Important Python concepts that every beginner should know
1. Variables & Data Types 🧠
Variables are like boxes where you store stuff.
Python automatically knows the type of data you're working with!
name = "Alice" # String
age = 25 # Integer
height = 5.6 # Float
is_student = True # Boolean
2. Conditional Statements 🔀
Want your program to make decisions?
Use if, elif, and else!
if age > 18:
print("You're an adult!")
else:
print("You're a kid!")
3. Loops 🔁
Repeat tasks without writing them 100 times!
For loop – Loop over a sequence
While loop – Loop until a condition is false
for i in range(5):
print(i) # 0 to 4
count = 0
while count < 3:
print("Hello")
count += 1
4. Functions ⚙️
Reusable blocks of code. Keeps your program clean and DRY (Don't Repeat Yourself)!
def greet(name):
print(f"Hello, {name}!")
greet("Bob")
5. Lists, Tuples, Dictionaries, Sets 📦
List: Ordered, changeable
Tuple: Ordered, unchangeable
Dict: Key-value pairs
Set: Unordered, unique items
my_list = [1, 2, 3]
my_tuple = (4, 5, 6)
my_dict = {"name": "Alice", "age": 25}
my_set = {1, 2, 3}
6. String Manipulation ✂️
Work with text like a pro!
text = "Python is awesome"
print(text.upper()) # PYTHON IS AWESOME
print(text.replace("awesome", "cool")) # Python is cool
7. Input from User ⌨️
Make your programs interactive!
name = input("Enter your name: ")
print("Hello " + name)
8. Error Handling ⚠️
Catch mistakes before they crash your program.
try:
x = 1 / 0
except ZeroDivisionError:
print("You can't divide by zero!")
9. File Handling 📁
Read or write files using Python.
with open("notes.txt", "r") as file:
content = file.read()
print(content)
10. Object-Oriented Programming (OOP) 🧱
Python lets you model real-world things using classes and objects.
class Dog:
def init(self, name):
self.name = name
def bark(self):
print(f"{self.name} says woof!")
my_dog = Dog("Buddy")
my_dog.bark()
React with ❤️ if you want me to cover each Python concept in detail.
For all resources and cheat sheets, check out my Telegram channel: https://news.1rj.ru/str/pythonproz
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Latest Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Hope it helps :)
1. Variables & Data Types 🧠
Variables are like boxes where you store stuff.
Python automatically knows the type of data you're working with!
name = "Alice" # String
age = 25 # Integer
height = 5.6 # Float
is_student = True # Boolean
2. Conditional Statements 🔀
Want your program to make decisions?
Use if, elif, and else!
if age > 18:
print("You're an adult!")
else:
print("You're a kid!")
3. Loops 🔁
Repeat tasks without writing them 100 times!
For loop – Loop over a sequence
While loop – Loop until a condition is false
for i in range(5):
print(i) # 0 to 4
count = 0
while count < 3:
print("Hello")
count += 1
4. Functions ⚙️
Reusable blocks of code. Keeps your program clean and DRY (Don't Repeat Yourself)!
def greet(name):
print(f"Hello, {name}!")
greet("Bob")
5. Lists, Tuples, Dictionaries, Sets 📦
List: Ordered, changeable
Tuple: Ordered, unchangeable
Dict: Key-value pairs
Set: Unordered, unique items
my_list = [1, 2, 3]
my_tuple = (4, 5, 6)
my_dict = {"name": "Alice", "age": 25}
my_set = {1, 2, 3}
6. String Manipulation ✂️
Work with text like a pro!
text = "Python is awesome"
print(text.upper()) # PYTHON IS AWESOME
print(text.replace("awesome", "cool")) # Python is cool
7. Input from User ⌨️
Make your programs interactive!
name = input("Enter your name: ")
print("Hello " + name)
8. Error Handling ⚠️
Catch mistakes before they crash your program.
try:
x = 1 / 0
except ZeroDivisionError:
print("You can't divide by zero!")
9. File Handling 📁
Read or write files using Python.
with open("notes.txt", "r") as file:
content = file.read()
print(content)
10. Object-Oriented Programming (OOP) 🧱
Python lets you model real-world things using classes and objects.
class Dog:
def init(self, name):
self.name = name
def bark(self):
print(f"{self.name} says woof!")
my_dog = Dog("Buddy")
my_dog.bark()
React with ❤️ if you want me to cover each Python concept in detail.
For all resources and cheat sheets, check out my Telegram channel: https://news.1rj.ru/str/pythonproz
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Latest Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Hope it helps :)
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Some useful PYTHON libraries for data science
NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++
SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.
Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.
Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.
Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of denoscriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.
Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.
Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.
SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.
Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.
Additional libraries, you might need:
os for Operating system and file operations
networkx and igraph for graph based data manipulations
regular expressions for finding patterns in text data
BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.
NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++
SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.
Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.
Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.
Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of denoscriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.
Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.
Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.
SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.
Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.
Additional libraries, you might need:
os for Operating system and file operations
networkx and igraph for graph based data manipulations
regular expressions for finding patterns in text data
BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.
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Python Projects & Resources
Important Python concepts that every beginner should know 1. Variables & Data Types 🧠 Variables are like boxes where you store stuff. Python automatically knows the type of data you're working with! name = "Alice" # String age = 25 …
10 Python Mini Projects for Beginners
Guys, once you've got the basics of Python down, it’s time to build stuff!
Here are 10 mini project ideas that are fun, practical, and boost your confidence!
1. Number Guessing Game 🎯
The computer picks a number, and the user keeps guessing until they get it right.
Perfect to practice loops, conditionals, and user input.
2. Calculator App ➕➖✖️➗
Build a simple calculator that takes two numbers and performs addition, subtraction, multiplication, or division.
3. To-Do List (Console Version) ✅
Let users add, view, and delete tasks. Great to practice lists and file handling if you want to save tasks.
4. Password Generator 🔐
Create random passwords using letters, numbers, and symbols. Use the random and string modules.
5. Dice Rolling Simulator 🎲
Simulate rolling a die. Add cool features like rolling multiple dice or counting the frequency.
6. Rock Paper Scissors Game ✊✋✌️
Let the user play against the computer. Introduces randomness and conditional logic.
7. Quiz App ❓
Create a multiple-choice quiz that gives a score at the end. Store questions and answers using dictionaries.
8. Countdown Timer ⏱️
User inputs minutes or seconds, and the timer counts down to zero. Helps practice time.sleep().
9. Tip Calculator 🍽️
Calculate how much each person should pay including tip. Useful for string formatting and arithmetic.
10. Weather App (Using API) ☁️☀️🌧️
Use a public weather API to fetch real-time weather for a city. Great to explore APIs and the requests library.
For all resources and cheat sheets, check out my Telegram channel: https://news.1rj.ru/str/pythonproz
Hope it helps :)
Guys, once you've got the basics of Python down, it’s time to build stuff!
Here are 10 mini project ideas that are fun, practical, and boost your confidence!
1. Number Guessing Game 🎯
The computer picks a number, and the user keeps guessing until they get it right.
Perfect to practice loops, conditionals, and user input.
2. Calculator App ➕➖✖️➗
Build a simple calculator that takes two numbers and performs addition, subtraction, multiplication, or division.
3. To-Do List (Console Version) ✅
Let users add, view, and delete tasks. Great to practice lists and file handling if you want to save tasks.
4. Password Generator 🔐
Create random passwords using letters, numbers, and symbols. Use the random and string modules.
5. Dice Rolling Simulator 🎲
Simulate rolling a die. Add cool features like rolling multiple dice or counting the frequency.
6. Rock Paper Scissors Game ✊✋✌️
Let the user play against the computer. Introduces randomness and conditional logic.
7. Quiz App ❓
Create a multiple-choice quiz that gives a score at the end. Store questions and answers using dictionaries.
8. Countdown Timer ⏱️
User inputs minutes or seconds, and the timer counts down to zero. Helps practice time.sleep().
9. Tip Calculator 🍽️
Calculate how much each person should pay including tip. Useful for string formatting and arithmetic.
10. Weather App (Using API) ☁️☀️🌧️
Use a public weather API to fetch real-time weather for a city. Great to explore APIs and the requests library.
For all resources and cheat sheets, check out my Telegram channel: https://news.1rj.ru/str/pythonproz
Hope it helps :)
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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
For all resources and cheat sheets, check out my Telegram channel: https://news.1rj.ru/str/pythonproz
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Latest Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Hope it helps :)
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
For all resources and cheat sheets, check out my Telegram channel: https://news.1rj.ru/str/pythonproz
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
Latest Jobs & Internship Opportunities: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Hope it helps :)
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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 🔍
React "❤️" for Part 2
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 🔍
React "❤️" for Part 2
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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 👨🏫
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
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 👨🏫
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
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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
Don’t forget to react ❤️ if you’d like to see more content like this!
Thank you all for joining! ❤️🙏
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
Don’t forget to react ❤️ if you’d like to see more content like this!
Thank you all for joining! ❤️🙏
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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.
Enjoy 😄👍
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.
Enjoy 😄👍
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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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#aiprompt
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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#aiprompt
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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.
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.
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🚀 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
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
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👉 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.
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.
👍4❤2
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
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
👍6
Source Code of Getting WiFi Passwords 👇👇-
Like for more Python Projects
Python Projects: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a
# 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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👍14❤4
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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ENJOY LEARNING 👍👍
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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ENJOY LEARNING 👍👍
👍9❤2🎄1
🚀 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
React "❤️" for More 👨💻
📂 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
React "❤️" for More 👨💻
❤14👍8