added range option

parser.add_argument("-c", "--combine", action="store_true", default=False, help="whether to combine or not")

parser.add_argument("-a", "--arg", default=None, help="analyze args or not")

parser.add_argument("-n", "--n", type=int, default=50, help="nr of top verbs to show")

parser.add_argument("-mf", "--min_freq", type=int, default=5, help="min verb frequency to eliminate rarest verbs")

parser.add_argument("-min", "--min_freq", type=int, default=100, help="min verb frequency to eliminate rarest verbs")

parser.add_argument("-max", "--max_freq", type=int, default=2000, help="min verb frequency to eliminate rarest verbs")

parser.add_argument("-l", "--lemmatize", action="store_true", default=False, help="whether to (re)do lemmatization")

args = parser.parse_args()

MIN_FREQ = args.min_freq

#print('Arg-0 only {}:'.format(df.shape))

# combine output

print(output.sent_id.value_counts().head())

output['sent_id'] = output['sent_id'].astype(float)

sent['sent_id'] = sent['sent_id'].astype(float)

df = pd.merge(output, sent, on=['sent_id'])

namlabs_df, namlabs = load_nameslab()

df = pd.merge(df, namlabs_df, on=['name'])

# compute overall counts

overall_freq = df.verb.value_counts()

print(overall_freq.head(3))

print(overall_freq.tail(3))

df["across"] = overall_freq.loc[df.verb].values

df = df[df.across > MIN_FREQ]

binned = pd.DataFrame()

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

    sorted_verbs.to_csv("frequent_verbs/{}{}.csv".format(n_edit, '_arg' if ARG else ''), sep="\t")

    binned[n] = sorted_verbs.index

    binned[n + 'across'] = sorted_verbs.across

    binned[n + 'across'] = sorted_verbs.across

print(binned.head(N))

binned.to_csv('frequent_verbs/most_freq_verbs{}.csv'.format('_arg' if ARG else ''))