Python Interview Questions:
Ready to test your Python skills? Letโs get started! ๐ป
1. How to check if a string is a palindrome?
2. How to find the factorial of a number using recursion?
3. How to merge two dictionaries in Python?
4. How to find the intersection of two lists?
5. How to generate a list of even numbers from 1 to 100?
6. How to find the longest word in a sentence?
7. How to count the frequency of elements in a list?
8. How to remove duplicates from a list while maintaining the order?
9. How to reverse a linked list in Python?
10. How to implement a simple binary search algorithm?
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Ready to test your Python skills? Letโs get started! ๐ป
1. How to check if a string is a palindrome?
def is_palindrome(s):
return s == s[::-1]
print(is_palindrome("madam")) # True
print(is_palindrome("hello")) # False
2. How to find the factorial of a number using recursion?
def factorial(n):
if n == 0 or n == 1:
return 1
return n * factorial(n - 1)
print(factorial(5)) # 120
3. How to merge two dictionaries in Python?
dict1 = {'a': 1, 'b': 2}
dict2 = {'c': 3, 'd': 4}
# Method 1 (Python 3.5+)
merged_dict = {**dict1, **dict2}
# Method 2 (Python 3.9+)
merged_dict = dict1 | dict2
print(merged_dict)
4. How to find the intersection of two lists?
list1 = [1, 2, 3, 4]
list2 = [3, 4, 5, 6]
intersection = list(set(list1) & set(list2))
print(intersection) # [3, 4]
5. How to generate a list of even numbers from 1 to 100?
even_numbers = [i for i in range(1, 101) if i % 2 == 0]
print(even_numbers)
6. How to find the longest word in a sentence?
def longest_word(sentence):
words = sentence.split()
return max(words, key=len)
print(longest_word("Python is a powerful language")) # "powerful"
7. How to count the frequency of elements in a list?
from collections import Counter
my_list = [1, 2, 2, 3, 3, 3, 4]
frequency = Counter(my_list)
print(frequency) # Counter({3: 3, 2: 2, 1: 1, 4: 1})
8. How to remove duplicates from a list while maintaining the order?
def remove_duplicates(lst):
return list(dict.fromkeys(lst))
my_list = [1, 2, 2, 3, 4, 4, 5]
print(remove_duplicates(my_list)) # [1, 2, 3, 4, 5]
9. How to reverse a linked list in Python?
class Node:
def __init__(self, data):
self.data = data
self.next = None
def reverse_linked_list(head):
prev = None
current = head
while current:
next_node = current.next
current.next = prev
prev = current
current = next_node
return prev
# Create linked list: 1 -> 2 -> 3
head = Node(1)
head.next = Node(2)
head.next.next = Node(3)
# Reverse and print the list
reversed_head = reverse_linked_list(head)
while reversed_head:
print(reversed_head.data, end=" -> ")
reversed_head = reversed_head.next
10. How to implement a simple binary search algorithm?
def binary_search(arr, target):
low, high = 0, len(arr) - 1
while low <= high:
mid = (low + high) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
low = mid + 1
else:
high = mid - 1
return -1
print(binary_search([1, 2, 3, 4, 5, 6, 7], 4)) # 3
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-> 1. Learn the Language of Data
Start with Python or R. Learn how to write clean scripts, automate tasks, and manipulate data like a pro.
-> 2. Master Data Handling
Use Pandas, NumPy, and SQL. These are your weapons for data cleaning, transformation, and querying.
Garbage in = Garbage out. Always clean your data.
-> 3. Nail the Basics of Statistics & Probability
You canโt call yourself a data scientist if you donโt understand distributions, p-values, confidence intervals, and hypothesis testing.
-> 4. Exploratory Data Analysis (EDA)
Visualize the story behind the numbers with Matplotlib, Seaborn, and Plotly.
EDA is how you uncover hidden gold.
-> 5. Learn Machine Learning the Right Way
Start simple:
Linear Regression
Logistic Regression
Decision Trees
Then level up with Random Forest, XGBoost, and Neural Networks.
-> 6. Build Real Projects
Kaggle, personal projects, domain-specific problemsโdonโt just learn, apply.
Make a portfolio that speaks louder than your resume.
-> 7. Learn Deployment (Optional but Powerful)
Use Flask, Streamlit, or FastAPI to deploy your models.
Turn models into real-world applications.
-> 8. Sharpen Soft Skills
Storytelling, communication, and business acumen are just as important as technical skills.
Explain your insights like a leader.
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Hope this helps you ๐
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๐ Top 10 Data Analytics Concepts Everyone Should Know ๐
1๏ธโฃ Data Cleaning ๐งน
Removing duplicates, fixing missing or inconsistent data.
๐ Tools: Excel, Python (Pandas), SQL
2๏ธโฃ Descriptive Statistics ๐
Mean, median, mode, standard deviationโbasic measures to summarize data.
๐ Used for understanding data distribution
3๏ธโฃ Data Visualization ๐
Creating charts and dashboards to spot patterns.
๐ Tools: Power BI, Tableau, Matplotlib, Seaborn
4๏ธโฃ Exploratory Data Analysis (EDA) ๐
Identifying trends, outliers, and correlations through deep data exploration.
๐ Step before modeling
5๏ธโฃ SQL for Data Extraction ๐๏ธ
Querying databases to retrieve specific information.
๐ Focus on SELECT, JOIN, GROUP BY, WHERE
6๏ธโฃ Hypothesis Testing โ๏ธ
Making decisions using sample data (A/B testing, p-value, confidence intervals).
๐ Useful in product or marketing experiments
7๏ธโฃ Correlation vs Causation ๐
Just because two things are related doesnโt mean one causes the other!
8๏ธโฃ Data Modeling ๐ง
Creating models to predict or explain outcomes.
๐ Linear regression, decision trees, clustering
9๏ธโฃ KPIs & Metrics ๐ฏ
Understanding business performance indicators like ROI, retention rate, churn.
๐ Storytelling with Data ๐ฃ๏ธ
Translating raw numbers into insights stakeholders can act on.
๐ Use clear visuals, simple language, and real-world impact
โค๏ธ React for more
1๏ธโฃ Data Cleaning ๐งน
Removing duplicates, fixing missing or inconsistent data.
๐ Tools: Excel, Python (Pandas), SQL
2๏ธโฃ Descriptive Statistics ๐
Mean, median, mode, standard deviationโbasic measures to summarize data.
๐ Used for understanding data distribution
3๏ธโฃ Data Visualization ๐
Creating charts and dashboards to spot patterns.
๐ Tools: Power BI, Tableau, Matplotlib, Seaborn
4๏ธโฃ Exploratory Data Analysis (EDA) ๐
Identifying trends, outliers, and correlations through deep data exploration.
๐ Step before modeling
5๏ธโฃ SQL for Data Extraction ๐๏ธ
Querying databases to retrieve specific information.
๐ Focus on SELECT, JOIN, GROUP BY, WHERE
6๏ธโฃ Hypothesis Testing โ๏ธ
Making decisions using sample data (A/B testing, p-value, confidence intervals).
๐ Useful in product or marketing experiments
7๏ธโฃ Correlation vs Causation ๐
Just because two things are related doesnโt mean one causes the other!
8๏ธโฃ Data Modeling ๐ง
Creating models to predict or explain outcomes.
๐ Linear regression, decision trees, clustering
9๏ธโฃ KPIs & Metrics ๐ฏ
Understanding business performance indicators like ROI, retention rate, churn.
๐ Storytelling with Data ๐ฃ๏ธ
Translating raw numbers into insights stakeholders can act on.
๐ Use clear visuals, simple language, and real-world impact
โค๏ธ React for more
๐1