Deep copying of objects with the copy module ππ¦
The link trap in Python ππ³οΈ
When you assign a list to another variable (A = B) or make a regular slice (A = B[:]), Python doesn't physically copy the data. It simply creates a new reference to the same objects in memory. If the list contains other mutable objects (lists, dictionaries, custom classes), standard copying methods will only create a shallow copy. The copy module allows you to control this process.
β Breaking the links: The deepcopy function recursively traverses the entire data structure and creates honest, independent duplicates for each nested element. This ensures that changes in the copy will not harm the original data. ππ
β Safe state: The use of deep copying is critical when implementing design patterns (for example, Snapshot/Memento), creating game state backups, or when you pass complex configurations to functions that may modify them accidentally. π‘οΈπΎ
β A sensible balance: It's worth remembering that deepcopy works slower and consumes more memory than shallow copying, as it spends resources on creating new objects and checking for cyclic references. Use it specifically when there are nested mutable containers within the structure. βοΈπ§
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import copy
# Original list with nested structure
original = [[1, 2, 3], [4, 5, 6]]
# 1. Shallow copy
shallow = copy.copy(original)
shallow[0][0] = 'X'
# Oh no! Both lists have changed, because the nested list wasn't copied, but passed by reference
print(f"Original after shallow: {original}") # [['X', 2, 3], [4, 5, 6]]
# Restore the data
original = [[1, 2, 3], [4, 5, 6]]
# 2. Deep copy
deep = copy.deepcopy(original)
deep[0][0] = 'X'
# Everything is fine! Only deep has changed, the original remains untouched
print(f"Original after deep: {original}") # [[1, 2, 3], [4, 5, 6]]
The link trap in Python ππ³οΈ
When you assign a list to another variable (A = B) or make a regular slice (A = B[:]), Python doesn't physically copy the data. It simply creates a new reference to the same objects in memory. If the list contains other mutable objects (lists, dictionaries, custom classes), standard copying methods will only create a shallow copy. The copy module allows you to control this process.
β Breaking the links: The deepcopy function recursively traverses the entire data structure and creates honest, independent duplicates for each nested element. This ensures that changes in the copy will not harm the original data. ππ
β Safe state: The use of deep copying is critical when implementing design patterns (for example, Snapshot/Memento), creating game state backups, or when you pass complex configurations to functions that may modify them accidentally. π‘οΈπΎ
β A sensible balance: It's worth remembering that deepcopy works slower and consumes more memory than shallow copying, as it spends resources on creating new objects and checking for cyclic references. Use it specifically when there are nested mutable containers within the structure. βοΈπ§
#Python #Programming #DeepCopy #Coding #Tech #Dev
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Regular for-loops are versatile but not always optimal: they add extra interpreter overhead, which is especially noticeable on large data π
In such cases, it's better to use standard Python tools, for example itertools βοΈ
For example, to get all unique pairs from a list, nested loops are not needed β just combinations():
Conclusion: instead of manual loops, it's better to use ready-made tools from the standard library β it's cleaner and more efficient π
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In such cases, it's better to use standard Python tools, for example itertools βοΈ
For example, to get all unique pairs from a list, nested loops are not needed β just combinations():
from itertools import combinations
def get_unique_pairs(items):
return list(combinations(items, 2))
print(get_unique_pairs(['A', 'B', 'C', 'D']))
# Output:
# [('A', 'B'), ('A', 'C'), ('A', 'D'), ('B', 'C'), ('B', 'D'), ('C', 'D')]
Conclusion: instead of manual loops, it's better to use ready-made tools from the standard library β it's cleaner and more efficient π
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π Python Roadmap 2026: Finally, a comprehensive and up-to-date map for learning Python, not just a list of "figure it out yourself" links
A large Russian-language Python roadmap for 2026 has been posted on GitHub - from the first scripts to the Middle+/Senior level.
The route is compiled for modern Python:
- Python 3.13+
- free-threaded mode without GIL
- JIT
- uv instead of the hassle with pip/venv/poetry
- ruff, pyright, pytest, hypothesis
- async-first approach
- typing
- CPython inside
- web, databases, ML/AI, DevOps, and architecture
The roadmap has a logical sequence: first the environment and foundation, then idioms, OOP, types, the standard library, asynchrony, testing, CPython internals, web, databases, the AI direction, production, and architecture.
A particular plus is the practical format. At each stage, there are tasks, checklists, code examples, and free resources. This is not a motivational document, but a roadmap that you can actually follow for several months and see progress.
For beginners - a clear path without chaos.
For juniors - a way to fill in the gaps.
For those who already write in Python - a good checklist to understand where you're still struggling.
Python in 2026 is about tooling, types, async, infrastructure, AI, and production discipline. And this roadmap is exactly about such a Python.
https://github.com/justxor/pythonroamap2026
#Python #PythonRoadmap #Programming #2026 #Coding #DevOps
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A large Russian-language Python roadmap for 2026 has been posted on GitHub - from the first scripts to the Middle+/Senior level.
The route is compiled for modern Python:
- Python 3.13+
- free-threaded mode without GIL
- JIT
- uv instead of the hassle with pip/venv/poetry
- ruff, pyright, pytest, hypothesis
- async-first approach
- typing
- CPython inside
- web, databases, ML/AI, DevOps, and architecture
The roadmap has a logical sequence: first the environment and foundation, then idioms, OOP, types, the standard library, asynchrony, testing, CPython internals, web, databases, the AI direction, production, and architecture.
A particular plus is the practical format. At each stage, there are tasks, checklists, code examples, and free resources. This is not a motivational document, but a roadmap that you can actually follow for several months and see progress.
For beginners - a clear path without chaos.
For juniors - a way to fill in the gaps.
For those who already write in Python - a good checklist to understand where you're still struggling.
Python in 2026 is about tooling, types, async, infrastructure, AI, and production discipline. And this roadmap is exactly about such a Python.
https://github.com/justxor/pythonroamap2026
#Python #PythonRoadmap #Programming #2026 #Coding #DevOps
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5 More Must-Know Python Concepts π
Let's take a look at five more fundamental concepts that every Python developer should have in their toolkit. π οΈ
Read: https://www.kdnuggets.com/5-more-must-know-python-concepts π
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Let's take a look at five more fundamental concepts that every Python developer should have in their toolkit. π οΈ
Read: https://www.kdnuggets.com/5-more-must-know-python-concepts π
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Forwarded from Machine Learning with Python
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βοΈ Pyneng β a large base for Python and network automation!
Detailed documentation and educational materials. The site contains lessons on Python syntax, working with files, functions, OOP, as well as separate sections on network technologies. The materials are presented with a large number of examples and practical tasks.
π I'll leave a link: https://pyneng.readthedocs.io/en/latest/
#Python #NetworkAutomation #Pyneng #LearnPython #DevOps #TechEducation
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Detailed documentation and educational materials. The site contains lessons on Python syntax, working with files, functions, OOP, as well as separate sections on network technologies. The materials are presented with a large number of examples and practical tasks.
π I'll leave a link: https://pyneng.readthedocs.io/en/latest/
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When you're doing a parser or migrating a site, there's often a pile of unreadable HTML markup on the screen. Converting this into neat Markdown is usually a hassle.
In the open code, I found a convenient tool called python-markdownify, which precisely solves the problem of converting HTML to Markdown.
The logic is simple: you take bulky HTML and get a clear and well-structured Markdown as a result.
The tool is easily customizable. You can clean up the necessary tags, change the format of headings, and neatly process tables and images. All of this can be configured.
It's installed via pip. It can be used both from Python code and from the command line, converting files in batches.
If desired, you can inherit and redefine the conversion rules for your own cases. The extensibility is fine there.
If you have to process large amounts of text or migrate a blog, the library saves a lot of time that would otherwise be spent on tedious work with regular expressions.
β‘οΈ Link to GitHub
http://github.com/matthewwithanm/python-markdownify
#python #markdown #html #coding #devtools #opensource
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In the open code, I found a convenient tool called python-markdownify, which precisely solves the problem of converting HTML to Markdown.
The logic is simple: you take bulky HTML and get a clear and well-structured Markdown as a result.
The tool is easily customizable. You can clean up the necessary tags, change the format of headings, and neatly process tables and images. All of this can be configured.
It's installed via pip. It can be used both from Python code and from the command line, converting files in batches.
pip install python-markdownify
If desired, you can inherit and redefine the conversion rules for your own cases. The extensibility is fine there.
If you have to process large amounts of text or migrate a blog, the library saves a lot of time that would otherwise be spent on tedious work with regular expressions.
β‘οΈ Link to GitHub
http://github.com/matthewwithanm/python-markdownify
#python #markdown #html #coding #devtools #opensource
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Advice for Python, UV, and Docker ππ³
Sometimes dependencies are better installed separately from the code β this noticeably speeds up the compilation of Docker images π
The idea is simple: first, we install dependencies, then we add the project π
Why is this necessary:
β’ Docker caches layers and does not rebuild them unnecessarily β‘οΈ
β’ if only the code changes β the dependencies are taken from the cache πΎ
β’ if the dependencies change β only the corresponding layer is rebuilt π
β’ without this, any minor change triggers a full reinstallation π
Example:
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π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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Sometimes dependencies are better installed separately from the code β this noticeably speeds up the compilation of Docker images π
The idea is simple: first, we install dependencies, then we add the project π
Why is this necessary:
β’ Docker caches layers and does not rebuild them unnecessarily β‘οΈ
β’ if only the code changes β the dependencies are taken from the cache πΎ
β’ if the dependencies change β only the corresponding layer is rebuilt π
β’ without this, any minor change triggers a full reinstallation π
Example:
RUN --mount=type=cache,target=/root/.cache/uv --mount=type=bind,source=uv.lock,target=uv.lock --mount=type=bind,source=pyproject.toml,target=pyproject.toml uv sync --locked --no-install-project
COPY . /app
RUN --mount=type=cache,target=/root/.cache/uv uv sync --locked
#Python #Docker #DevOps #UV #SoftwareEngineering #TechTips
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π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
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Limiting program resources using the resource module π‘οΈ
Protecting the server from "greedy" code π§
When you run someone else's code, process user files, or write parsers, there's always a risk of a memory leak or an infinite loop. If such a script runs on the server, it can fill up all the RAM and bring down neighboring important processes (for example, the database). The built-in resource module (works on Unix/Linux/macOS) allows you to strictly limit the program's appetites.
β Safe environment: You can limit not only RAM (RLIMIT_AS), but also CPU time (RLIMIT_CPU). If the code goes into an infinite loop, the system will gracefully terminate it after a specified number of seconds.
β File system control: Using RLIMIT_FSIZE, you can prevent the script from creating files larger than a certain size. This will save the server's disks from being accidentally overwritten by gigantic logs.
β Precise audit: The getrusage function provides detailed statistics on the current process: how much time the CPU spent on calculations, how many I/O operations there were, and what the maximum amount of memory used was during the entire operation.
#Python #ResourceManagement #ServerSafety #Coding #DevOps #Linux
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import resource
import sys
# 1. Limiting the size of RAM (soft and hard limits in bytes)
# Limit the memory to ~50 MB
memory_limit = 50 * 1024 * 1024
resource.setrlimit(resource.RLIMIT_AS, (memory_limit, memory_limit))
# 2. Checking the protection's working
try:
print("Trying to allocate a huge array of memory...")
huge_list = [i for i in range(10_000_000)]
except MemoryError:
print("The limit worked! The program didn't crash, but caught the error.")
# 3. Finding out how many resources the script has already consumed
usage = resource.getrusage(resource.RUSAGE_SELF)
print(f"Peak memory consumption (in KB): {usage.ru_maxrss}")
Protecting the server from "greedy" code π§
When you run someone else's code, process user files, or write parsers, there's always a risk of a memory leak or an infinite loop. If such a script runs on the server, it can fill up all the RAM and bring down neighboring important processes (for example, the database). The built-in resource module (works on Unix/Linux/macOS) allows you to strictly limit the program's appetites.
β Safe environment: You can limit not only RAM (RLIMIT_AS), but also CPU time (RLIMIT_CPU). If the code goes into an infinite loop, the system will gracefully terminate it after a specified number of seconds.
β File system control: Using RLIMIT_FSIZE, you can prevent the script from creating files larger than a certain size. This will save the server's disks from being accidentally overwritten by gigantic logs.
β Precise audit: The getrusage function provides detailed statistics on the current process: how much time the CPU spent on calculations, how many I/O operations there were, and what the maximum amount of memory used was during the entire operation.
#Python #ResourceManagement #ServerSafety #Coding #DevOps #Linux
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π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
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π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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π SPOTO Mid-Year Sale β Grab Your IT Certification Success Kit!
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β Free Resources:
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γ»IT Exams Skill Test: https://bit.ly/4dVPapB
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A 14-day tutorial where you build a Python code-agent CLI in the style of Claude Code from scratch and simultaneously understand how the Agent Harness actually works. π οΈπ€
In the end, you don't just call a ready-made agent via the API, but you understand the components that make up a Claude Code-like tool. π§ βοΈ
https://github.com/bozhouDev/14days-build-claude-code-cli/blob/main/README.en.md
#Python #AI #ClaudeCode #CLI #CodingTutorial #Tech
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β 13 courses live + 40+ coming soon
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In the end, you don't just call a ready-made agent via the API, but you understand the components that make up a Claude Code-like tool. π§ βοΈ
https://github.com/bozhouDev/14days-build-claude-code-cli/blob/main/README.en.md
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GitHub
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η½ι‘΅ηζη¨οΌηθ΅·ζ₯δΌθζδΈηΉ. Contribute to bozhouDev/14days-build-claude-code-cli development by creating an account on GitHub.
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Python can substitute an empty context manager without conditions inside!
It often happens that a resource needs to be opened via
This usually leads to code duplication or conditions around
`nullcontext(obj)
But note that
π₯
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It often happens that a resource needs to be opened via
with, and sometimes the object is already ready and there's no need to open anything.This usually leads to code duplication or conditions around
with:if need_open:
f = open(...)
else:
f = existing_file
`nullcontext(obj)
behaves like an empty context manager and allows you to maintain a single execution flow.
This is especially useful for APIs, tests, optional resources, dependency injection, and functions that can accept both a path and a ready-made object.
with ctx as resource:
process(resource)
But note that
nullcontext() does not close the passed object β it simply passes it on further.π₯
nullcontext() helps to unify scenarios with optional context managers and significantly simplifies the architecture of IO code.#Python #ContextManager #CodingTips #DevLife #Programming #Tech
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β€1
Do you know that Python can shift sequences without slicing and creating new lists?
When you need to cyclically shift data, many use slicing:
data = data[-1:] + data[:-1]
But deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations.
q.rotate(1)
A negative value rotates the queue in the other direction.
q.rotate(-2)
This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms.
workers.rotate(-1)
π₯ deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists.
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When you need to cyclically shift data, many use slicing:
data = data[-1:] + data[:-1]
But deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations.
q.rotate(1)
A negative value rotates the queue in the other direction.
q.rotate(-2)
This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms.
workers.rotate(-1)
π₯ deque.rotate() allows you to implement cyclical data structures without manual index logic and without creating new lists.
#Python #DataStructures #CodingTips #Programming #Deque #Tech
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Shuffling without repetitions:
β¨ Honest selection and generation of unique sets
When it's necessary to implement the logic of prize draws, random task distribution, or generating test questions, developers often use
β Guarantee of uniqueness: The main property of
β Safety of the original: The function does not modify the original list (unlike
β Strict control of size: If you pass a parameter
#Python #Random #Coding #NoRepetition #DataScience #UniqueSets
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import random
# Initial list of candidates or prizes
participants = ["Alexey", "Maria", "Ivan", "Olga", "Dmitry"]
# 1. Selecting 3 unique winners (sample without replacement)
winners = random.sample(participants, k=3)
print(f"Winners: {winners}")
# The result is different each time, but there will be no repetitions within the list of winners!
# 2. Shuffling an entire string (creating an anagram)
word = "python"
shuffled_word = "".join(random.sample(word, len(word)))
print(f"Anagram: {shuffled_word}")
# 3. Important difference: random.choices allows repetitions
print(f"With repetitions: {random.choices(participants, k=3)}")
β¨ Honest selection and generation of unique sets
When it's necessary to implement the logic of prize draws, random task distribution, or generating test questions, developers often use
random.choice() in a loop. But this approach requires manually ensuring that the same element is not selected twice. The random.sample function takes on this routine.β Guarantee of uniqueness: The main property of
random.sample is "without replacement". The extracted element no longer participates in the next selection cycle, which completely eliminates duplicates in the resulting list.β Safety of the original: The function does not modify the original list (unlike
random.shuffle()), but creates a completely new array with the results. This allows the structure of the original data to remain intact.β Strict control of size: If you pass a parameter
k (the number of elements) that exceeds the length of the original list, Python will not start duplicating elements and will immediately throw an ValueError error. This protects the program logic from incorrect data.#Python #Random #Coding #NoRepetition #DataScience #UniqueSets
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Convert PDF to structured JSON β in a couple of lines and without hassle! πβ¨
Today, we'll create a mini-service that takes a PDF document, extracts the text from it, and asks GPT to neatly organize the content into sections: title, author, date, and a list of sections. π
First, let's connect the necessary libraries and API key:
Now, let's extract the text from the PDF. We'll loop through all the pages and combine them into a single string:
Next, we'll send the obtained text to GPT. We'll ask the model to return a structured JSON with the necessary fields:
Output the result:
π₯ Suitable for contracts, reports, methodologies, and any PDFs β we immediately get a JSON ready for use.
#PDF #JSON #Python #GPT #Automation #DataScience
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Today, we'll create a mini-service that takes a PDF document, extracts the text from it, and asks GPT to neatly organize the content into sections: title, author, date, and a list of sections. π
First, let's connect the necessary libraries and API key:
import os
from PyPDF2 import PdfReader
from openai import OpenAI
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
Now, let's extract the text from the PDF. We'll loop through all the pages and combine them into a single string:
reader = PdfReader("document.pdf")
text = "
".join(page.extract_text() for page in reader.pages)Next, we'll send the obtained text to GPT. We'll ask the model to return a structured JSON with the necessary fields:
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": (
"You are a PDF parser. Return a JSON with the fields: title, author, date, sections. "
"Each section is an object with name and summary."
)},
{"role": "user", "content": text}
]
)
Output the result:
structured = response.choices[0].message.content.strip()
print(structured)
π₯ Suitable for contracts, reports, methodologies, and any PDFs β we immediately get a JSON ready for use.
#PDF #JSON #Python #GPT #Automation #DataScience
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π Level up your AI & Data Science skills with HelloEncyclo β a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
β 13 courses live + 40+ coming soon
π― One access, lifetime updates
π Use code: PRESALE-BOOK-WAVE-2GFG
π https://helloencyclo.com/?ref=HUSSEINSHEIKHO
Telegram
AI PYTHON π
Youβve been invited to add the folder βAI PYTHON πβ, which includes 14 chats.
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