π Marking Deprecated Code in Python: deprecated()
In large projects, old functions are not always immediately removed. They are often left for compatibility, but it's important to warn developers that they should no longer be used.
Previously,
In Python 3.13, a
Now, the information about deprecation is part of the API itself. It can be recognized not only during runtime, but also by editors and static analysis tools.
This also works for classes:
This is convenient for libraries, SDKs, and large projects where the API is gradually changing.
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In large projects, old functions are not always immediately removed. They are often left for compatibility, but it's important to warn developers that they should no longer be used.
Previously,
warnings.warn()was often used for this purpose:
import warnings
def old_api():
warnings.warn(
"Use new_api()",
DeprecationWarning
)
In Python 3.13, a
deprecated()decorator has been introduced:
from warnings import deprecated
@deprecated("Use new_api()")
def old_api():
return "old"
Now, the information about deprecation is part of the API itself. It can be recognized not only during runtime, but also by editors and static analysis tools.
This also works for classes:
@deprecated("Use NewClient")
class OldClient:
passThis is convenient for libraries, SDKs, and large projects where the API is gradually changing.
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π©βπ» heapq.merge(): Combining sorted data!
If you have multiple sources of data that are already sorted, you don't need to collect them into a single collection and sort them again.
In this guide:
β’ We will combine multiple sorted sequences;
β’ We will explore lazy processing of large data sources;
β’ We will configure comparison using the
β’ We will combine data sorted in reverse order.
This is especially useful when working with logs, files, and query results, where each source already provides data in the correct order.
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If you have multiple sources of data that are already sorted, you don't need to collect them into a single collection and sort them again.
heapq.merge() combines such sources into a single, ordered iterator.In this guide:
β’ We will combine multiple sorted sequences;
β’ We will explore lazy processing of large data sources;
β’ We will configure comparison using the
key argument;β’ We will combine data sorted in reverse order.
This is especially useful when working with logs, files, and query results, where each source already provides data in the correct order.
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The repository contains:
π«‘ Linters and formatters;
π«‘ Type checkers;
π«‘ Testing tools;
π«‘ Debugging and profiling tools;
π«‘ Package managers;
π«‘ Logging tools;
π«‘ Security tools;
π«‘ Documentation and package building tools.
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Python Lists: A Quick Reference π
* Definition: Lists are ordered, mutable collections that can contain various data types and duplicates.
* Creating Lists: Creating empty, numerical, mixed, nested lists, and lists with duplicates.
* Accessing List Elements: Accessing elements using positive and negative indexing, as well as slicing with
.
* Common Operations: Adding, inserting, concatenating, deleting, clearing, searching, sorting, and reversing lists.
* List Methods:
,
,
,
,
,
,
,
,
, and
.
* List Comprehension: Quickly creating lists using expressions and conditions.
* List vs. Tuple: Differences between mutable lists and immutable tuples.
* Important Points: Specifics of indexing, slicing, data types, and working with lists.
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* Definition: Lists are ordered, mutable collections that can contain various data types and duplicates.
* Creating Lists: Creating empty, numerical, mixed, nested lists, and lists with duplicates.
* Accessing List Elements: Accessing elements using positive and negative indexing, as well as slicing with
start:end:step
.
* Common Operations: Adding, inserting, concatenating, deleting, clearing, searching, sorting, and reversing lists.
* List Methods:
append()
,
insert()
,
extend()
,
remove()
,
pop()
,
clear()
,
index()
,
count()
,
sort()
, and
reverse()
.
* List Comprehension: Quickly creating lists using expressions and conditions.
* List vs. Tuple: Differences between mutable lists and immutable tuples.
* Important Points: Specifics of indexing, slicing, data types, and working with lists.
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1. Think Python β OβReilly
https://t.co/X7y3pX68IW
2. The Big Book of Small Python Projects
https://inventwithpython.com/bigbookpython/
3. Data Structures
https://opendatastructures.org/ods-python.pdf
4. Data Science Handbook
https://t.co/9aOLdiAbim
5. Data Analysis
https://wesmckinney.com/book/
6. Data Science
https://t.co/TusyBWGfrx
7. Machine Learning
https://t.co/7veOwESe8q
8. Statistics
https://t.co/waWDQylQpY
9. Think Bayes
https://allendowney.github.io/ThinkBayes2/
10. Clean Code in Python β Packt
https://t.co/OHhVgF1L65
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