quick operations, increasing efficiency.

15.  What are exceptions in Python?

Exceptions are errors that occur during program execution, causing the program to terminate unexpectedly. Common exceptions include division by zero, attempting to access a non-existent list index, or trying to open a missing file. Python provides a way to handle exceptions using the try-except block, allowing the program to continue running smoothly even when an error occurs. Exception handling is crucial in data analysis when dealing with missing values, unexpected input formats, or database connection failures.

Learn more in the Python exception handling guide.

16.  What is the difference between “is” and == in Python?

The “is” operator determines whether two variables point to the same memory location, whereas the == operator determines whether two variables have the same value. Two variables may point to separate memory locations even if they have the same values. When working with changeable data types like lists and dictionaries, it is helpful to utilize is to determine whether two variables relate to the same item.

17.  How does Python handle memory management?

Python manages memory automatically through a mechanism known as garbage collection. It tracks the number of references to each object and removes objects that are no longer needed to free up memory. Python also uses reference counting, where an object's memory is freed when the reference count reaches zero. This ensures efficient memory usage and prevents memory leaks, especially when dealing with large datasets in data analysis.

18.  What is a dictionary in Python?

A dictionary is a data structure that holds key-value pairs. Dictionaries are frequently used in data analysis to store structured information, such as mapping column names to data values or storing configuration settings. Unlike lists, which store elements in a sequence, dictionaries enable quick lookups by retrieving corresponding values using unique keys.

19.  What is a tuple in Python?

A tuple is similar to a list but is immutable, meaning its values cannot be changed after creation. Tuples are used when a collection of values should remain constant throughout the program, such as storing coordinates, database records, or fixed configurations. Since tuples are immutable, they are faster and consume less memory compared to lists.

20.  What is the difference between a list and a set in Python?

Whereas a set is an unordered collection that forbids duplicate values, a list is an ordered collection of elements that permits duplicates. Sets are frequently used to carry out operations like unions and intersections between various groupings of values, as well as to eliminate duplicate data from datasets.

21.  What is the difference between deep copy and shallow copy?

A shallow copy creates a new object, but the inner elements are still references to the original data. This means that changes to nested or mutable elements in the copy may affect the original.

A deep copy, on the other hand, creates a new object along with copies of all the nested elements, ensuring complete independence from the original object. This prevents any changes in the copy from affecting the original structure.

Example:

• Shallow copy:

import copy

a = [[1, 2], [3, 4]]

b = copy.copy(a)  # Only outer list is copied

• Deep copy:

import copy

a = [[1, 2], [3, 4]]

b = copy.deepcopy(a)  # Entire structure is copied

22.  What is the difference between mutable and immutable data types in Python?

Mutable data types can be changed after creation, while immutable data types cannot. Examples of mutable types include lists and dictionaries, whereas strings and tuples are immutable.

23.  How do you filter data in a DataFrame using Pandas?

You can filter data in a DataFrame using boolean indexing in Pandas. For example, to filter rows where the 'Score' is greater than 30: filtered_df = df[df['Score'] > 30]

24.  What is the difference between positional and keyword arguments in Python?

Positional arguments are passed to a function based on their position, meaning the order in which they appear matters. In contrast, keyword arguments are passed by explicitly specifying the parameter names, which improves clarity and allows arguments to be provided in any order. Using keyword arguments enhances readability, especially when dealing with functions that take many parameters.

25.  What is the purpose of the pass statement in Python?

The pass statement is a placeholder that allows writing syntactically correct code without executing any actions. It is commonly used in situations where code needs to be written later, such as defining empty functions or loops.

Conclusion

Python is a fundamental tool for data analysts, and a solid understanding of these concepts is crucial for problem-solving and efficiency. Want to deepen your expertise? Join IOTA Academy’s Data Analysis Course today and enhance your data analysis skills with hands-on projects!