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Hi I'm new to data science. Learning data science from course-era. I'm having pandas data frame as follows,

   time   value

A   9      5
A   8      4
A   7      3
B   9      3
B   8      2
B   7      1
C   9      3
C   8      2
C   7      1

I want to convert this as ,

       A   B  C

9      5   3  3 
8      4   2  2
7      3   1  1

As I start to write query for this, it is getting complicated. Is there any easy way to do this? Thanks for the help.

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For me, when it comes to reshaping a dataframe(switching columns/indices/rows and such) its fairly intuitive using the pivot_table function.

my_df.pivot_table(index='time', columns=my_df.index, values='value')
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  • $\begingroup$ perfect! you saved my day.... Thank you so much.... $\endgroup$ – Gokulakannan Oct 25 '18 at 15:10
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Recreating your problem:

import pandas as pd
d={'time':[9, 8, 7, 9, 8, 7, 9, 8, 7],'value':[5, 4, 3, 3, 2, 1, 3, 2, 1]}
df = pd.DataFrame(data=d,index=['A', 'A', 'A', 'B','B','B','C','C','C'])

Using a function with for loop.

def change_my_df(give_df,column_to_index,value_in_cell):
"""Specify the dataframe, the column that will become the new inex, the column that will populate the cell of the new df"""
    new_index=give_df[column_to_index].unique().tolist()
    new_columns=list(set(give_df.index.values))
    new_df=pd.DataFrame(index=new_index,columns=new_columns)
    for l,r in give_df.iterrows():
        new_df[l][r[str(column_to_index)]]=r[str(value_in_cell)]
    return new_df
new_df=change_my_df(df,'time','value')
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