python del vs pandas drop

Question

I know it might be old debate, but out of pandas.drop and python del function which is better in terms of performance over large dataset?

I am

Answer 1

tested it on a 10Mb data of stocks, got the following results:

for drop with the following code

t=time.time()
d.drop(labels="2")
print(time.time()-t)

0.003617525100708008

for del with the following code on the same column:

t=time.time()
del d[2]
print(time.time()-t)

time i got was:

0.0045168399810791016

reruns on different datasets and columns didn't make any significant difference