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| import streamlit as st | |
| import pandas as pd | |
| def load_and_preprocess_data(): | |
| df = pd.read_csv( | |
| "Ratings.csv", | |
| encoding="latin-1", | |
| ) | |
| df_users=pd.read_csv( | |
| "Users.csv", | |
| encoding="latin-1", | |
| ) | |
| df_books=pd.read_csv( | |
| "Books.csv", | |
| encoding="latin-1", | |
| ) | |
| # Remove nans values | |
| df = df.dropna() | |
| df_users = df_users.dropna() | |
| df_books = df_books.dropna() | |
| # Use only positive quantites. This is not a robust approach, | |
| # but to keep things simple it quite good. | |
| df = df[df["Book-Rating"] > 0] | |
| # Parse the date column and add 10 years, just to better visualization | |
| #df["InvoiceDate"] = pd.to_datetime(df["InvoiceDate"]).dt.floor( "d") + pd.offsets.DateOffset(years=10) | |
| # Change customer id to int | |
| df["User-ID"] = df["User-ID"].astype(int) | |
| df_users["Age"] = df_users["Age"].astype(int) | |
| df_books["Year-Of-Publication"] = df_books["Year-Of-Publication"].astype(int) | |
| # Add price column | |
| #df["Price"] = df["Quantity"] * df["UnitPrice"] | |
| # Get unique entries in the dataset of users and products | |
| users = df["User-ID"].unique() | |
| products = df["ISBN"].unique() | |
| location=df_users["Location"] | |
| age=df_users["Age"] | |
| title=df_books["Book-Title"] | |
| author=df_books["Book-Author"] | |
| year=df_books["Year-Of-Publication"] | |
| # Create a categorical type for users and product. User ordered to ensure | |
| # reproducibility | |
| user_cat = pd.CategoricalDtype(categories=sorted(users), ordered=True) | |
| product_cat = pd.CategoricalDtype(categories=sorted(products), ordered=True) | |
| # Transform and get the indexes of the columns | |
| user_idx = df["User-ID"].astype(product_cat).cat.codes | |
| product_idx = df["ISBN"].astype(product_cat).cat.codes | |
| # Add the categorical index to the starting dataframe | |
| # Merging both DataFrames based on respective common columns | |
| merged_df = pd.merge(df, df_users[['User-ID', 'Location', 'Age']], on='User-ID', how='left') | |
| merged_df = pd.merge(merged_df, df_books[['ISBN', 'Book-Title', 'Book-Author', 'Year-Of-Publication']], on='ISBN', how='left') | |
| merged_df["ProductIndex"] = product_idx | |
| merged_df["CustomerIndex"] = user_idx | |
| return merged_df, user_idx, product_idx | |