Fix slow loops in python

this question is effectively the same as asking for any form of performance tuning advice. python assignment help.

That is to say that they only time you’re going to be aware of performance limitations in your code is when the operations are performed in some sort of loop (or applied across some expanse or data).

So what makes a loop in Python any slower than it would be in native machine code?

Python is a very high level and dynamic language. It’s variables are references to objects, and these are mostly very general purpose and any reference might be repeatedly changed to objects of entirely different types (classes) throughout the execution of any loop. These changes might occur as side effects of any function or method called within any iteration of any loop … including indirectly… through arbitrary levels of nesting.

Handling this generality comes at a cost, That cost is minimal for most code over most common datasets. But it’s the main thing that makes Python “slow” compared to what’s possible with compiled native code, and even what’s possible with statically typed scripting and some JIT (just-in-time compilation technologies).

There are a number of tweaks you can make to generic Python code to tune some performance.

For example, you can use “properties” (via a defined set of __slots__) in your classes, or naked tuples in lieu of generic objects and dictionaries. These will mitigate some of that object dispatch overhead on those objects being used within your loops. You can bind some objects to local variables (outside of a loop, but within the scope in which that loop will be executed. You can avoid certain performance pitfalls (such as inadvertently instantiating new strings and incurring heavy copying costs within a loop, as opposed to appending to a list and using the string ‘’.join() method after the loop).

These tend to small tweaks and there are about a bajillion of them. Far too many to cover here and now.

The deeper approach is to use smarter classes of objects and to use them in ways which obviate processing by explicit looping.

For example, when you use NumPy and Pandas, you can apply operations to whole datasets (NumPy multidimensional arrays and Pandas DataFrames). These implicitly execute loops which are natively compiled into the libraries. But you’re not explicitly looping over their contents in Python. You’re “broadcasting” these operations over the dataset.