🐍📰 Python Mappings: A Comprehensive Guide
https://realpython.com/python-mappings/
#python
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https://realpython.com/python-mappings/
#python
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Regular Expressions in Python
Regular expressions (regex) in #Python are used for searching, matching, and manipulating strings based on patterns. In Python, regular expressions are implemented in the
Main functions of the re module:
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Usage examples:
Explanation of the example:
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Additional pattern examples:
Regular expressions are a powerful tool for working with text and can be useful in a wide range of tasks, from simple input validation to complex text parsing.💊
Regular expressions (regex) in #Python are used for searching, matching, and manipulating strings based on patterns. In Python, regular expressions are implemented in the
re module.Main functions of the re module:
re.match(): Checks if the beginning of a string matches a given pattern.re.search(): Searches for a pattern in a string and returns the first matching object found.re.findall(): Finds all occurrences of a pattern in a string and returns them as a list.re.finditer(): Finds all occurrences of a pattern and returns them as an iterator.re.sub(): Replaces all occurrences of a pattern with a given string.re.split(): Splits a string by a given pattern.Usage examples:
import re
# Example string
text = "The rain in Spain falls mainly in the plain."
# 1. re.match()
match = re.match(r'The', text)
if match:
print("Match found:", match.group())
else:
print("No match found")
# 2. re.search()
search = re.search(r'rain', text)
if search:
print("Search found:", search.group())
else:
print("No search found")
# 3. re.findall()
findall = re.findall(r'in', text)
print("Findall results:", findall)
# 4. re.finditer()
finditer = re.finditer(r'in', text)
for match in finditer:
print("Finditer match:", match.group(), "at position", match.start())
# 5. re.sub()
substitute = re.sub(r'rain', 'snow', text)
print("Substitute result:", substitute)
# 6. re.split()
split = re.split(r'\s', text)
print("Split result:", split)
Explanation of the example:
>
re.match(r'The', text): Checks if the string text starts with "The".>
re.search(r'rain', text): Searches for the first occurrence of "rain" in the string text.>
re.findall(r'in', text): Finds all occurrences of "in" in the string text.>
re.finditer(r'in', text): Returns an iterator that iterates over all occurrences of "in" in the string text.>
re.sub(r'rain', 'snow', text): Replaces all occurrences of "rain" with "snow" in the string text.>
re.split(r'\s', text): Splits the string text by spaces (whitespace characters).Additional pattern examples:
\d: Any digit.\D: Any character except a digit.\w: Any letter, digit, or underscore.\W: Any character except a letter, digit, or underscore.\s: Any whitespace character.\S: Any non-whitespace character..: Any character except a newline.^: Start of the string.$: End of the string.*: 0 or more repetitions.+: 1 or more repetitions.?: 0 or 1 repetition.{n}: Exactly n repetitions.{n,}: n or more repetitions.{n,m}: Between n and m repetitions.
Regular expressions are a powerful tool for working with text and can be useful in a wide range of tasks, from simple input validation to complex text parsing.
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https://realpython.com/python-string-formatting/
#python
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Master Python Interviews with These 150 Essential Questions.pdf
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Master Python Interviews with These 150 Essential Questions
Preparing for a Python-based role in data science, analytics, software development, or AI?
You need more than just coding skills — you need clarity on concepts, frameworks, and best practices.
This document contains 150 most commonly asked Python interview questions with clear, concise answers covering:
-Core Python – data types, control flow, OOP, memory management, iterators, decorators, and more
-Data Science Libraries – NumPy, Pandas, Matplotlib, Seaborn
-Frameworks – Flask, Django, Pyramid
-Data Handling – CSV reading, DataFrames, joins, merges, file handling
-Advanced Topics – GIL, multithreading, pickling, deep vs. shallow copy, generators
-Coding Challenges – from Fibonacci to palindrome checkers, sorting algorithms, and data structure problems
https://news.1rj.ru/str/DataScienceQ 🧠
Preparing for a Python-based role in data science, analytics, software development, or AI?
You need more than just coding skills — you need clarity on concepts, frameworks, and best practices.
This document contains 150 most commonly asked Python interview questions with clear, concise answers covering:
-Core Python – data types, control flow, OOP, memory management, iterators, decorators, and more
-Data Science Libraries – NumPy, Pandas, Matplotlib, Seaborn
-Frameworks – Flask, Django, Pyramid
-Data Handling – CSV reading, DataFrames, joins, merges, file handling
-Advanced Topics – GIL, multithreading, pickling, deep vs. shallow copy, generators
-Coding Challenges – from Fibonacci to palindrome checkers, sorting algorithms, and data structure problems
https://news.1rj.ru/str/DataScienceQ 🧠
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🐍📰 Skip Ahead in Loops With Python's Continue Keyword
Learn how #Python's continue statement works, when to use it, common mistakes to avoid, and what happens under the hood in CPython byte code
https://realpython.com/python-continue/
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Learn how #Python's continue statement works, when to use it, common mistakes to avoid, and what happens under the hood in CPython byte code
https://realpython.com/python-continue/
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Stelvio v0.3.0 is here!
The easiest way to deploy a Python application on AWS.
Only Python.
No YAML. No JSON. No clicking around in the AWS Console.
✓ CLI with no prior setup
✓ Environment support
Watch how I deploy an API from an empty folder — in less than 60 seconds.
Try it right now💊
Documentation: https://docs.stelvio.dev
GitHub: https://github.com/michal-stlv/stelvio/
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The easiest way to deploy a Python application on AWS.
Only Python.
No YAML. No JSON. No clicking around in the AWS Console.
✓ CLI with no prior setup
✓ Environment support
Watch how I deploy an API from an empty folder — in less than 60 seconds.
Try it right now
Documentation: https://docs.stelvio.dev
GitHub: https://github.com/michal-stlv/stelvio/
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Forwarded from Machine Learning with Python
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
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✅ https://news.1rj.ru/str/Codeprogrammer
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Clean code advice for Python:
Do not add redundant context.
Avoid adding unnecessary data to variable names, especially when working with classes.
Example:
This is bad:
This is good:
👉 @DataScience4
Do not add redundant context.
Avoid adding unnecessary data to variable names, especially when working with classes.
Example:
This is bad:
class Person:
def __init__(self, person_first_name, person_last_name, person_age):
self.person_first_name = person_first_name
self.person_last_name = person_last_name
self.person_age = person_age
This is good:
class Person:
def __init__(self, first_name, last_name, age):
self.first_name = first_name
self.last_name = last_name
self.age = age
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python-docx: Create and Modify Word Documents #python
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
Example
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python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
pip install python-docx
Example
from docx import Document
document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")
document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'
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Get a weather forecast without API and complex settings in Python
We use the wttr.in: https://github.com/chubin/wttr.in
service — a simple and powerful tool that shows the weather right in the console.
To work with the HTTP request, you only need one library - requests. Installing it is very easy:
Here is the minimal and clear code to get the forecast:
Just enter the desired city and get a detailed forecast with temperature, precipitation
Try it yourself😏
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We use the wttr.in: https://github.com/chubin/wttr.in
service — a simple and powerful tool that shows the weather right in the console.
To work with the HTTP request, you only need one library - requests. Installing it is very easy:
pip install requests
Here is the minimal and clear code to get the forecast:
import requests
city = input("Enter the city name: ")
url = f"https://wttr.in/{city}"
try:
response = requests.get(url)
print(response.text)
except Exception:
print("Oops! Something went wrong. Please try again later.")
Just enter the desired city and get a detailed forecast with temperature, precipitation
Try it yourself
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Django REST Framework and Vue versus Django and HTMX
https://testdriven.io/blog/drf-vue-vs-django-htmx/
Learn how the development process varies between working with Django REST Framework and Vue versus #Django and #HTMX.
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https://testdriven.io/blog/drf-vue-vs-django-htmx/
Learn how the development process varies between working with Django REST Framework and Vue versus #Django and #HTMX.
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Nested Loops in Python
Nested loops in Python allow you to place one loop inside another, enabling you to perform repeated actions over multiple sequences. Understanding nested loops helps you write more efficient code, manage complex data structures, and avoid common pitfalls such as poor readability and performance issues.
Learn: https://realpython.com/nested-loops-python/
Nested loops in Python allow you to place one loop inside another, enabling you to perform repeated actions over multiple sequences. Understanding nested loops helps you write more efficient code, manage complex data structures, and avoid common pitfalls such as poor readability and performance issues.
Learn: https://realpython.com/nested-loops-python/
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Top 150 Python Interview Questions
This PDF covers the most frequently asked Python questions with answers to help you prepare for upcoming interviews.
🔸 Link to PDF
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This PDF covers the most frequently asked Python questions with answers to help you prepare for upcoming interviews.
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Building a Real-time Dashboard with FastAPI and Svelte
In this tutorial, you'll learn how to build a real-time analytics dashboard using FastAPI and Svelte. We'll use server-sent events (SSE) to stream live data updates from FastAPI to our Svelte frontend, creating an interactive dashboard that updates in real-time.
Start: https://testdriven.io/blog/fastapi-svelte/
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In this tutorial, you'll learn how to build a real-time analytics dashboard using FastAPI and Svelte. We'll use server-sent events (SSE) to stream live data updates from FastAPI to our Svelte frontend, creating an interactive dashboard that updates in real-time.
Start: https://testdriven.io/blog/fastapi-svelte/
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python-docx: Create and Modify Word Documents #python
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
Example
https://news.1rj.ru/str/DataScienceN 🚗
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
pip install python-docx
Example
from docx import Document
document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")
document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'
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🐍📰 What Are Mixin Classes in Python?
Learn how to use Python mixin classes to write modular, reusable, and flexible code with practical examples and design tips
https://realpython.com/python-mixin/
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Learn how to use Python mixin classes to write modular, reusable, and flexible code with practical examples and design tips
https://realpython.com/python-mixin/
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Top 482 Flask Developer Interview Questions & Answers
Part 1: https://hackmd.io/@husseinsheikho/flask-int1
Part 2: https://hackmd.io/@husseinsheikho/flask-int2
Part 3: https://hackmd.io/@husseinsheikho/flask-int3
Part 4: https://hackmd.io/@husseinsheikho/flask-int4
Part 5: https://hackmd.io/@husseinsheikho/flask-int5
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Part 1: https://hackmd.io/@husseinsheikho/flask-int1
Part 2: https://hackmd.io/@husseinsheikho/flask-int2
Part 3: https://hackmd.io/@husseinsheikho/flask-int3
Part 4: https://hackmd.io/@husseinsheikho/flask-int4
Part 5: https://hackmd.io/@husseinsheikho/flask-int5
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marimo: A Reactive, Reproducible Notebook
marimo notebooks redefine the notebook experience by offering a reactive environment that addresses the limitations of traditional linear notebooks. With marimo, you can seamlessly reproduce and share content while benefiting from automatic cell updates and a correct execution order. Discover how marimo’s features make it an ideal tool for documenting research and learning activities.
Link: https://realpython.com/marimo-notebook/
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marimo notebooks redefine the notebook experience by offering a reactive environment that addresses the limitations of traditional linear notebooks. With marimo, you can seamlessly reproduce and share content while benefiting from automatic cell updates and a correct execution order. Discover how marimo’s features make it an ideal tool for documenting research and learning activities.
Link: https://realpython.com/marimo-notebook/
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