python - Infer header names pandas dataframe for numeric type - 


id         91  57 60  79  888 111 06/03/2015  1   2   2   4   1   1 03/03/2015  1   2   2   2   2   3 06/04/2015  1   2   2   2   1   1 17/04/2015  1   3   2   2   1   3 21/04/2015  3   2   1   1   2   1 12/05/2015  1   3   2   2   2   3

i have csv file columns of id's (numeric value) , value (1-4) assigned each id dates. have data in following format:

    date       score id     06/03/2015  1   91     03/03/2015  1   91     06/04/2015  1   91     17/04/2015  1   91     21/04/2015  3   91     12/05/2015  1   91          06/03/2015  2   57     03/03/2015  2   57     06/04/2015  2   57       17/04/2015  3   57       21/04/2015  2   57       12/05/2015  3   57

etc...

attempt:

my thinking start creating pandas dataframe follows:

df = pd.read_csv("file.csv", sep=', ', delimiter=none, header='infer')

the problem having infer not seem able detect header names values numeric?

from here, hoping perform dataframe operations data desired format

use melt rename columns if necessary:

#s\+ space separator, if necessary change df = pd.read_csv("file.csv", sep='\s+')  d = {'id':'date'} cols = ['date','score','id'] df = df.rename(columns=d).melt('date', var_name='id', value_name='score')[cols]  #convert id column int df['id'] = df['id'].astype(int) print (df)          date  score   id 0  2015-06-03      1   91 1  2015-03-03      1   91 2  2015-06-04      1   91 3  2015-04-17      1   91 4  2015-04-21      3   91 5  2015-12-05      1   91 6  2015-06-03      2   57 7  2015-03-03      2   57 8  2015-06-04      2   57 ...

but if first column index possible use unstack:

df = pd.read_csv("file.csv", sep='\s+', index_col=[0], parse_dates=[0])

then possible convert columns int:

df.columns = df.columns.astype(int)  cols = ['date','score','id'] df = df.unstack().rename_axis(('id','date')).reset_index(name='score')[cols] print (df)           date  score   id 0  2015-06-03      1   91 1  2015-03-03      1   91 2  2015-06-04      1   91 3  2015-04-17      1   91 4  2015-04-21      3   91 5  2015-12-05      1   91 6  2015-06-03      2   57 ... ...

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