3.4. Optimization Membership

3.4.1. List Membership

Small list:

>>> users = ['alice', 'bob', 'carol']

>>> # doctest: +SKIP
... %%timeit -r 1000 -n 1000
... 'mallory' in users
...
30.2 ns ± 5.99 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
30.2 ns ± 6.1 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
30.7 ns ± 6.89 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
30.9 ns ± 6.84 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
31.7 ns ± 6.22 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)

Larger list:

>>> users = ['alice', 'bob', 'carol', 'dave', 'eve', 'frank']

>>> # doctest: +SKIP
... %%timeit -r 1000 -n 1000
... 'mallory' in users
...
45 ns ± 9.23 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
45.1 ns ± 9.61 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
45.7 ns ± 9.65 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
45.8 ns ± 10.1 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
45.9 ns ± 10.1 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)

3.4.2. Set Membership

Small set:

>>> users = {'alice', 'bob', 'carol'}

>>> # doctest: +SKIP
... %%timeit -r 1000 -n 1000
... 'mallory' in users
...
15.1 ns ± 1.85 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
15.3 ns ± 3.73 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
15.5 ns ± 4.36 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
15.8 ns ± 4.65 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
15.9 ns ± 3.46 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)

Larger set:

>>> users = {'alice', 'bob', 'carol', 'dave', 'eve', 'frank'}

>>> # doctest: +SKIP
... %%timeit -r 1000 -n 1000
... 'mallory' in users
...
15.4 ns ± 4.6 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
15.8 ns ± 4.69 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
15.9 ns ± 3.7 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
16 ns ± 3.02 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
16.1 ns ± 3.26 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)

3.4.3. Assignments

# %% About
# - Name: Performance Complexity UniqueKeys
# - Difficulty: easy
# - Lines: 3
# - Minutes: 5

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% English
# 1. Collect unique keys from all rows in one sequence `result`
# 2. Run doctests - all must succeed

# %% Polish
# 1. Zbierz unikalne klucze z wszystkich wierszy w jednej sekwencji `result`
# 2. Uruchom doctesty - wszystkie muszą się powieść

# %% Example
# >>> result
# ['age', 'firstname', 'lastname']

# %% Hints
# - `row.keys()`
# - Compare solutions with `Micro-benchmarking`


# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> result is not Ellipsis
True
>>> type(result) in (set, list, tuple, frozenset)
True
>>> sorted(result)
['age', 'firstname', 'lastname']
"""

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% Imports

# %% Types
result: set[str]

# %% Data
DATA = [
    {'firstname': 'Alice', 'lastname': 'Apricot'},
    {'firstname': 'Bob', 'age': 31},
    {'lastname': 'Corn', 'firstname': 'Carol'},
    {'lastname': 'Durian', 'age': 33},
    {'age': 34, 'firstname': 'Eve'},
    {'age': 15, 'lastname': 'Mallory', },
]

# %% Result
result = ...