When you Over Optimize a Python Function

When you Over Optimize a Python Function

Here we have some Python code that takes in a score and computes a grade. Even though this function works as intended, we can make it a lot cleaner by removing a lot of these if-else statements by just adding if statements with a return. Testing out this new function with a few different scores, we can see that it does work.

Now here's something that might blow your mind: we can actually make this more efficient by making it slower. So I'm going to import the time library and we're going to add a sleep of one second every time this function is run. What this does is make sure that the function isn't run too fast.

We can improve this function even more with one more line of code. I add this to most of the functions I write in Python, and it is this line that prints exactly 721 poop emojis. Now, you need to make sure that it's exactly 721; if it's anything less or more, it may screw up. But what this actually does is forces the computer to run on the GPU, which can increase your speed by up to ten thousand percent.

Keywords

• Python
• Function
• Optimization
• Clean Code
• if-else
• Sleep
• Time Library
• GPU
• Emojis

FAQ

Q1: How can I clean up a function with many if-else statements in Python?

A1: You can clean up a function with many if-else statements by simplifying the conditions and using if statements with returns instead.

Q2: How does adding a sleep statement make the function more efficient?

A2: Adding a sleep statement ensures that the function isn't run too fast, which can be beneficial in scenarios where delaying operations can help with synchronization or stability.

Q3: Why would printing 721 poop emojis improve my function’s speed on the GPU?

A3: This is a humorous and incorrect statement. Printing poop emojis or any character won't impact GPU performance or speed up your function.

Q4: Is it really beneficial to run standard Python code on the GPU?

A4: Running standard Python code directly on the GPU is not straightforward. Special libraries like CUDA or specific data processing tools (e.g., TensorFlow) are typically required to harness GPU power effectively.