You can convert a pandas groupby object into a pivot table using the pivot_table() method in pandas. Here's an example:

import pandas as pd
 
# create a pandas DataFrame
df = pd.DataFrame({'Region': ['North', 'North', 'South', 'South', 'West', 'West'],
                   'Salesperson': ['Alice', 'Bob', 'Charlie', 'Dave', 'Eve', 'Frank'],
                   'Sales': [100, 200, 150, 50, 75, 125]})
 
# group by region and salesperson and calculate the total sales for each group
grouped = df.groupby(['Region', 'Salesperson']).sum()
 
# convert the groupby object to a pivot table
pivot = grouped.pivot_table(values='Sales', index=['Region'], columns=['Salesperson'], aggfunc=sum)
 
print(pivot)

This will group the DataFrame by region and salesperson and calculate the total sales for each group. Then, the pivot_table() method is used to convert the resulting groupby object into a pivot table that shows the total sales for each salesperson broken down by region. The resulting output will be the same as the previous example:

Salesperson  Alice  Bob  Charlie  Dave  Eve  Frank
Region                                            
North          100  200      NaN   NaN  NaN    NaN
South          NaN  NaN    150.0  50.0  NaN    NaN
West           NaN  NaN      NaN   NaN  75.0  125.0
Note that the parameters passed to the pivot_table() method are similar to those used in the previous example. The values parameter specifies the column to summarize (in this case, 'Sales'), the index parameter specifies the rows to group by (in this case, 'Region'), and the columns parameter specifies the columns to group by (in this case, 'Salesperson'). The aggfunc parameter specifies the function to use to summarize the data (in this case, sum(), which is the default function).