Created
May 16, 2018 17:13
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# Extract the columns to plot | |
plot_data = features[['score', 'Site EUI (kBtu/ft²)', | |
'Weather Normalized Source EUI (kBtu/ft²)', | |
'log_Total GHG Emissions (Metric Tons CO2e)']] | |
# Replace the inf with nan | |
plot_data = plot_data.replace({np.inf: np.nan, -np.inf: np.nan}) | |
# Rename columns | |
plot_data = plot_data.rename(columns = {'Site EUI (kBtu/ft²)': 'Site EUI', | |
'Weather Normalized Source EUI (kBtu/ft²)': 'Weather Norm EUI', | |
'log_Total GHG Emissions (Metric Tons CO2e)': 'log GHG Emissions'}) | |
# Drop na values | |
plot_data = plot_data.dropna() | |
# Function to calculate correlation coefficient between two columns | |
def corr_func(x, y, **kwargs): | |
r = np.corrcoef(x, y)[0][1] | |
ax = plt.gca() | |
ax.annotate("r = {:.2f}".format(r), | |
xy=(.2, .8), xycoords=ax.transAxes, | |
size = 20) | |
# Create the pairgrid object | |
grid = sns.PairGrid(data = plot_data, size = 3) | |
# Upper is a scatter plot | |
grid.map_upper(plt.scatter, color = 'red', alpha = 0.6) | |
# Diagonal is a histogram | |
grid.map_diag(plt.hist, color = 'red', edgecolor = 'black') | |
# Bottom is correlation and density plot | |
grid.map_lower(corr_func); | |
grid.map_lower(sns.kdeplot, cmap = plt.cm.Reds) | |
# Title for entire plot | |
plt.suptitle('Pairs Plot of Energy Data', size = 36, y = 1.02); |
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