Python Interview Questions for Data Science

Python interview questions are a staple in data science technical evaluations. You can expect questions that span key Python coding principles during a typical interview. This comprehensive list of updated Python data science interview questions will help you prepare, covering topics like statistics, probability, string parsing, NumPy matrices, and Pandas.

Python data science interview questions may ask you to differentiate between a list and a tuple, find all bigrams in a sentence, or even implement the K-means algorithm from scratch. Commonly covered topics include:

• Basic Python
• String Manipulation
• Statistics and Probability
• Pandas
• Matrices and NumPy
• Data Structures and Algorithms
• Machine Learning

Table of Contents

1. Why Is Python Asked in Data Science Interviews?
2. Basic Python Interview Questions
3. String Manipulation Python Interview Questions
4. Python Statistics and Probability Interview Questions
5. Python Pandas Interview Questions
6. Python Data Manipulation Interview Questions
7. Matrices and NumPy Python Interview Questions
8. Python Data Structures and Algorithms Interview Questions
9. Python Machine Learning Interview Questions
10. The Bottom Line

Why Is Python Asked in Data Science Interviews?

Python has ascended as the preeminent language in data science, overshadowing R, Julia, Spark, and Scala. This prominence is largely due to its extensive array of data science libraries and its supportive community.

Python’s versatility extends across the entire data science stack, simplifying tasks from exploratory data analysis and visualization to model building and deployment.

Basic Python Interview Questions

1) What built-in data types are used in Python?

Python utilizes several built-in data types, including:

• Number (int, float, complex)
• String (str)
• Tuple (tuple)
• Range (range)
• List (list)
• Set (set)
• Dictionary (dict)

2) How are data analysis libraries used in Python? Name some common ones.

Python's popularity in data science is driven by its extensive library collection, which includes:

• Pandas
• NumPy
• SciPy
• TensorFlow
• SciKit
• Seaborn
• Matplotlib

These libraries provide tools for data processing, analysis, visualization, and more.

3) How is a negative index used in Python?

Negative indexing in Python allows access to list elements from the end. For instance, n-1 retrieves the last item, while n-2 fetches the second-to-last.

4) What is the difference between lists and tuples in Python?

• Syntax: Lists use square brackets [ ], while tuples use parentheses ( ).
• Mutability: Lists are mutable; tuples are immutable.
• Operations: Lists support more operations, such as insert and pop.
• Performance: Tuples, being immutable, are faster and consume less memory.

5) Which library would you prefer for plotting: Seaborn or Matplotlib?

Seaborn, built on top of Matplotlib, offers more customization and faster implementation for many common tasks. Matplotlib is better suited for fine-tuning.

6) Is Python an object-oriented programming language?

Python integrates features from both object-oriented programming (OOP) and aspect-oriented programming. However, it lacks strong encapsulation, a core OOP feature.

7) What is the difference between a series and a data frame in Pandas?

• Series: One-dimensional array with axis labels (index).
• Data Frame: Two-dimensional, tabular data structure with labeled axes (rows and columns).

8) How would you find duplicate values in a dataset in Python?

Use Pandas' duplicated() method to check for duplicates, returning a Boolean series indicating duplicate entries.

9) What is a lambda function in Python?

Lambda functions, or anonymous functions, are defined using the lambda keyword and can take multiple parameters but are restricted to a single expression.

10) Is memory de-allocated when you exit Python?

Not always. Modules with circular references might not be freed, and some memory reserved by the C library may remain.

11) What is a compound datatype?

Compound data structures represent multiple values:

• Lists: An ordered collection of values.
• Tuples: Ordered sequence of values.
• Sets: Unordered collection of unique values.

12) What is list comprehension in Python? Provide an example.

List comprehension provides a concise way to create lists. For example:

rletters = [letter for letter in 'retain']
print(rletters)  # Output: ['r', 'e', 't', 'a', 'i', 'n']        

13) What is tuple unpacking and why is it important?

Tuple unpacking assigns elements of a tuple to multiple variables, useful for swapping variables without a temporary variable:

x, y = 20, 30
x, y = y, x
print(f"x: {x}, y: {y}")  # Output: x: 30, y: 20        

14) What's the difference between '/' and '//' in Python?

• / performs float division (e.g., 9 / 2 returns 4.5).
• // performs floor division, returning the largest integer less than or equal to the division result (e.g., 9 // 2 returns 4).

15) How do you convert integers to strings in Python?

The str() function converts integers to strings. Alternatives include f-strings and the .format() method.

16) What are arrays in Python?

Arrays store multiple values in a single variable, e.g.,

faang = ["Facebook", "Apple", "Amazon", "Netflix", "Google"]
print(faang)  # Output: ['Facebook', 'Apple', 'Amazon', 'Netflix', 'Google']        

17) What's the difference between mutable and immutable objects?

• Mutable: Values can change (e.g., lists, sets, dictionaries).
• Immutable: Values cannot change (e.g., tuples, strings).

18) What are some limitations of Python?

• Speed: Slower than languages like Java and C.
• Mobile Development: Less effective for mobile apps.
• Memory Consumption: High memory usage.
• Python 2 vs Python 3: Incompatibilities between versions.

19) Explain the 'zip' and 'enumerate' functions.

• enumerate(): Returns indexes and items from an iterable.
• zip(): Combines multiple iterables into tuples.

20) Define PYTHONPATH.

PYTHONPATH tells the Python interpreter where to locate module files, akin to the PATH variable in the operating system.

String Manipulation Python Interview Questions

String parsing is common in data science interviews, especially for text-heavy companies like Twitter, LinkedIn, Indeed, or Netflix. These questions test your ability to clean and transform text data.

21) Write a function that returns a list of bigrams from a string.

def bigrams(sentence):
    words = sentence.split()
    return [words[i] + ' ' + words[i+1] for i in range(len(words) - 1)]

print(bigrams("Have free hours and love children"))
# Output: ['Have free', 'free hours', 'hours and', 'and love', 'love children']        

22) Given two strings, determine if one can be shifted to become the other.

def can_shift(A, B):
    return len(A) == len(B) and B in A + A

print(can_shift("abcde", "cdeab"))  # Output: True
print(can_shift("abc", "acb"))  # Output: False        

23) Determine if there is a one-to-one character mapping between two strings.

def is_one_to_one(string1, string2):
    if len(string1) != len(string2):
        return False
    mapping = {}
    for char1, char2 in zip(string1, string2):
        if char1 in mapping:
            if mapping[char1] != char2:
                return False
        elif char2 in mapping.values():
            return False
        else:
            mapping[char1] = char2
    return True

print(is_one_to_one("qwe", "asd"))  # Output: True
print(is_one_to_one("donut", "fatty"))  # Output: False        

24) Return the first recurring character in a string.

def first_recurring_char(s):
    seen = set()
    for char in s:
        if char in seen:
            return char
        seen.add(char)
    return None

print(first_recurring_char("interviewquery"))  # Output: 'i'        

25) Check if one string is a subsequence of another.

def is_subsequence(string1, string2):
    it = iter(string2)
    return all(char in it for char in string1)

print(is_subsequence("abc", "ahbgdc"))  # Output: True
print(is_subsequence("axc", "ahbgdc"))  # Output: False        

Python Statistics and Probability Interview Questions

These questions assess your ability to apply statistical and probability concepts using Python.

26) Generate N samples from a normal distribution and plot them.

import numpy as np
import matplotlib.pyplot as plt

def plot_normal_distribution(N):
    samples = np.random.randn(N)
    plt.hist(samples, bins=30, alpha=0.5, edgecolor='black')
    plt.title('Histogram of Normal Distribution')
    plt.xlabel('Value')
    plt.ylabel('Frequency')
    plt.show()

plot_normal_distribution(1000)        

27) How do you handle missing data in a dataset?

Common methods include:

• Dropping Missing Values: Using dropna() in Pandas.
• Imputation: Replacing missing values with the mean, median, or mode using fillna().

28) Calculate the mean, median, and mode of a dataset in Python.

from scipy import stats

data = [1, 2, 2, 3, 4, 5, 5, 5, 6]

mean = np.mean(data)
median = np.median(data)
mode = stats.mode(data)

print(f"Mean: {mean}, Median: {median}, Mode: {mode.mode[0]}")        

29) Perform a t-test to compare the means of two samples.

from scipy.stats import ttest_ind

sample1 = np.random.randn(100)
sample2 = np.random.randn(100)

t_stat, p_value = ttest_ind(sample1, sample2)

print(f"T-statistic: {t_stat}, P-value: {p_value}")        

30) How do you calculate the Pearson correlation coefficient in Python?

from scipy.stats import pearsonr

data1 = np.random.randn(100)
data2 = np.random.randn(100)

corr, _ = pearsonr(data1, data2)

print(f"Pearson correlation coefficient: {corr}")        

Python Pandas Interview Questions

Pandas is a must-know library for any data science interview, encompassing data wrangling and pre-processing skills.

31) How do you read a CSV file in Pandas?

import pandas as pd

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

32) How do you handle missing values in a Data Frame?

# Dropping rows with missing values
df.dropna()

# Filling missing values with the mean
df.fillna(df.mean())        

33) How do you group data in a Data Frame?

grouped = df.groupby('column_name').agg({'other_column': 'mean'})        

34) How do you merge two Data Frames in Pandas?

merged_df = pd.merge(df1, df2, on='common_column')        

35) How do you create a pivot table in Pandas?

pivot_table = df.pivot_table(index='column1', columns='column2', values='values_column', aggfunc='mean')        

36) Explain how to use the 'apply' function in Pandas.

df['new_column'] = df['column'].apply(lambda x: x * 2)        

37) How do you handle categorical data in Pandas?

# Using pd.get_dummies for one-hot encoding
df = pd.get_dummies(df, columns=['categorical_column'])

# Using LabelEncoder
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['encoded_column'] = le.fit_transform(df['categorical_column'])        

38) How do you concatenate two Data Frames?

concatenated_df = pd.concat([df1, df2], axis=0)        

Python Data Manipulation Interview Questions

These questions test your ability to transform data for analysis.

39) How do you filter rows in a Data Frame?

filtered_df = df[df['column'] > value]        

40) How do you reshape data in a Data Frame?

reshaped_df = df.pivot(index='index_column', columns='columns_column', values='values_column')        

41) How do you sort a Data Frame?

sorted_df = df.sort_values(by='column')
        

42) How do you handle time series data in Pandas?

# Parsing dates while reading the CSV 
df = pd.read_csv('data.csv', parse_dates=['date_column']) 
# Setting the date column as index 
df.set_index('date_column', inplace=True) 
# Resampling time series data 
resampled_df = df.resample('M').mean() # Monthly resampling        

43) How do you add a new column to a Data Frame?

df['new_column'] = df['existing_column'] * 2        

Matrices and NumPy Python Interview Questions

NumPy is essential for numerical computing and manipulating matrices.

44) Create a 3x3 identity matrix using NumPy.

import numpy as np

identity_matrix = np.eye(3)        

45) How do you perform matrix multiplication in NumPy?

matrix1 = np.array([[1, 2], [3, 4]]) 
matrix2 = np.array([[5, 6], [7, 8]]) 
result = np.dot(matrix1, matrix2)        

46) How do you calculate the inverse of a matrix in NumPy?

matrix = np.array([[1, 2], [3, 4]]) 
inverse_matrix = np.linalg.inv(matrix)        

47) How do you find the eigenvalues and eigenvectors of a matrix?

matrix = np.array([[1, 2], [2, 3]]) 
eigenvalues, eigenvectors = np.linalg.eig(matrix)        

48) How do you generate random numbers in NumPy?

random_numbers = np.random.rand(3, 3) # 3x3 matrix of random numbersCopy code        

Python Machine Learning Interview Questions

These questions cover applying machine learning principles using Python.

49) Implement the K-means algorithm from scratch.

import numpy as np 

def kmeans(data, k, max_iters=100): 
# Initialize centroids randomly from the data points 
centroids = data[np.random.choice(data.shape[0], k, replace=False)] 

for _ in range(max_iters): 
# Assign each data point to the nearest centroid 
distances = np.linalg.norm(data[:, np.newaxis] - centroids, axis=2) 
labels = np.argmin(distances, axis=1) 

# Recompute the centroids 
new_centroids = np.array([data[labels == i].mean(axis=0) for i in range(k)]) 

# Check for convergence 
if np.all(centroids == new_centroids): 
break 
centroids = new_centroids 

return labels, centroids 

# Example usage 
data = np.random.rand(100, 2) 
labels, centroids = kmeans(data, 3)        

The Bottom Line:

Mastering Python is essential for aspiring data scientists. This list of 49 interview questions covers key areas like Python syntax, string manipulation, statistics, Pandas, NumPy, and machine learning.

By understanding and practicing these questions, you'll be well-prepared for technical interviews, and equipped with the skills to tackle real-world data science challenges.

If you are keen to Earn a Professional Certificate on Data Science, I'll Strongly Recommend this IBM Data Science Professional Certificate on Coursera

Pros:

1. Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles.
2. Learn the tools, languages, and libraries used by professional data scientists, including Python and SQL.
3. Import and clean data sets, analyze and visualize data, and build machine learning models and pipelines.
4. Apply your new skills to real-world projects and build a portfolio of data projects that showcase your proficiency to employers.
5. Earn an employer-recognized certificate from IBM and Coursera

I hope this month's newsletter is insightful for you all about this topic, "Python Interview Questions for Data Science."

To get ready for Data Science interviews, consider reading the book "Elements of Programming Interviews in Python" by Adnan Aziz , Tsung-Hsien L. , and Amit Prakash .

Good luck with your preparation and your journey to becoming a proficient data scientist!

SUBSCRIBE to My Newsletter to Get Notified First?when I Publish My Next Week Newsletter Edition ??

If you feel my Newsletter may help someone you know, Share and?Enlighten them!

Also, If you have?any critics, Enlighten me in the comments section???

Affiliate Disclosure: As Per the USA’s Federal Trade Commission laws, I’d like to disclose that these links to the web services are affiliate links. I’m an affiliate marketer with links to an online retailer on my website. When people read what I’ve written about a particular product and then click on those links and buy something from the retailer, I earn a commission from the retailer.