python - Find target values in pandas dataframe -

i have multilevel dataframe df. columns, have different "objects" analyze. rows index , have case id lc, , time t.

i need find, each case lc, time t (ideally interpolated, closest value fine enough) @ each object reached target value.

this target value function of given object @ time t==0.

import pandas pd print(pd.__version__)  0.16.2 

dummy data set example:

data = {1: {(1014, 0.0): 20.25,      (1014, 0.0991): 19.08,      (1014, 0.1991): 18.43,      (1014, 0.2991): 19.03,      (1014, 0.3991): 18.71,      (1015, 0.0): 20.22,      (1015, 0.0991): 19.3,      (1015, 0.1991): 18.68,      (1015, 0.2991): 18.22,      (1015, 0.3991): 17.84,      (1016, 0.0): 21.75,      (1016, 0.0991): 19.97,      (1016, 0.1991): 19.65,      (1016, 0.2991): 19.29,      (1016, 0.3991): 18.94     },  2: {(1014, 0.0): 29.11,      (1014, 0.0991): 28.68,      (1014, 0.1991): 28.27,      (1014, 0.2991): 27.46,      (1014, 0.3991): 26.96,      (1015, 0.0): 29.22,      (1015, 0.0991): 28.64,      (1015, 0.1991): 28.18,      (1015, 0.2991): 27.74,      (1015, 0.3991): 27.25,      (1016, 0.0): 29.17,      (1016, 0.0991): 28.68,      (1016, 0.1991): 28.17,      (1016, 0.2991): 27.68,      (1016, 0.3991): 27.18     },  3: {(1014, 0.0): 22.01,      (1014, 0.0991): 21.5,      (1014, 0.1991): 21.18,      (1014, 0.2991): 20.58,      (1014, 0.3991): 20.21,      (1015, 0.0): 21.81,      (1015, 0.0991): 21.46,      (1015, 0.1991): 21.11,      (1015, 0.2991): 20.78,      (1015, 0.3991): 20.42,      (1016, 0.0): 21.82,      (1016, 0.0991): 21.49,      (1016, 0.1991): 21.11,      (1016, 0.2991): 20.75,      (1016, 0.3991): 20.37     }}  df = pd.dataframe(data).sort() df.index.names=['case', 't'] 

dataframe looks like:

                 1      2      3 case t                           1014 0.0000  20.25  29.11  22.01      0.0991  19.08  28.68  21.50      0.1991  18.43  28.27  21.18      0.2991  19.03  27.46  20.58      0.3991  18.71  26.96  20.21 1015 0.0000  20.22  29.22  21.81      0.0991  19.30  28.64  21.46      0.1991  18.68  28.18  21.11      0.2991  18.22  27.74  20.78      0.3991  17.84  27.25  20.42 1016 0.0000  21.75  29.17  21.82      0.0991  19.97  28.68  21.49      0.1991  19.65  28.17  21.11      0.2991  19.29  27.68  20.75      0.3991  18.94  27.18  20.37 

target values function of values @ time t==0. typically, k=0.5 half-time period. current sample,we take k=0.926

since values sorted, ok take first lines each case.

targets = df.groupby(level='case').first() * 0.926 print(targets)               1         2         3 case                               1014  18.75150  26.95586  20.38126 1015  18.72372  27.05772  20.19606 1016  20.14050  27.01142  20.20532 

now, how build following dataframe, shows time t @ wich each object reach target value calculated above?

             1         2         3 case                               1014    0.3991    0.3991    0.2991 1015    0.1991    0.3991    0.3991 1016    0.0991    0.3991    0.3991 

these of hack, let's see if there better solutions:

in [36]: targets['t']=0  in [37]: df2 = df.reset_index().set_index('case') - targets  in [38]: df3 = df2.groupby(df2.index).transform(lambda x: x.abs()==np.min(x.abs()))  in [39]: df4 = pd.dataframe({'1': df2.t[df3[1]],                     '2': df2.t[df3[2]],                     '3': df2.t[df3[3]]})  print df4             1       2       3 case                         1014  0.3991  0.3991  0.3991 1015  0.1991  0.3991  0.3991 1016  0.0991  0.3991  0.3991