Python Detailed Roadmap ๐
๐ 1. Basics
โผ Data Types & Variables
โผ Operators & Expressions
โผ Control Flow (if, loops)
๐ 2. Functions & Modules
โผ Defining Functions
โผ Lambda Functions
โผ Importing & Creating Modules
๐ 3. File Handling
โผ Reading & Writing Files
โผ Working with CSV & JSON
๐ 4. Object-Oriented Programming (OOP)
โผ Classes & Objects
โผ Inheritance & Polymorphism
โผ Encapsulation
๐ 5. Exception Handling
โผ Try-Except Blocks
โผ Custom Exceptions
๐ 6. Advanced Python Concepts
โผ List & Dictionary Comprehensions
โผ Generators & Iterators
โผ Decorators
๐ 7. Essential Libraries
โผ NumPy (Arrays & Computations)
โผ Pandas (Data Analysis)
โผ Matplotlib & Seaborn (Visualization)
๐ 8. Web Development & APIs
โผ Web Scraping (BeautifulSoup, Scrapy)
โผ API Integration (Requests)
โผ Flask & Django (Backend Development)
๐ 9. Automation & Scripting
โผ Automating Tasks with Python
โผ Working with Selenium & PyAutoGUI
๐ 10. Data Science & Machine Learning
โผ Data Cleaning & Preprocessing
โผ Scikit-Learn (ML Algorithms)
โผ TensorFlow & PyTorch (Deep Learning)
๐ 11. Projects
โผ Build Real-World Applications
โผ Showcase on GitHub
๐ 12. โ Apply for Jobs
โผ Strengthen Resume & Portfolio
โผ Prepare for Technical Interviews
Like for more โค๏ธ๐ช
๐ 1. Basics
โผ Data Types & Variables
โผ Operators & Expressions
โผ Control Flow (if, loops)
๐ 2. Functions & Modules
โผ Defining Functions
โผ Lambda Functions
โผ Importing & Creating Modules
๐ 3. File Handling
โผ Reading & Writing Files
โผ Working with CSV & JSON
๐ 4. Object-Oriented Programming (OOP)
โผ Classes & Objects
โผ Inheritance & Polymorphism
โผ Encapsulation
๐ 5. Exception Handling
โผ Try-Except Blocks
โผ Custom Exceptions
๐ 6. Advanced Python Concepts
โผ List & Dictionary Comprehensions
โผ Generators & Iterators
โผ Decorators
๐ 7. Essential Libraries
โผ NumPy (Arrays & Computations)
โผ Pandas (Data Analysis)
โผ Matplotlib & Seaborn (Visualization)
๐ 8. Web Development & APIs
โผ Web Scraping (BeautifulSoup, Scrapy)
โผ API Integration (Requests)
โผ Flask & Django (Backend Development)
๐ 9. Automation & Scripting
โผ Automating Tasks with Python
โผ Working with Selenium & PyAutoGUI
๐ 10. Data Science & Machine Learning
โผ Data Cleaning & Preprocessing
โผ Scikit-Learn (ML Algorithms)
โผ TensorFlow & PyTorch (Deep Learning)
๐ 11. Projects
โผ Build Real-World Applications
โผ Showcase on GitHub
๐ 12. โ Apply for Jobs
โผ Strengthen Resume & Portfolio
โผ Prepare for Technical Interviews
Like for more โค๏ธ๐ช
๐8
Drawing Beautiful Design Using Python
๐๐
๐๐
from turtle import *
import turtle as t
def my_turtle():
# Choices
sides = str(3)
loops = str(450)
pen = 1
for i in range(int(loops)):
forward(i * 2/int(sides) + i)
left(360/int(sides) + .350)
hideturtle()
pensize(pen)
speed(30)
my_turtle()
t.done()
๐2
TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
๐5
Python Most Important Interview Questions
Question 1: Calculate the average stock price for Company X over the last 6 months.
Question 2: Identify the month with the highest total sales for Company Y using their monthly sales data.
Question 3: Find the maximum and minimum stock price for Company Z on any given day in the last year.
Question 4: Create a column in the DataFrame showing the percentage change in stock price from the previous day for Company X.
Question 5: Determine the number of days when the stock price of Company Y was above its 30-day moving average. Question
6: Compare the average stock price of Companies X and Z in the first quarter of the year.
#Data#
----------------------------------------------
import pandas as pd
data = { 'Date': pd.date_range(start='2023-01-01', periods=180, freq='D'), 'CompanyX_StockPrice': pd.np.random.randint(50, 150, 180), 'CompanyY_Sales': pd.np.random.randint(20000, 50000, 180), 'CompanyZ_StockPrice': pd.np.random.randint(70, 200, 180) }
df = pd.DataFrame(data)
Question 1: Calculate the average stock price for Company X over the last 6 months.
Question 2: Identify the month with the highest total sales for Company Y using their monthly sales data.
Question 3: Find the maximum and minimum stock price for Company Z on any given day in the last year.
Question 4: Create a column in the DataFrame showing the percentage change in stock price from the previous day for Company X.
Question 5: Determine the number of days when the stock price of Company Y was above its 30-day moving average. Question
6: Compare the average stock price of Companies X and Z in the first quarter of the year.
#Data#
----------------------------------------------
import pandas as pd
data = { 'Date': pd.date_range(start='2023-01-01', periods=180, freq='D'), 'CompanyX_StockPrice': pd.np.random.randint(50, 150, 180), 'CompanyY_Sales': pd.np.random.randint(20000, 50000, 180), 'CompanyZ_StockPrice': pd.np.random.randint(70, 200, 180) }
df = pd.DataFrame(data)
๐2
Type of problem, while solving DSA problem in Array
โ There are many types of problems that can be solved using arrays and different techniques in Data Structures and Algorithms. Here are some common problem types and techniques that you might encounter:
๐. ๐๐ฅ๐ข๐๐ข๐ง๐ ๐ฐ๐ข๐ง๐๐จ๐ฐ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: In these problems, you are given an array and a window size, and you have to find a subarray of that size that satisfies certain conditions. You can use a sliding window technique to efficiently search through the array by maintaining a current window of fixed size and updating it as you move forward.
๐. ๐๐ฐ๐จ ๐ฉ๐จ๐ข๐ง๐ญ๐๐ซ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: In these problems, you use two pointers to traverse the array from both ends and find a certain pattern or condition. For example, you can use two pointers to find a pair of elements that sum up to a target value, or to reverse an array.
๐. ๐๐จ๐ซ๐ญ๐ข๐ง๐ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: In these problems, you are asked to sort an array in a certain way, such as in ascending or descending order, or according to certain criteria such as frequency or value. You can use sorting algorithms such as merge sort or quick sort to efficiently sort the array.
๐. ๐๐๐๐ซ๐๐ก๐ข๐ง๐ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: In these problems, you are asked to find a specific element in the array or to search for a certain pattern. You can use searching algorithms such as binary search or linear search to efficiently search through the array.
๐. ๐๐ฎ๐๐๐ซ๐ซ๐๐ฒ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: In these problems, you are asked to find a contiguous subarray that satisfies certain conditions. You can use techniques such as prefix sum or Kadane's algorithm to efficiently find the subarray with the maximum sum.
๐. ๐๐จ๐ฎ๐ง๐ญ๐ข๐ง๐ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: In these problems, you are asked to count the occurrences of certain elements or to count the number of subarrays or subsequences that satisfy certain conditions. You can use techniques such as hashing or dynamic programming to efficiently count the occurrences or number of subarrays.
โ There are many types of problems that can be solved using arrays and different techniques in Data Structures and Algorithms. Here are some common problem types and techniques that you might encounter:
๐. ๐๐ฅ๐ข๐๐ข๐ง๐ ๐ฐ๐ข๐ง๐๐จ๐ฐ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: In these problems, you are given an array and a window size, and you have to find a subarray of that size that satisfies certain conditions. You can use a sliding window technique to efficiently search through the array by maintaining a current window of fixed size and updating it as you move forward.
๐. ๐๐ฐ๐จ ๐ฉ๐จ๐ข๐ง๐ญ๐๐ซ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: In these problems, you use two pointers to traverse the array from both ends and find a certain pattern or condition. For example, you can use two pointers to find a pair of elements that sum up to a target value, or to reverse an array.
๐. ๐๐จ๐ซ๐ญ๐ข๐ง๐ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: In these problems, you are asked to sort an array in a certain way, such as in ascending or descending order, or according to certain criteria such as frequency or value. You can use sorting algorithms such as merge sort or quick sort to efficiently sort the array.
๐. ๐๐๐๐ซ๐๐ก๐ข๐ง๐ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: In these problems, you are asked to find a specific element in the array or to search for a certain pattern. You can use searching algorithms such as binary search or linear search to efficiently search through the array.
๐. ๐๐ฎ๐๐๐ซ๐ซ๐๐ฒ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: In these problems, you are asked to find a contiguous subarray that satisfies certain conditions. You can use techniques such as prefix sum or Kadane's algorithm to efficiently find the subarray with the maximum sum.
๐. ๐๐จ๐ฎ๐ง๐ญ๐ข๐ง๐ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ๐ฌ: In these problems, you are asked to count the occurrences of certain elements or to count the number of subarrays or subsequences that satisfy certain conditions. You can use techniques such as hashing or dynamic programming to efficiently count the occurrences or number of subarrays.
๐4
10 Must-Know Python Libraries for LLMs in 2025
1. Hugging Face Transformers
Best for: Pre-trained LLMs, fine-tuning, inference
2. LangChain
Best for: LLM-powered apps, chatbots, AI agents
3. SpaCy
Best for: Tokenization, named entity recognition (NER), dependency parsing
4. Natural Language Toolkit (NLTK)
Best for: Linguistic analysis, tokenization, POS tagging
5. SentenceTransformers
Best for: Semantic search, similarity, clustering
6. FastText
Best for: Word embeddings, text classification
7. Gensim
Best for: Word2Vec, topic modeling, document embeddings
8. Stanza
Best for: Named entity recognition (NER), POS tagging
9. TextBlob
Best for: Sentiment analysis, POS tagging, text processing
10. Polyglot
Best for: Multi-language NLP, named entity recognition, word embeddings
1. Hugging Face Transformers
Best for: Pre-trained LLMs, fine-tuning, inference
2. LangChain
Best for: LLM-powered apps, chatbots, AI agents
3. SpaCy
Best for: Tokenization, named entity recognition (NER), dependency parsing
4. Natural Language Toolkit (NLTK)
Best for: Linguistic analysis, tokenization, POS tagging
5. SentenceTransformers
Best for: Semantic search, similarity, clustering
6. FastText
Best for: Word embeddings, text classification
7. Gensim
Best for: Word2Vec, topic modeling, document embeddings
8. Stanza
Best for: Named entity recognition (NER), POS tagging
9. TextBlob
Best for: Sentiment analysis, POS tagging, text processing
10. Polyglot
Best for: Multi-language NLP, named entity recognition, word embeddings
๐3
How to Learn API Development?
Learning how to develop APIs is an important skill for modern-day developers. Hereโs a mind map of what all you need to learn about API development:
1 - API Fundamentals
What is an API, types of API (REST, SOAP, GraphQL, gRPC, etc.), and API vs SDK.
2 - API Request/Response
HTTP Methods, Response Codes, and Headers.
3 - Authentication and Security
Authentication mechanisms (JWT, OAuth 2, API Keys, Basic Auth) and security strategies.
4 - API Design and Development
RESTful API principles include stateless, resource-based URL, versioning, and pagination. Also, API documentation tools like OpenAPI, Postman, Swagger.
5 - API Testing
Tools for testing APIs such as Postman, cURL, SoapUI, and so on.
6 - API Deployment and Integration
Consuming APIs in different languages like JS, Python, and Java. Also, working with 3rd party APIs like the Google Maps API and the Stripe API. Learn about API Gateways like AWS, Kong, Apigee.
Over to you: What else will you add to the list for learning API development?
Learning how to develop APIs is an important skill for modern-day developers. Hereโs a mind map of what all you need to learn about API development:
1 - API Fundamentals
What is an API, types of API (REST, SOAP, GraphQL, gRPC, etc.), and API vs SDK.
2 - API Request/Response
HTTP Methods, Response Codes, and Headers.
3 - Authentication and Security
Authentication mechanisms (JWT, OAuth 2, API Keys, Basic Auth) and security strategies.
4 - API Design and Development
RESTful API principles include stateless, resource-based URL, versioning, and pagination. Also, API documentation tools like OpenAPI, Postman, Swagger.
5 - API Testing
Tools for testing APIs such as Postman, cURL, SoapUI, and so on.
6 - API Deployment and Integration
Consuming APIs in different languages like JS, Python, and Java. Also, working with 3rd party APIs like the Google Maps API and the Stripe API. Learn about API Gateways like AWS, Kong, Apigee.
Over to you: What else will you add to the list for learning API development?
๐6