Differences between functools.partial and a similar lambda?

  1. A lambda function has the same type as a standard function, so it will behave like an instance method.

  2. The partial object in your example can be called like this:

    g1(x, y, z)

    leading to this call (not valid Python syntax, but you get the idea):

    f(*secondary_args, x, y, z, **secondary_kwargs)

    The lambda only accepts a single argument and uses a different argument order. (Of course both of these differences can be overcome – I'm just answering what the differences between the two versions you gave are.)

  3. Execution of the partial object is slightly faster than execution of the equivalent lambda.


partials are not only about 20% faster than equivalent lambdas as already said but they keep a direct ref to they function the relate to. While in lambdas that function is 'buried' within the function body.

=> If you need to only solve the problem of deferring evaluation of one function until all args are known then use partials. You'll have way better introspection methods compared to bury the calls into anonymous functions, i.e. lambdas.


Summary

The practical differences between lambda and functools.partial in the common use cases seems to be

  • functools.partial needs an import, lambda does not.
  • The function definition for functions created with functools.partial is visible just by printing the created function. The functions created with lambda should be inspected with inspect.getsource().

These were found to be practically identical for lambda and functools.partial

  • Speed
  • Tracebacks

Speed (lambda vs functools.partial)

I think that tests and real data speaks louder than just guesses about which one is faster than the another.

Looks like that there is no statistical proof for speed difference between lambda and functools.partial. I ran different tests with different amount of repetitions, getting slightly different results each time; any of the three approaches could be the fastest. The speeds were identical with 95% (2 sigma) confidence. Here are some numerical results*

# When functions are defined beforehand
In [1]: timeit -n 1000 -r 1000 f_partial(data)
23.6 µs ± 2.92 µs per loop (mean ± std. dev. of 1000 runs, 1000 loops each)

In [2]: timeit -n 1000 -r 1000 f_lambda(data)
22.6 µs ± 2.6 µs per loop (mean ± std. dev. of 1000 runs, 1000 loops each)

# When function is defined each time again
In [3]: timeit -n 1000 -r 1000 (lambda x: trim_mean(x, 0.1))(data)
22.6 µs ± 1.98 µs per loop (mean ± std. dev. of 1000 runs, 1000 loops each)

In [4]: timeit -n 1000 -r 1000 f_lambda = lambda x: trim_mean(x, 0.1); f_lambda(data)
23.7 µs ± 3.89 µs per loop (mean ± std. dev. of 1000 runs, 1000 loops each)

In [5]: timeit -n 1000 -r 1000 f_partial = partial(trim_mean, proportiontocut=0.1); f_partial(data)
24 µs ± 3.38 µs per loop (mean ± std. dev. of 1000 runs, 1000 loops each)

Tracebacks

I also tried running the f_lambda and f_partial using list with string element inserted, and the tracebacks were equal (except for the very first entry, of course). So there is no difference there.

Inspecting the source code

  • The function definition for functions created with functools.partial is visible just by printing the created function. The functions created with lambda should be inspected with inspect.getsource().
# Can be inspected with just printing the function
In [1]: f_partial
Out[1]: functools.partial(<function trim_mean at 0x000001463262D0D0>, proportiontocut=0.1)

In [2]: print(f_partial)
functools.partial(<function trim_mean at 0x000001463262D0D0>, proportiontocut=0.1)

# Lambda functions do not show the source directly
In [3]: f_lambda
Out[3]: <function __main__.<lambda>(x)>

# But you can use inspect.getsource()
In [4]: inspect.getsource(f_lambda)
Out[4]: 'f_lambda = lambda x: trim_mean(x, 0.1)\n'

# This throws a ValueError, though.
In [5]: inspect.getsource(f_partial)

Appendix

* Setup used in the tests

from functools import partial
from scipy.stats import trim_mean
import numpy as np
data = np.hstack((np.random.random(1000), np.random.random(50)*25000))

f_lambda = lambda x: trim_mean(x, 0.1)
f_partial = partial(trim_mean, proportiontocut=0.1)

The tests were performed on Python 3.7.3 64-bit (Windows 10).

Tags:

Python

Lambda

Functools

Partial

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