Does loc in pandas use vectorised logic or a for loop?

Question

I access rows in pandas with the loc function as below:

pdf.loc[pdf.a>2]

Is this vectorised? Is it better than using numpy

pdf[pdf.a>2]

i think loc[] is better then a for loop when you do a conditional update based on columns. — anky
– anky, Commented Apr 26, 2019 at 20:03
numpy will be faster, but then you lose the indices, which are super useful and inherent to pandas. pdf.to_numpy()[np.where(pdf.a > 2)[0]] should be faster than .loc — ALollz
– ALollz, Commented Apr 27, 2019 at 2:14

cs95 · Accepted Answer · 2019-04-26 20:01:36Z

1

This timing suggests there is no slow down with loc

testa = pd.DataFrame(np.arange(10000000),columns =['q'])
%timeit testb = testa.loc[testa.q>6] 
%timeit testc = testa[testa.q>7]

1 loop, best of 3: 207 ms per loop
1 loop, best of 3: 208 ms per loop

edited Apr 26, 2019 at 20:01

cs95

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answered Apr 26, 2019 at 19:52

Chogg

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Chogg Over a year ago

Reading a bit more into this, vectorisation just means that the for loop is done at the c level. Presumably this can be done in loc. One other thing I'm confused about is why the loc command uses [ ] rather than ( ). Presumably this implies something about what loc is doing that I have never understood.

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