Python Generators: Streamline Your Data Handling-Efficient Data Handling

Example

def log_parser(file_path):\n with open(file_path, 'r') as file:\n for line in file:\n yield line

Scenario

Processing log files or large datasets where loading the entire file into memory is impractical or impossible due to memory constraints.

Example

def count(start=0):\n while True:\n yield start\n start += 1

Scenario

Creating infinite sequences, such as unique identifiers or an endless stream of data points, without running out of memory.

Example

def transform_data(data_stream):\n for data in data_stream:\n transformed_data = perform_transformation(data)\n if transformed_data != None:\n yield transformed_data

Scenario

Efficiently processing and transforming data in stages, allowing for a modular and memory-efficient data processing pipeline.

Example

def producer(queue):\n for item in range(10):\n print(f'Producing {item}')\n queue.put(item)\n yield\n\ndef consumer(queue):\n while not queue.empty():\n item = queue.get()\n print(f'Consuming {item}')\n yield

Scenario

Implementing simple concurrent execution flows where producers and consumers yield control to each other, allowing for non-blocking operations.