In a numpy array is dtype np.datetime64 or pandas object datetime.date faster?

In a numpy array is dtype np.datetime64 or pandas object datetime.date faster?

I want to filter through a date array the fastest possible? Should I use pandas or numpy? If I use numpy what data type should I use?

I want to filter through a date array the fastest possible? Should I use pandas or numpy? If I use numpy what data type should I use?

I have a large dataframe where I need to loop through a range and pull the data from a date range. It's taking longer than I need since I'm also testing the data and re-running it is getting quite tedious.

*NOTE: After looking for questions that answer this, I wasn't able to find a suitable answer so I posted this in case anyone else is in a similar situation. The answer is below.

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Python NumPy Tutorial for Beginners

Learn the basics of the NumPy library in this tutorial for beginners. It provides background information on how NumPy works and how it compares to Python's Built-in lists. This video goes through how to write code with NumPy. It starts with the basics of creating arrays and then gets into more advanced stuff. The video covers creating arrays, indexing, math, statistics, reshaping, and more.

Python NumPy Tutorial for Beginners

NumPy, which stands for Numerical Python, is a library consisting of multidimensional array objects and a collection of routines for processing those arrays. Using NumPy, mathematical and logical operations on arrays can be performed.