Explain {isinstance} in iPython prun output?

Question

I'm trying to profile a few lines of Pandas code, and when I run %prun i'm finding most of my time is taken by {isinstance}. This seems to happen a lot -- can anyone suggest what that means and, for bonus points, suggest a way to avoid it?

This isn't meant to be application specific, but here's a thinned out version of the code if that's important:

def flagOtherGroup(df):

    try:mostUsed0 = df[df.subGroupDummy == 0].siteid.iloc[0]
    except: mostUsed0 = -1

    try: mostUsed1 = df[df.subGroupDummy == 1].siteid.iloc[0]
    except: mostUsed1 = -1

    df['mostUsed'] = 0 

    df.loc[(df.subGroupDummy == 0) & (df.siteid == mostUsed1), 'mostUsed'] = 1
    df.loc[(df.subGroupDummy == 1) & (df.siteid == mostUsed0), 'mostUsed'] = 1

    return df[['mostUsed']]

%prun -l15  temp = test.groupby('userCode').apply(flagOtherGroup)

And top lines of prun:

   Ordered by: internal time
   List reduced from 531 to 15 due to restriction <15>

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
   834472    1.908    0.000    2.280    0.000 {isinstance}
497048/395400    1.192    0.000    1.572    0.000 {len}
    32722    0.879    0.000    4.479    0.000 series.py:114(__init__)
    34444    0.613    0.000    1.792    0.000 internals.py:3286(__init__)
    25990    0.568    0.000    0.568    0.000 {method 'reduce' of 'numpy.ufunc' objects}
82266/78821    0.549    0.000    0.744    0.000 {numpy.core.multiarray.array}
    42201    0.544    0.000    1.195    0.000 internals.py:62(__init__)
    42201    0.485    0.000    1.812    0.000 internals.py:2015(make_block)
   166244    0.476    0.000    0.615    0.000 {getattr}
     4310    0.455    0.000    1.121    0.000 internals.py:2217(_rebuild_blknos_and_blklocs)
    12054    0.417    0.000    2.134    0.000 internals.py:2355(apply)
     9474    0.385    0.000    1.284    0.000 common.py:727(take_nd)

Answer 1

isinstance, len and getattr are just the built-in functions. There are a huge number of calls to the isinstance() function here; it is not that the call itself takes a lot of time, but the function was used 834472 times.

Presumably it is the pandas code that uses it.