What are some ways to optimize Python code for memory usage?

In the first example, we are going to optimize Python code for memory usage by using generators instead of lists. When working with large datasets, using generators can significantly reduce memory consumption. Generators produce items one at a time and only when needed, unlike lists which store all items in memory at once. 
# Using a list to generate Fibonacci numbers
def fibonacci_list(n):
    fib_list = [0, 1]
    for i in range(2, n):
        fib_list.append(fib_list[i-1] + fib_list[i-2])
    return fib_list

# Using a generator to generate Fibonacci numbers
def fibonacci_generator(n):
    a, b = 0, 1
    for _ in range(n):
        yield a
        a, b = b, a + b

# Comparing memory usage
import sys
n = 1000000
print(sys.getsizeof(fibonacci_list(n)))  # Memory usage with list
print(sys.getsizeof(fibonacci_generator(n)))  # Memory usage with generator

In this example, we have two functions to generate Fibonacci numbers - one using a list and the other using a generator. By comparing the memory usage of both approaches, we can see the benefits of using generators for memory optimization. The generator approach consumes significantly less memory compared to the list approach when dealing with large datasets. In the second example, we are going to optimize Python code for memory usage by using data structures like sets and dictionaries instead of lists. Sets and dictionaries offer faster lookups and require less memory compared to lists when storing unique or key-value pairs of data. 
# Using a list to store unique elements
unique_list = [1, 2, 3, 4, 5, 1, 2, 3]
unique_list = list(set(unique_list))

# Using a dictionary to store key-value pairs
data = {'name': 'John', 'age': 30, 'city': 'New York'}

In this example, we demonstrate how to use sets to store unique elements and dictionaries to store key-value pairs efficiently. By utilizing these data structures instead of lists, we can optimize memory usage and improve the performance of our Python code. Sets automatically remove duplicate elements, while dictionaries provide fast access to values based on keys, making them ideal for memory-efficient data storage.

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