Python Projects & Resources
56.8K subscribers
776 photos
342 files
327 links
Perfect channel to learn Python Programming 🇮🇳
Download Free Books & Courses to master Python Programming
- Free Courses
- Projects
- Pdfs
- Bootcamps
- Notes

Admin: @Coderfun
Download Telegram
Python Interview Questions:

Ready to test your Python skills? Let’s get started! 💻


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


Here you can find essential Python Interview Resources👇
https://t.iss.one/DataSimplifier

Like for more resources like this 👍 ♥️

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)
10
Data Science Learning Plan

Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)

Step 2: Python for Data Science (Basics and Libraries)

Step 3: Data Manipulation and Analysis (Pandas, NumPy)

Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)

Step 5: Databases and SQL for Data Retrieval

Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)

Step 7: Data Cleaning and Preprocessing

Step 8: Feature Engineering and Selection

Step 9: Model Evaluation and Tuning

Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)

Step 11: Working with Big Data (Hadoop, Spark)

Step 12: Building Data Science Projects and Portfolio
4
Project ideas for college students
👍21
Boost your python speed by 300% 👆
4👍4
Top 10 important data science concepts

1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.

2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.

3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.

4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.

5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.

6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.

7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.

8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.

9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.

10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.iss.one/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊
4
Important Python Functions
3
AI & ML Project Ideas
4