In [5]:

    

import pandas
import numpy as np
import pandas as pd

#from __future__ import print_function

from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score


import matplotlib.pyplot as plt
import matplotlib.cm as cm

%matplotlib inline  
import seaborn as sns


    

In [6]:

    

df = pd.read_csv("hUSCensus1990raw50K.csv.bz2",compression = "bz2")


    

In [7]:

    

df_demo = pd.DataFrame()


df_demo["AGE"] = df[["AGE"]].copy()
df_demo["INCOME"] = df[["INCOME" + str(i) for i in range(1,8)]].sum(axis = 1)

df_demo["YEARSCH"] = df[["YEARSCH"]].copy()
df_demo["ENGLISH"] = df[["ENGLISH"]].copy()
df_demo["FERTIL"] = df[["FERTIL"]].copy()
df_demo["YRSSERV"] = df[["YRSSERV"]].copy()



df_demo = pd.get_dummies(df_demo, columns = ["ENGLISH", "FERTIL" ])


    

In [8]:

    

X = df_demo.values[np.random.choice(df_demo.values.shape[0], 10000)]

from sklearn import metrics
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_db = sc.fit_transform(X)


n_clusters = 3

labels = KMeans(n_clusters = n_clusters).fit_predict(X_db)




print('Number of clusters: %d' % n_clusters)

print("Silhouette Coefficient: %0.3f"
      % metrics.silhouette_score(X_db, labels))

unique_labels = set(labels)
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
for k, col in zip(unique_labels, colors):
    if k == -1:
        # Black used for noise.
        col = 'k'

    class_member_mask = (labels == k)

    xy = X[class_member_mask]
    plt.scatter(xy[:, 0], xy[:, 1],  c = col, edgecolor='k')

    xy = X[class_member_mask]
    plt.scatter(xy[:, 0], xy[:, 1],  c = col, edgecolor='k')


    

    




Number of clusters: 3
Silhouette Coefficient: 0.167






    

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