Use time.time() to measure the elapsed wall-clock time between two points:

import time

start = time.time()
print("hello")
end = time.time()
print(end - start)

This gives the execution time in seconds.

Another option since Python 3.3 might be to use perf_counter or process_time, depending on your requirements. Before 3.3 it was recommended to use time.clock (thanks Amber). However, it is currently deprecated:

On Unix, return the current processor time as a floating point number expressed in seconds. The precision, and in fact the very definition of the meaning of “processor time”, depends on that of the C function of the same name.

On Windows, this function returns wall-clock seconds elapsed since the first call to this function, as a floating point number, based on the Win32 function QueryPerformanceCounter(). The resolution is typically better than one microsecond.

Deprecated since version 3.3: The behaviour of this function depends on the platform: use perf_counter() or process_time() instead, depending on your requirements, to have a well defined behaviour.

Have a look at timeit, the python profiler and pycallgraph. Also make sure to have a look at the comment below by nikicc mentioning "SnakeViz". It gives you yet another visualisation of profiling data which can be helpful.

timeit

def test():
    """Stupid test function"""
    lst = []
    for i in range(100):
        lst.append(i)

if __name__ == '__main__':
    import timeit
    print(timeit.timeit("test()", setup="from __main__ import test"))

    # For Python>=3.5 one can also write:
    print(timeit.timeit("test()", globals=locals()))

Essentially, you can pass it python code as a string parameter, and it will run in the specified amount of times and prints the execution time. The important bits from the docs:

timeit.timeit(stmt='pass', setup='pass', timer=<default timer>, number=1000000, globals=None) Create a Timer instance with the given statement, setup code and timer function and run its timeit method with number executions. The optional globals argument specifies a namespace in which to execute the code.

... and:

Timer.timeit(number=1000000) Time number executions of the main statement. This executes the setup statement once, and then returns the time it takes to execute the main statement a number of times, measured in seconds as a float. The argument is the number of times through the loop, defaulting to one million. The main statement, the setup statement and the timer function to be used are passed to the constructor.

Note: By default, timeit temporarily turns off garbage collection during the timing. The advantage of this approach is that it makes independent timings more comparable. This disadvantage is that GC may be an important component of the performance of the function being measured. If so, GC can be re-enabled as the first statement in the setup string. For example:

timeit.Timer('for i in xrange(10): oct(i)', 'gc.enable()').timeit()

Profiling

Profiling will give you a much more detailed idea about what's going on. Here's the "instant example" from the official docs:

import cProfile
import re
cProfile.run('re.compile("foo|bar")')

Which will give you:

      197 function calls (192 primitive calls) in 0.002 seconds

Ordered by: standard name

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
     1    0.000    0.000    0.001    0.001 <string>:1(<module>)
     1    0.000    0.000    0.001    0.001 re.py:212(compile)
     1    0.000    0.000    0.001    0.001 re.py:268(_compile)
     1    0.000    0.000    0.000    0.000 sre_compile.py:172(_compile_charset)
     1    0.000    0.000    0.000    0.000 sre_compile.py:201(_optimize_charset)
     4    0.000    0.000    0.000    0.000 sre_compile.py:25(_identityfunction)
   3/1    0.000    0.000    0.000    0.000 sre_compile.py:33(_compile)

Both of these modules should give you an idea about where to look for bottlenecks.

Also, to get to grips with the output of profile, have a look at this post

pycallgraph

NOTE pycallgraph has been officially abandoned since Feb. 2018. As of Dec. 2020 it was still working on Python 3.6 though. As long as there are no core changes in how python exposes the profiling API it should remain a helpful tool though.

This module uses graphviz to create callgraphs like the following:

You can easily see which paths used up the most time by colour. You can either create them using the pycallgraph API, or using a packaged script:

pycallgraph graphviz -- ./mypythonscript.py

The overhead is quite considerable though. So for already long-running processes, creating the graph can take some time.