plotting

# Customize the plot
ax.grid(1, ls='--', color='#777777', alpha=0.5, lw=1)
ax.tick_params(labelsize=12, length=0)
ax.set_axis_bgcolor('w')
# add a legend
leg = plt.legend( ['text'], loc=1 )
fr = leg.get_frame()
fr.set_facecolor('w')
fr.set_alpha(.7)
plt.draw()
 
from statsmodels.stats.outliers_influence import variance_inflation_factor 
  
# creating dummies for gender 
data['Gender'] = data['Gender'].map({'Male':0, 'Female':1}) 
  
# the independent variables set 
X = data[['Gender', 'Height', 'Weight']] 
  
# VIF dataframe 
vif_data = pd.DataFrame() 
vif_data["feature"] = X.columns 
  
# calculating VIF for each feature 
vif_data["VIF"] = [variance_inflation_factor(X.values, i) 
                          for i in range(len(X.columns))] 
  
print(vif_data)

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