Generators in Python

Why use generators for processing large data?

Generators are Python constructs that yield values lazily, which means they don’t load everything into memory. This is ideal for Data Streaming large datasets, such as:

• Reading files line by line
• Processing massive CSV or JSONL logs
• Streaming from APIs or sockets
• Working with NLP corpora or web-scraped text

Benefits:

• Memory efficiency
• Composability of operations (via generator chains)
• Useful in I/O-heavy or parallelized workflows

Example:

def read_lines(filepath):
    with open(filepath, 'r') as f:
        for line in f:
            yield line.strip()
 
for line in read_lines("large_file.txt"):
    process(line)  # custom processing function

Exploratory Questions:

• Can we apply the same strategy for batched ML inference pipelines?
• When should we materialize a generator vs. keep it lazy?
• What limitations arise when debugging generators?