imporve normalizing

def cust(x):

    return ' (' + str(round(x, 2)) + ')'

    return ' (' + str(x) + ')'

grs = pd.DataFrame()

for n, gr in df.groupby(['bias', 'name_bias']):

    n_edit = '{}_coverage_of_{}_person'.format(n[0], n[1]) 

    counts = gr.verb.value_counts()

    counts = counts / overall_counts.loc[counts.index]

    counts2 = overall_counts.loc[counts.index]

    counts.index += counts.apply(cust)

    counts.index += counts2.apply(cust)

    sorted_verbs = counts.sort_values(ascending=False).head(args.n)

    sorted_verbs.to_csv("{}.csv".format(n_edit))

    counts = pd.DataFrame(gr.verb.value_counts())#, columns=["within"])

    #print(counts.columns)

    counts["across"] = overall_counts.loc[counts.index]

    #print(counts)

    counts["normalized"] = counts.verb / counts.across

    #print(counts)

    #counts.normalized = counts.normalized.apply(round)

    #print(counts)

    sorted_verbs = counts.sort_values(by="normalized", ascending=False).head(args.n)

    #print(sorted_verbs)

    #exit(0)

    sorted_verbs.to_csv("frequent_verbs/{}.csv".format(n_edit), sep="\t")

    grs[n] = sorted_verbs.index

print(grs)

grs.to_csv('most_freq_verbs.csv')

grs.to_csv('frequent_verbs/most_freq_verbs.csv')

exit(0)