pandas example:
>>> import pandas as pd
>>> df = pd.read_csv("grouped.csv", sep="[,\s]*")
>>> df
epochtime name score level extralives
0 1234455 suzy 120 3 0
1 1234457 billy 123 1 2
2 1234459 billy 124 2 4
3 1234459 suzy 224 5 4
4 1234460 suzy 301 7 1
5 1234461 billy 201 3 1
>>> g = df.groupby("name").describe()
>>> g
epochtime score level extralives
name
billy count 3.000000 3.000000 3.0 3.000000
mean 1234459.000000 149.333333 2.0 2.333333
std 2.000000 44.747439 1.0 1.527525
min 1234457.000000 123.000000 1.0 1.000000
25% 1234458.000000 123.500000 1.5 1.500000
50% 1234459.000000 124.000000 2.0 2.000000
75% 1234460.000000 162.500000 2.5 3.000000
max 1234461.000000 201.000000 3.0 4.000000
suzy count 3.000000 3.000000 3.0 3.000000
mean 1234458.000000 215.000000 5.0 1.666667
std 2.645751 90.835015 2.0 2.081666
min 1234455.000000 120.000000 3.0 0.000000
25% 1234457.000000 172.000000 4.0 0.500000
50% 1234459.000000 224.000000 5.0 1.000000
75% 1234459.500000 262.500000 6.0 2.500000
max 1234460.000000 301.000000 7.0 4.000000
Or simply:
>>> df.groupby("name").mean()
epochtime score level extralives
name
billy 1234459 149.333333 2 2.333333
suzy 1234458 215.000000 5 1.666667
And then:
>>> g.ix[("billy","mean")]
epochtime 1234459.000000
score 149.333333
level 2.000000
extralives 2.333333
Name: (billy, mean), dtype: float64
>>> g.ix[("billy","mean")]["score"]
149.33333333333334
>>> g["score"]
name
billy count 3.000000
mean 149.333333
std 44.747439
min 123.000000
25% 123.500000
50% 124.000000
75% 162.500000
max 201.000000
suzy count 3.000000
mean 215.000000
std 90.835015
min 120.000000
25% 172.000000
50% 224.000000
75% 262.500000
max 301.000000
Name: score, dtype: float64
Et cetera. If you're thinking in R/SQL ways, but want to use Python, then definitely give pandas a try.
Note that you can also do multi-column groupbys:
>>> df.groupby(["epochtime", "name"]).mean()
score level extralives
epochtime name
1234455 suzy 120 3 0
1234457 billy 123 1 2
1234459 billy 124 2 4
suzy 224 5 4
1234460 suzy 301 7 1
1234461 billy 201 3 1