python - pandas - get values from multindex columns -


i have following dataframe df:

h,nu,city,code,code2 0.965392,15,madrid,es,es 0.920614,15,madrid,it,es 0.726219,16,madrid,tn,es 0.739119,17,madrid,fr,es 0.789923,55,dublin,mt,en 0.699239,57,dublin,en,en 0.890462,68,dublin,ar,en 0.746863,68,dublin,pt,en 0.789923,55,milano,it,it 0.699239,57,milano,es,it 0.890462,68,milano,ar,it 0.746863,68,milano,pt,it

i add new column hcode, each city, h value corresponding code mapped code2 string, resulting dataframe appears as:

h,nu,city,code,code2,hcode 0.965392,15,madrid,es,es,0.965392 0.920614,15,madrid,it,es,0.965392 0.726219,16,madrid,tn,es,0.965392 0.739119,17,madrid,fr,es,0.965392 0.789923,55,dublin,mt,en,0.699239 0.699239,57,dublin,en,en,0.699239 0.890462,68,dublin,ar,en,0.699239 0.746863,68,dublin,pt,en,0.699239 0.789923,55,milano,it,it,0.789923 0.699239,57,milano,es,it,0.789923 0.890462,68,milano,ar,it,0.789923 0.746863,68,milano,pt,it,0.789923

so far tried groupby city , code2, no results.

you can groupby on 'city' , 'code2', call first on , reset index resulting in following:

in [172]: gp = df.groupby(['city','code2'])['h'].first().reset_index() gp  out[172]:      city code2         h 0  dublin    en  0.789923 1  madrid    es  0.965392 2  milano     0.789923

then perform left merge on original df , select 'h_y' column, name comes fact columns clash , ffill this:

in [173]: df['hcode'] = df.merge(gp, left_on=['city', 'code'], right_on=['city', 'code2'], how='left')['h_y'].ffill() df  out[173]:            h  nu    city code code2     hcode 0   0.965392  15  madrid   es    es  0.965392 1   0.920614  15  madrid      es  0.965392 2   0.726219  16  madrid   tn    es  0.965392 3   0.739119  17  madrid   fr    es  0.965392 4   0.789923  55  dublin   mt    en  0.965392 5   0.699239  57  dublin   en    en  0.789923 6   0.890462  68  dublin   ar    en  0.789923 7   0.746863  68  dublin   pt    en  0.789923 8   0.789923  55  milano       0.789923 9   0.699239  57  milano   es     0.789923 10  0.890462  68  milano   ar     0.789923 11  0.746863  68  milano   pt     0.789923

result of merge show produces:

in [165]: df.merge(gp, left_on=['city', 'code'], right_on=['city', 'code2'])['h_y']  out[165]: 0    0.965392 1    0.789923 2    0.789923 name: h_y, dtype: float64

edit

ok, iiuc can group before filter group 'code2' equals 'code' , use merge against:

in [200]: gp = df.groupby('city') mask = gp.apply(lambda x: x['code2'] == x['code']) lookup = df.loc[mask[mask].reset_index(level=0).index] lookup  out[200]:           h  nu    city code code2 5  0.699239  57  dublin   en    en 0  0.965392  15  madrid   es    es 8  0.789923  55  milano       in [202]: df['hcode'] = df.merge(lookup, left_on=['city', 'code'], right_on=['city', 'code2'], how='left')['h_y'].ffill() df  out[202]:            h  nu    city code code2     hcode 0   0.965392  15  madrid   es    es  0.965392 1   0.920614  15  madrid      es  0.965392 2   0.726219  16  madrid   tn    es  0.965392 3   0.739119  17  madrid   fr    es  0.965392 4   0.789923  55  dublin   mt    en  0.965392 5   0.699239  57  dublin   en    en  0.699239 6   0.890462  68  dublin   ar    en  0.699239 7   0.746863  68  dublin   pt    en  0.699239 8   0.789923  55  milano       0.789923 9   0.699239  57  milano   es     0.789923 10  0.890462  68  milano   ar     0.789923 11  0.746863  68  milano   pt     0.789923

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