PERF: Series.apply is slower on single element dict compared with multi elements dict

[Alexia-I]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this issue exists on the latest version of pandas.

• I have confirmed this issue exists on the main branch of pandas.

Reproducible Example

Hello, I noticed that applying the apply function to a Series with a single element dictionary takes more time than its counterpart with a multi-element dictionary. I'm curious if this is due to something I did wrong.

import pandas as pd
import timeit
import random
import string
import time

# Create dictionary inputs
single_pair_dict = {'a': 42}
# Create a dictionary containing 10 key-value pairs
multi_pair_dict = {letter: random.randint(1, 100) for letter in string.ascii_lowercase[:10]}

# Self-defined apply function
def complex_operation(x):
    return x * x - x + 42

n = 10000
# Create Series and test the execution time of apply() method
times = time.time()
for i in range(n):
    pd.Series(single_pair_dict).apply(complex_operation)
time_now_single = time.time() - times

timem = time.time()
for i in range(n):
    pd.Series(multi_pair_dict).apply(complex_operation)
time_now_multi = time.time() - timem

# Print the results
print(f"Time for apply() on Series with a single key-value pair: {time_now_single} seconds")
print(f"Time for apply() on Series with multiple key-value pairs: {time_now_multi} seconds")

Time for apply() on Series with single key-value pair: 1.1293659210205078 seconds
Time for apply() on Series with multiple key-value pairs: 1.0656580924987793 seconds

Installed Versions

INSTALLED VERSIONS

commit : d4c8d82
python : 3.9.18.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.0-88-generic
Version : #98~20.04.1-Ubuntu SMP Mon Oct 9 16:43:45 UTC 2023
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.0rc0
numpy : 1.26.3
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 68.2.2
pip : 23.3.1
Cython : 3.0.8
pytest : None
hypothesis : None
...
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None

Prior Performance

No response

[rhshadrach]

Thanks for the report, I can't reproduce on main. Can you reliably reproduce the results you posted?

Time for apply() on Series with a single key-value pair: 0.777146577835083 seconds
Time for apply() on Series with multiple key-value pairs: 0.80838942527771 seconds

Also confirmed using timeit on just the apply operation:

# Create dictionary inputs
single_pair_dict = {'a': 42}
# Create a dictionary containing 10 key-value pairs
multi_pair_dict = {letter: random.randint(1, 100) for letter in string.ascii_lowercase[:10]}

# Self-defined apply function
def complex_operation(x):
    return x * x - x + 42

ser1 = pd.Series(single_pair_dict)
%timeit ser1.apply(complex_operation)
# 25 µs ± 94.4 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

ser2 = pd.Series(multi_pair_dict)
%timeit ser2.apply(complex_operation)
# 26.8 µs ± 93.7 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)