How can I profile Python code line-by-line?

I've been using cProfile to profile my code, and it's been working great. I also use to visualize the results (makes it a little clearer).

However, cProfile (and most other Python profilers I've seen so far) seem to only profile at the function-call level. This causes confusion when certain functions are called from different places - I have no idea if call #1 or call #2 is taking up the majority of the time. This gets even worse when the function in question is six levels deep, called from seven other places.

How do I get a line-by-line profiling?

Instead of this:

function #12, total time: 2.0s

I'd like to see something like this:

function #12 (called from 0.5s
function #12 (called from 1.5s

cProfile does show how much of the total time "transfers" to the parent, but again this connection is lost when you have a bunch of layers and interconnected calls.

Ideally, I'd love to have a GUI that would parse through the data, then show me my source file with a total time given to each line. Something like this:

a = 1 # 0.0s
result = func(a) # 0.4s
c = 1000 # 0.0s
result = func(c) # 5.0s

Then I'd be able to click on the second "func(c)" call to see what's taking up time in that call, separate from the "func(a)" call.

Does that make sense? Is there any profiling library that collects this type of information? Is there some awesome tool I've missed?


I believe that's what Robert Kern's line_profiler is intended for. From the link:

Function: Proc2 at line 149
Total time: 0.606656 s

Line #      Hits         Time  Per Hit   % Time  Line Contents
   149                                           @profile
   150                                           def Proc2(IntParIO):
   151     50000        82003      1.6     13.5      IntLoc = IntParIO + 10
   152     50000        63162      1.3     10.4      while 1:
   153     50000        69065      1.4     11.4          if Char1Glob == 'A':
   154     50000        66354      1.3     10.9              IntLoc = IntLoc - 1
   155     50000        67263      1.3     11.1              IntParIO = IntLoc - IntGlob
   156     50000        65494      1.3     10.8              EnumLoc = Ident1
   157     50000        68001      1.4     11.2          if EnumLoc == Ident1:
   158     50000        63739      1.3     10.5              break
   159     50000        61575      1.2     10.1      return IntParIO

Hope that helps!

You could also use pprofile(pypi). If you want to profile the entire execution, it does not require source code modification. You can also profile a subset of a larger program in two ways:

  • toggle profiling when reaching a specific point in the code, such as:

    import pprofile
    profiler = pprofile.Profile()
    with profiler:
    # Process profile content: generate a cachegrind file and send it to user.
  • toggle profiling asynchronously from call stack (requires a way to trigger this code in considered application, for example a signal handler or an available worker thread) by using statistical profiling:

    import pprofile
    profiler = pprofile.StatisticalProfile()
    statistical_profiler_thread = pprofile.StatisticalThread(
    with statistical_profiler_thread:
    # Likewise, process profile content

Code annotation output format is much like line profiler:

$ pprofile --threads 0 demo/
Command line: ['demo/']
Total duration: 1.00573s
File: demo/
File duration: 1.00168s (99.60%)
Line #|      Hits|         Time| Time per hit|      %|Source code
     1|         2|  3.21865e-05|  1.60933e-05|  0.00%|import threading
     2|         1|  5.96046e-06|  5.96046e-06|  0.00%|import time
     3|         0|            0|            0|  0.00%|
     4|         2|   1.5974e-05|  7.98702e-06|  0.00%|def func():
     5|         1|      1.00111|      1.00111| 99.54%|  time.sleep(1)
     6|         0|            0|            0|  0.00%|
     7|         2|  2.00272e-05|  1.00136e-05|  0.00%|def func2():
     8|         1|  1.69277e-05|  1.69277e-05|  0.00%|  pass
     9|         0|            0|            0|  0.00%|
    10|         1|  1.81198e-05|  1.81198e-05|  0.00%|t1 = threading.Thread(target=func)
(call)|         1|  0.000610828|  0.000610828|  0.06%|# /usr/lib/python2.7/ __init__
    11|         1|  1.52588e-05|  1.52588e-05|  0.00%|t2 = threading.Thread(target=func)
(call)|         1|  0.000438929|  0.000438929|  0.04%|# /usr/lib/python2.7/ __init__
    12|         1|  4.79221e-05|  4.79221e-05|  0.00%|t1.start()
(call)|         1|  0.000843048|  0.000843048|  0.08%|# /usr/lib/python2.7/ start
    13|         1|  6.48499e-05|  6.48499e-05|  0.01%|t2.start()
(call)|         1|   0.00115609|   0.00115609|  0.11%|# /usr/lib/python2.7/ start
    14|         1|  0.000205994|  0.000205994|  0.02%|(func(), func2())
(call)|         1|      1.00112|      1.00112| 99.54%|# demo/ func
(call)|         1|  3.09944e-05|  3.09944e-05|  0.00%|# demo/ func2
    15|         1|  7.62939e-05|  7.62939e-05|  0.01%|t1.join()
(call)|         1|  0.000423908|  0.000423908|  0.04%|# /usr/lib/python2.7/ join
    16|         1|  5.26905e-05|  5.26905e-05|  0.01%|t2.join()
(call)|         1|  0.000320196|  0.000320196|  0.03%|# /usr/lib/python2.7/ join

Note that because pprofile does not rely on code modification it can profile top-level module statements, allowing to profile program startup time (how long it takes to import modules, initialise globals, ...).

It can generate cachegrind-formatted output, so you can use kcachegrind to browse large results easily.

Disclosure: I am pprofile author.

PyVmMonitor has a live-view which can help you there (you can connect to a running program and get statistics from it).


You can take help of line_profiler package for this

1. 1st install the package:

    pip install line_profiler

2. Use magic command to load the package to your python/notebook environment

    %load_ext line_profiler

3. If you want to profile the codes for a function then do as follows: %lprun -f function_name function_call

    %lprun -f function_defined_by_you function_defined_by_you(arg1, arg2)


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