text_plain_output_2.png application_vnd.jupyter.stderr_output_1.png image_output_1.png
import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn

data = pd.read_csv('../input/Iris.csv')

import seaborn

# Calculate correlation coefficient
cols = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm']
cm = np.corrcoef(data[cols].values.T)
heatmap = seaborn.heatmap(cm, cbar=True, 
                          annot=True, 
                          square=True, 
                          fmt='.2f',
                          annot_kws={'size': 15},
                          yticklabels=cols,
                          xticklabels=cols)

seaborn.set(style='dark')

seaborn.reset_orig()
seaborn.pairplot(data.drop('Id', axis=1), hue='Species', size=2.2, diag_kind='kde')