feat: generated files

app.py

@@ -114,16 +114,22 @@ elif page == "Customer Analysis":
             # Get percentage of units sold for each manufacturer
             all_manufacturers = customer_data.iloc[:, 1:].T  # Exclude CLIENTE column
             all_manufacturers.index = all_manufacturers.index.astype(str)
-            
+
             # Get total sales for each manufacturer
             sales_data = customer_euros.iloc[:, 1:].T  # Exclude CLIENTE column
             sales_data.index = sales_data.index.astype(str)
 
+            # Remove the 'CLIENTE' row from sales_data to avoid issues with mixed types
+            sales_data_filtered = sales_data.drop(index='CLIENTE', errors='ignore')
+
+            # Ensure all values are numeric (optional, but adds robustness)
+            sales_data_filtered = sales_data_filtered.apply(pd.to_numeric, errors='coerce')
+
             # Sort manufacturers by percentage of units and get top 10
             top_units = all_manufacturers.sort_values(by=all_manufacturers.columns[0], ascending=False).head(10)
-            
+
             # Sort manufacturers by total sales and get top 10
-            top_sales = sales_data.sort_values(by=sales_data.columns[0], ascending=False).head(10)
+            top_sales = sales_data_filtered.sort_values(by=sales_data_filtered.columns[0], ascending=False).head(10)
 
             # Combine top manufacturers from both lists
             combined_top = pd.concat([top_units, top_sales]).index.unique()
@@ -133,10 +139,10 @@ elif page == "Customer Analysis":
             amounts = []  # Will store total sales
 
             for m in combined_top:
-                if m in all_manufacturers.index and m in sales_data.index:
+                if m in all_manufacturers.index and m in sales_data_filtered.index:
                     values.append(float(all_manufacturers.loc[m, all_manufacturers.columns[0]]))
                     manufacturers.append(get_supplier_name(m))
-                    amounts.append(float(sales_data.loc[m, sales_data.columns[0]]))
+                    amounts.append(float(sales_data_filtered.loc[m, sales_data_filtered.columns[0]]))
 
             st.write(f"### Results for top {len(manufacturers)} manufacturers (balanced by units % and total sales):")
             for manufacturer, value, amount in zip(manufacturers, values, amounts):

test.py

@@ -0,0 +1,80 @@
+import pandas as pd
+import numpy as np
+
+# Simulate loading data
+print("Loading data...")
+df = pd.read_csv(r"D:\01A-TRABAJO\PYTHON\DATASCIENCE\EJERCICIOS\final_project_space\Final_Project\df_clean.csv")
+nombres_proveedores = pd.read_csv(r"D:\01A-TRABAJO\PYTHON\DATASCIENCE\EJERCICIOS\final_project_space\Final_Project\nombres_proveedores.csv", sep=';')
+euros_proveedor = pd.read_csv(r"D:\01A-TRABAJO\PYTHON\DATASCIENCE\EJERCICIOS\final_project_space\Final_Project\euros_proveedor.csv", sep=',')
+
+print("\nInitial data types:")
+print(df.dtypes)
+print(nombres_proveedores.dtypes)
+print(euros_proveedor.dtypes)
+
+# Convert columns to string
+df['CLIENTE'] = df['CLIENTE'].astype(str)
+nombres_proveedores['codigo'] = nombres_proveedores['codigo'].astype(str)
+euros_proveedor['CLIENTE'] = euros_proveedor['CLIENTE'].astype(str)
+
+print("\nData types after conversion:")
+print(df.dtypes)
+print(nombres_proveedores.dtypes)
+print(euros_proveedor.dtypes)
+
+# Convert numerical columns in euros_proveedor
+for col in euros_proveedor.columns:
+    if col != 'CLIENTE':
+        euros_proveedor[col] = pd.to_numeric(euros_proveedor[col], errors='coerce')
+
+print("\nData types in euros_proveedor after numeric conversion:")
+print(euros_proveedor.dtypes)
+
+# Simulate customer selection
+customer_code = df['CLIENTE'].iloc[0]  # Take the first customer as an example
+print(f"\nAnalysis for customer: {customer_code}")
+
+customer_data = df[df["CLIENTE"] == str(customer_code)]
+customer_euros = euros_proveedor[euros_proveedor["CLIENTE"] == str(customer_code)]
+
+print("\nCustomer data:")
+print(customer_data)
+print("\nCustomer euro data:")
+print(customer_euros)
+
+# Obtain percentage of units sold by manufacturer
+all_manufacturers = customer_data.iloc[:, 1:].T
+all_manufacturers.index = all_manufacturers.index.astype(str)
+
+print("\nAll manufacturers:")
+print(all_manufacturers)
+print(all_manufacturers.dtypes)
+
+# Get total sales by manufacturer
+sales_data = customer_euros.iloc[:, 1:].T
+sales_data.index = sales_data.index.astype(str)
+
+print("\nSales data:")
+print(sales_data)
+print(sales_data.dtypes)
+
+# Remove the 'CLIENTE' row before attempting to sort sales data
+sales_data_filtered = sales_data.drop(index='CLIENTE')
+
+# Ensure all values are numeric
+sales_data_filtered = sales_data_filtered.apply(pd.to_numeric, errors='coerce')
+
+# Attempt to sort the sales data after filtering
+try:
+    print("\nAttempting to sort the sales data...")
+    top_sales = sales_data_filtered.sort_values(by=sales_data_filtered.columns[0], ascending=False).head(10)
+    print("Sorting successful:")
+    print(top_sales)
+except Exception as e:
+    print(f"Error sorting: {str(e)}")
+    print("Values in the first column:")
+    print(sales_data_filtered[sales_data_filtered.columns[0]])
+    print("Data types in the first column:")
+    print(sales_data_filtered[sales_data_filtered.columns[0]].apply(type))
+
+print("\nDebugging script completed.")