multi_dimensional_data_analysis.py

# https://archive.ics.uci.edu/ml/datasets/Zoo

import pandas as pd

zoo_data = pd.read_csv('zoo_data-1.csv', encoding='utf-8', index_col=['animal_name'])

# print first 5 rows of zoo data
print(zoo_data.head())

from sklearn.cluster import KMeans
clusters = 7

kmeans = KMeans(n_clusters=clusters)
kmeans.fit(zoo_data)

print(kmeans.labels_)
print(kmeans.inertia_)

from sklearn.decomposition import PCA
pca = PCA(3)
pca.fit(zoo_data)

pca_data = pd.DataFrame(pca.transform(zoo_data))
print(pca_data.head())


from matplotlib import colors as mcolors 
import math 
   
''' Generating different colors in ascending order  
                                of their hsv values '''
colors = list(zip(*sorted(( 
                    tuple(mcolors.rgb_to_hsv( 
                          mcolors.to_rgba(color)[:3])), name) 
                     for name, color in dict( 
                            mcolors.BASE_COLORS, **mcolors.CSS4_COLORS 
                                                      ).items())))[1] 
   
   
# number of steps to taken generate n(clusters) colors  
skips = math.floor(len(colors[5 : -5])/clusters) 
cluster_colors = colors[5 : -5 : skips] 


# plot reduced dimension data
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(pca_data[0], pca_data[1], pca_data[2], c=list(map(lambda label: cluster_colors[label], kmeans.labels_)))

str_labels = list(map(lambda label:'% s' % label, kmeans.labels_)) 

list(map(lambda data1, data2, data3, str_label:
        ax.text(data1, data2, data3, s=str_label, size=16.5,
        zorder=20, color='k'), pca_data[0], pca_data[1],
        pca_data[2], str_labels))

plt.show()

import seaborn as sns

# generating correlation heatmap
sns.heatmap(zoo_data.corr(), annot=True)

# posting correlation heatmap to output console
plt.show()


# ---------------------------------------------------------------
# Code for generating trisurface plot for correlation matrix:
from matplotlib import cm 

# generating correlation data 
df = zoo_data.corr() 
df.index = range(0, len(df)) 
df.rename(columns = dict(zip(df.columns, df.index)), inplace = True) 
df = df.astype(object) 

''' Generating coordinates with  
corresponding correlation values '''
for i in range(0, len(df)): 
    for j in range(0, len(df)): 
        if i != j: 
            df.iloc[i, j] = (i, j, df.iloc[i, j]) 
        else : 
            df.iloc[i, j] = (i, j, 0) 

df_list = [] 

# flattening dataframe values 
for sub_list in df.values: 
    df_list.extend(sub_list) 

# converting list of tuples into trivariate dataframe 
plot_df = pd.DataFrame(df_list) 

fig = plt.figure() 
ax = Axes3D(fig) 

# plotting 3D trisurface plot 
ax.plot_trisurf(plot_df[0], plot_df[1], plot_df[2],  
                    cmap = cm.jet, linewidth = 0.2) 

plt.show()