Python Devs Rejoice:

Software engineer Timothy was struggling with a Python code that ran out of memory while simulating a large dataset of 100 million random sensor readings. His code, which used a list to store the data, was causing his computer to freeze. Margaret, an experienced programmer, explained that the issue was due to the "Eager Baker" approach, where Python tries to build and store all the data in memory at once. She introduced Timothy to the "Lazy Baker" approach, which uses generators to produce data on demand, one item at a time, using the yield keyword. This approach significantly reduces memory usage, with a generator using only 104 bytes compared to a list using 8 megabytes for a million items. By using generators, Timothy was able to process his large dataset efficiently, and the fan on his laptop quieted down. This approach is particularly useful for handling massive datasets and infinite streams, and is a key concept in efficient programming.