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| import streamlit as st | |
| import pandas as pd | |
| import plotly.express as px | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| # Page configuration | |
| st.set_page_config(page_title="Customer Insights App", page_icon=":bar_chart:") | |
| # Load CSV files | |
| df = pd.read_csv("df_clean.csv") | |
| nombres_proveedores = pd.read_csv("nombres_proveedores.csv", sep=';') | |
| euros_proveedor = pd.read_csv("euros_proveedor.csv", sep=',') | |
| nombres_proveedores['codigo'] = nombres_proveedores['codigo'].astype(str) | |
| euros_proveedor['CLIENTE'] = euros_proveedor['CLIENTE'].astype(str) | |
| # Ignore the last two columns | |
| df = df.iloc[:, :-2] | |
| # Ensure customer code is a string | |
| df['CLIENTE'] = df['CLIENTE'].astype(str) | |
| # Function to get supplier name | |
| def get_supplier_name(code): | |
| name = nombres_proveedores[nombres_proveedores['codigo'] == code]['nombre'].values | |
| return name[0] if len(name) > 0 else code | |
| # Function to create radar chart with square root transformation | |
| def radar_chart(categories, values, amounts, title): | |
| N = len(categories) | |
| angles = [n / float(N) * 2 * np.pi for n in range(N)] | |
| angles += angles[:1] | |
| fig, ax = plt.subplots(figsize=(12, 12), subplot_kw=dict(projection='polar')) | |
| # Apply square root transformation | |
| sqrt_values = np.sqrt(values) | |
| sqrt_amounts = np.sqrt(amounts) | |
| max_sqrt_value = max(sqrt_values) | |
| normalized_values = [v / max_sqrt_value for v in sqrt_values] | |
| total_sqrt_amount = sum(sqrt_amounts) | |
| normalized_amounts = [a / total_sqrt_amount for a in sqrt_amounts] | |
| normalized_values += normalized_values[:1] | |
| ax.plot(angles, normalized_values, 'o-', linewidth=2, color='#FF69B4', label='% Units (sqrt)') | |
| ax.fill(angles, normalized_values, alpha=0.25, color='#FF69B4') | |
| normalized_amounts += normalized_amounts[:1] | |
| ax.plot(angles, normalized_amounts, 'o-', linewidth=2, color='#4B0082', label='% Spend (sqrt)') | |
| ax.fill(angles, normalized_amounts, alpha=0.25, color='#4B0082') | |
| ax.set_xticks(angles[:-1]) | |
| ax.set_xticklabels(categories, size=8, wrap=True) | |
| ax.set_ylim(0, max(max(normalized_values), max(normalized_amounts)) * 1.1) | |
| circles = np.linspace(0, 1, 5) | |
| for circle in circles: | |
| ax.plot(angles, [circle]*len(angles), '--', color='gray', alpha=0.3, linewidth=0.5) | |
| ax.set_yticklabels([]) | |
| ax.spines['polar'].set_visible(False) | |
| plt.title(title, size=16, y=1.1) | |
| plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1)) | |
| return fig | |
| # Main page design | |
| st.title("Welcome to Customer Insights App") | |
| st.markdown(""" | |
| This app helps businesses analyze customer behaviors and provide personalized recommendations based on purchase history. | |
| Use the tools below to dive deeper into your customer data. | |
| """) | |
| # Navigation menu | |
| page = st.selectbox("Select the tool you want to use", ["", "Customer Analysis", "Customer Recommendations"]) | |
| # Home Page | |
| if page == "": | |
| st.markdown("## Welcome to the Customer Insights App") | |
| st.write("Use the dropdown menu to navigate between the different sections.") | |
| # Customer Analysis Page | |
| elif page == "Customer Analysis": | |
| st.title("Customer Analysis") | |
| st.markdown("Use the tools below to explore your customer data.") | |
| partial_code = st.text_input("Enter part of Customer Code (or leave empty to see all)") | |
| if partial_code: | |
| filtered_customers = df[df['CLIENTE'].str.contains(partial_code)] | |
| else: | |
| filtered_customers = df | |
| customer_list = filtered_customers['CLIENTE'].unique() | |
| customer_code = st.selectbox("Select Customer Code", customer_list) | |
| if customer_code: | |
| customer_data = df[df["CLIENTE"] == customer_code] | |
| customer_euros = euros_proveedor[euros_proveedor["CLIENTE"] == customer_code] | |
| if not customer_data.empty and not customer_euros.empty: | |
| st.write(f"### Analysis for Customer {customer_code}") | |
| all_manufacturers = customer_data.iloc[:, 1:].T[customer_data.iloc[:, 1:].T[customer_data.index[0]] > 0] | |
| # Convert to numeric and handle any non-numeric values | |
| all_manufacturers = all_manufacturers.apply(pd.to_numeric, errors='coerce') | |
| customer_euros = customer_euros.apply(pd.to_numeric, errors='coerce') | |
| top_units = all_manufacturers.sort_values(by=customer_data.index[0], ascending=False).head(10) | |
| # Ensure we're working with numeric data for sorting | |
| numeric_euros = customer_euros.select_dtypes(include=[np.number]) | |
| if not numeric_euros.empty: | |
| top_sales = numeric_euros.iloc[0].sort_values(ascending=False).head(10) | |
| else: | |
| st.warning("No numeric sales data available for this customer.") | |
| top_sales = pd.Series() | |
| combined_top = pd.concat([top_units, top_sales]).index.unique() | |
| values = [] | |
| manufacturers = [] | |
| amounts = [] | |
| for m in combined_top: | |
| if m in all_manufacturers.index: | |
| values.append(all_manufacturers[m]) | |
| manufacturers.append(get_supplier_name(m)) | |
| amounts.append(customer_euros[m].values[0] if m in customer_euros.columns else 0) | |
| st.write(f"### Results for top {len(manufacturers)} manufacturers (balanced by units and sales):") | |
| for manufacturer, value, amount in zip(manufacturers, values, amounts): | |
| st.write(f"{manufacturer} = {value:.4f} units, €{amount:.2f}") | |
| if manufacturers: # Only create the chart if we have data | |
| fig = radar_chart(manufacturers, values, amounts, f'Radar Chart for Top {len(manufacturers)} Manufacturers of Customer {customer_code}') | |
| st.pyplot(fig) | |
| else: | |
| st.warning("No data available to create the radar chart.") | |
| # Customer sales 2021-2024 (if data exists) | |
| if 'VENTA_2021' in df.columns and 'VENTA_2022' in df.columns and 'VENTA_2023' in df.columns and 'VENTA_2024' in df.columns: | |
| years = ['2021', '2022', '2023', '2024'] | |
| sales_columns = ['VENTA_2021', 'VENTA_2022', 'VENTA_2023', 'VENTA_2024'] | |
| customer_sales = customer_data[sales_columns].values[0] | |
| fig_sales = px.line(x=years, y=customer_sales, markers=True, title=f'Sales Over the Years for Customer {customer_code}') | |
| fig_sales.update_layout(xaxis_title="Year", yaxis_title="Sales") | |
| st.plotly_chart(fig_sales) | |
| else: | |
| st.warning("Sales data for 2021-2024 not available.") | |
| else: | |
| st.warning(f"No data found for customer {customer_code}. Please check the code.") | |
| # Customer Recommendations Page | |
| elif page == "Customer Recommendations": | |
| st.title("Customer Recommendations") | |
| st.markdown(""" | |
| Get tailored recommendations for your customers based on their purchasing history. | |
| """) | |
| partial_code = st.text_input("Enter part of Customer Code for Recommendations (or leave empty to see all)") | |
| if partial_code: | |
| filtered_customers = df[df['CLIENTE'].str.contains(partial_code)] | |
| else: | |
| filtered_customers = df | |
| customer_list = filtered_customers['CLIENTE'].unique() | |
| customer_code = st.selectbox("Select Customer Code for Recommendations", customer_list) | |
| if customer_code: | |
| customer_data = df[df["CLIENTE"] == customer_code] | |
| if not customer_data.empty: | |
| st.write(f"### Purchase History for Customer {customer_code}") | |
| st.write(customer_data) | |
| st.write(f"### Recommended Products for Customer {customer_code}") | |
| # Placeholder for recommendation logic | |
| st.write("Product A, Product B, Product C") | |
| else: | |
| st.warning(f"No data found for customer {customer_code}. Please check the code.") |