Plot the explained variance in iris dataset

exp_var_cor_cov.py

# PLOTTING THE EXPLAINED VARIANCE CHARTS

##################################
#USING THE COVARIANCE MATRIX
##################################
tot = sum(eig_vals1)
explained_variance1 = [(i / tot)*100 for i in sorted(eig_vals1, reverse=True)]
cum_explained_variance1 = np.cumsum(explained_variance1)

trace3 = Bar(
        x=['PC %s' %i for i in range(1,5)],
        y=explained_variance1,
        showlegend=False)

trace4 = Scatter(
        x=['PC %s' %i for i in range(1,5)], 
        y=cum_explained_variance1,
        name='cumulative explained variance')

data2 = Data([trace3, trace4])

layout2=Layout(
        yaxis=YAxis(title='Explained variance in percent'),
        title='Explained variance by different principal components using the covariance matrix')

fig2 = Figure(data=data2, layout=layout2)
py.iplot(fig2)

##################################
#USING CORRELATION MATRIX
##################################
tot = sum(eig_vals3)
explained_variance = [(i / tot)*100 for i in sorted(eig_vals3, reverse=True)]
cum_explained_variance = np.cumsum(explained_variance)

trace1 = Bar(
        x=['PC %s' %i for i in range(1,5)],
        y=explained_variance,
        showlegend=False)

trace2 = Scatter(
        x=['PC %s' %i for i in range(1,5)], 
        y=cum_explained_variance,
        name='cumulative explained variance')

data = Data([trace1, trace2])

layout=Layout(
        yaxis=YAxis(title='Explained variance in percent'),
        title='Explained variance by different principal components using the correlation matrix')

fig = Figure(data=data, layout=layout)
py.iplot(fig)