Inspired by:
Diagnose slow Python code
(YouTube)
Introduction
Timing and profiling you code (or program, or algorithm)
can give you meaningful information about your code.
Sometimes your code runs too slow. Sometimes you need
to make your code more efficient.
Sometimes you need to know how well your code will scale to
larger datasets.
Python has several modules that can be used
to collect performance statistics. Two of which
are shown in the code examples below.
Project #1
Time and/or profile one of your programs or algorithms.
You should to run it long enough to
to collect meaningful statistics.
For small programs, you might execute it 1,000 times
in a loop. Be careful that your looping code
is short enough so you are collecting data on your program
and not the loop code.
Project #2
If your are analyzing how well your code scales, test various
size datasets and plot the resulting statistics.
Time and Profile Code Examples
import time
# -------------------------------------------------------------------
# ---- test code
# -------------------------------------------------------------------
def test001():
ii = 0
for i in range(1000000):
ii += i
return ii
def test002():
ff = 0.0
for i in range(1000000):
ff += float(i)
return ff
def test_001_and_002():
test001()
test002()
# -------------------------------------------------------------------
# ---- main
# -------------------------------------------------------------------
start = time.perf_counter()
test_001_and_002()
end = time.perf_counter()
elapsed = end - start
print(f'Completed in {elapsed:.2f} seconds')
import cProfile
import pstats
# -------------------------------------------------------------------
# ---- test code
# -------------------------------------------------------------------
def test001():
ii = 0
for i in range(1000000):
i += i
return ii
def test002():
ff = 0.0
for i in range(1000000):
ff += float(i)
return ff
def test_001_and_002():
test001()
test002()
# -------------------------------------------------------------------
# ---- main
# -------------------------------------------------------------------
with cProfile.Profile() as pr:
test_001_and_002()
stats = pstats.Stats(pr)
stats.sort_stats(pstats.SortKey.TIME)
stats.print_stats()
##stats.dump_stats(filename='needs_profiling.prof')
Create your own timing decorator
