Python’s Lazy Evaluation

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Today were going to talk about one of the most misunderstood concepts in Python lazy evaluation. But before we dive into that, let me ask you a question: have you ever heard someone say Python is slow?

You see, Python has this magical feature called lazy evaluation. It allows us to write code that looks like we’re doing a lot of work, but in reality, we’re just sitting back and relaxing while the computer does all the heavy lifting for us. And let me tell you, its pretty ***** cool!

So how exactly does this lazy evaluation thingy work? Well, imagine that you have a list with 10 million elements, but you only need to access the first one. In most programming languages, when you create a list like that, all of its elements are immediately loaded into memory. But not in Python!

In Python, we can use lazy evaluation to load only the element we need and leave the rest for later. This is done using a generator expression or a list comprehension with an if statement inside it. Let me show you what I mean:

# In Python, we can use lazy evaluation to load only the elements we need and leave the rest for later.
# This is done using a generator expression or a list comprehension with an if statement inside it.



# First, we define a list called "my_list" and use a list comprehension to populate it with numbers from 0 to 9999999.
# The if statement inside the list comprehension filters out any numbers that are not divisible by 2.
my_list = [x for x in range(10000000) if x % 2 == 0]

In this example, we’re creating a list with all the even numbers between 0 and 999,999. But instead of loading all those elements into memory at once, Python is only generating them as needed. This means that if you only need to access the first element (which happens to be 0), then Python will only load that one element into memory.

Now let’s compare this with a similar operation in C++:

// This script creates an empty vector of size 10 million and populates it with elements as needed.
// This is more efficient than loading all elements into memory at once.
// The vector is named "my_list" and contains integers.

vector<int> my_list; // create an empty vector
for (int i = 0; i < 5000000; ++i) { // loop through the first 5 million elements
    if (i % 2 == 0) { // check if the current index is even
        my_list.push_back(i * 2); // multiply the index by 2 and add it to the vector
    }
}
// The vector now contains 5 million elements, with only the even indices populated.
// This is more efficient than creating a vector of size 10 million and populating all elements, as it saves memory.

In this example, we’re creating a vector with all the even numbers between 0 and 499,999. But instead of using lazy evaluation like Python does, C++ is loading all those elements into memory at once! This means that if you only need to access the first element (which happens to be 0), then C++ will still load all 10 million elements into memory before generating the ones we actually care about.

It allows us to write code that looks like we’re doing a lot of work, but in reality, we’re just sitting back and relaxing while the computer does all the heavy lifting for us. And let me tell you, its pretty ***** cool!

But don’t take my word for it try it out yourself! Create a list with 10 million elements using lazy evaluation in Python and compare its performance to doing the same thing in C++. I bet you’ll be surprised at how much faster Python is!

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