If your data are continuous, then you can use a Kolmogorov-Smirnov 2-sample test to test the hypothesis that they are the same. If your data are discrete, you can try the Anderson-Darling test, which does support continuous data in scipy as well. Another quick check is to use z-scores and set some threshold around the number SDs.
Not sure if you want to exclude your events' values when comparing (makes some assumptions about your data so that is up to you), but here is an example:
import numpy as np
from scipy import stats
V1 = [300, 120, 450, 700, 880, 400, 100, 60, 44, 91]
events = [(2,4), (7,8)]
for e in events:
e_vals = V1[e:e]
noe_vals = [V1[i] for i in range(0,len(V1)) if i not in range(e,e)]
print("Event ", e, ": ", stats.ks_2samp(noe_vals, e_vals))
print("Event ", e, ": ", stats.anderson_ksamp([noe_vals, e_vals]))
print("Event ", e, ": z-scores(", stats.zscore(V1)[e:e], ")")
Event (2, 4) : Ks_2sampResult(statistic=0.875, pvalue=0.072496346049057803)
Event (2, 4) : Anderson_ksampResult(statistic=1.0583965447073287, critical_values=array([ 0.325, 1.226, 1.961, 2.718, 3.752]), significance_level=0.11909520945783537)
Event (2, 4) : z-scores( [ 0.48957677 1.39285493] )
Event (7, 8) : Ks_2sampResult(statistic=0.88888888888888884, pvalue=0.21744842363651484)
Event (7, 8) : Anderson_ksampResult(statistic=0.071240617635128525, critical_values=array([ 0.325, 1.226, 1.961, 2.718, 3.752]), significance_level=0.32330529012728515)
Event (7, 8) : z-scores( [-0.91953717] )
Lots of things to toy around with:
If your event index is inclusive, just add 1 when slicing:
e_vals = V1[e:e+1], etc.;
if you want to exclude all other events, just add another loop to remove those before testing;
if you want to combine all events, slice using all the indexes.
If you want to go further and classify your event, you can utilize these values as additional data for a supervised classification model if you have past labeled events and other data, and build a better predictor that doesn't rely entirely on distribution assumptions.
Alternatively, you can use Monte Carlo simulation, GLM, OLS, etc. to fit your data and test the resulting statistics, similar to what IrishStat did.