Do Python lambda functions help in reducing the execution times?

No. The function objects generated by lambda behave exactly like those generated by def. They do not execute any faster. (Also, inline in modern C++ is no longer a directive telling the compiler to inline a function, and has very little to do with inlining.)

If you want, you can take a look at the bytecode disassembly for a lambda and an equivalent def:

import dis

dis.dis(lambda x: x + 2)

print()
def f(x): return x + 2

dis.dis(f)

Output:

  3           0 LOAD_FAST                0 (x)
              3 LOAD_CONST               1 (2)
              6 BINARY_ADD
              7 RETURN_VALUE

  6           0 LOAD_FAST                0 (x)
              3 LOAD_CONST               1 (2)
              6 BINARY_ADD
              7 RETURN_VALUE

No difference. You can also time them:

import timeit

def f(x): return x + 2
g = lambda x: x + 2

print(timeit.timeit('f(3)', globals=globals()))
print(timeit.timeit('g(3)', globals=globals()))

Output:

0.06977041810750961
0.07760106027126312

The lambda actually took longer in this run. (There seems to be some confusion in the comments about whether we're timing enough work to be meaningful. timeit wraps the timed statement in a million-iteration loop by default, so yes, we are.)

Before you ask, no, lambda has no performance disadvantage over def either. The winner of the above race is basically up to luck. lambda and def do have a significant disadvantage over avoiding the use of a callback function entirely, though. For example, map-with-lambda has a significant performance penalty relative to list comprehensions:

import timeit

print(timeit.timeit('list(map(lambda x: x*x, range(10)))'))
print(timeit.timeit('[x*x for x in range(10)]'))

Output:

1.5655903220176697
0.7803761437535286

Whether lambda or def, Python functions are expensive to call.