Create a new Dataframe based on conditions from another Dataframe for loop

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Working with 2017 NFL Quarterback Data, looking to put the top 10 qbs each week in a dataframe (along with the rest of the data).

qb = {'week': [1, 1, 1, 2, 2, 2], 'qb': ['Rodgers', 'Brady', 'Wilson', 'Rodgers', 'Brady', 'Wilson'], 'pts': [30, 24, 20, 31, 20, 26]}

qb_df = pd.DataFrame(data=qb)

week    qb        pts
1       Rodgers   30
1       Brady     24
1       Wilson    20
2       Rodgers   31
2       Brady     20
3       Wilson    26

For this sake looking to return the top 2 from each week into a new dataframe.

week    qb        pts
1       Rodgers   30
1       Brady     24
2       Rodgers   31
2       Wilson    26

I tried a for loop that works as far as getting the data, but can't figure out to put it in a dataframe

top10_17 = pd.DataFrame()
for i in range(1, 18):
    i_17 = qb_2017.loc[qb_2017['Week'] == i].sort_values('FantasyPoints', ascending=False)[:10]
    top10_17 = pd.concat(i_17)

Used range(1,18) for the 17 weeks in an NFL season

IIUC sort_values with groupby + head

   pts       qb  week
0   30  Rodgers     1
1   24    Brady     1
3   31  Rodgers     2
5   26   Wilson     2

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grouped = qb_df.groupby('week')

This assumes your list is sorted, which can be done with pandas.sort_values()

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You could also do this:


week  qb     
1     Rodgers    30
      Brady      24
2     Rodgers    31
      Wilson     26
Name: pts, dtype: int64

If format is really important to stay constant:


week       qb  pts
0     1  Rodgers   30
1     1    Brady   24
2     2  Rodgers   31
3     2   Wilson   26

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