7 Useful Python One-Liners

1. Reverse a string

print("Python"[::-1]) # Output: nohtyP

2. Check for Palindrome

is_palindrome = lambda s: s == s[::-1]
print(is_palindrome("madam")) # Output: True

3. Get all even numbers from a list

print([x for x in range(20) if x % 2 == 0])

4. Flatten a nested list

print([item for sublist in [[1,2],[3,4]] for item in sublist])

5. Find factorial of a number

import math; print(math.factorial(5)) # Output: 120

6. Count frequency of elements

from collections import Counter
print(Counter("banana")) # Output: {'a': 3, 'b': 1, 'n': 2}

7. Swap two variables

a, b = 5, 10
a, b = b, a
print(a, b) # Output: 10 5

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🔰 Deep Python Roadmap for Beginners 🐍

Setup & Installation 🖥⚙️
• Install Python, choose an IDE (VS Code, PyCharm)
• Set up virtual environments for project isolation 🌎

Basic Syntax & Data Types 📝🔢
• Learn variables, numbers, strings, booleans
• Understand comments, basic input/output, and simple expressions ✍️

Control Flow & Loops 🔄🔀
• Master conditionals (if, elif, else)
• Practice loops (for, while) and use control statements like break and continue 👮

Functions & Scope ⚙️🎯
• Define functions with def and learn about parameters and return values
• Explore lambda functions, recursion, and variable scope 📜

Data Structures 📊📚
• Work with lists, tuples, sets, and dictionaries
• Learn list comprehensions and built-in methods for data manipulation ⚙️

Object-Oriented Programming (OOP) 🏗👩‍💻
• Understand classes, objects, and methods
• Dive into inheritance, polymorphism, and encapsulation 🔍

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How to master Python from scratch🚀

1. Setup and Basics 🏁
   - Install Python 🖥️: Download Python and set it up.
   - Hello, World! 🌍: Write your first Hello World program.

2. Basic Syntax 📜
   - Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.
   - Control Structures 🔄: Understand if-else statements, for loops, and while loops.
   - Functions 🛠️: Write reusable blocks of code.

3. Data Structures 📂
   - Lists 📋: Manage collections of items.
   - Dictionaries 📖: Store key-value pairs.
   - Tuples 📦: Work with immutable sequences.
   - Sets 🔢: Handle collections of unique items.

4. Modules and Packages 📦
   - Standard Library 📚: Explore built-in modules.
   - Third-Party Packages 🌐: Install and use packages with pip.

5. File Handling 📁
   - Read and Write Files 📝
   - CSV and JSON 📑

6. Object-Oriented Programming 🧩
   - Classes and Objects 🏛️
   - Inheritance and Polymorphism 👨‍👩‍👧

7. Web Development 🌐
   - Flask 🍼: Start with a micro web framework.
   - Django 🦄: Dive into a full-fledged web framework.

8. Data Science and Machine Learning 🧠
   - NumPy 📊: Numerical operations.
   - Pandas 🐼: Data manipulation and analysis.
   - Matplotlib 📈 and Seaborn 📊: Data visualization.
   - Scikit-learn 🤖: Machine learning.

9. Automation and Scripting 🤖
   - Automate Tasks 🛠️: Use Python to automate repetitive tasks.
   - APIs 🌐: Interact with web services.

10. Testing and Debugging 🐞
    - Unit Testing 🧪: Write tests for your code.
    - Debugging 🔍: Learn to debug efficiently.

11. Advanced Topics 🚀
    - Concurrency and Parallelism 🕒
    - Decorators 🌀 and Generators ⚙️
    - Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy.

12. Practice Projects 💡
    - Calculator 🧮
    - To-Do List App 📋
    - Weather App ☀️
    - Personal Blog 📝

13. Community and Collaboration 🤝
    - Contribute to Open Source 🌍
    - Join Coding Communities 💬
    - Participate in Hackathons 🏆

14. Keep Learning and Improving 📈
    - Read Books 📖: Like "Automate the Boring Stuff with Python".
    - Watch Tutorials 🎥: Follow video courses and tutorials.
    - Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars.

15. Teach and Share Knowledge 📢
    - Write Blogs ✍️
    - Create Video Tutorials 📹
    - Mentor Others 👨‍🏫

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Python Roadmap

5 GitHub Repo to Master Python

1. The Algorithms: https://github.com/TheAlgorithms/Python
2. Vinta: https://github.com/vinta/awesome-python
3. Avinash Kranjan: https://tinyurl.com/Amazing-Python-Scripts
4. Geek Computers: https://github.com/geekcomputers/Python
5. Practical Tutorials: https://tinyurl.com/project-based-learningg

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If you're a data science beginner, Python is the best programming language to get started.

Here are 7 Python libraries for data science you need to know if you want to learn:

- Data analysis
- Data visualization
- Machine learning
- Deep learning

NumPy

NumPy is a library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

Pandas

Widely used library for data manipulation and analysis, offering data structures like DataFrame and Series that simplify handling of structured data and performing tasks such as filtering, grouping, and merging.

Matplotlib

Powerful plotting library for creating static, interactive, and animated visualizations in Python, enabling data scientists to generate a wide variety of plots, charts, and graphs to explore and communicate data effectively.

Scikit-learn

Comprehensive machine learning library that includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection, as well as utilities for data preprocessing and evaluation.

Seaborn

Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics, making it easier to generate complex visualizations with minimal code.

TensorFlow or PyTorch

TensorFlow, Keras, or PyTorch are three prominent deep learning frameworks utilized by data scientists to construct, train, and deploy neural networks for various applications, each offering distinct advantages and capabilities tailored to different preferences and requirements.

SciPy

Collection of mathematical algorithms and functions built on top of NumPy, providing additional capabilities for optimization, integration, interpolation, signal processing, linear algebra, and more, which are commonly used in scientific computing and data analysis workflows.

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Machine Learning Algorithm cheat sheet

Copy & paste these 7 ChatGPT prompts to create an irresistible Resume/CV 👇

Showcase your strengths. Turn applications into interview invites!

Use these 10 proven ChatGPT prompts:


📈 Prompt 1: ATS Keyword Optimizer

Analyze the job denoscription for [Position] and my resume. Identify 10 crucial keywords. Suggest natural placements in my resume, ensuring ATS compatibility. Present results as a table with Keyword, Relevance Score (1-10), and Suggested Placement. My resume: [Paste Resume]. Job denoscription: [Paste Denoscription].


📈 Prompt 2: Experience Section Enhancer

Optimize the bullet points for my most recent role as [Job Title]. Focus on achievements, skills utilized, and quantifiable results. Use strong action verbs. Present a before/after comparison with explanations for changes. Current job denoscription: [Paste Current Bullets]. 


📈 Prompt 3: Skills Hierarchy Creator

Evaluate my skills for [Job Denoscription]. Create a skills hierarchy with 3 tiers: core, advanced, and distinguishing skills. Suggest how to demonstrate each skill briefly. Present a visual skills pyramid with examples. My resume: [Paste Resume]. Job requirements: [Paste Requirements].


📈 Prompt 4: Professional Summary Crafter

Write a compelling professional summary for my resume for [Job Title]. Incorporate my unique value proposition, key skills, and career experience. Limit to 3-4 sentences. Provide 3 versions: conservative, balanced, and bold. My resume: [Paste Resume]. Job denoscription: [Paste Denoscription].


📈 Prompt 5:  Education Optimizer

Refine my education section for [Job Title]. Highlight relevant coursework, projects, or academic achievements. Suggest how to present ongoing education/certifications effectively. Provide a before/after version with explanations. My resume: [Paste Resume]. Job denoscription: [Paste Denoscription].


📈 Prompt 6: Technical Skills Showcase

List my technical skills for [Industry/Role]. Create a visual representation (Described in Text) that organizes these skills by proficiency level and relevance to [Target Role]. Suggestion skills to acquire/improve. My resume: [Paste Resume]. Job denoscription: [Paste Denoscription].


📈 Prompt 7:  Positive Career Gap Framing

Write an explanation for my [X months/years] career gap between [Start Date] and [End Date]. Focus on growth, skills gained, and valuable experiences. Show how these enhance my fit for [Target Job Title]. Create 3 versions for resume, cover letter, and interview response. My resume: [Paste Resume]. Job denoscription: [Paste Job Denoscription].

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⌨️ Benefits of learning Python Programming

1. Web Development: Python frameworks like Django and Flask are popular for building dynamic websites and web applications.

2. Data Analysis: Python has powerful libraries like Pandas and NumPy for data manipulation and analysis, making it widely used in data science and analytic.

3. Machine Learning: Python's libraries such as TensorFlow, Keras, and Scikit-learn are extensively used for implementing machine learning algorithms and building predictive models.

4. Artificial Intelligence: Python is commonly used in AI development due to its simplicity and extensive libraries for tasks like natural language processing, image recognition, and neural network implementation.

5. Cybersecurity: Python is utilized for tasks such as penetration testing, network scanning, and creating security tools due to its versatility and ease of use.

6. Game Development: Python, along with libraries like Pygame, is used for developing games, prototyping game mechanics, and creating game noscripts.

7. Automation: Python's simplicity and versatility make it ideal for automating repetitive tasks, such as noscripting, data scraping, and process automation.

🚀 Essential Python/ Pandas snippets to explore data:
 
1.   .head() - Review top rows
2.   .tail() - Review bottom rows
3.   .info() - Summary of DataFrame
4.   .shape - Shape of DataFrame
5.   .describe() - Denoscriptive stats
6.   .isnull().sum() - Check missing values
7.   .dtypes - Data types of columns
8.   .unique() - Unique values in a column
9.   .nunique() - Count unique values
10.   .value_counts() - Value counts in a column
11.   .corr() - Correlation matrix

👉 What is Python Data Structures?
You can think of a data structure as a way of organizing and storing data such that we can access and modify it efficiently.
We have primitive data types like integers, floats, Booleans, and strings.

👉 What is Python List?
A list in Python is a heterogeneous container for items. This would remind you of an array in C++, but since Python does not support arrays, we have Python Lists.

👉 Python Tuple
This Python Data Structure is like a, like a list in Python, is a heterogeneous container for items.
But the major difference between the two (tuple and list) is that a list is mutable, but a tuple is immutable.
This means that while you can reassign or delete an entire tuple, you cannot do the same to a single item or a slice.

👉 Python Dictionaries
Finally, we will take a look at Python dictionaries. Think of a real-life dictionary. What is it used for? It holds word-meaning pairs. Likewise, a Python dictionary holds key-value pairs. However, you may not use an unhashable item as a key.
To declare a Python dictionary, we use curly braces. But since it has key-value pairs instead of single values, this differentiates a dictionary from a set.

What is Python Loop?
When you want some statements to execute a hundred times, you don’t repeat them 100 times.
Think of when you want to print numbers 1 to 99. Or that you want to say Hello to 99 friends.
In such a case, you can use loops in python.
Here, we will discuss 4 types of Python Loop:
Python For Loop
Python While Loop
Python Loop Control Statements
Nested For Loop in Python
Python While Loop
A while loop in python iterates till its condition becomes False. In other words, it executes the statements under itself while the condition it takes is True.
Python For Loop
Python for loop can iterate over a sequence of items. The structure of a for loop in Python is different than that in C++ or Java.
That is, for(int i=0;i<n;i++) won’t work here. In Python, we use the ‘in’ keyword.
Nested for Loops in Python
You can also nest a loop inside another. You can put a for loop inside a while, or a while inside a for, or a for inside a for, or a while inside a while.
Or you can put a loop inside a loop inside a loop. You can go as far as you want.
Loop Control Statements in Python
Sometimes, you may want to break out of normal execution in a loop.
For this, we have three keywords in Python- break, continue, and Python

Source Code of Getting WiFi Passwords 👇👇-

# importing subprocess
import subprocess
 
# getting meta data
meta_data = subprocess.check_output(['netsh', 'wlan', 'show', 'profiles'])
 
# decoding meta data
data = meta_data.decode('utf-8', errors ="backslashreplace")
 
# splitting data by line by line
data = data.split('\n')
 
# creating a list of profiles
profiles = []
 
# traverse the data
for i in data:
     
    # find "All User Profile" in each item
    if "All User Profile" in i :
         
        # if found
        # split the item
        i = i.split(":")
         
        # item at index 1 will be the wifi name
        i = i[1]
         
        # formatting the name
        # first and last character is use less
        i = i[1:-1]
         
        # appending the wifi name in the list
        profiles.append(i)
         
 
# printing heading       
print("{:<30}| {:<}".format("Wi-Fi Name", "Password"))
print("----------------------------------------------")
 
# traversing the profiles       
for i in profiles:
     
    # try catch block begins
    # try block
    try:
        # getting meta data with password using wifi name
        results = subprocess.check_output(['netsh', 'wlan', 'show', 'profile', i, 'key = clear'])
         
        # decoding and splitting data line by line
        results = results.decode('utf-8', errors ="backslashreplace")
        results = results.split('\n')
         
        # finding password from the result list
        results = [b.split(":")[1][1:-1] for b in results if "Key Content" in b]
         
        # if there is password it will print the pass word
        try:
            print("{:<30}| {:<}".format(i, results[0]))
         
        # else it will print blank in front of pass word
        except IndexError:
            print("{:<30}| {:<}".format(i, ""))
             
     
             
    # called when this process get failed
    except subprocess.CalledProcessError:
        print("Encoding Error Occurred")

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Here is an A-Z list of essential programming terms:

1. Array: A data structure that stores a collection of elements of the same type in contiguous memory locations.

2. Boolean: A data type that represents true or false values.

3. Conditional Statement: A statement that executes different code based on a condition.

4. Debugging: The process of identifying and fixing errors or bugs in a program.

5. Exception: An event that occurs during the execution of a program that disrupts the normal flow of instructions.

6. Function: A block of code that performs a specific task and can be called multiple times in a program.

7. GUI (Graphical User Interface): A visual way for users to interact with a computer program using graphical elements like windows, buttons, and menus.

8. HTML (Hypertext Markup Language): The standard markup language used to create web pages.

9. Integer: A data type that represents whole numbers without any fractional part.

10. JSON (JavaScript Object Notation): A lightweight data interchange format commonly used for transmitting data between a server and a web application.

11. Loop: A programming construct that allows repeating a block of code multiple times.

12. Method: A function that is associated with an object in object-oriented programming.

13. Null: A special value that represents the absence of a value.

14. Object-Oriented Programming (OOP): A programming paradigm based on the concept of "objects" that encapsulate data and behavior.

15. Pointer: A variable that stores the memory address of another variable.

16. Queue: A data structure that follows the First-In-First-Out (FIFO) principle.

17. Recursion: A programming technique where a function calls itself to solve a problem.

18. String: A data type that represents a sequence of characters.

19. Tuple: An ordered collection of elements, similar to an array but immutable.

20. Variable: A named storage location in memory that holds a value.

21. While Loop: A loop that repeatedly executes a block of code as long as a specified condition is true.

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🚀 Roadmap to Master Python Programming 🔰

📂 Python Fundamentals
 ∟📂 Learn Syntax, Variables & Data Types
  ∟📂 Master Control Flow & Functions
   ∟📂 Practice with Simple Projects

📂 Intermediate Concepts
 ∟📂 Object-Oriented Programming (OOP)
  ∟📂 Work with Modules & Packages
   ∟📂 Understand Exception Handling & File I/O

📂 Data Structures & Algorithms
 ∟📂 Lists, Tuples, Dictionaries & Sets
  ∟📂 Algorithms & Problem Solving
   ∟📂 Master Recursion & Iteration

📂 Python Libraries & Tools
 ∟📂 Get Comfortable with Pip & Virtual Environments
  ∟📂 Learn NumPy & Pandas for Data Handling
   ∟📂 Explore Matplotlib & Seaborn for Visualization

📂 Web Development with Python
 ∟📂 Understand Flask & Django Frameworks
  ∟📂 Build RESTful APIs
   ∟📂 Integrate Front-End & Back-End

📂 Advanced Topics
 ∟📂 Concurrency: Threads & Asyncio
  ∟📂 Learn Testing with PyTest
   ∟📂 Dive into Design Patterns

📂 Projects & Real-World Applications
 ∟📂 Build Command-Line Tools & Scripts
  ∟📂 Contribute to Open-Source
   ∟📂 Showcase on GitHub & Portfolio

📂 Interview Preparation & Job Hunting
 ∟📂 Solve Python Coding Challenges
  ∟📂 Master Data Structures & Algorithms Interviews
   ∟📂 Network & Apply for Python Roles

✅️ Happy Coding

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