%time

%time print('Outputs CPU time,Wall Clock time') 
#CPU times: user 2 µs, sys: 0 ns, total: 2 µs Wall time: 5.96 µs

Gives:

• CPU times: CPU level execution time
• sys times: system level execution time
• total: CPU time + system time
• Wall time: Wall Clock Time

%timeit

%timeit -r 7 -n 1000 print('Outputs execution time of the snippet') 
#1000 loops, best of 7: 7.46 ns per loop

• Gives best time out of given number of runs(r) in looping (n) times.
• Outputs details on system caching:
  • When code snippets are executed multiple times, system caches a few opearations and doesn't execute them again that may hamper the accuracy of the profile reports.

%prun

%prun -s cumulative 'Code to profile' 

Gives:

• number of function calls(ncalls)
• has entries per function call(distinct)
• time taken per call(percall)
• time elapsed till that function call(cumtime)
• name of the func/module called etc...

%memit

%memit 'Code to profile'
#peak memory: 199.45 MiB, increment: 0.00 MiB

Gives:

• Memory usage

%lprun

#Example function
def fun():
  for i in range(10):
    print(i)

#Usage: %lprun <name_of_the_function> function
%lprun -f fun fun()

Gives:

• Line wise stats

sys.getsizeof

sys.getsizeof('code to profile')
# 64 bytes

Returns the size of an object in bytes.

asizeof() from pympler

from pympler import asizeof
obj = [1,2,("hey","ha"),3]
print(asizeof.asizeof(obj,stats=4))

pympler.asizeof can be used to investigate how much memory certain Python objects consume. In contrast to sys.getsizeof, asizeof sizes objects recursively

tracker from pympler

from pympler import tracker
tr = tracker.SummaryTracker()
def fun():
  li = [1,2,3]
  di = {"ha":"haha","duh":"Umm"}
fun()
tr.print_diff()

Tracks the lifetime of a function.

Profiling options:
1. line_profiler is another profiler used commonly to find out timing metrics line-by-line.
2. memory_profiler is a tool to profile memory usage.
3. heapy (from project Guppy) Profile how objects in the heap are used.

These are some of the common ones I tend to use. But if you want to find out more, try reading this book It is a pretty good book on starting out with performance in mind. You can move onto advanced topics on using Cython and JIT(Just-in-time) compiled python.

Ever want to know what the hell that python script is doing? Enter the Inspect Shell. Inspect Shell lets you print/alter globals and run functions without interrupting the running script. Now with auto-complete and command history (only on linux).

Inspect Shell is not a pdb-style debugger.

https://github.com/amoffat/Inspect-Shell

You could use that (and your wristwatch).

There's also a statistical profiler called statprof. It's a sampling profiler, so it adds minimal overhead to your code and gives line-based (not just function-based) timings. It's more suited to soft real-time applications like games, but may be have less precision than cProfile.

The version in pypi is a bit old, so can install it with pip by specifying the git repository:

pip install git+git://github.com/bos/statprof.py@1a33eba91899afe17a8b752c6dfdec6f05dd0c01

You can run it like this:

import statprof

with statprof.profile():
    my_questionable_function()

See also https://stackoverflow.com/a/10333592/320036