python - about concat in pandas : using row data to create new columns -


the same question has been posted on pydata google group.

i want custom concat i.e using rows in group object create new cols.

here contrived example:

input data frame name age foo     12 bar     14  df = pandas.dataframe({  'name':['foo','bar'],'age': [12,14] })    expected output, pandas data frame 4 cols  foo 12 bar 14

ps: looking efficient solution applied grouped pandas object containing 800k odd groupings.

sample 800k data have following structure. still using analogy actual data scientific , column names might not intuitive

subject (grouped col)           name     age        mark1              foo      12         80                bar      14         90

what want grouped data following data frame

subject foo 12 80 bar 14 90

you want reshape values of dataframe so:

in [43]: pandas.dataframe(df[['name', 'age']].values.reshape(1, 4)) out[43]:      0   1    2   3 0  foo  12  bar  14

this should efficient reshape() returns view. credits @wouter overmeire.


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