✨ ftplib | Python Standard Library ✨
📖 Provides tools for connecting to FTP servers and transferring files using the File Transfer Protocol (FTP).
🏷️ #Python
📖 Provides tools for connecting to FTP servers and transferring files using the File Transfer Protocol (FTP).
🏷️ #Python
✨ Quiz: Testing Your Code With Python's unittest ✨
📖 Test your understanding of Python unittest basics, including TestCase, assertions, fixtures, subtests, and test discovery.
🏷️ #intermediate #stdlib #testing
📖 Test your understanding of Python unittest basics, including TestCase, assertions, fixtures, subtests, and test discovery.
🏷️ #intermediate #stdlib #testing
✨ Quiz: Use Codex CLI to Enhance Your Python Projects ✨
📖 Test your understanding of how to install Codex CLI, use Plan mode, and refine features with natural language in your terminal.
🏷️ #intermediate #ai #tools
📖 Test your understanding of how to install Codex CLI, use Plan mode, and refine features with natural language in your terminal.
🏷️ #intermediate #ai #tools
✨ Quiz: ChatterBot: Build a Chatbot With Python ✨
📖 Test your understanding of the ChatterBot Python library, from training a basic bot with ListTrainer to wiring in a local LLM through Ollama.
🏷️ #intermediate #data-science #projects
📖 Test your understanding of the ChatterBot Python library, from training a basic bot with ListTrainer to wiring in a local LLM through Ollama.
🏷️ #intermediate #data-science #projects
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✨ A New Python Packaging Council and Other News for May 2026 ✨
📖 A new Python Packaging Council, PEP 803 stabilizes the free-threaded ABI, the incremental GC gets reverted, and Astral changes hands.
🏷️ #community #news
📖 A new Python Packaging Council, PEP 803 stabilizes the free-threaded ABI, the incremental GC gets reverted, and Astral changes hands.
🏷️ #community #news
Rounding dates/times in Pandas:
*
*
*
Example:
https://t.iss.one/DataScience4
*
dt.floor — down (to the previous interval)*
dt.ceil — up (to the next interval)*
dt.round — to the nearest intervalExample:
s.dt.floor('3h') # previous 3-hour slot
s.dt.ceil('15m') # next 15-minute slot
s.dt.round('1D') # nearest dayhttps://t.iss.one/DataScience4
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✨ getopt | Python Standard Library ✨
📖 Provides tools for parsing command line options from sys.argv following Unix getopt() conventions, with support for both short and long option styles.
🏷️ #Python
📖 Provides tools for parsing command line options from sys.argv following Unix getopt() conventions, with support for both short and long option styles.
🏷️ #Python
✨ Quiz: Using Python for Data Analysis ✨
📖 Test your understanding of a data analysis workflow in Python, from cleansing raw data with pandas to spotting insights with regression.
🏷️ #intermediate #best-practices #data-science #python
📖 Test your understanding of a data analysis workflow in Python, from cleansing raw data with pandas to spotting insights with regression.
🏷️ #intermediate #best-practices #data-science #python
✨ How to Use OpenCode for AI-Assisted Python Coding ✨
📖 Learn how to use OpenCode, an open-source AI coding assistant, with a free Gemini API key to analyze and refactor Python code in your terminal.
🏷️ #intermediate #ai #python #tools
📖 Learn how to use OpenCode, an open-source AI coding assistant, with a free Gemini API key to analyze and refactor Python code in your terminal.
🏷️ #intermediate #ai #python #tools
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Forwarded from Machine Learning with Python
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Areas covered: #Python #AI #Cisco #PMP #Fortinet #AWS #Azure #Excel #CompTIA #ITIL #Cloud + more
🔗 Download each free resource here:
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👉 https://bit.ly/42y8owh
• IT Exams Skill Test
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• Free AI Materials & Support Tools
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This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0
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✨ netrc | Python Standard Library ✨
📖 Provides tools for parsing .netrc credentials files and looking up logins, accounts, and passwords by host.
🏷️ #Python
📖 Provides tools for parsing .netrc credentials files and looking up logins, accounts, and passwords by host.
🏷️ #Python
✨ Quiz: The Factory Method Pattern and Its Implementation in Python ✨
📖 Check your grasp of the Factory Method pattern in Python: when to use it, the roles involved, and how to implement a flexible object factory.
🏷️ #intermediate #best-practices
📖 Check your grasp of the Factory Method pattern in Python: when to use it, the roles involved, and how to implement a flexible object factory.
🏷️ #intermediate #best-practices
What is a Lambda Function?
A lambda function is a small anonymous function defined using the lambda keyword. It's often used for short, throwaway functions that are only needed temporarily.
Basic Syntax- The syntax of a lambda function is:
"lambda arguments: expression"
-arguments: A comma-separated list of parameters.
-expression: An expression that is evaluated and returned.
Examples
1️⃣ Basic Lambda Function:
Here, lambda x, y: x + y is a lambda function that adds two numbers.
2️⃣ Lambda with map():
map() applies the lambda function to each item in the numbers list.
3️⃣ Lambda with filter():
filter() uses the lambda function to filter out only the even numbers.
4️⃣ Lambda with reduce():
reduce() applies the lambda function cumulatively to the items in the list.
Pros and Cons-
Pros:
-> Concise and readable.
-> Useful for small, simple functions.
-> Handy for functional programming (e.g., map, filter, reduce).
Cons:
-> Limited to single expressions.
-> Can be less readable if overused.
-> Lack of function name can make debugging harder.
Lambda functions are an excellent tool for any Python developer to have in their toolkit. They can help streamline your code and make your functions more elegant and efficient.
A lambda function is a small anonymous function defined using the lambda keyword. It's often used for short, throwaway functions that are only needed temporarily.
Basic Syntax- The syntax of a lambda function is:
"lambda arguments: expression"
-arguments: A comma-separated list of parameters.
-expression: An expression that is evaluated and returned.
Examples
1️⃣ Basic Lambda Function:
add = lambda x, y: x + y
print(add(2, 3)) # Output: 5
Here, lambda x, y: x + y is a lambda function that adds two numbers.
2️⃣ Lambda with map():
numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x ** 2, numbers))
print(squared) # Output: [1, 4, 9, 16, 25]
map() applies the lambda function to each item in the numbers list.
3️⃣ Lambda with filter():
numbers = [1, 2, 3, 4, 5]
even = list(filter(lambda x: x % 2 == 0, numbers))
print(even) # Output: [2, 4]
filter() uses the lambda function to filter out only the even numbers.
4️⃣ Lambda with reduce():
from functools import reduce
numbers = [1, 2, 3, 4, 5]
product = reduce(lambda x, y: x * y, numbers)
print(product) # Output: 120
reduce() applies the lambda function cumulatively to the items in the list.
Pros and Cons-
Pros:
-> Concise and readable.
-> Useful for small, simple functions.
-> Handy for functional programming (e.g., map, filter, reduce).
Cons:
-> Limited to single expressions.
-> Can be less readable if overused.
-> Lack of function name can make debugging harder.
Lambda functions are an excellent tool for any Python developer to have in their toolkit. They can help streamline your code and make your functions more elegant and efficient.
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Top 10 Python One Liners!
1️⃣ Reverse a string:
2️⃣ Check if a number is even:
3️⃣ Find the factorial of a number:
4️⃣ Read a file and print its contents:
5️⃣ Create a list of squares:
6️⃣ Flatten a list of lists:
7️⃣ Find the length of a list:
8️⃣ Create a dictionary from two lists:
9️⃣ Generate a list of random numbers:
🔟 Check if a string is a palindrome:
Mastering these one-liners can significantly improve your coding efficiency and make your code more concise.
https://t.iss.one/pythonRe✉️
1️⃣ Reverse a string:
reversed_string = "Hello World"[::-1]
2️⃣ Check if a number is even:
is_even = lambda x: x % 2 == 0
3️⃣ Find the factorial of a number:
factorial = lambda x: 1 if x == 0 else x * factorial(x - 1)
4️⃣ Read a file and print its contents:
[print(line.strip()) for line in open('file.txt')]5️⃣ Create a list of squares:
squares = [x**2 for x in range(10)]
6️⃣ Flatten a list of lists:
flat_list = [item for sublist in [[1, 2], [3, 4], [5, 6]] for item in sublist]
7️⃣ Find the length of a list:
length = len([1, 2, 3, 4])
8️⃣ Create a dictionary from two lists:
keys = ['a', 'b', 'c']; values = [1, 2, 3]; dictionary = dict(zip(keys, values))
9️⃣ Generate a list of random numbers:
import random; random_numbers = [random.randint(0, 100) for _ in range(10)]
🔟 Check if a string is a palindrome:
is_palindrome = lambda s: s == s[::-1]
Mastering these one-liners can significantly improve your coding efficiency and make your code more concise.
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Lesson: Mastering Python Lists: Common Pitfalls and Best Practices 🐍
1. The Peril of Shallow Copies: Understanding References 🧠
Description: When you assign one list to another using =, you're not creating a new list; you're creating a new reference to the same list object. Modifications through one reference will affect the other. ⚠️
Correct Usage: Create a true copy to ensure independence. ✅
Incorrect Usage: Direct assignment creates an alias. ❌
Brief Explanation: = assigns a reference. Use slicing [:] or .copy() for shallow copies, and copy.deepcopy() for independent copies of nested lists. 🔑
---
2. Modifying a List During Iteration 🔄
Description: Modifying a list while iterating over it (e.g., removing elements) can lead to unpredictable behavior because the list's length and indices change during the loop. ⚠️
Correct Usage: Iterate over a copy of the list or use a list comprehension. ✅
Incorrect Usage: Modifying the original list directly while iterating. ❌
Brief Explanation: Changing the list's size or order mid-iteration confuses the loop's internal index. Use list comprehensions or iterate over a copy to ensure stable iteration. 🛡️
---
3. Append vs. Extend for Adding Elements ➕
Description: append() adds a single element (which can be another list) to the end of the list. extend() iterates over an iterable and adds each of its elements to the list.
Correct Usage: Choose based on whether you want to add one item or multiple items individually. ✅
Incorrect Usage: Using append() when you want to flatten an iterable into the current list. ❌
Brief Explanation: append() adds one item; extend() adds items from an iterable one by one. 🧩
---
4. Efficient Membership Testing 🔍
Description: Checking if an item is present in a list is a common operation. Python provides an optimized in operator for this, which is generally more efficient and readable than manual iteration.
Correct Usage: Use the in operator. ✅
Incorrect Usage: Manually looping to find an item. ❌
1. The Peril of Shallow Copies: Understanding References 🧠
Description: When you assign one list to another using =, you're not creating a new list; you're creating a new reference to the same list object. Modifications through one reference will affect the other. ⚠️
Correct Usage: Create a true copy to ensure independence. ✅
original = [1, 2, [3, 4]]
copy_slice = original[:] # or original.copy() for shallow copy
copy_slice[2][0] = 99
print(f"Correct (original): {original}") # Output: [1, 2, [99, 4]] (still shallow)
import copy
deep_copy = copy.deepcopy(original) # for nested structures
deep_copy[2][0] = 100
print(f"Correct (original after deep_copy): {original}") # Output: [1, 2, [99, 4]]
Incorrect Usage: Direct assignment creates an alias. ❌
list_a = [1, 2, 3]
list_b = list_a # list_b now refers to the SAME object as list_a
list_b.append(4)
print(f"Incorrect (list_a): {list_a}") # Output: [1, 2, 3, 4]
Brief Explanation: = assigns a reference. Use slicing [:] or .copy() for shallow copies, and copy.deepcopy() for independent copies of nested lists. 🔑
---
2. Modifying a List During Iteration 🔄
Description: Modifying a list while iterating over it (e.g., removing elements) can lead to unpredictable behavior because the list's length and indices change during the loop. ⚠️
Correct Usage: Iterate over a copy of the list or use a list comprehension. ✅
my_numbers = [1, 2, 3, 4, 5, 6]
new_numbers = [num for num in my_numbers if num % 2 == 0]
print(f"Correct: {new_numbers}") # Output: [2, 4, 6]
# Alternatively, iterate over a copy for removals:
# for item in my_numbers[:]: ...
Incorrect Usage: Modifying the original list directly while iterating. ❌
nums = [1, 2, 3, 4, 5]
for num in nums:
if num % 2 != 0:
nums.remove(num) # This will skip elements or raise errors
print(f"Incorrect: {nums}") # Output: [2, 4] (missed 3, removed 1 and 5)
Brief Explanation: Changing the list's size or order mid-iteration confuses the loop's internal index. Use list comprehensions or iterate over a copy to ensure stable iteration. 🛡️
---
3. Append vs. Extend for Adding Elements ➕
Description: append() adds a single element (which can be another list) to the end of the list. extend() iterates over an iterable and adds each of its elements to the list.
Correct Usage: Choose based on whether you want to add one item or multiple items individually. ✅
list1 = [1, 2]
list1.append([3, 4]) # Adds the list [3, 4] as one element
print(f"Correct (append list): {list1}") # Output: [1, 2, [3, 4]]
list2 = [1, 2]
list2.extend([3, 4]) # Adds 3, then 4 as separate elements
print(f"Correct (extend list): {list2}") # Output: [1, 2, 3, 4]
Incorrect Usage: Using append() when you want to flatten an iterable into the current list. ❌
data = [1, 2]
extra_data = [3, 4]
data.append(extra_data) # Appends the entire extra_data list as a single element
print(f"Incorrect: {data}") # Output: [1, 2, [3, 4]]
Brief Explanation: append() adds one item; extend() adds items from an iterable one by one. 🧩
---
4. Efficient Membership Testing 🔍
Description: Checking if an item is present in a list is a common operation. Python provides an optimized in operator for this, which is generally more efficient and readable than manual iteration.
Correct Usage: Use the in operator. ✅
student_ids = [101, 105, 112, 115]
if 105 in student_ids:
print("Correct: Student 105 is enrolled.")
Incorrect Usage: Manually looping to find an item. ❌
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Learn Python Coding
Lesson: Mastering Python Lists: Common Pitfalls and Best Practices 🐍 1. The Peril of Shallow Copies: Understanding References 🧠 Description: When you assign one list to another using =, you're not creating a new list; you're creating a new reference to the…
codes = ["A", "B", "C"]
found = False
for code in codes:
if code == "B":
found = True
break
if found:
print("Incorrect: Code B found (less efficient).")
Brief Explanation: The
in operator is optimized for membership checks, offering better performance and cleaner code than manual loops, especially for larger lists.---
5. Avoiding Unnecessary List Conversions
Description: Many functions and methods return iterators or generator objects for efficiency. Converting these directly to a list without need can waste memory and computation if you only need to process elements one by one.
Correct Usage: Process iterators directly when possible, convert to list only if multiple passes or random access is needed.
squares_gen = (x*x for x in range(5)) # Generator expression
for s in squares_gen: # Process elements one by one
print(f"Correct: {s}", end=" ") # Output: 0 1 4 9 16
print()
# If you need the full list:
squares_list = list(x*x for x in range(5))
print(f"Correct (list conversion): {squares_list}") # Output: [0, 1, 4, 9, 16]
Incorrect Usage: Unnecessarily converting iterators to lists when single-pass processing suffices.
data_stream = map(str.upper, ['apple', 'banana', 'cherry'])
# If you only need to print them once:
full_list = list(data_stream) # Unnecessary list creation
for item in full_list:
print(f"Incorrect: {item}", end=" ") # Output: APPLE BANANA CHERRY
print()
Brief Explanation: Iterators/generators are memory-efficient for single-pass operations. Convert to
list() only when random access, repeated iteration, or a material collection is strictly required.https://t.iss.one/pythonRe
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🧐 Python Cheatsheet — a convenient cheat sheet for Python that really saves time at work!
The repository contains a summary of key topics: from basic syntax and data structures to working with files, environments, and OOP with classes and magic methods. Everything is presented compactly, without unnecessary theory, with examples that can be immediately applied in code.
Repo: https://github.com/onyxwizard/python-cheatsheet📱
https://t.iss.one/pythonRe👩💻
The repository contains a summary of key topics: from basic syntax and data structures to working with files, environments, and OOP with classes and magic methods. Everything is presented compactly, without unnecessary theory, with examples that can be immediately applied in code.
Repo: https://github.com/onyxwizard/python-cheatsheet📱
https://t.iss.one/pythonRe
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