Grouping your DataFrame
can get the job done for you. I’ll demonstrate using this simplified version of your data:
import pandas as pd
data = {
'Team':['L.A. Lakers', 'Denver',
'Houston', 'Utah', 'Oklahoma',
'L.A. Lakers', 'Dallas'],
'G':[59,60,59,59,60,58,60],
'W':[46,41,39,37,37,45,36],
'L':[13,19,20,22,23,13,24],
}
df = pd.DataFrame(data)
print(df)
Out:
Team G W L
0 L.A. Lakers 59 46 13
1 Denver 60 41 19
2 Houston 59 39 20
3 Utah 59 37 22
4 Oklahoma 60 37 23
5 L.A. Lakers 58 45 13
6 Dallas 60 36 24
Now I can use groupby
and aggregate by max values:
grouped = df.groupby('Team')[['G', 'W', 'L']].agg('max')
print(grouped)
Out:
G W L
Team
Dallas 60 36 24
Denver 60 41 19
Houston 59 39 20
L.A. Lakers 59 46 13
Oklahoma 60 37 23
Utah 59 37 22
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