def get_subsitute(): name = t1.get() # Get the index of the item that matches the title idx = indices[name] # Get the pairwsie similarity scores of all items with that items sim_scores = list(enumerate(cosine_sim[idx])) # Sort the items based on the similarity scores def get_key(elem): return elem[1] sim_scores.sort(key=get_key, reverse=True) # Get the scores of the 10 most similar items sim_scores = sim_scores[1:11] # Get the items indices item_indices = [i[0] for i in sim_scores] # Return the top 10 most similar items #print (type( df_new['name'].iloc[item_indices])) df = ((df_new.iloc[item_indices].sort_values(by='price'))) pt = Table(frame, dataframe=df) pt.place(x=0, y=300) pt.show()
def get_comp(): value = t1.get() df = (support_data[(support_data['name_x'] == value) | (support_data['name_y'] == value)].sort_values( by='support', ascending=False).head(10)) pt = Table(frame, dataframe=df) pt.place(x=0, y=300) pt.show()