When the python interpreter encounters a yield statement in a function, it knows that the function is a generator function. If you are interested in learning about list comprehensions take a look at my post about them here. The syntax is very similar to list comprehensions, the only difference being that ( ) round brackets are required for a generator expression, as opposed to the brackets in a list comprehension.
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In cases where a full generator function may be overly verbose for something like a simple sequence generator, there is the option to create a generator using a generator expression. Fibonacci SequenceĪ Fibonacci sequence in a memory-constrained environment is another great candidate to solve using a generator function as loading all of the values in the generated Fibonacci sequence into memory can be avoided. Infinite Number SequenceĪ generator function is a perfect candidate to solve the problem of generating an infinite sequence of numbers, as the state of the sequence generation can be neatly encapsulated within the one function definition. Sequence GenerationĪnother common and popular use of generator functions is to generate large or potentially infinite sequences of values. This will open the file and yield chunks of data equal to chunk_size bytes. Now in the case of a file that needs to be read in n sized chunks of bytes, the following generator function could be used. It can simply be achieved as in the following example. The built-in open function in Python already lazily evaluates the file line by line so for reading a large file in one line at a time there is no requirement to implement a generator function. Note: When using a 32-bit version of Python, only 2GB of memory will be addressable by the Python process so no matter how much physical memory is available, a MemoryError will be raised as soon once 2GB has been consumed by Python.ĭepending on the type and content of the file it is likely that you will want to read it in either line by line ( common for a text file), or in chunks of bytes ( common for a binary file). When dealing with large files, this can be problematic either by consuming excessive volumes of memory or in the worst-case scenario consuming all the available memory, raising a MemoryError and ultimately crashing the application. Likely the most common usage of generator functions is when reading large files, reading a file in Python it is common to read the entire contents of the file into memory at once. The difference here is that the generator produces one value at a time rather than loading all 50,000,000 integers into memory up-front as a list would. The generator returned by basic_generator is assigned to the variable gen which can then be iterated over like a normal iterator.
This will return a generator that will yield integers up to 50,000,000 one at a time. In the below ( contrived) example, the generator function basic_generator is defined.
They are defined as a normal function, with the addition of the yield statement in place of the familiar return. Generator functions revolve around the yield statement ( more on that later). Unlike a regular iterator in Python however, the entire set of data is not loaded into memory up front. Generator's at their core are functions that return an iterator, meaning that the generator can be looped over. This is especially useful when working with things like very large files in an efficient and pythonic way. Generators provide a means of processing data without loading the entire data-source into memory first. Have you ever had to process a large dataset and encountered the dreaded MemoryError in Python, or simply needed to limit the memory consumption of a large dataset? If the answer to that is yes, consider using a generator.
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