#Solution for #Assignment by @MINtaa911

You can forward your feedback via @pyDiscussion

#Guess the number
from random import randint
def guess_number():
num = randint(0,20)
guess = int(input("Enter your guess number: "))
while(num != guess):
if(guess > num):
print("Your guess is too high")
elif(guess < num):
print("your guess is too low")
else:
break
guess = int(input("Enter your guess number: "))

print("you win")

guess_number()

#LearnDataScience #LearnPython #StayHome #PreventCOVId19

Another #Solution for #Assignment by @Amalright

You can forward your feedback via @pyDiscussion

from random import*

Def fun_game(number_chosen):
data=randrange(0,20)
if(nuber_chosen> data):
print("too much")
elif(nuber_chosen < data):
print("too low")
else:
print("correct answer :")

J = 1
While(j !=0)
choice =int(input("Enter 1 to continue"))
if(choice ==1):
choice =int(input("Enter your try"))
fun_game(choice)
else:
j =0

#StayHome #PreventCOVId19 #LearnPython #LearnDataScience #LearnRandomNumber

Python is a programming language. Like other languages, it gives us a way to communicate ideas. In the case of a programming language, these ideas are “commands” that people use to communicate with a computer!

We convey our commands to the computer by writing them in a text file using a programming language. These files are called programs. Running a program means telling a computer to read the text file, translate it to the set of operations that it understands, and perform those actions.

#LearnPython #LearnDataAnalysis #StayHome #PreventCOVID19

Thanks for your participation in the Q&A poll. The answer is double asterisks(**)

Example: print(2 ** 3) is 8. 2 the power of 3.

Ask @pyDiscussion if you have any question.
#QuarantineYourself #LearnPython #LearnDataScience

#Python is a multi-paradigm, dynamically typed, multipurpose programming language, designed to be quick (to learn, to use, and to understand), and to enforce a clean and uniform syntax. Please note that Python 2 is officially out of support as of January 1, 2020. Therefore automatically shift yourself from Python-2 to the latest and most improved version of python [python-3.x] .

#QuarantineYourself #LearnPython #LearnDataScience

Reading a File using Python

Computers use file systems to store and retrieve data. Each file is an individual container of related information. If you’ve ever saved a document, downloaded a song, or even sent an email you’ve created a file on some computer somewhere. Even script.py, the Python program you’re editing in the learning environment, is a file.
So, how do we interact with files using Python?
Let’s say we had a file called hello_python.txt with these contents:

Python is a powerful tool for Data Ananlysis. So, stay at home and learn python for future Data Science.

We could read that file like this:

script.py

with open('hello_python.txt') as python_file:
python_contents = python_file.read()
print(python_contents)

This
opens a file object called python_file and creates a new indented block where you can read the contents of the opened file. We then read the contents of the file python_file using python_file.read() and save the resulting string into the variable python_contents. Then we print python_contents, which outputs the statement written in the above!.

#QuarantineYourself #LearnPython #LearnDataScience

Python is a general-purpose programming language. It can do almost all of what other languages can do with comparable, or faster, speed. It is often chosen by Data Analysts and Data Scientists for prototyping, visualization, and execution of data analyses on datasets.

There’s an important question here. Plenty of other programming languages, like R, can be useful in the field of data science. Why are so many people choosing Python?

One major factor is Python’s versatility. There are over 125,000 third-party Python libraries. These libraries make Python more useful for specific purposes, from the traditional (e.g. web development, text processing) to the cutting edge (e.g. AI and machine learning). For example, a biologist might use the Biopython library to aid their work in genetic sequencing.

Additionally, Python has become a go-to language for data analysis. With data-focused libraries like pandas, NumPy, and Matplotlib, anyone familiar with Python’s syntax and rules can use it as a powerful tool to process, manipulate, and visualize data.

#FaceMask #KeepDistancing #LearnPython #LearnDataScience

Join @python4fds for more information

Writing a File using Python

In the previous post we have seen that how to open and read a file using python script. Today, I have posting about how to write a file or create your own file using the script.

Reading a file is all well and good, but what if we want to create a file of our own? With Python we can do just that. It turns out that our open() function that we’re using to open a file to read needs another argument to open a file to write to.

script.py

with open('generated_file.txt', 'w') as gen_file:
  gen_file.write("I love python!")

Here we pass the argument 'w' to open() **in order to indicate to open the file in write-mode. The default argument is 'r' and passing 'r' to **open() opens the file in read-mode as we’ve been doing.

This code creates a new file in the same folder as script.py and gives it the text What an incredible file!. It’s important to note that if there is already a file called generated_file.txt it will completely overwrite that file, erasing whatever its contents were before.

#QuarantineYourself #LearnPython #LearnDataScience

What Is a CSV File?

Text files aren’t the only thing that Python can read, but they’re the only thing that we don’t need any additional parsing library to understand. CSV files are an example of a text file that impose a structure to their data. CSV stands for Comma-Separated Values and CSV files are usually the way that data from spreadsheet software (like Microsoft Excel or Google Sheets) is exported into a portable format. A spreadsheet that looks like the following
Name Username Email
Asibeh Tenager asibeh asibeh@yahoo.com
Asibeh Tenager asibeh asibeh@yahoo.com



In a CSV file that same exact data would be rendered like this:

users.csv

Name,Username,Email, Asibeh Tenager, asibeh, asibeh@yahoo.com

Notice that the first row of the CSV file doesn’t actually represent any data, just the labels of the data that’s present in the rest of the file. The rest of the rows of the file are the same as the rows in the spreadsheet software, just instead of being separated into different cells they’re separated by… well I suppose it’s fair to say they’re separated by commas.


#FaceMask #KeepDistancing #LearnPython #LearnDatascience

#Assignment

Guess The Number

Write a programme where the computer randomly generates a number between 0 and 20. The user needs to guess what the number is. If the user guesses wrong, tell them their guess is either too high, or too low. This will get you started with the random library if you haven't already used it.

Post your solution in the comment box

#LearnDataScience #LearnPython #StayHome

Python is a general-purpose programming language. It can do almost all of what other languages can do with comparable, or faster, speed. It is often chosen by Data Analysts and Data Scientists for prototyping, visualization, and execution of data analyses on datasets.

There’s an important question here. Plenty of other programming languages, like R, can be useful in the field of data science. Why are so many people choosing Python?

One major factor is Python’s versatility. There are over 125,000 third-party Python libraries. These libraries make Python more useful for specific purposes, from the traditional (e.g. web development, text processing) to the cutting edge (e.g. AI and machine learning). For example, a biologist might use the Biopython library to aid their work in genetic sequencing.

Additionally, Python has become a go-to language for data analysis. With data-focused libraries like pandas, NumPy, and Matplotlib, anyone familiar with Python’s syntax and rules can use it as a powerful tool to process, manipulate, and visualize data.

#FaceMask #KeepDistancing #LearnPython #LearnDataScience

Join @python4fds for more information

Bubble Sort Algorithm Explained! Python Implementation & Step-by-Step Guide
https://www.youtube.com/watch?v=x6WGF8zDWZA

Learn More About Algorithmic Thinking:

If you're interested in diving deeper into algorithmic problem-solving, check out these additional tutorials:

📌 Bubble Sort Algorithm Explained! Python Implementation & Step-by-Step Guide
https://www.youtube.com/watch?v=x6WGF8zDWZA

📌 Linear Search Algorithm: https://www.youtube.com/watch?v=f0KsENxdTGI

📌 Binary Search Algorithm: https://www.youtube.com/watch?v=_MjGCuwFDuw

🙏 Support My Work:
🎁 Send a thanks gift or become a member: https://www.youtube.com/channel/UCsFz0IGS9qFcwrh7a91juPg/join

💬 Join Our Telegram Discussion Group: https://t.me/epythonlab

💡 Researchers & Beginners in Python!

This step-by-step guide walks you through installing and setting up Python on Windows using the Microsoft Store, along with VS Code setup to get you coding in no time!

🔗 https://www.youtube.com/watch?v=EGdhnSEWKok

Like & share if you found this helpful!

#PythonForResearch #PythonSetup #DataScience #AI #MachineLearning #CodingForBeginners #ResearchTools #Academia #PythonOnWindows

Python podcast with AI Agent

https://youtu.be/fRww_ZVJZNI

🚀 How to Become a Self-Taught AI Developer?

AI is transforming the world, and the best part? You don’t need a formal degree to break into the field! With the right roadmap and hands-on practice, anyone can become an AI developer. Here’s how you can do it:

1️⃣ Master the Fundamentals of Programming

Start with Python, as it’s the most popular language for AI. Learn data structures, algorithms, and object-oriented programming (OOP). Practice coding on LeetCode and HackerRank.

👉How to get started Python: https://www.youtube.com/watch?v=EGdhnSEWKok
How to Create & Use Python Virtual Environments | ML Project Setup + GitHub Actions CI/CD https://youtu.be/qYYYgS-ou7Q

👉Beginner's Guide to Python Programming. Getting started now: https://youtu.be/ISv6XIl1hn0

👉Data Structures with Projects full tutorial for beginners
https://www.youtube.com/watch?v=lbdKQI8Jsok

👉OOP in Python - beginners Crash Course https://www.youtube.com/watch?v=I7z6i1QTdsw

2️⃣ Build a Strong Math Foundation

AI relies on:
🔹 Linear Algebra – Matrices, vectors (used in deep learning) https://youtu.be/BNa2s6OtWls
🔹 Probability & Statistics – Bayesian reasoning, distributions https://youtube.com/playlist?list=PL0nX4ZoMtjYEl_1ONxAZHu65DPCQcsHmI&si=tAz0B3yoATAjE8Fx
🔹 Calculus – Derivatives, gradients (used in optimization)

📚 Learn from 3Blue1Brown, Khan Academy, or MIT OpenCourseWare.

3️⃣ Learn Machine Learning (ML)

Start with traditional ML before deep learning:
✔ Supervised Learning – Linear regression, decision trees https://youtube.com/playlist?list=PL0nX4ZoMtjYGV8Ff_s2FtADIPfwlHst8B&si=buC-eP3AZkIjzI_N
✔ Unsupervised Learning – Clustering, PCA
✔ Reinforcement Learning – Q-learning, deep Q-networks

🔗 Best course? Andrew Ng’s ML Course on Coursera.

4️⃣ Dive into Deep Learning

Once comfortable with ML, explore:
⚡ Neural Networks (ANNs, CNNs, RNNs, Transformers)
⚡ TensorFlow & PyTorch (Industry-standard deep learning frameworks)
⚡ Computer Vision & NLP

Try Fast.ai or the Deep Learning Specialization by Andrew Ng.

5️⃣ Build Real-World Projects

The best way to learn AI? DO AI. 🚀
💡 Train models with Kaggle datasets
💡 Build a chatbot, image classifier, or recommendation system
💡 Contribute to open-source AI projects

6️⃣ Stay Updated & Join the AI Community

AI evolves fast! Stay ahead by:
🔹 Following Google AI, OpenAI, DeepMind
🔹 Engaging in Reddit r/MachineLearning, LinkedIn AI discussions
🔹 Attending AI conferences like NeurIPS & ICML

7️⃣ Create a Portfolio & Apply for AI Roles

📌 Publish projects on GitHub
📌 Share insights on Medium/Towards Data Science
📌 Network on LinkedIn & Kaggle

No CS degree? No problem! AI is about curiosity, consistency, and hands-on experience. Start now, keep learning, and let’s build the future with AI. 🚀

Tagging AI learners & enthusiasts: What’s your AI learning journey like? Let’s connect!. 🔥👇

#AI #MachineLearning #DeepLearning #Python #ArtificialIntelligence #SelfTaught

✅ Parse XML → Export to CSV using pure Python — no external libraries, no fluff. https://youtu.be/ii1UqhJwAkg

This beginner-friendly project walks you through:

🔍 Extracting structured data from XML files

⚙️ Automating file conversion and cleanup

📂 Working with realistic data formats used in enterprise tools, APIs, and fan databases

I used character data from the Dexter TV series as a sample XML source, making it fun and practical at the same time.

🎓 Perfect for:

Students & junior devs building portfolio projects

Data analysts working with legacy XML feeds

Anyone learning Python automation and data wrangling



#Python #Pandas #DataProjects #Automation #XMLtoCSV #DataExtraction #BeginnerFriendly #LearnPython #RealWorldPython #PortfolioProject #PythonForData

🚀 New Python Tutorial Alert!

Boolean logic is the foundation of every programming decision. Whether it’s controlling the flow of your code, building smarter conditions, or making algorithms more efficient—understanding it well is a must for every Python developer.

In my latest tutorial, I break down Boolean logic in Python step by step, with simple explanations and clear examples for beginners.

👉 Watch here: https://www.youtube.com/watch?v=DRiifF9SX2w

If you’re just starting out or want to sharpen your fundamentals, this one’s for you.

#Python #Programming #CodingForBeginners #LearnPython #BooleanLogic

🚀 New Python Tutorial!
Python Lists | Beginner-Friendly Tutorial with Real-World Examples
https://youtu.be/KDZcMK7FoA0