如何调整数据框以调整某些条件？（除以列，将结构更改为T）

Question

How can I split the dataframe adjusting some condition?(divid by columns change structure by T)

I have dataframe like

   df:

        id   f1   c1     c2

   0    a    x     1     2
   1    b    x     3     4
   3    c    x     5     6
   4    a    y     7     8
   5    b    y     9     10
   6    c    y     11    12

and expected result

   dfX (filtered f1=x value):

       newid   a     b     c

   0    c1     1     3     5
   1    c2     2     4     6

  dfY (filtered f1=y value):

   0    c1     7     9     11
   1    c2     8     10    12

I need to split dataframe by f1 value and change the sturutre

I tried with 5~6 for loop and more than 20~30 lines and did it.

But it seems it's not the best way to do it. It would be appreciated if you give me some tips to handle the data more efficent way

Answer 1

#Set ID as index and transpose the DF and rename the column name.
df[df.f1=='x'][['id','c1','c2']].set_index('id').T.reset_index().rename(columns={'index':'newid'})
Out[115]: 
id newid  a  b  c
0     c1  1  3  5
1     c2  2  4  6

Similarly you can filter f1 on 'y':

df[df.f1=='y'][['id','c1','c2']].set_index('id').T.reset_index().rename(columns={'index':'newid'})
Out[120]: 
id newid  a   b   c
0     c1  7   9  11
1     c2  8  10  12

Answer 2

You can use groupby.

>>> for _, b in df.groupby(['f1']):
...     print b.pivot_table(columns='id').reset_index().rename(columns={'index':'newid'})
...     
id newid  a  b  c
0     c1  1  3  5
1     c2  2  4  6
id newid  a   b   c
0     c1  7   9  11
1     c2  8  10  12