numpy datetime64 comparison slower than pandas Timestamp

Solution for numpy datetime64 comparison slower than pandas Timestamp
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I’ve been quite surprised to find that comparing scalar numpy datetime64 objects is significantly slower than comparing pandas Timestamp objects. My understanding is that internally pd.Timestamp is using datetime64[ns] so I’m a bit baffled as to how pd.Timestamp is faster in this case.

Here’s my simple attempt at comparing the performance of doing a less than comparison.

import pandas as pd
import numpy as np

# create datetime64 and timestamp objects
dt1 = np.datetime64("1900-01-01", "ns")
dt2 = np.datetime64("2020-01-01", "ns")
ts1 = pd.Timestamp("1900-01-01")
ts2 = pd.Timestamp("2020-01-01")

# time datetime64 comparisons
%% timeit
for _ in range(1000000):
    _ = dt1 < dt2
# NOTE: 3.07 s +/- 796 ms per loop

# time Timestamp comparisons
%%timeit
for _ in range(1000000):
    _ = ts1 < ts2
# NOTE: 125 ms +/- 6.2 ms per loop

It seems that Pandas is approximately 25x faster here. I’ve tried looking at the source code but am not sufficiently familiar with C or cython to understand what Pandas might be doing to achieve such an improvement. I did look at this somewhat related question but it’s quite old and the timings there were not consistent with what I found (quite possibly due to updates to the libraries over the last 6 years).