What useful functions are there in the functools module?
Among the most popular ones: lru_cache for caching results, partial for partial application of arguments, wraps for preserving metadata in decorators and cmp_to_key for transforming a comparison function into a sorting key πβ¨
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Among the most popular ones: lru_cache for caching results, partial for partial application of arguments, wraps for preserving metadata in decorators and cmp_to_key for transforming a comparison function into a sorting key πβ¨
# functools module usage
from functools import lru_cache, partial, wraps, cmp_to_key
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Do you know that Python allows you to get all the object's attributes with one function without additional code? π
When you need to quickly check the state of an object, many manually access the attributes or dig into
But for this, Python already has a built-in function
It returns a dictionary of all the object's attributes, including the current state of the instance.
This is useful for debugging, logging, serialization, ORM, and analyzing the runtime state of objects.
Also,
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When you need to quickly check the state of an object, many manually access the attributes or dig into
__dict__.But for this, Python already has a built-in function
vars().vars(obj)
It returns a dictionary of all the object's attributes, including the current state of the instance.
{'x': 10}This is useful for debugging, logging, serialization, ORM, and analyzing the runtime state of objects.
print(vars(user))
Also,
vars() works with modules, classes, and any objects with dict.
vars(module)
π₯ vars()` β a rather underrated tool for quickly analyzing the state of objects during execution.#Python #Coding #Programming #Debugging #TechTips #DevLife
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Python there is a small convenience that not everyone knows about: in startswith and endswith you can pass an tuple of values directly
This allows you to check multiple options with one call, without long conditions and unnecessary code
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This allows you to check multiple options with one call, without long conditions and unnecessary code
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3 quick ways to merge dictionaries in Python π
1οΈβ£ The operator | (Python 3.9+) β the most modern and elegant way. Creates a new dictionary.
2οΈβ£ The in-place update operator |= (Python 3.9+) β if you need to modify the first dictionary in place.
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1οΈβ£ The operator | (Python 3.9+) β the most modern and elegant way. Creates a new dictionary.
dict1 = {'a': 1, 'b': 2}
dict2 = {'b': 99, 'c': 4}
combined = dict1 | dict2
# Result: {'a': 1, 'b': 99, 'c': 4} (values of the second dictionary replace the first)2οΈβ£ The in-place update operator |= (Python 3.9+) β if you need to modify the first dictionary in place.
dict1 |= dict2
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**Today we will examine
It allows a class instance to work as a function, preserving the state and filtering rules. βοΈ
Let's create a filter for numbers that only passes even ones and strictly greater than a specified threshold:
Let's use the filter in practice:
Now each instance can have its own rules:
π₯ So,
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__call__ β a data filter object!** π§ It allows a class instance to work as a function, preserving the state and filtering rules. βοΈ
Let's create a filter for numbers that only passes even ones and strictly greater than a specified threshold:
class EvenFilter:
def __init__(self, threshold):
self.threshold = threshold
def __call__(self, numbers):
return [n for n in numbers if n % 2 == 0 and n > self.threshold]
Let's use the filter in practice:
f = EvenFilter(5)
nums = [1, 4, 6, 7, 10]
print(f(nums)) # [6, 10]
Now each instance can have its own rules:
f2 = EvenFilter(8)
print(f2(nums)) # [10]
π₯ So,
__call__ turns an object into a "smart function" with memory and customizable logic. π‘#Python #DataScience #Programming #Coding #Tech #HelloEncyclo
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collections.Counter β counting elements in a single line. π
Counting elements without loops with Counter π
Do you need to count how many times each word appears in a text or how many duplicates there are in a list? Don't reinvent the wheel with for loops and dictionaries. The built-in collections module will do everything for you. π
π Code:
Ideal for basic data analysis and solving tasks on LeetCode. π»
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#Python #DataScience #Coding #Programming #LearnToCode #TechSkills
Counting elements without loops with Counter π
Do you need to count how many times each word appears in a text or how many duplicates there are in a list? Don't reinvent the wheel with for loops and dictionaries. The built-in collections module will do everything for you. π
π Code:
from collections import Counter
words = ["apple", "banana", "apple", "cherry", "banana", "apple"]
word_counts = Counter(words)
print(word_counts)
# Output: Counter({'apple': 3, 'banana': 2, 'cherry': 1})
# Bonus: the top 2 most frequent elements
print(word_counts.most_common(2))
# Output: [('apple', 3), ('banana', 2)]
Ideal for basic data analysis and solving tasks on LeetCode. π»
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Search for a substring in Python π
In this example, two simple ways of finding a substring in a string are shown, which allow to solve the task without unnecessary code π»
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In this example, two simple ways of finding a substring in a string are shown, which allow to solve the task without unnecessary code π»
# Example implementation
def find_substring(text, sub):
return text.find(sub)
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What's the difference between is and == in Python?
The == operator checks whether the values of two objects are equal. In contrast, is determines whether variables refer to same object in memory. That is, == compares the content, while is checks the identity of the objects ππ
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The == operator checks whether the values of two objects are equal. In contrast, is determines whether variables refer to same object in memory. That is, == compares the content, while is checks the identity of the objects ππ
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π‘ Replacing if-else with Match-Case
Starting with Python 3.10, we have a powerful tool: Structural Pattern Matching (match-case). This is not just an analog of switch-case from other languages; it's much more flexible. π
Imagine you're writing a command handler for a bot. π€
β How NOT to do it:
β‘ How to do it properly:
The code looks like a clear table, and your eye doesn't get caught up in a bunch of
You can pass data structures in the
It's easy to combine cases. π§©
#Python #Programming #MatchCase #CodingTips #Python310 #Developer
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Starting with Python 3.10, we have a powerful tool: Structural Pattern Matching (match-case). This is not just an analog of switch-case from other languages; it's much more flexible. π
Imagine you're writing a command handler for a bot. π€
β How NOT to do it:
def handle_command(command):
if command == "start":
return "Hello! I'm a bot."
elif command == "help":
return "Here's a list of available commands..."
elif command == "stop":
return "Goodbye!"
else:
return "Unknown command."
β‘ How to do it properly:
def handle_command(command):
match command:
case "start":
return "Hello! I'm a bot."
case "help":
return "Here's a list of available commands..."
case "stop":
return "Goodbye!"
case _: # The underscore symbol catches everything else (default)
return "Unknown command."
The code looks like a clear table, and your eye doesn't get caught up in a bunch of
elif statements. π§You can pass data structures in the
case statements and check their structure and content on the fly. πIt's easy to combine cases. π§©
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β¨ Unpacking the remaining elements π§©
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 (*). βοΈ
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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 (*). βοΈ
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.
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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.
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