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I have a Dataframe like so:
p_rel y_BET sq_resid
1 0.069370 41.184996 0.292942
2 0.116405 43.101090 0.010953
3 0.173409 44.727748 0.036832
4 0.225629 46.681293 0.540616
5 0.250682 46.980616 0.128191
6 0.294650 47.446113 0.132367
7 0.322530 48.078038 0.235047
How do I get rid of the fourth row because it has the max value of sq_resid? note: the max will change from dataset to dataset so just removing the 4th row isn't enough.
I have tried several things such as I can remove the max value which leaves the dataframe like below but haven't been able to remove the whole row.
p_rel y_BET sq_resid
1 0.069370 41.184996 0.292942
2 0.116405 43.101090 0.010953
3 0.173409 44.727748 0.036832
4 0.225629 46.681293 Nan
5 0.250682 46.980616 0.128191
6 0.294650 47.446113 0.132367
7 0.322530 48.078038 0.235047
818
Use pandas. DataFrame. drop() method to delete/remove rows with condition(s).
To drop a specific row from the data frame – specify its index value to the Pandas drop function. It can be useful for selection and aggregation to have a more meaningful index. For our sample data, the “name” column would make a good index also, and make it easier to select country rows for deletion from the data.
You could just filter the df like so:
In [255]:
df.loc[df['sq_resid']!=df['sq_resid'].max()]
Out[255]:
p_rel y_BET sq_resid
1 0.069370 41.184996 0.292942
2 0.116405 43.101090 0.010953
3 0.173409 44.727748 0.036832
5 0.250682 46.980616 0.128191
6 0.294650 47.446113 0.132367
or drop using idxmax which will return the label row of the max value:
In [257]:
df.drop(df['sq_resid'].idxmax())
Out[257]:
p_rel y_BET sq_resid
1 0.069370 41.184996 0.292942
2 0.116405 43.101090 0.010953
3 0.173409 44.727748 0.036832
5 0.250682 46.980616 0.128191
6 0.294650 47.446113 0.132367
7 0.322530 48.078038 0.235047
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