Clustering example using cities

Here is the Python code [#1]

import os
import matplotlib as mpl
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import sklearn as sk
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
import json
imagePos1 = 0
rlen1 = 1
clen1 = 1
dpi1 = 72
width1 = 640
height1 = 480
figSize1 = (width1 / dpi1, height1/dpi1)
fig1, ax1 = plt.subplots(rlen1,clen1,figsize=figSize1,dpi=dpi1)
fig1,ax1 = plt.subplots(1,1)
centers1 = 6
jsonFs1 = "././mapdata1.json"
print("Read: \"{0:s}\"".format(jsonFs1))
with open(jsonFs1,"r") as jsonF1:
	jsonDict1 = json.load(jsonF1)
dataList1 = jsonDict1["data"]
dataList2 = []
for lat1,lng1,city1 in dataList1:
	dataList2.append([lng1,lat1])
dataArray2 = np.array(dataList2)
kmeans1 = KMeans(
	n_clusters=centers1,
	init="k-means++",
	max_iter=300,
	n_init=10,
	random_state=0,
	)
pred_y = kmeans1.fit_predict(dataArray2)
plt.scatter(
	dataArray2[:,0],
	dataArray2[:,1],
	c="green",
	)
plt.scatter(
	kmeans1.cluster_centers_[:, 0],
	kmeans1.cluster_centers_[:, 1],
	s=200,
	c="red",
	)
plt.xlabel("Latitude")
plt.ylabel("Longitude")
plt.title("Cities scatter plot")
fig1.tight_layout()
imagePos1 += 1
saveFs1 = "./group23-1-0{0:d}.png".format(imagePos1)
fig1.savefig(saveFs1,bbox_inches="tight")
savedImage1 = Image.open(saveFs1)
savedWidth1, savedHeight1= savedImage1.size
(_, _, _, _, _, _, savedSize1, _, savedTime1, _) = os.stat(saveFs1)
print("\t{0:s} : {1:d} x {2:d} , {3:,} bytes".format(saveFs1,savedWidth1,savedHeight1,savedSize1))

Here is the output of the Python code.

Read: "././mapdata1.json"
	./group23-1-01.png : 630 x 470 , 66,496 bytes