How to flag tukey outliers using python pandas groupby

I would like to use pandas groupby to flag values in a df that are outliers. I think I've got it working, but as I'm new to python, wanted to ask if there is a more obvious / pythonic approach.

Given input data with two groups, two variables X and Y:

n=10000
df= pd.DataFrame({'key': ['a']*n+['b']*n
                 ,"x"  : np.hstack((
                  np.random.normal(10, 1.0, size=n)
                 ,np.random.normal(100, 1.0, size=n)
                  ))
                 ,"y"  : np.hstack((
                  np.random.normal(20, 1.0, size=n)
                 ,np.random.normal(200, 1.0, size=n)
                  ))              
                 })

To identify outliers I need to calculate the quartiles and inter-quartile range for each group to calculate the limits. Seemed reasonable to create a function:

def get_outlier(x,tukeymultiplier=2):
    Q1=x.quantile(.25)
    Q3=x.quantile(.75)
    IQR=Q3-Q1
    lowerlimit = Q1 - tukeymultiplier*IQR
    upperlimit = Q3 + tukeymultiplier*IQR
    return (x<lowerlimit) | (x>upperlimit)

And then use groupby and call the function via transform, e.g.:

g=df.groupby('key')[['x','y']]
df['x_outlierflag']=g.transform(get_outlier).x
df['y_outlierflag']=g.transform(get_outlier).y

df.loc[df.x_outlierflag==True]
df.loc[df.y_outlierflag==True]

I'm not worried about performance at this point, because the data are small. But not sure if there is a more natural way to do this? For example, it's not clear to me how apply() differs from transform(). Is there an apply() approach that would be better?

Solution

Here is the alternative more efficient and concise approach with groupby and transform

c = ['x', 'y']
Q1 = df.groupby('key')[c].transform('quantile', 0.25)
Q3 = df.groupby('key')[c].transform('quantile', 0.75)
IQR = Q3 - Q1
    
lower = df[c].lt(Q1 - IQR * tukeymultiplier)
upper = df[c].gt(Q3 + IQR * tukeymultiplier)

df.join((lower | upper).add_suffix('_outlier_flag'))

  key           x           y  x_outlier_flag  y_outlier_flag
0   a   37.179263  124.097101           False            True
1   a  -20.823970  -22.472672           False           False
2   a  123.438609 -121.588755            True            True
3   a  -53.817197   31.569917           False           False
4   a  -83.406668   48.850183            True           False
5   a  125.069615 -119.059600            True            True
6   a -177.185957  -39.235412            True           False
7   a  -57.935018  -36.746727           False           False
8   a   95.881448  -27.108608            True           False
9   a  -15.649324   86.914889           False            True
....