CODEWITHSUNDEEP
pythonpandaspivotreshapetranspose
k song
k song

Reputation: 69

Pandas partial transpose

I want to reformat a dataframe by transeposing some columns with fixing other columns.

original data :

ID  subID  values_A  
--  -----  --------  
A    aaa     10      
B    baa     20      
A    abb     30      
A    acc     40      
C    caa     50      
B    bbb     60

Pivot once : 

 pivot_table( df, index = ["ID", "subID"] )

Output:

ID  subID  values_A 
--  -----  -------- 
A    aaa     10     
     abb     30     
     acc     40     

B    baa     20     
     bbb     60     

C    caa     50

What I want to do ( Fix ['ID'] columns and partial transpose ) : 

ID  subID_1  value_1   subID_2  value_2  subID_3  value_3
--  -------  -------  --------  -------  -------  -------
A    aaa       10       abb       30       acc       40
B    baa       20       bbb       60       NaN       NaN 
C    caa       50       NaN       NaN      NaN       NaN

what I know max subIDs count value which are under each IDs.

I don't need any calculating value when pivot and transepose dataframe.

Please help

Upvotes: 1

Views: 1619

Answers (1)

jezrael
jezrael

Reputation: 862581

Use cumcount for counter, create MultiIndex by set_index, reshape by unstack and sort first level of MultiIndex in columns by sort_index. Last flatten it by list comprehension with reset_index:

g = df.groupby('ID').cumcount()

df = df.set_index(['ID', g]).unstack().sort_index(level=1, axis=1)
#python 3.6+
df.columns = [f'{a}_{b+1}' for a, b in df.columns]
#python bellow
#df.columns = ['{}_{}'.format(a, b+1) for a, b in df.columns]
df = df.reset_index()
print (df)
  ID subID_1  values_A_1 subID_2  values_A_2 subID_3  values_A_3
0  A     aaa        10.0     abb        30.0     acc        40.0
1  B     baa        20.0     bbb        60.0     NaN         NaN
2  C     caa        50.0     NaN         NaN     NaN         NaN

Upvotes: 2

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