how to pivot complex dataframe

Question

I have a dataframe shown in below.

df =pd.DataFrame({'ID': [1, 2, 3, 4, 5], 'contract1' :["A", "B", "C", "D", "B"],
           'contract2' :["C", "A", np.nan, "A", np.nan],
           'contract3' :[np.nan, "C", np.nan, np.nan, np.nan] })
df

ID        contract1   contract2   contract3    
1             A           C         nan
2             B           A          C
3             C          nan        nan
4             D           A         nan
5             B          nan        nan

I would like the flag result like this;

ID     A     B     C      D
1      1     0     1      0
2      1     1     1      0 
3      0     0     1      0
4      1     0     0      1
5      0     1     0      0  

This flag table show whether each ID have a each contract. Maybe pivot is available,but I couldn't handle this kind of complex dataframe... Can I ask how to transform ?

Answer 1

A Faster implementation would be to use melt in conjunction with str.get_dummies as shown:

pd.melt(df, id_vars=['ID']).set_index('ID')['value']        \
                           .str.get_dummies()               \
                           .groupby(level=0)                \
                           .agg(np.sum)

    A  B  C  D
ID            
1   1  0  1  0
2   1  1  1  0
3   0  0  1  0
4   1  0  0  1
5   0  1  0  0

Answer 2

You can melt your original data frame to long format and then use crosstab() on the ID and value column:

import pandas as pd
df1 = df.set_index('ID').stack().rename("Type").reset_index()    
pd.crosstab(df1.ID, df1.Type)

# Type  A   B   C   D
#   ID              
#   1   1   0   1   0
#   2   1   1   1   0
#   3   0   0   1   0
#   4   1   0   0   1
#   5   0   1   0   0