Is it possible to do applymap using the groupby in

In a pandas Dataframe I want to applymap(somefunction) using groupby (using some column index values).

mcve_01.txt

pos         index      M1      M2      F1_x 
16230484    141      G/G      G/G       G
16230491    141      C/C      C/C       C
16230503    141      T/T      T/T       T
16230524    141      T/T      T/T       T
16230535    141      .    .         T
16232072    211      A/A      A/A       A
16232072    211      A/A      A/A       A
16229783    211      C/C      C/C       G
16229992    211      A/A      A/A       G
16230007    211      T/T      T/T       A
16230011    263      G/G      G/G       C
16230049    263      A/A      A/A       T
16230174    263      .         .        T
16230190    263      A/A      A/A       T
16230260    263      A/A      A/A       G

I have function written to do some analyses for columns A, B, C, D where the values in A, B, C and D are list.

mcve_data = pd.read_csv('mcve_01.txt', sep='\t')

mcve_data.set_index(['pos', 'index'], append= True, inplace = True)
mcve_list = mcve_data.applymap(lambda c:[list(c)])

say the function is,

def mapfun(c):
if any(['.' in l for l in c]):
    return '.'

if all(['|' in l for l in c]):
    fun = zip

else:
    fun = product

filt_set = set(['|','/'])
filt = partial(filter,lambda l: not (l in filt_set))

return ','.join('g'.join(t) for t in fun(*map(filt, c)))

Finally:

mcve_mm = (mcve_list+mcve_list.shift(1)).dropna(how='all').\
    applymap(mapfun)

which gives me (final output):

pos    index      M1        M2      F1_x    
16230484    141  CgG,CgG,CgG,CgG        CgG,CgG,CgG,CgG         CgG
16230491    141  TgC,TgC,TgC,TgC        TgC,TgC,TgC,TgC         TgC
.....      ...   TgT,TgT,TgT,TgT        TgT,TgT,TgT,TgT         TgT
               .        .       TgT
               .        .       AgT
               AgA,AgA,AgA,AgA          AgA,AgA,AgA,AgA         AgA
               CgA,CgA,CgA,CgA          CgA,CgA,CgA,CgA         GgA
               AgC,AgC,AgC,AgC          AgC,AgC,AgC,AgC         GgG
               TgA,TgA,TgA,TgA          TgA,TgA,TgA,TgA         AgG
               GgT,GgT,GgT,GgT          GgT,GgT,GgT,GgT         CgA
               AgG,AgG,AgG,AgG          AgG,AgG,AgG,AgG         TgC

So, this code works if I want to run the function (mapfun) for the whole dataframe without grouping. But, i want to run the function by grouping them by index values.

Unfortunately, I don't see any example of groupby and applymap together.

I tried then reindexing the index column and then wrap the function (mapfun) within apply, which didn't work.

mcve_mm = (mcve_list+mcve_list.shift(1)).dropna(how='all').groupby(['f1_index'], group_keys = False).apply(lambda x: [mapfun])

I didn't get any error but the function part got messed up when trying to group and then apply.

Output I am getting:

f1_index
141.0     [<function mapfun at 0x7fee93550f28>]
211.0     [<function mapfun at 0x7fee93550f28>]
263.0     [<function mapfun at 0x7fee93550f28>]
dtype: object

Expected output:

same as final output but the output (functional part) grouped by the common index values

Now, I want to take this function and applymap in this column by grouping the data/frame using the values in one of the column or index.

data_groupby = (df+df.shift(1)).dropna(how='all').\
applymap(fnc) using groupby

I tried resetting the index and then groupby using the index name. But, the def fnc() is specific to the data from columns A, B, C, D. Also, I am not finding any examples and tutorial that uses applymap along with groupby in pandas df.

A DataFrameGroupBy is a dictionary of DataFrames, not a single DataFrame. You can use applymap on the subgroups:

import pandas as pd
from numpy.random import random, randint

# Dummy data
vdata = pd.DataFrame(randint(2, size=(32,4)))
vdata.columns=[list('ABCD')]

vgb = vdata.groupby(('A','B'))
altered = []
for index, subframe in vgb:
    subframe = subframe.applymap(lambda x: x*2)
    altered.append(subframe)
    print index
    print subframe
    assert(subframe.A.mean() == index[0]*2)
    assert(subframe.B.mean() == index[1]*2)

vdata = pd.concat(altered)
print vdata