lecithin.py

# split those ingredients values
choco_data['ingredients'] = choco_data['ingredients'].str.strip(' ') 
choco_data['num_ingredients'] = choco_data['ingredients'].str.split('-', expand=True)[0] 
choco_data['main_ingredients'] = choco_data['ingredients'].str.split('-', expand=True)[1] 
choco_data['main_ingredients'] = choco_data['main_ingredients'].str.strip(' ') 

# encoding the values
ingre_encode = choco_data['main_ingredients'].str.get_dummies(sep=',')

# concatenating lecithin column with the main data. Containing lecithin denoted by 1 
# and otherwise 0 
choco_data_lecithin = pd.concat([choco_data, ingre_encode['L']], axis=1) 
choco_data_lecithin.rename(columns={'L': 'ingredient_L'}, inplace=True) 

# split the chocolate data into two parts - chocolate containing lecithin and 
# chocolate doesn't contain lecithin. 
choco_has_lecithin = choco_data_lecithin[choco_data_lecithin['ingredient_L'] == 1] 
choco_has_no_lecithin = choco_data_lecithin[choco_data_lecithin['ingredient_L'] == 0] 

# calculating the average for both chocolates 
rating_by_choco_has_lecithin = choco_has_lecithin['rating'].mean() 
rating_by_choco_has_no_lecithin = choco_has_no_lecithin['rating'].mean() 

# plotting the graph 
fig = go.Figure() 
fig.add_trace(number_indicator(val=rating_by_choco_has_lecithin,
                               title_text="Average Rating of Bars having Lecithin", 
                               row_num=0, col_num=0)) 
fig.add_trace(number_indicator(val=rating_by_choco_has_no_lecithin, 
                               title_text="Average Rating of Bars having no Lecithin", 
                               row_num=0, col_num=1)) 
fig.update_layout(grid={'rows': 1, 'columns': 2, 'pattern': 'independent'}) 
fig.show()