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Python generator functions are an incredibly useful feature that allows developers to create iterators in a more elegant and efficient way. In essence, generator functions are functions that use the “yield” keyword to return values one at a time, rather than returning a full list or other collection.
This makes them particularly useful for large datasets or situations where you only need to process a portion of a dataset at a time. Here are three real-world examples of how you can use Python generator functions to your advantage.
1. Processing large files
When working with large files that won’t fit in memory, it’s often necessary to read them in chunks. One way to do this is to use a generator function to read a file one line at a time. Here’s an example:
def read_large_file(file): with open(file) as f: while True: line = f.readline() if not line: break yield line.strip()
In this example, the function reads a file one line at a time using a while loop. Each line is stripped of whitespace and returned using the “yield” keyword. This allows you to iterate over the lines in the file without having to load the entire file into memory at once.
2. Generating prime numbers
Generating a list of prime numbers can be a computationally expensive task, particularly when you’re dealing with large numbers. A generator function can be used to generate prime numbers on the fly, as needed. Here’s an example:
def generate_primes(n): primes =  for num in range(2, n): is_prime = True for prime in primes: if num % prime == 0: is_prime = False break if is_prime: primes.append(num) yield num
In this example, the function generates prime numbers up to a given number “n”. It uses a list to keep track of the primes it has generated so far and checks each new number against the existing primes to determine whether it is a prime. If a number is determined to be prime, it is added to the list and returned using the “yield” keyword.
3. Processing streaming data
When dealing with streaming data, it’s often necessary to process it as it comes in, rather than waiting for the entire dataset to arrive before processing it. A generator function can be used to process streaming data in real-time. Here’s an example:
def process_stream(stream): while True: data = stream.get_data() if not data: break result = process_data(data) yield result
In this example, the function uses a while loop to continually read data from a streaming source. If there is no more data, the loop exits. Otherwise, the data is processed using a separate function and the result is returned using the “yield” keyword. This allows you to process streaming data as it arrives, rather than waiting for the entire dataset to be available.
In conclusion, Python generator functions are a powerful tool that can be used to process large datasets, generate complex data structures, and handle streaming data in real-time. By using generator functions, you can write more efficient, more elegant code that can handle a wide range of data processing tasks.