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I've written a bunch of code on the assumption that I was going to use Numpy arrays. Turns out the data I am getting is loaded through Pandas. I remember now that I loaded it in Pandas because I was having some problems loading it in Numpy. I believe the data was just too large.
Therefore I was wondering, is there a difference in computational ability when using Numpy vs Pandas?
If Pandas is more efficient then I would rather rewrite all my code for Pandas but if there is no more efficiency then I'll just use a numpy array...
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The indexing of NumPy arrays is faster than that of the Pandas Series.
Numpy is memory efficient. Pandas has a better performance when a number of rows is 500K or more. Numpy has a better performance when number of rows is 50K or less. Indexing of the pandas series is very slow as compared to numpy arrays.
It provides high-performance, easy to use structures and data analysis tools. Unlike NumPy library which provides objects for multi-dimensional arrays, Pandas provides in-memory 2d table object called Dataframe. It is like a spreadsheet with column names and row labels.
numpy consumes less memory compared to pandas. numpy generally performs better than pandas for 50K rows or less.
There can be a significant performance difference, of an order of magnitude for multiplications and multiple orders of magnitude for indexing a few random values.
I was actually wondering about the same thing and came across this interesting comparison: http://penandpants.com/2014/09/05/performance-of-pandas-series-vs-numpy-arrays/
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