# 7.2. Micro-benchmarking¶

We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil -- Donald Knuth

## 7.2.1. Evaluation¶

• Fresh start of Python process

• Clean memory before start

• Same data

• Same start conditions, CPU load, RAM usage, iostat

• Do not measure how long Python wakes up

• Check what you measure

## 7.2.2. timeit¶

### 7.2.2.1. Programmatic use¶

Listing 7.15. Timeit simple statement
from timeit import timeit

setup = """name = 'José Jiménez'"""
stmt = """result = f'My name... {name}'"""

duration = timeit(stmt, setup, number=10000)

print(duration)
# 0.0005737080000000061

Listing 7.16. Timeit multiple statements with setup code
from timeit import timeit

setup = """
firstname = 'José'
lastname = 'Jiménez'
"""

TEST = dict()
TEST[0] = 'name = f"{firstname} {lastname}"'
TEST[1] = 'name = "{0} {1}".format(firstname, lastname)'
TEST[2] = 'name = firstname + " " + lastname'
TEST[3] = 'name = " ".join([firstname, lastname])'

for stmt in TEST.values():
duration = timeit(stmt, setup, number=10000)
print(f'{duration:.5}\t{stmt}')

# 0.00071559    name = f"{firstname} {lastname}"
# 0.0026514     name = "{0} {1}".format(firstname, lastname)
# 0.001015      name = firstname + " " + lastname
# 0.0013494     name = " ".join([firstname, lastname])

Listing 7.17. Timeit with globals()
from timeit import timeit

def factorial(n: int) -> int:
if n == 0:
return 1
else:
return n * factorial(n-1)

duration = timeit(
stmt='factorial(500); factorial(400); factorial(450)',
globals=globals(),
number=10000,
)

duration = round(duration, 6)

print(f'factorial time: {duration} seconds')
# factorial time: 2.845382 seconds


### 7.2.2.2. Console use¶

Listing 7.18. Timeit
python3 -m timeit -n100000 -r100 --setup='name="Jose Jimenez"' 'output = f"My name... {name}"'
# 100000 loops, best of 100: 55.9 nsec per loop

python3 -m timeit -n100000 -r100 --setup='name="Jose Jimenez"' 'output = "My name... {name}".format(name=name)'
# 100000 loops, best of 100: 327 nsec per loop

python3 -m timeit -n100000 -r100 --setup='name="Jose Jimenez"' 'output = "My name... %s" % name'
# 100000 loops, best of 100: 124 nsec per loop

-n N, --number=N
how many times to execute ‘statement’

-r N, --repeat=N
how many times to repeat the timer (default 5)

-s S, --setup=S
statement to be executed once initially (default pass)

-p, --process
measure process time, not wallclock time, using time.process_time() instead of time.perf_counter(), which is the default

-u, --unit=U
specify a time unit for timer output; can select nsec, usec, msec, or sec

-v, --verbose
print raw timing results; repeat for more digits precision

-h, --help
print a short usage message and exit


## 7.2.3. String Concatenation¶

from time import time

class Timeit:
def __init__(self, name):
self.name = name

def __enter__(self):
self.start = time()
return self

def __exit__(self, *arg):
end = time()
print(f'Duration of {self.name} is {end-self.start:.2f} second')

a = 1
b = 2
repetitions = int(1e7)

with Timeit('f-string'):
for _ in range(repetitions):
f'{a}{b}'

with Timeit('string concat'):
for _ in range(repetitions):
a + b

with Timeit('str.format()'):
for _ in range(repetitions):
'{0}{1}'.format(a, b)

with Timeit('str.format()'):
for _ in range(repetitions):
'{}{}'.format(a, b)

with Timeit('str.format()'):
for _ in range(repetitions):
'{a}{b}'.format(a=a, b=b)

with Timeit('%-style'):
for _ in range(repetitions):
'%s%s' % (a, b)

with Timeit('%-style'):
for _ in range(repetitions):
'%d%d' % (a, b)

with Timeit('%-style'):
for _ in range(repetitions):
'%f%f' % (a, b)

# Duration of f-string is 2.70 second
# Duration of string concat is 0.68 second
# Duration of str.format() is 3.46 second
# Duration of str.format() is 3.37 second
# Duration of str.format() is 4.85 second
# Duration of %-style is 2.59 second
# Duration of %-style is 2.59 second
# Duration of %-style is 3.82 second


## 7.2.4. Case Studies - Unique Keys¶

• Runtime: Jupyter %%timeit

Listing 7.19. Setup code used for all examples
DATA = [
{'Sepal length': 5.1, 'Sepal width': 3.5, 'Species': 'setosa'},
{'Petal length': 4.1, 'Petal width': 1.3, 'Species': 'versicolor'},
{'Sepal length': 6.3, 'Petal width': 1.8, 'Species': 'virginica'},
{'Sepal length': 5.0, 'Petal width': 0.2, 'Species': 'setosa'},
{'Sepal width': 2.8, 'Petal length': 4.1, 'Species': 'versicolor'},
{'Sepal width': 2.9, 'Petal width': 1.8, 'Species': 'virginica'},
]

Listing 7.20. Append if object not in the list
%%timeit -r 10 -n 1000000

fieldnames = list()

for row in DATA:
for key in row.keys():
if key not in fieldnames:
fieldnames.append(key)

# 2.16 µs ± 26.5 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)

Listing 7.21. Append to list and deduplicate at the end
%%timeit -r 10 -n 1000000

fieldnames = list()

for row in DATA:
for key in row.keys():
fieldnames.append(key)

set(fieldnames)

# 2.5 µs ± 32.9 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)

%%timeit -r 10 -n 1000000

fieldnames = set()

for row in DATA:
for key in row.keys():

# 2.12 µs ± 32.4 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)

Listing 7.23. Update set
%%timeit -r 10 -n 1000000

unique_keys = set()

for row in DATA:
unique_keys.update(row.keys())

# 1.57 µs ± 26.7 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)

Listing 7.24. Set Comprehension
%%timeit -r 10 -n 1000000

fieldnames = set(key
for record in DATA
for key in record.keys())

# 2.06 µs ± 79.7 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)

Listing 7.25. Add to Set Comprehension. Code appends generator object not values, this is why it is so fast!
%%timeit -r 10 -n 1000000

fieldnames = set()

for record in DATA
for key in record.keys())

# 447 ns ± 9.52 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)

Listing 7.26. Update Set Comprehension
%%timeit -r 10 -n 1000000

fieldnames = set()
fieldnames.update(tuple(x.keys()) for x in DATA)

# 2.06 µs ± 45.9 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)

%%timeit -r 10 -n 1000000

unique_keys = set()

for row in DATA:
unique_keys.update(tuple(row))

# 2.09 µs ± 16.1 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)

%%timeit -r 10 -n 1000000

unique_keys = set()

for row in DATA:
unique_keys.update(list(row))

# 2.33 µs ± 30.2 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)

%%timeit -r 10 -n 1000000

unique_keys = set()

for row in DATA:
unique_keys.update(set(row))

# 1.71 µs ± 54 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)


## 7.2.5. Case Study - Factorial¶

Listing 7.27. Recap information about factorial (n!)
"""
5! = 5 * 4!
4! = 4 * 3!
3! = 3 * 2!
2! = 2 * 1!
1! = 1 * 0!
0! = 1
"""

factorial(5)                                    # = 120
return 5 * factorial(4)                     # 5 * 24 = 120
return 4 * factorial(3)                 # 4 * 6 = 24
return 3 * factorial(2)             # 3 * 2 = 6
return 2 * factorial(1)         # 2 * 1 = 2
return 1 * factorial(0)     # 1 * 1 = 1
return 1                # 1

Listing 7.28. Cache with global scope
_cache = {}

def cache(func):
def wrapper(n):
if n not in _cache:
_cache[n] = func(n)
return _cache[n]
return wrapper

@cache
def factorial(n):
if n == 0:
return 1
else:
return n * factorial(n-1)

factorial(500)
factorial(400)
factorial(450)

Listing 7.29. Cache with local scope
def cache(func):
_cache = {}
def wrapper(n):
if n not in _cache:
_cache[n] = func(n)
return _cache[n]
return wrapper

@cache
def factorial(n):
if n == 0:
return 1
else:
return n * factorial(n-1)

factorial(500)
factorial(400)
factorial(450)

Listing 7.30. Cache with embedded scope
def cache(func):
def wrapper(n):
if n not in wrapper._cache:
wrapper._cache[n] = func(n)
return wrapper._cache[n]
if not hasattr(wrapper, '_cache'):
setattr(wrapper, '_cache', {})
return wrapper

@cache
def factorial(n: int) -> int:
if n == 0:
return 1
else:
return n * factorial(n-1)

factorial(500)
factorial(400)
factorial(450)

Listing 7.31. Without cache
%%timeit

def factorial(n: int) -> int:
if n == 0:
return 1
else:
return n * factorial(n-1)

factorial(500)
factorial(400)
factorial(450)

# 283 µs ± 6.63 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Listing 7.32. Cache contains
%%timeit

_cache = {}

def factorial(n: int) -> int:
if n in _cache:
return _cache[n]

if n == 0:
return 1
else:
result = _cache[n] = n * factorial(n-1)
return result

factorial(500)
factorial(400)
factorial(450)

# 153 µs ± 2.49 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Listing 7.33. Cache get
%%timeit

_cache = {}

def factorial(n: int) -> int:
result = _cache.get(n)

if result:
return result

if n == 0:
return 1
else:
result = _cache[n] = n * factorial(n-1)
return result

factorial(500)
factorial(400)
factorial(450)

# 181 µs ± 10.3 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Listing 7.34. Cache contains with exceptions
%%timeit

_cache = {}

def factorial(n: int) -> int:
if n == 0:
return 1

try:
return _cache[n]
except KeyError:
_cache[n] = result = n * factorial(n-1)
return result

factorial(500)
factorial(400)
factorial(450)

# 618 µs ± 6.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%%timeit

_cache = {}

def fac(n: int) -> int:

def factorial(n: int) -> int:
if n == 0:
return 1
return n * factorial(n-1)

if not n in _cache:
_cache[n] = factorial(n)

return _cache[n]

fac(500)
fac(400)
fac(450)

# 283 µs ± 6.44 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Listing 7.36. Get from cache
%%timeit

_cache = {}

def factorial(n: int) -> int:
if n == 0:
return 1

if n in _cache:
return _cache[n]

result = _cache[n] = n * factorial(n-1)
return result

factorial(500)
factorial(400)
factorial(450)

# 153 µs ± 9.64 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

_cache = {}

def factorial(n):
if n == 0:
return 1

if (result := _cache.get(n)):
return result

result = n * factorial(n-1)
_cache[n] = result
return result