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Using Generators in Python: The Why, The What, and The When
Python generators explained through 4Ws
Today, “what are Generators in Python” and “what are Generators used for in Python” are some of the most popular Python interview questions.
Often, Generator is considered as one of the slightly more intermediate concepts in Python. If you are new to learning Python, you may not have come across Generator before. Here’s a tip, it has something to do with the use of yield statements inside a function.
In this post, I am going to highlight some of the use cases, reasons, and advantages of using Generators in Python. In short, you should consider using Generators when dealing with large datasets with memory constraints.
Let’s dive a little bit deeper, shall we?
TL;DR
- Consider using Generator when dealing with a huge dataset
- Consider using Generator in scenarios where we do not need to reiterate it more than once
- Generators give us lazy evaluation
- They are a great way to generate sequences in a memory-efficient manner
Why Should I Care About Using Generators
Memory constraints
To understand why you should use Generators, we have to first understand that computers have a finite amount of memory (RAM). Whenever we are storing or manipulating variables, lists, etc., all of that is being stored inside our memory.
You might ask, why do computer programs store them in memory? Because it’s the fastest way for us to write and retrieve data.
Scenarios
Have you ever had to work with a list so large that you run into MemoryError? Perhaps, you have tried reading rows from a super large Excel (or .csv) file. All I remember was that performing these tasks is painfully slow or impossible.
What Is a Generator Function
To put it simply, a Generator function is a special kind of function that returns multiple items. The point here is that the items are returned one by one rather than all at once.
