Python interview questions are crucial in evaluating candidates during data science technical assessments. Expect inquiries that cover vital Python coding principles in standard interviews.

This extensive compilation of current Python data science interview questions will aid your preparation, focusing on various subjects such as statistics, probability, string manipulation, NumPy matrices, and Pandas.

Questions in Python data science interviews may require you to explain the distinctions between lists and tuples, identify all bigrams within a sentence, or even create the K-means algorithm from scratch.

Common topics generally 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 Machine Learning Interview Questions 9. The Bottom Line

Why Is Python Asked in Data Science Interviews? Python has emerged as the leading language in data science, surpassing others like R, Julia, Spark, and Scala. This dominance is primarily due to its vast array of libraries tailored for data science and a strong community backing.

Its flexibility allows it to be utilized throughout the data science workflow, facilitating tasks from exploratory data analysis and visualization to model development and deployment.

Basic Python Interview Questions 1) What built-in data types are utilized in Python? Python offers 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 utilized in Python? Name some common ones. The popularity of Python in data science stems from its extensive collection of libraries, which includes: - Pandas - NumPy - SciPy - TensorFlow - SciKit - Seaborn - Matplotlib

These libraries equip users with tools for data processing, analysis, visualization, and beyond.

3) How is a negative index applied in Python? Negative indexing enables access to list elements from the end. For example, n-1 retrieves the last element, while n-2 fetches the second-to-last.

4) What distinguishes lists from tuples in Python? - Syntax: Lists are defined using square brackets [ ], whereas tuples use parentheses ( ). - Mutability: Lists can be modified; tuples remain unchanged. - Operations: Lists support more operations, such as insert and pop. - Performance: Being immutable, tuples are generally faster and require less memory.

5) Which library would you prefer for plotting: Seaborn or Matplotlib? Seaborn, built on Matplotlib, allows for greater customization and quicker implementation of many common tasks. Matplotlib is more suitable for detailed adjustments.

6) Is Python an object-oriented programming language? Python combines features from both object-oriented programming (OOP) and aspect-oriented programming but lacks strong encapsulation, a fundamental aspect of OOP.

7) What is the distinction between a series and a data frame in Pandas? - Series: A one-dimensional array with axis labels (index). - Data Frame: A two-dimensional, labeled data structure with rows and columns.

8) How would you identify duplicate values in a dataset using Python? Utilize the duplicated() method in Pandas to check for duplicates, returning a Boolean series that indicates duplicate entries.

9) What is a lambda function in Python? Lambda functions, also known as anonymous functions, are defined using the lambda keyword and can accept multiple parameters but are limited to a single expression.

10) Is memory released when exiting Python? Not necessarily. Modules with circular references may not be released, and some memory allocated by the C library could remain.

11) What constitutes a compound datatype? Compound data structures can hold multiple values: - Lists: An ordered collection of values. - Tuples: An ordered sequence of values. - Sets: An unordered collection of unique values.

12) What is list comprehension in Python? Provide an example. List comprehension is a concise method for creating 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 significant? Tuple unpacking assigns elements of a tuple to multiple variables, which is useful for variable swapping without needing 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 floating-point division (e.g., 9 / 2 returns 4.5). - // performs floor division, yielding 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 into strings. Alternatives include f-strings and the .format() method.

16) What are arrays in Python? Arrays allow for storing multiple values within 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 applications. - 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 informs the Python interpreter where to find module files, similar to the PATH variable in operating systems.

String Manipulation Python Interview Questions String parsing is prevalent in data science interviews, particularly for text-centric companies like Twitter, LinkedIn, or Netflix. These queries evaluate your capability 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) Assess 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 inquiries evaluate 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 an essential library for any data science interview, encompassing skills in data wrangling and preprocessing.

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 inquiries assess 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 manage 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 critical for numerical computing and matrix manipulation.

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 compute 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 numbers

Python Machine Learning Interview Questions These questions involve the application of machine learning principles using Python.

49) Implement the K-means algorithm from scratch. import numpy as np

def kmeans(data, k, max_iters=100):

# Randomly initialize centroids 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 closest centroid

distances = np.linalg.norm(data[:, np.newaxis] - centroids, axis=2)

labels = np.argmin(distances, axis=1)

# Recalculate 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 crucial for aspiring data scientists. This collection of 49 interview questions covers essential areas, including Python syntax, string manipulation, statistics, Pandas, NumPy, and machine learning.

By grasping and practicing these questions, you'll be well-equipped for technical interviews and ready to tackle real-world data science challenges.

> If you're interested in earning a Professional Certificate in Data Science, I highly recommend the IBM Data Science Professional Certificate on Coursera.

Good luck with your preparation and your journey toward becoming a skilled data scientist!

Pros: 1. Master the latest practical skills and knowledge that data scientists utilize daily. 2. Learn the tools, languages, and libraries employed by professional data scientists, including Python and SQL. 3. Import and clean datasets, analyze and visualize data, and build machine learning models and pipelines. 4. Apply your newfound skills to real-world projects and develop a portfolio of data projects that demonstrate your expertise to potential employers. 5. Earn a certificate recognized by employers from IBM and Coursera.

> To prepare for Data Science interviews, consider reading the book “Elements of Programming Interviews in Python” by Adnan Aziz, Tsung-Hsien L., and Amit Prakash.

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