AssertionError when running K means Main Function - python-2.7

Question

When Running the below code, I receive an AssertionError in the Main Function, assert len(args) > 1. Any idea where in the code the issue occurs?
K-Means clustering implementation
import numpy as np
from math import sqrt
import csv
import sys
====
Define a function that computes the distance between two data points
GAP = 2
MIN_VAL = 1000000
def get_distance(point1, point2):
dis = sqrt(pow(point1[0] - point2[0],2) + pow(point1[1] - point2[1],2))
return dis
====
Define a function that reads data in from the csv
def csvreader(data_file):
sampleData = []
global Countries
with open(data_file, 'r') as csvfile:
read_data = csv.reader(csvfile, delimiter=' ', quotechar='|')
for row in read_data:
print ', '.join(row)
if read_data <> None:
for f in read_data:
values = f.split(",")
if values[0] <> 'Countries':
sampleData.append([values[1],values[2]])
return sampleData
====
Write the initialisation procedure
def cluster_dis(centroid, cluster):
dis = 0.0
for point in cluster:
dis += get_distance(centroid, point)
return dis
def update_centroids(centroids, cluster_id, cluster):
x, y = 0.0, 0.0
length = len(cluster)
if length == 0:
return
for item in cluster:
x += item[0]
y += item[1]
centroids[cluster_id] = (x / length, y / length)
====
Implement the k-means algorithm, using appropriate looping
def kmeans(data, k):
assert k <= len(data)
seed_ids = np.random.randint(0, len(data), k)
centroids = [data[idx] for idx in seed_ids]
clusters = [[] for _ in xrange(k)]
cluster_idx = [-1] * len(data)
pre_dis = 0
while True:
for point_id, point in enumerate(data):
min_distance, tmp_id = MIN_VAL, -1
for seed_id, seed in enumerate(centroids):
distance = get_distance(seed, point)
if distance < min_distance:
min_distance = distance
tmp_id = seed_id
if cluster_idx[point_id] != -1:
dex = clusters[cluster_idx[point_id]].index(point)
del clusters[cluster_idx[point_id]][dex]
clusters[tmp_id].append(point)
cluster_idx[point_id] = tmp_id
now_dis = 0.0
for cluster_id, cluster in enumerate(clusters):
now_dis += cluster_dis(centroids[cluster_id], cluster)
update_centroids(centroids, cluster_id, cluster)
delta_dis = now_dis - pre_dis
pre_dis = now_dis
if delta_dis < GAP:
break
print(centroids)
print(clusters)
return centroids, clusters
def main():
args = sys.argv[1:]
assert len(args) > 1
data_file, k = args[0], int(args[1])
data = csvreader(data_file)
kmeans(data, k)
if __name__ == '__main__':
main()