Conditional grouping in pandas and transpose - dataframe

Question

With an input dataframe framed out of a given CSV, I need to transpose the data based on certain conditions. The groupby should be applied based on Key value.
For any value in the same 'Key' group, if the 'Type' is "T", these values should be written on "T" columns labelled as T1, T2, T3...and so on.
For any value in the same 'Key' group, if the 'Type' is "P" and 'Code' ends with "00" these values should be written on "U" columns labelled as U1, U2, U3...and so on.
For any value in the same 'Key' group, if the 'Type' is "P" and 'Code' doesn't end with "00" these values should be written on "P" columns labelled as P1, P2, P3...and so on.
There might be n number of values of type T & P for any Key value and the output columns for T & P should be updated accordingly
Input Dataframe:
df = pd.DataFrame({'Key': ['1', '1', '1', '1', '1', '2', '2', '2', '2', '2'],
'Value': ['T101', 'T102', 'P101', 'P102', 'P103', 'T201', 'T202', 'P201', 'P202', 'P203'],
'Type': ['T', 'T', 'P', 'P', 'P', 'T', 'T', 'P', 'P', 'P'],
'Code': ['0', '0', 'ABC00', 'TWY01', 'JTH02', '0', '0', 'OUJ00', 'LKE00', 'WDF45']
})
Expected Dataframe:
Can anyone suggest an effective solution for this case?

Answer 1

Here's a possible solution using pivot.
import pandas as pd
df = pd.DataFrame({'Key': ['1', '1', '1', '1', '1', '2', '2', '2', '2', '2'],
'Value': ['T101', 'T102', 'P101', 'P102', 'P103', 'T201', 'T202', 'P201', 'P202', 'P203'],
'Type': ['T', 'T', 'P', 'P', 'P', 'T', 'T', 'P', 'P', 'P'],
'Code': ['0', '0', 'ABC00', 'TWY01', 'JTH02', '0', '0', 'OUJ00', 'LKE00', 'WDF45']
})
# Set up the U label
df.loc[(df['Code'].apply(lambda x: x.endswith('00'))) & (df['Type'] == 'P'), 'Type'] = 'U'
# Type indexing by key by type
df = df.join(df.groupby(['Key','Type']).cumcount().rename('Tcount').to_frame() + 1)
df['Type'] = df['Type'] + df['Tcount'].astype('str')
# Pivot the table
pv =df.loc[:,['Key','Type','Value']].pivot(index='Key', columns='Type', values='Value')
>>>pv
Type P1 P2 T1 T2 U1 U2
Key
1 P102 P103 T101 T102 P101 NaN
2 P203 NaN T201 T202 P201 P202
cdf = df.loc[df['Code'] != '0', ['Key', 'Code']].groupby('Key')['Code'].apply(lambda x: ','.join(x))
>>>cdf
Key
1 ABC00,TWY01,JTH02
2 OUJ00,LKE00,WDF45
Name: Code, dtype: object
>>>pv.join(cdf)
P1 P2 T1 T2 U1 U2 Code
Key
1 P102 P103 T101 T102 P101 None ABC00,TWY01,JTH02
2 P203 None T201 T202 P201 P202 OUJ00,LKE00,WDF45