pythonpandasdataframepandas.excelwriter
Pandas Dataframe Groupby with multiple columns and sum it
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
- Calculation for Utilization columns : Utilization = df[Hours]/9*100
-> If billable column value as "YES"" - create column as Billable utilization
- Calculation for Billable Utilization in column: Billable Utilization = df[Hours]/9 * 100
-> If billable column value as "NO"" - create column as Non-Billable utilization
- 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
Solution
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