pandas DataFrame and pandas.groupby to calculate Salaries

For my assignment, I need to import baseball salary data into a pandas DataFrame.
From there, one of my objectives is to get the salaries of all the teams per year.

I was successful however in order to move onto the next task, I need a pandas DataFrame. sumofSalaries.dtype is returning int64.

Questions:
1. How do I convert the data in the code following into a DataFrame?
2. How do I delete the indexes in sumofSalaries?

Code:

 import pandas as pd
 salariesData = pd.read_csv('Salaries.csv')

 #sum salaries by year and team
 sumOfSalaries = salariesData.groupby(by=['yearID','teamID'])['salary'].sum()

 del sumOfSalaries.index.names #line giving me errors

 #create DataFrame from grouped data 
 df = pd.DataFrame(sumOfSalaries, columns = ['yearID', 'teamID', 'salary'])
 df

 _____________________________________________________________________________

 sumofSalaries:
 yearID  teamID
 1985    ATL        14807000
         BAL        11560712
         BOS        10897560
         CAL        14427894
         CHA         9846178

 ...and so on
 _____________________________________________________________________________

  df:

            yearID  teamID  salary
 yearID teamID          
 1985   ATL NaN NaN 14807000
        BAL NaN NaN 11560712
        BOS NaN NaN 10897560
        CAL NaN NaN 14427894

Solution

I think you need only add parameter as_index=False to groupby, output is DataFrame without MultiIndex:

sumOfSalaries = salariesData.groupby(by=['yearID','teamID'], as_index=False)['salary'].sum()

Sample:

import pandas as pd

salariesData = pd.DataFrame({
'yearID': {0: 1985, 1: 1985, 2: 1985, 3: 1985, 4: 1985, 5: 1986, 6: 1986, 7: 1986, 8: 1987, 9: 1987}, 
'teamID': {0: 'ATL', 1: 'ATL', 2: 'ATL', 3: 'CAL', 4: 'CAL', 5: 'CAL', 6: 'CAL', 7: 'BOS', 8: 'BOS', 9: 'BOS'}, 
'salary': {0: 10, 1: 20, 2: 30, 3: 40, 4: 50, 5: 10, 6: 20, 7: 30, 8: 40, 9: 50}
},
columns = ['yearID','teamID','salary']
)

print (salariesData)
   yearID teamID  salary
0    1985    ATL      10
1    1985    ATL      20
2    1985    ATL      30
3    1985    CAL      40
4    1985    CAL      50
5    1986    CAL      10
6    1986    CAL      20
7    1986    BOS      30
8    1987    BOS      40
9    1987    BOS      50

sumOfSalaries = salariesData.groupby(by=['yearID','teamID'], as_index=False)['salary'].sum()

print (sumOfSalaries)
   yearID teamID  salary
0    1985    ATL      60
1    1985    CAL      90
2    1986    BOS      30
3    1986    CAL      30
4    1987    BOS      90

Also if need remove index names, use assign to (None, None), but if use solution above, it is not necessary:

sumOfSalaries.index.names = (None, None)

Sample:

sumOfSalaries = salariesData.groupby(by=['yearID','teamID'])['salary'].sum()
sumOfSalaries.index.names = (None, None)

print (sumOfSalaries)

1985  ATL    60
      CAL    90
1986  BOS    30
      CAL    30
1987  BOS    90
Name: salary, dtype: int64