Pass percentiles to pandas agg function

Being more specific, if you just want to aggregate your pandas groupby results using the percentile function, the python lambda function offers a pretty neat solution. Using the question's notation, aggregating by the percentile 95, should be:

dataframe.groupby('AGGREGATE').agg(lambda x: np.percentile(x['COL'], q = 95))

You can also assign this function to a variable and use it in conjunction with other aggregation functions.

Perhaps not super efficient, but one way would be to create a function yourself:

def percentile(n):
    def percentile_(x):
        return np.percentile(x, n)
    percentile_.__name__ = 'percentile_%s' % n
    return percentile_

Then include this in your agg:

In [11]: column.agg([np.sum, np.mean, np.std, np.median,
                     np.var, np.min, np.max, percentile(50), percentile(95)])
Out[11]:
           sum       mean        std  median          var  amin  amax  percentile_50  percentile_95
AGGREGATE
A          106  35.333333  42.158431      12  1777.333333    10    84             12           76.8
B           36  12.000000   8.888194       9    79.000000     5    22             12           76.8

Note sure this is how it should be done though...

I believe the idiomatic way to do this in pandas is:

df.groupby("AGGREGATE").quantile([0, 0.25, 0.5, 0.75, 0.95, 1])

You can have agg() use a custom function to be executed on specified column:

# 50th Percentile
def q50(x):
    return x.quantile(0.5)

# 90th Percentile
def q90(x):
    return x.quantile(0.9)

my_DataFrame.groupby(['AGGREGATE']).agg({'MY_COLUMN': [q50, q90, 'max']})