Drop pandas dataframe row based on max value of a column

Question

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

Answer 1

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