What are lambda functions in Python?

Lambda functions — are anonymous functions, which are created using the lambda keyword. They can accept any number of arguments, but contain only one expression, the result of which is automatically returned.

They are most often used for short operations, for example during sorting, filtering or data processing.

# Example:
sorted_data = sorted(data, key=lambda x: x[1])
filtered_data = list(filter(lambda x: x > 0, numbers))

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👍 Fluent Python — practical examples from one of the best Python books!

Here, the language's capabilities are explained, which many only know superficially. The material focuses not on basic syntax, but on a deep understanding and proper use of its capabilities in real projects. There are many examples of working with objects, collections, functions, decorators, generators, async code, and Python's internal logic.

I'll leave a link: https://github.com/fluentpython/example-code-2e

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What is the difference between copy() and deepcopy() in Python?

copy() creates a shallow copy of the object: nested elements remain shared between both copies. In contrast, deepcopy() recursively copies all nested objects, making the new structure completely independent. This is particularly important when working with mutable data

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When applying multiple filters to a series in Pandas, it's better to break the condition into several lines:

s = pd.Series([10, 15, 20, 25, 30])

s.loc[
    (s > 20) &
    (s % 2 == 1)
]

Such code is easier to read, write, and maintain. 🛠️✨

As a result, the value:
25
will be selected,
since it is both greater than 20 and an odd number. 🎯

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Learn Python in a day with the IDE used for vibe coding Replit+data types: Python string , Python list Python with+more…

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Cheat sheet on Python Data Types 🐍

Python Data Types — the main data types in Python.

🔢 Numbers — numerical types (int, float, complex)
⚖️ Bool — logical values (True, False)
📝 String — text strings
📋 List — mutable ordered collections
🔒 Tuple — immutable ordered collections
🎲 Set — unique unordered elements
🗂️ Dict — collections of "key-value" pairs.

Helps to quickly orient yourself in data types and choose the appropriate structure for storing information 💡

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Python is like New Year. 🎉 You can pass a tuple to startswith and endswith. 💻🐍

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Variables in Python are stickers, not boxes.

A variable doesn't store a value, but points to it. If you change a list, both variables will see the change. This makes sense with stickers. But with boxes, it's magic: two boxes mysteriously change simultaneously.

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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 🐍✨

# 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? 🐍

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)


🔥 vars()` — a rather underrated tool for quickly analyzing the state of objects during execution.

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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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3 quick ways to merge dictionaries in Python 🐍

1️⃣ The operator | (Python 3.9+) — 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)

2️⃣ The in-place update operator |= (Python 3.9+) — if you need to modify the first dictionary in place.

dict1 |= dict2

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**Today we will examine __call__ — a data filter object!** 🧠

It allows a class instance to work as a function, preserving the state and filtering rules. ⚙️

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]

🔥 So, __call__ turns an object into a "smart function" with memory and customizable logic. 💡

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collections.Counter — counting elements in a single line. 📊

Counting elements without loops with Counter 🔄

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. 🚀

🛠 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. 💻

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Search for a substring in Python 🐍

In this example, two simple ways of finding a substring in a string are shown, which allow to solve the task without unnecessary code 💻

# 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 🐍🔍

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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 (*). ⭐️

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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