Transposing one column in python pandas with the simplest index possible -


i have following data (data_current):

import pandas pd import numpy np  data_current=pd.dataframe({'medicine':['green tea','fried tomatoes','meditation','meditation'],'disease':['acne','hypertension', 'cancer','lupus']}) data_current

what transpose 1 of columns, instead of having multiple rows same medicine , different diseases have 1 row each medicine several columns diseases. important keep index simple possible, i.e. 0,1,2... i.e. don't want assign 'medicines' index column because merge on other key. so, need data_needed

data_needed=pd.dataframe({'medicine':['green tea','fried tomatoes','meditation'],'disease_1':['acne','hypertension','cancer'], 'disease_2':['np.nan','np.nan','lupus']}) data_needed

here's 1 achieve output

firstly, groupby on medicine , disease list

in [368]: md = (data_current.groupby('medicine')                             .apply(lambda x: x['disease'].tolist())                             .reset_index())  in [369]: md out[369]:          medicine                0 0  fried tomatoes   [hypertension] 1       green tea           [acne] 2      meditation  [cancer, lupus]

then convert lists in column separate columns

in [370]: dval = pd.dataframe(md[0].tolist(), )  in [371]: dval out[371]:               0      1 0  hypertension   none 1          acne   none 2        cancer  lupus

now, can concat -- md dval

in [372]: md = md.drop(0, axis=1)  in [373]: data_final = pd.concat([md, dval], axis=1)

and, rename columns want.

in [374]: data_final.columns = ['medicine', 'disease_1', 'disease_2']  in [375]: data_final out[375]:          medicine     disease_1 disease_2 0  fried tomatoes  hypertension      none 1       green tea          acne      none 2      meditation        cancer     lupus

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