Given data frame:
Group | count | status | Duration |
---|---|---|---|
A | 2 | 1 | 2.4 |
A | 4 | 0 | 7 |
A | 2 | 1 | 4 |
B | 3 | 1 | 6 |
B | 2 | 0 | 7 |
df.groupby("Group")["Duration"].max()
Expected Result data frame:
Group | count | status | Duration |
---|---|---|---|
A | 4 | 0 | 7 |
B | 2 | 0 | 7 |
Given data frame:
Group | count | status | Duration |
---|---|---|---|
A | 2 | 1 | 2.4 |
A | 4 | 0 | 7 |
A | 2 | 1 | 4 |
B | 3 | 1 | 6 |
B | 2 | 0 | 7 |
df.groupby("Group")["Duration"].max()
Expected Result data frame:
Group | count | status | Duration |
---|---|---|---|
A | 4 | 0 | 7 |
B | 2 | 0 | 7 |
You'll also need as_index=False
to prevent the group columns from becoming the index in your output.
df.groupby("Group",as_index=False)[["count","status","Duration"]].max()