Overview

tests
package

Tired of writing the same code again and again when comparing the runtime of more than one function? timethese helps with this type of micro-benchmarking. It basically runs timeit (or actually repeat) on multiple functions and spits out a report.

In one sentence: timethese is timeit for multiple functions with better reporting

• Free software: MIT License

Installation

pip install timethese

You can also install the in-development version with:

pip install https://github.com/jwbargsten/python-timethese/archive/master.zip

Usage

Microbenchmark

timethese has a 3 step approach:

1. define the functions you want to compare
2. feed them to cmpthese as list or dict (see below)
3. format the results, aka pretty print

Let's have a look:

from timethese import cmpthese, pprint_cmp, timethese

xs = range(10)


# 1. DEFINE FUNCTIONS

def map_hex():
    list(map(hex, xs))


def list_compr_hex():
    list([hex(x) for x in xs])


def map_lambda():
    list(map(lambda x: x + 2, xs))


def map_lambda_fn():
    fn = lambda x: x + 2
    list(map(fn, xs))


def list_compr_nofn():
    list([x + 2 for x in xs])


# 2. FEED THE FUNCTIONS TO CMPTHESE

# AS DICT:

cmp_res_dict = cmpthese(
    10000,
    {
        "map_hex": map_hex,
        "list_compr_hex": list_compr_hex,
        "map_lambda": map_lambda,
        "map_lambda_fn": map_lambda_fn,
        "list_compr_nofn": list_compr_nofn,
    },
    repeat=3,
)


# OR AS LIST:

cmp_res_list = cmpthese(
    10000, [map_hex, list_compr_hex, map_lambda, map_lambda_fn, list_compr_nofn,], repeat=3,
)

# 3. PRETTY PRINT THE RESULTS

print(pprint_cmp(cmp_res_dict))
print(pprint_cmp(cmp_res_list))

What do you get if you run this?

Depending on the runtime of the supplied functions, either rate (unit: 1/s) or the seconds per iteration (s/iter) are shown.

For dict something like:

                      Rate  list_compr_nofn  map_hex  map_lambda  map_lambda_fn  list_compr_hex
list_compr_nofn  1385057/s                .      43%         47%            48%             88%
        map_hex   969501/s             -30%        .          3%             4%             31%
     map_lambda   940257/s             -32%      -3%           .             1%             27%
  map_lambda_fn   935508/s             -32%      -4%         -1%              .             27%
 list_compr_hex   738367/s             -47%     -24%        -21%           -21%               .

For list something like:

                        Rate  4.list_compr_nofn  0.map_hex  2.map_lambda  3.map_lambda_fn  1.list_compr_hex
4.list_compr_nofn  1360009/s                  .        31%           42%              46%               78%
        0.map_hex  1037581/s               -24%          .            9%              11%               36%
     2.map_lambda   955513/s               -30%        -8%             .               2%               25%
  3.map_lambda_fn   933666/s               -31%       -10%           -2%                .               22%
 1.list_compr_hex   763397/s               -44%       -26%          -20%             -18%                 .

(the function names are taken from fn.__name__ and prefixed with the list index.)

Timing

timethese also has the function timethese, which is used by cmpthese internally. To get the timings directly, you can run:

from timethese import timethese

xs = range(10)


def map_hex():
    list(map(hex, xs))


def list_compr_hex():
    list([hex(x) for x in xs])


def map_lambda():
    list(map(lambda x: x + 2, xs))


def map_lambda_fn():
    fn = lambda x: x + 2
    list(map(fn, xs))


def list_compr_nofn():
    list([x + 2 for x in xs])


timings_dict = timethese(
    10000,
    {
        "map_hex": map_hex,
        "list_compr_hex": list_compr_hex,
        "map_lambda": map_lambda,
        "map_lambda_fn": map_lambda_fn,
        "list_compr_nofn": list_compr_nofn,
    },
    repeat=3,
)

timings_list = timethese(
    10000,
    [ map_hex, list_compr_hex, map_lambda, map_lambda_fn, list_compr_nofn ],
    repeat=3,
)

# if you want, you can create a pandas df from it

import pandas as pd

timings_df = pd.DataFrame(timings_dict.values())
print(timings_df)

# BEWARE: if you pass a list to timings, you have to skip the .values() call

timings_df = pd.DataFrame(timings_list)
print(timings_df)

Timing functions with decorators

timethese also provides decorators to time single functions:

import time
import timethese

@timethese.print_time
def calculate_something():
    time.sleep(1)

calculate_something()

Four decorators are provided, 2 for normal stuff

• timethese.print_time
• timethese.log_time(logger, level=logging.INFO)

and 2 for pandas dataframes (they also print the shape of the resulting dataframe). Useful when using df.pipe(...)

• timethese.log_time_df(logger, level=logging.INFO)
• timethese.print_time_df

E.g. to log execution times of pipe operations on pandas dataframes, you could write:

import time
import logging
import timethese
import numpy as np
import pandas as pd

logging.basicConfig(level=logging.DEBUG)

logger = logging.getLogger(__name__)


@timethese.log_time_df(logger, logging.DEBUG)
def sum_by_group(df):
    time.sleep(1)  # introduce some artificial delay
    return df.groupby("A").sum()


df = pd.DataFrame({"A": np.arange(100) % 2, "B": np.random.normal(size=100)})

res = df.pipe(sum_by_group)

See the function documentation in the source code for better examples.

Development

To run the all tests run:

tox

Note, to combine the coverage data from all the tox environments run:

Windows	set PYTEST_ADDOPTS=--cov-append tox
Other	PYTEST_ADDOPTS=--cov-append tox

See also

The idea came from Perl's Benchmark.pm, which I used a lot in the Good Ol' Days.