Generating sequences with Python yield keyword
Jul 16, 2025 am 04:50 AM
The yield keyword is used to create generators, generate values on demand, and save memory. 1. Replace return to generate finite sequences, such as Fibonacci sequences; 2. Implement infinite sequences, such as natural sequences; 3. Process big data or file readings, and process them line by line to avoid memory overflow; 4. Note that the generator can only traverse once, and can be called by next() or for loop.
If you want to know how to use Python's yield keyword to generate sequences, its biggest function is to help you create a generator to generate values as needed when needed, rather than store all data in memory at once.
This is especially useful when dealing with large amounts of data or infinite sequences.
Let’s take a look at several common usage scenarios and writing methods.
Create a simple numerical sequence with yield
The most common way is return a sequence instead. The difference is that when a function encounters yield , it will pause execution and retain the current state, and then continue to execute after the next call.
For example, you want to write a function that generates a Fibonacci sequence:
def fib_sequence(n):
a, b = 0, 1
while a < n:
yield a
a, b = b, abThis way you can get the value step by step like this:
for num in fib_sequence(100):
print(num)This method is much more memory-saving than creating a complete list, especially when you only need to iterate over iterate.
yield implements an infinite sequence generator
Sometimes you may want to generate an "infinite" sequence, such as a natural sequence that starts with 1 and is added to 1. In this case, it will definitely not work if you use a normal list, but it is easy to use yield .
For example:
def infinite_numbers():
num = 1
While True:
yield num
num = 1You can take the first few numbers like this:
counter = infinite_numbers() print(next(counter)) # 1 print(next(counter)) # 2
Of course, when using it in actual use, you should pay attention to controlling the number of cycles, otherwise it will enter a dead cycle.
yield handles big data or file reading
In addition to mathematical sequences, yield is also useful when dealing with large files or data streams. For example, if you read a log file of several GB, it is obviously inappropriate to load it into memory at one time, you can use the generator to read it line by line.
Example:
def read_large_file(file_path):
with open(file_path, 'r') as f:
for line in f:
yield line.strip()In this way, you only read one line at a time and will not take up too many resources:
for log_line in read_large_file('huge_log.txt'):
process(log_line) # Assume this is your processing logicThis method is very practical when doing data analysis, ETL or log processing.
A few tips: Where yield is easy to ignore when using
- The yield function returns a generator object , not a direct result, and needs to be triggered with
next()orforloop. - The generator can only be traversed once, and you have to call the function again to create a new object.
- If you want to get all the results from the generator, you can convert it into a list:
list(my_generator()) - yield can be used with the send() method to achieve bidirectional communication, but this is an advanced usage and is generally not commonly used.
Basically that's it. Using yield well can make your code more efficient and elegant, especially when dealing with sequences, streaming data or large data sets.
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