Python has a built-in topological dependency sorter!🚀
If you're working with tasks that have dependencies — for example, in build systems, CI/CD pipelines, or workflow orchestration — the order of execution often has to be determined manually.
Usually through graphs, DFS,, or custom execution order logic.
But Python's standard library already has graphlib.TopologicalSorter.
After preparation, the sorter returns the correct execution order.
Result:
Especially useful for workflow management systems, dependency resolution, orchestration systems, and any tasks with a dependency graph.
🔥 TopologicalSorter allows you to solve dependency problems using Python's built-in tools without having to implement graph algorithms manually.
#Python #DependencyResolution #WorkflowOrchestration #CICD #BuildSystems #TopologicalSort
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If you're working with tasks that have dependencies — for example, in build systems, CI/CD pipelines, or workflow orchestration — the order of execution often has to be determined manually.
Usually through graphs, DFS,, or custom execution order logic.
But Python's standard library already has graphlib.TopologicalSorter.
ts = TopologicalSorter()
ts.add("deploy", "test")
ts.add("test", "build")
After preparation, the sorter returns the correct execution order.
tuple(ts.static_order())
Result:
("build", "test", "deploy")Especially useful for workflow management systems, dependency resolution, orchestration systems, and any tasks with a dependency graph.
#Python #DependencyResolution #WorkflowOrchestration #CICD #BuildSystems #TopologicalSort
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✨ Unpacking the remaining elements 🧩
Sometimes you need to extract the first and last elements from a list, while grouping everything in the middle separately. Instead of struggling with slicing ([1:-1]), use the asterisk (*). ⭐️
#Python #Coding #DataScience #DevLife #Programming #Tech
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Sometimes you need to extract the first and last elements from a list, while grouping everything in the middle separately. Instead of struggling with slicing ([1:-1]), use the asterisk (*). ⭐️
data = ["CEO", "Middle Python Dev", "Junior Dev", "QA", "HR"]
# The asterisk automatically collects everything "extra" into a separate list.
boss, *team, hr = data
print(boss) # CEO
print(team) # ['Middle Python Dev', 'Junior Dev', 'QA']
print(hr) # HR
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Cheat sheet on Python Frameworks:
Django: A full-featured web framework with built-in ORM, admin panel, and security features.
Flask: A lightweight microframework with a minimal set of features and high flexibility.
ORM & Admin: Built-in to Django, but need to be connected separately in Flask.
Security: Django has built-in security mechanisms, while in Flask, they need to be configured manually.
Testing: Django offers built-in testing tools, while Flask relies on third-party libraries.
Use Cases: Django is suitable for large and complex projects, while Flask is better for small applications, APIs, and prototypes.
#Python #WebDev #Django #Flask #Backend #Programming
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Django: A full-featured web framework with built-in ORM, admin panel, and security features.
Flask: A lightweight microframework with a minimal set of features and high flexibility.
ORM & Admin: Built-in to Django, but need to be connected separately in Flask.
Security: Django has built-in security mechanisms, while in Flask, they need to be configured manually.
Testing: Django offers built-in testing tools, while Flask relies on third-party libraries.
Use Cases: Django is suitable for large and complex projects, while Flask is better for small applications, APIs, and prototypes.
#Python #WebDev #Django #Flask #Backend #Programming
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🐱 Awesome Python Typing — Everything for Learning Typing in Python! 🐍✨
If you want to understand type annotations in Python, this repository is definitely worth saving. It contains the best articles, books, tools, libraries, and other materials dedicated to typing and its use in real-world projects. 💻📚
Here's the link: GitHub 📱
https://github.com/typeddjango/awesome-python-typing
#Python #Typing #TypeAnnotations #Programming #Developer #GitHub
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If you want to understand type annotations in Python, this repository is definitely worth saving. It contains the best articles, books, tools, libraries, and other materials dedicated to typing and its use in real-world projects. 💻📚
Here's the link: GitHub 📱
https://github.com/typeddjango/awesome-python-typing
https://github.com/typeddjango/awesome-python-typing
#Python #Typing #TypeAnnotations #Programming #Developer #GitHub
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Python allows you to create enumerations that are also regular strings!
Previously, when working with APIs, JSON, and configurations, it was often necessary to manually extract the value from an Enum.
For example:
When serializing, you would get an enumeration object:
rather than a regular string:
In Python 3.11,
Now, the value can be used wherever a string is expected:
The result:
At the same time, the advantages of enumerations are preserved:
You cannot accidentally pass an incorrect value:
will result in an error.
🔥
#Python #Coding #StrEnum #DevTips #Programming #Python311
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Previously, when working with APIs, JSON, and configurations, it was often necessary to manually extract the value from an Enum.
For example:
class Status(Enum):
ACTIVE = "active"
When serializing, you would get an enumeration object:
Status.ACTIVErather than a regular string:
"active"In Python 3.11,
StrEnum was introduced to solve this problem.from enum import StrEnum
class Status(StrEnum):
ACTIVE = "active"
BLOCKED = "blocked"
Now, the value can be used wherever a string is expected:
json.dumps({"status": Status.ACTIVE})The result:
{"status": "active"}At the same time, the advantages of enumerations are preserved:
Status.ACTIVEStatus.BLOCKEDYou cannot accidentally pass an incorrect value:
Status("unknown")will result in an error.
🔥
StrEnum allows you to combine the strict typing of enumerations with the convenience of regular strings, without manual conversion when working with APIs, JSON, and configurations.#Python #Coding #StrEnum #DevTips #Programming #Python311
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