Today I want to introduce you to two very useful features of the python language: list derivatives and generator expressions. Both of these features allow you to create a sequence with one concise line of code, without writing loops or functions. But there are some important differences between them, so let's take a look.
list-deductive formula
A list derivation is an expression surrounded by square brackets that generates a list based on one or more iterators. For example, if you want to generate a list containing square numbers from 1 to 10, you could write it like this:
squares = [x**2 for x in range(1, 11)] print(squares) # [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
You can also add conditional judgments to list derivatives to filter out some unwanted elements. For example, if you only want to generate even squares, you could write it like this:
even_squares = [x**2 for x in range(1, 11) if x % 2 == 0] print(even_squares) # [4, 16, 36, 64, 100]
You can also use multiple iterators in list derivatives to generate Cartesian products. For example, if you want to generate all possible combinations of two lists, you could write it like this:
colors = ["red", "green", "blue"]
shapes = ["circle", "square", "triangle"]
combinations = [(c, s) for c in colors for s in shapes]
print(combinations)
# [('red', 'circle'), ('red', 'square'), ('red', 'triangle'), ('green', 'circle'), ('green', 'square'), ('green', 'triangle'), ('blue', 'circle'), ('blue', 'square'), ('blue', 'triangle')]The advantages of list derivation are that it creates a list quickly and the syntax is concise and easy to read. But it also has the disadvantage that it stores all the elements in memory at once, which can take up a lot of space, especially if the generated list is large or infinite. This is where we can use generator expressions to solve this problem.
A generator expression is an expression surrounded by parentheses that is very similar to a list derivation, except that instead of immediately generating a list, it returns a generator object. A generator object is a special kind of iterator that produces the next element on demand, without having to calculate and store all the elements in advance. For example, if you wanted to generate a generator object containing the square numbers from 1 to 10, you could write it like this:
squares_gen = (x**2 for x in range(1, 11)) print(squares_gen) # <generator object <genexpr> at 0x000001F7E8C6D740>
Note that what is printed out here is not a list, but a generator object. If you want to get the elements in the generator object, you can use the next() function or a for loop to traverse it. Example:
print(next(squares_gen))
# 1
print(next(squares_gen))
# 4
for square in squares_gen:
print(square)
# 9
# 16
# ...Note that each time the next() function is called or the generator object is traversed, it dynamically computes the next element and remembers the current state.
generator expression (math.)
The syntax of generator expressions is basically the same as list derivatives, except that you use parentheses instead of square brackets. You can also add conditional judgments and multiple iterators to a generator expression, just like list derivatives. Example:
even_squares_gen = (x**2 for x in range(1, 11) if x % 2 == 0) combinations_gen = ((c, s) for c in colors for s in shapes)
The advantage of a generator expression is that it saves memory space because instead of creating a list all at once, it generates the next element on demand. This way, you can handle very large or infinite sequences without worrying about memory overflow. For example, if you wanted to generate an infinite Fibonacci sequence, you could write it like this:
def fib():
a, b = 0, 1
while True:
yield a
a, b = b, a + b
fib_gen = (x for x in fib())Note that here we use a generator function to define the Fibonacci series and then wrap it in a generator expression. A generator function is a function that uses a yield statement to return a value, and it also returns a generator object. Both generator functions and generator expressions are two different ways of writing generators, and they can both be used to create sequences of inert evaluations.
Another advantage of generator expressions is that it improves performance because it avoids unnecessary calculations and intermediate variables. For example, if you want to calculate the sum of all the elements in a sequence, you can write it like this:
total = sum([x**2 for x in range(1, 11)])
But this creates a list and then sums the elements of the list, which wastes time and space. If you use a generator expression, you can write it like this:
total = sum(x**2 for x in range(1, 11))
Instead of creating a list, this will pass the number of squares of each element directly to the sum() function, which will be faster and more space-efficient. In fact, many built-in functions can take a generator as an argument, such as min(), max(), all(), any(), and so on. You can also pass a generator to the list() function or the set() function to convert to a list or a set.
summarize
I can use a restaurant example as an analogy for list derivatives and generator expressions. Suppose you're a restaurant owner, and you want to give your guests a menu and let them choose their favorite dishes. You have two ways to create a menu:
One way is to use list derivation, which means that all the dishes are made ahead of time and placed on a large plate for the guests to freely pick from. The advantage of this is that guests can see all the dishes and can also take them multiple times, and it's fast. But the downside of this is that you need to take up a lot of kitchen space and ingredients, and some of the dishes may get cold or spoiled, resulting in waste.
The other is to use a generator expression, which means that you make a dish on the spot and deliver it to the guest's table according to their needs. The advantage of this is that you don't need to take up a lot of kitchen space and ingredients, and each dish is fresh and not wasted. But the downside of this is that the guest can't see all the dishes and can't take them multiple times, and it can be a little slower.
So, you should choose the right way to make your menu depending on the situation. If you have a lot of guests and they all like to eat different dishes, then you might be better suited to use list derivatives. If you have only a few guests and they all like to eat fresh dishes, then you might be better suited to use generator expressions.
In summary, both list derivatives and generator expressions are very useful features that allow you to create a sequence with one concise line of code. List derivatives are good for sequences that need to be traversed or manipulated multiple times, while generator expressions are good for sequences that need to be traversed only once or processed very large or infinite sequences. You should choose the right way to improve the efficiency and readability of your code depending on the scenario.
To this point this article on the Python features of the list of derivatives and generator expressions detailed article is introduced to this, more related Python list of derivatives and generator expressions, please search for my previous posts or continue to browse the following related articles I hope you will support me in the future more!