"Introduction to Algorithms" 📘 - an outstanding university resource for everyone studying algorithms and computer science. 🎓💻

The book covers computational complexity, data structures, algorithms on graphs, dynamic programming, divide-and-conquer methods, greedy algorithms, randomized algorithms, and many mathematical foundations of modern computer science. 🧮📊🔍

What's particularly valuable here is the combination of mathematical rigor and practical algorithmic thinking. 🧠✨ This is one of those books that greatly change the approach to problem analysis, efficiency, and computing itself. 🚀🛠

An essential tool in the library of any developer and engineer working in the field of computer science. 🏗💾

https://www.cs.mcgill.ca/~akroit/math/compsci/Cormen%20Introduction%20to%20Algorithms.pdf 🔗

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Why is enumerate() used in Python? 🤔🐍

It allows you to simultaneously obtain the value of an element and its index when iterating through a list. 📊✨

This is more convenient and more readable than manually working with a counter. ✅🚀

for i, item in enumerate(items):
    print(i, item)

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# Cheat sheet on high-order functions in Python:

🐍 map() - applies a function to every element of an iterable and returns an iterator with the results
🔍 filter() - filters elements based on a condition and leaves only those for which the function returns True
🔄 reduce() - successively combines all elements of an iterable into a single value
⚡ lambda functions - anonymous functions for short expressions and working with map/filter/reduce
📦 iterable objects - lists, tuples, and other collections for processing
📚 functools - a Python module that contains reduce()
🧠 functional programming - an approach to programming through functions and data processing without changing the state

```python
# Example usage
from functools import reduce

# map
squared = map(lambda x: x**2, [1, 2, 3, 4])
print(list(squared))

# filter
evens = filter(lambda x: x % 2 == 0, [1, 2, 3, 4, 5])
print(list(evens))

# reduce
total = reduce(lambda x, y: x + y, [1, 2, 3, 4])
pr

int(total)```

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Why in Python it is better to check None using is 🐍

In Python, you should not write obj == None, even if sometimes it works the same ⚠️

The reason is that == calls the comparison method eq, which can be overridden in the class — and then the behavior becomes unpredictable 🎲

For example:

class Weird:
    def eq(self, other):
        return True  # always says "equal"

obj = Weird()

print(obj == None)  # True
print(obj is None)  # False

Here obj == None gives a false result due to custom logic 🤔

Instead:

obj is None

is checks the identity of the object and cannot be overridden. Since None is a singleton, such a check is always correct and predictable ✅

Conclusion: to check for None always use is None — it is the right and safe approach 🛡️

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