effective_data_viz_8.py

# Scaling attribute values to avoid few outiers
cols = ['density', 'residual sugar', 'total sulfur dioxide', 'fixed acidity']
subset_df = wines[cols]

from sklearn.preprocessing import StandardScaler
ss = StandardScaler()

scaled_df = ss.fit_transform(subset_df)
scaled_df = pd.DataFrame(scaled_df, columns=cols)
final_df = pd.concat([scaled_df, wines['wine_type']], axis=1)
final_df.head()

# plot parallel coordinates
from pandas.plotting import parallel_coordinates
pc = parallel_coordinates(final_df, 'wine_type', color=('#FFE888', '#FF9999'))