Python Learning
الذهاب إلى القناة على Telegram
Python learning resources Beginner to advanced Python guides, cheatsheets, books and projects. For data science, backend and automation. Join 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
إظهار المزيد
الشبكة:5 791
المشتركون
-124 ساعات
-287 أيام
-5230 أيام
جاري تحميل البيانات...
القنوات المماثلة
سحابة العلامات
الإشارات الواردة والصادرة
---
---
---
---
---
---
جذب المشتركين
أغسطس '26
أغسطس '26
+70
في 0 قنوات
يوليو '26
+90
في 0 قنوات
Get PRO
يونيو '26
+114
في 2 قنوات
Get PRO
مايو '26
+92
في 1 قنوات
Get PRO
أبريل '26
+72
في 2 قنوات
Get PRO
مارس '26
+82
في 3 قنوات
Get PRO
فبراير '26
+84
في 1 قنوات
Get PRO
يناير '26
+106
في 10 قنوات
Get PRO
ديسمبر '25
+68
في 1 قنوات
Get PRO
نوفمبر '25
+77
في 0 قنوات
Get PRO
أكتوبر '25
+87
في 1 قنوات
Get PRO
سبتمبر '25
+73
في 2 قنوات
Get PRO
أغسطس '25
+86
في 1 قنوات
Get PRO
يوليو '25
+58
في 0 قنوات
Get PRO
يونيو '25
+49
في 1 قنوات
Get PRO
مايو '25
+47
في 1 قنوات
Get PRO
أبريل '25
+39
في 1 قنوات
Get PRO
مارس '25
+34
في 1 قنوات
Get PRO
فبراير '25
+41
في 0 قنوات
Get PRO
يناير '25
+74
في 0 قنوات
Get PRO
ديسمبر '24
+57
في 1 قنوات
Get PRO
نوفمبر '24
+44
في 1 قنوات
Get PRO
أكتوبر '24
+43
في 0 قنوات
Get PRO
سبتمبر '24
+225
في 0 قنوات
Get PRO
أغسطس '24
+147
في 0 قنوات
Get PRO
يوليو '24
+318
في 1 قنوات
Get PRO
يونيو '24
+125
في 0 قنوات
Get PRO
مايو '24
+276
في 2 قنوات
Get PRO
أبريل '24
+631
في 2 قنوات
Get PRO
مارس '24
+608
في 0 قنوات
Get PRO
فبراير '24
+762
في 1 قنوات
Get PRO
يناير '24
+406
في 0 قنوات
Get PRO
ديسمبر '23
+715
في 1 قنوات
Get PRO
نوفمبر '23
+38
في 0 قنوات
Get PRO
أكتوبر '23
+27
في 0 قنوات
Get PRO
سبتمبر '23
+40
في 0 قنوات
Get PRO
أغسطس '23
+53
في 0 قنوات
Get PRO
يوليو '23
+96
في 0 قنوات
Get PRO
يونيو '23
+192
في 0 قنوات
Get PRO
مايو '23
+82
في 0 قنوات
Get PRO
أبريل '23
+67
في 0 قنوات
Get PRO
مارس '23
+99
في 0 قنوات
Get PRO
فبراير '23
+142
في 0 قنوات
Get PRO
يناير '23
+119
في 0 قنوات
Get PRO
ديسمبر '22
+204
في 0 قنوات
Get PRO
نوفمبر '22
+56
في 0 قنوات
Get PRO
أكتوبر '22
+215
في 0 قنوات
Get PRO
سبتمبر '22
+325
في 0 قنوات
Get PRO
أغسطس '22
+24
في 0 قنوات
Get PRO
يوليو '22
+178
في 0 قنوات
Get PRO
يونيو '22
+18
في 0 قنوات
Get PRO
مايو '22
+33
في 0 قنوات
Get PRO
أبريل '22
+311
في 0 قنوات
Get PRO
مارس '22
+884
في 0 قنوات
| التاريخ | نمو المشتركين | الإشارات | القنوات | |
| 26 أغسطس | 0 | |||
| 25 أغسطس | +1 | |||
| 24 أغسطس | +1 | |||
| 23 أغسطس | +1 | |||
| 22 أغسطس | 0 | |||
| 21 أغسطس | 0 | |||
| 20 أغسطس | +2 | |||
| 19 أغسطس | +5 | |||
| 18 أغسطس | +2 | |||
| 17 أغسطس | +2 | |||
| 16 أغسطس | 0 | |||
| 15 أغسطس | +1 | |||
| 14 أغسطس | +4 | |||
| 13 أغسطس | +4 | |||
| 12 أغسطس | +2 | |||
| 11 أغسطس | +3 | |||
| 10 أغسطس | +5 | |||
| 09 أغسطس | +5 | |||
| 08 أغسطس | +1 | |||
| 07 أغسطس | +5 | |||
| 06 أغسطس | +5 | |||
| 05 أغسطس | +2 | |||
| 04 أغسطس | +2 | |||
| 03 أغسطس | +11 | |||
| 02 أغسطس | +4 | |||
| 01 أغسطس | +2 |
منشورات القناة
📖 Reading Python Error Messages
Suppose you see this.
TypeError: can only concatenate str (not "int") to strInstead of panicking, read it from left to right. TypeError → The operation uses the wrong data type. str → Python found a string. int → It also found an integer. 👉 You're trying to combine two incompatible types.
| 2 | 📚 10 Python Modules You Probably Didn't Know Existed
1. textwrap - Format long blocks of text.
2. difflib - Compare files or strings.
3. fractions - Work with exact fractions.
4. decimal - High precision decimal arithmetic.
5. calendar - Generate calendars programmatically.
6. uuid - Generate unique IDs.
7. secrets - Create cryptographically secure tokens.
8. pprint - Print nested data structures beautifully.
9. platform - Detect operating system information.
10. getpass - Securely read passwords from the terminal. | 143 |
| 3 | What does the Python slice list[::-1] do? | 183 |
| 4 | ⚡️ append() vs extend()
These two methods look similar, but they do completely different things.
numbers = [1, 2, 3]
numbers.append([4, 5])
print(numbers)
Output:
[1, 2, 3, [4, 5]]
Now compare it with:
numbers = [1, 2, 3]
numbers.extend([4, 5])
print(numbers)
Output:
[1, 2, 3, 4, 5]
👉 append() adds one object.
👉 extend() adds every element.
This small difference causes countless beginner bugs. | 239 |
| 5 | 📦 What Should You Learn After Python Basics?
✅ Functions & Modules
⬇️
✅ Object-Oriented Programming
⬇️
✅ File Handling
⬇️
✅ Exception Handling
⬇️
✅ Virtual Environments
⬇️
✅ Git & GitHub
⬇️
✅ Choose a Path:
• Web Development
• Automation
• Data Science
• Machine Learning
• Cybersecurity
• Backend APIs
Python is just the language.
Your specialization is what turns it into a career. | 229 |
| 6 | Python Script to Retrieve Saved Wi-Fi Passwords (Windows)
Someone requested this… We thought it might help.
import subprocess
def get_wifi_passwords():
# To get list of all saved Wi-Fi profiles
profiles_data = subprocess.check_output(['netsh', 'wlan', 'show', 'profiles']).decode('utf-8', errors="ignore").split('\n')
profiles = [line.split(":")[1].strip() for line in profiles_data if "All User Profile" in line]
print("\nSaved Wi-Fi Networks & Passwords:\n" + "-"*40)
for profile in profiles:
try:
# To get password for each profile
profile_info = subprocess.check_output(
['netsh', 'wlan', 'show', 'profile', profile, 'key=clear']
).decode('utf-8', errors="ignore").split('\n')
password = [line.split(":")[1].strip() for line in profile_info if "Key Content" in line]
print(f"Network : {profile}")
print(f"Password: {password[0] if password else 'None / Open Network'}\n")
except:
print(f"Network : {profile}")
print("Password: Unable to retrieve\n")
if __name__ == "__main__":
get_wifi_passwords()
💻 How to use the above code:
1. Open any text editor (Notepad, VS Code, etc.)
2. Copy and paste the code above
3. Save the file as wifi_passwords.py
4. Open Command Prompt or PowerShell as Administrator
5. Navigate to the folder where you saved the file
6. Run the command:
python wifi_passwords.py
✅ The script will list all the Wi-Fi networks that are saved on your computer along with their passwords.
⚠️ This only works for networks that are already saved on your Windows PC. It cannot crack or find passwords of networks you have never connected to. | 246 |
| 7 | +1 Python Notes for AI was requested by one of you. And here it is...
You can drop any future resource requests here. | 257 |
| 8 | 🧠 Think Like Python
Suppose you want to know if a username exists.
🔻 Many beginners write:
found = False
for user in users:
if user == "Alex":
found = True
break
🟢 Python gives you a simpler solution.
found = "Alex" in users
Less code. More readable. Usually faster to understand.
Whenever Python has a built-in way to express an idea, prefer it. | 340 |
| 9 | What exception does Python raise when you divide by zero? | 326 |
| 10 | Python Machine Learning Workbook.pdf | 400 |
| 11 | 🔍 10 Useful String Methods in Python
1. split() → Break text into pieces.
2. join() → Combine multiple strings.
3. replace() → Replace part of a string.
4. strip() → Remove extra spaces.
5. startswith() → Check prefixes.
6. endswith() → Check suffixes.
7. find() → Locate text.
8. count() → Count occurrences.
9. upper() / lower() → Change case.
10. capitalize() → Capitalize the first letter.
These methods appear in almost every real-world Python project. | 477 |
| 12 | One of our members asked for a Python Book
This book, Think Python, is an introduction to Python programming for beginners.
It starts with basic concepts of programming; it is carefully designed to define all terms when they are first used and to develop each new concept in a logical progression. | 540 |
| 13 | 🚀 Python Time Complexity Cheat Sheet
✅ List
• Access by index → O(1)
• Append → O(1)
• Insert at beginning → O(n)
• Delete from middle → O(n)
• Search (in) → O(n)
Best for: Ordered collections where fast indexing matters.
✅ Dictionary (dict)
• Lookup → O(1)
• Insert → O(1)
• Update → O(1)
• Delete → O(1)
Best for: Fast lookups using keys.
✅ Set
• Add → O(1)
• Remove → O(1)
• Membership test → O(1)
Best for: Removing duplicates and fast membership checks.
✅ Tuple
• Access → O(1)
• Search → O(n)
Best for: Read-only collections that shouldn't change. | 567 |
| 14 | 📖 Reading Python Error Messages
Suppose you see this.
TypeError: 'NoneType' object is not iterable
Instead of guessing, break it down.
TypeError → You're performing an operation on an incompatible type.
NoneType → The value is None.
not iterable → Python expected something it could loop over, like a list or tuple.
A common cause:
def get_users():
print("Loading users...")
for user in get_users():
print(user)
get_users() doesn't return anything, so it returns None by default. Python can't loop over None.
When you see this error, ask yourself:
"Which variable was supposed to contain a list but ended up being None?" | 537 |
| 15 | 🐍 15 Python Built-in Functions Every Developer Should Know
You don't always need another library.
Python already ships with powerful built-in functions that can make your code cleaner, shorter, and faster.
1. enumerate() - Loop through items while automatically keeping track of their index.
2. zip() - Combine multiple lists together element by element.
3. map() - Apply the same function to every item in an
iterable.
4. filter() - Keep only the elements that satisfy a condition.
5. sorted() - Return a new sorted list without changing the original.
6. any() - Returns True if at least one item is truthy.
7. all() - Returns True only if every item is truthy.
8. sum() - Quickly calculate the total of numeric values.
9. min() / max() - Find the smallest or largest value instantly.
10. len() - Count the number of items in any iterable.
11. set() - Remove duplicate values while creating a collection of unique items.
12. isinstance() - Check whether an object belongs to a specific type.
13. range() - Generate sequences of numbers efficiently.
14. reversed() - Iterate over data in reverse order without modifying it.
15. help() - Open the built-in documentation for almost any Python object.
Learning these built-ins will make your code look much more "Pythonic" and save you from writing unnecessary loops. | 509 |
| 16 | What is the biggest advantage of using a set instead of a list when checking whether an item exists? | 384 |
| 17 |  A Japanese AI company called Preferred Networks has a mature open-source library for NumPy/SciPy calculations on GPUs.
It's called CuPy 🚀.
For massive datasets, it is often enough to replace a single line: import cupy as cp
The same array operations can run on CUDA up to 100 times faster.
What it can do:
🛠 Highly compatible with existing NumPy and SciPy code
📝 Dramatically reduces the need to rewrite code or learn new syntax
💻 Supports not only NVIDIA CUDA but also AMD ROCm architectures
Keep in mind:
→ Only faster for massive arrays; small datasets will run slower due to CPU-to-GPU data transfer lag
→ Strictly bound by your physical GPU VRAM limits (can cause out-of-memory errors).
→ Covers most major math functions, but does not replicate 100% of NumPy/SciPy modules.
The project is completely open-source and battle-tested since 2015 📂: https://github.com/cupy/cupy | 506 |
| 18 | 100+ Python Problems with Solutions.pdf | 576 |
| 19 |  📘 Biopython: Tutorial and Cookbook
✍️ Authors: Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck, Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich, Bartek Wilczyński
🔗 Read Online
#Python
────────────────────
👉 @free_programming_books_bds 👈 | 660 |
| 20 | The Unofficial Python Graph Gallery
If you work with data, you already know the pain of making charts look decent in Python. You spend 5 minutes writing the logic to process your data, and then 45 minutes wrestling with matplotlib or seaborn trying to figure out why your labels are overlapping, how to change a specific hex color, or how to remove those ugly default borders.
This repository completely solves that. Instead of just listing libraries, it is a massive, beautifully organized collection of hundreds of data visualization examples.
🔗 Link | 615 |
