Benchmark result of lambda and itemgetter used in sort method in Python

benchmark_lambda_itemgetter.py

"""
Description: In this script, I plot the benchmark result of lambda compared
to itemgetter in the operator package. We sort a list of tuple (which has two
elements) to benchmark. The list element number ranges from 100 to 1000000.
"""
# import numpy as np
import matplotlib
import matplotlib.pyplot as plt

colors = ["#e6194b",
          "#3cb44b"]

# the number of tuple in list
N = [100, 1000, 10000, 100000, 1000000]

# time for lambda
t1 = [8.19, 81.4, 855, 14600, 172000]

# time for itemgetter
t2 = [5.09, 47, 498, 10100, 131000]

fig, ax = plt.subplots()
ax.semilogx(N, t1, marker='.',  label="lambda")
ax.semilogx(N, t2, marker='.', label="itemgetter")

ax.set_xlabel("The number of elements in list")
ax.set_ylabel("Time to sort the list " + r"(in $\mu s$)")

ax.set_xticks(N)
ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.legend()

# use the following method to turn off minor ticks
ax.tick_params(axis='x', which='minor', bottom='off')
# ax.minorticks_off()  # or use minorticks_off method
# turn grid on
ax.grid(linestyle='--')

plt.show()

itemgetter vs lambda

a = list(range(1000))
b = list(range(1000))
import random
random.shuffle(a)
random.shuffle(b)
c = list(zip(a, b))
%timeit c.sort(key=lambda x: x[1])
# 81.4 µs ± 433 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

random.shuffle(c)
from operator import itemgetter
%timeit c.sort(key=itemgetter(1))
# 47 µs ± 202 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

I have also tested the performance (run time in µs) of this two method for various list size.

+-----------+--------+------------+
| List size | lambda | itemgetter |
+-----------+--------+------------+
| 100       | 8.19   | 5.09       |
+-----------+--------+------------+
| 1000      | 81.4   | 47         |
+-----------+--------+------------+
| 10000     | 855    | 498        |
+-----------+--------+------------+
| 100000    | 14600  | 10100      |
+-----------+--------+------------+
| 1000000   | 172000 | 131000     |
+-----------+--------+------------+

https://gist.github.com/jdhao/5569afa7efc13abf75a5baf18e7c29d6 for graph