Pandas Dataframe Groupby with multiple columns and sum it

Question

I have a dataframe df which i need to groupby multiple column based on a condition.

Input

Employee Name	Subjects	Billable	Hours	Date
Anu	Java	Yes	8	01-03-2021
Anu	Python	Yes	9	02-03-2021
Anu	SQL	No	6	03-03-2021
Anu	React	Yes	5	03-03-2021
Anu	.Net	No	8	04-03-2021
Bala	SQL	No	5	01-03-2021
Bala	Python	Yes	4	01-03-2021
Bala	Java	Yes	2	02-03-2021
Bala	.Net	No	8	03-03-2021
Bala	React	Yes	7	04-03-2021

Columns should be add in output file: Utilization, Billable Utilization, Non-Billable Utilization

1. Calculation for Utilization columns : Utilization = df[Hours]/9*100

-> If billable column value as "YES"" - create column as Billable utilization

2. Calculation for Billable Utilization in column: Billable Utilization = df[Hours]/9 * 100

-> If billable column value as "NO"" - create column as Non-Billable utilization

3. Calculation for Non-Billing Utilization in column: Non-Billable Utilization = df[Hours]/9*100

Code

available =9
utilization= (df['Hours']/available*100).round(2)
df = df.assign(Utilization = utilization)
df1 = df.groupby(['Date','Employee Name'])['Utilization'].sum()
df['Billable'] = np.where(df['Billable'] == 'Yes', 'Billable Utilization','Non Billable Utilization')
df2 = (df.groupby(['Date','Employee Name']).agg({'Hours':sum}).div(available).mul(100)).round(2)

Expected Output

My output

Answer 1

You can "pivot" Hours into Billable and Non-Billable columns using pivot_table() and then convert to utilization:

# sample data
df = pd.DataFrame({'Employee Name':{0:'Anu',1:'Anu',2:'Anu',3:'Anu',4:'Anu',5:'Bala',6:'Bala',7:'Bala',8:'Bala',9:'Bala'},'Subjects':{0:'Java',1:'Python',2:'SQL',3:'React',4:'.Net',5:'SQL',6:'Python',7:'Java',8:'.Net',9:'React'},'Billable':{0:'Yes',1:'Yes',2:'No',3:'Yes',4:'No',5:'No',6:'Yes',7:'Yes',8:'No',9:'Yes'},'Hours':{0:8,1:9,2:6,3:5,4:8,5:5,6:4,7:2,8:8,9:7},'Date':{0:'01-03-2021',1:'02-03-2021',2:'03-03-2021',3:'03-03-2021',4:'04-03-2021',5:'01-03-2021',6:'01-03-2021',7:'02-03-2021',8:'03-03-2021',9:'04-03-2021'}})

# pivot hours into (non)billable
df = df.pivot_table(index=['Date', 'Employee Name'], columns='Billable').reset_index()
df.columns = ['Date', 'Employee Name', 'Non-Billable', 'Billable']

# compute utilization
df['Non-Billable'] = df['Non-Billable'].div(9).mul(100).round(2)
df['Billable'] = df['Billable'].div(9).mul(100).round(2)
df['Utilization'] = df[['Billable', 'Non-Billable']].fillna(0).sum(axis=1)

#          Date Employee Name  Non-Billable  Billable  Utilization
# 0  01-03-2021           Anu           NaN     88.89        88.89
# 1  01-03-2021          Bala         55.56     44.44       100.00
# 2  02-03-2021           Anu           NaN    100.00       100.00
# 3  02-03-2021          Bala           NaN     22.22        22.22
# 4  03-03-2021           Anu         66.67     55.56       122.23
# 5  03-03-2021          Bala         88.89       NaN        88.89
# 6  04-03-2021           Anu         88.89       NaN        88.89
# 7  04-03-2021          Bala           NaN     77.78        77.78