Performance difference between append and insert in Python

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Goal

This post aims to compare the performance between append and insert in Python. The performance comparison is simply done by the piece of code that counts a number, append it to a list, and then reverse it.

We will see the significant difference between two codes: one using append is linear and another using insert is quadratic run time growth as below.

Reference

Libraries

In [26]:
from timeit import Timer
import pandas as pd
%matplotlib inline

Append

In [6]:
count = 10**5
In [9]:
def count_by_append(count):
    nums = []
    for i in range(count):
        nums.append(i)
    nums.reverse()
count_by_append(count)

The execution time is 22ms

Insert

In [10]:
def count_by_insert(count):
    nums = []
    for i in range(count):
        nums.insert(0, i)
count_by_insert(count)

The execution time is 3.53s

Comparison

In [24]:
counts = [10 ** i for i in range(5)]
time_by_append = []
time_by_insert = []

for count in counts:
    print(f'Processing {count}')
    t = Timer(lambda: count_by_append(count))
    time_by_append.append(t.timeit(number=10))
    t = Timer(lambda: count_by_insert(count))
    time_by_insert.append(t.timeit(number=10))

df_performance = pd.DataFrame({'count': counts,
                               'count_by_append': time_by_append,
                               'count_by_insert': time_by_insert})
df_performance

Processing 1
Processing 10
Processing 100
Processing 1000
Processing 10000
Out[24]:
count count_by_append count_by_insert
0 1 0.000013 0.000011
1 10 0.000019 0.000027
2 100 0.000107 0.000227
3 1000 0.000927 0.005318
4 10000 0.009438 0.379457
In [34]:
# Plot the performance difference
df_performance.set_index('count').plot(title='Performance Comparison beteen Append and Insert');

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