Update app.py

app.py

@@ -51,6 +51,9 @@ api_secret_key = st.sidebar.text_input("API Secret Key", "YOUR_API_SECRET_KEY",
 start_date = st.sidebar.date_input("Start Date", datetime.now() - timedelta(days=30))
 end_date = st.sidebar.date_input("End Date", datetime.now())
 
+# Load the merchantid to organisation name mapping
+merchant_mapping = pd.read_csv('/mnt/data/a-599431.CSV')  # Modify this path as needed
+
 # Fetch data button
 if st.sidebar.button("Fetch Data"):
     # Fetch data from ShareASale
@@ -60,26 +63,29 @@ if st.sidebar.button("Fetch Data"):
     # Parse the CSV data into a DataFrame
     df = parse_csv_to_df(ledger_data)
     
+    # Merge with merchant mapping
+    df = pd.merge(df, merchant_mapping, on='merchantid', how='left')
+    
     # Filter rows where action is "Transaction Created"
     df_filtered = df.loc[df['action'] == 'Transaction Created']
     
     # Remove 'ledgerid' and 'transid' columns
-    df_filtered = df_filtered.drop(columns=['ledgerid', 'transid', 'action'])
+    df_filtered = df_filtered.drop(columns=['ledgerid', 'transid', 'action', 'merchantid'])
     
     # Display the DataFrame as a table
     st.write("Transaction Data")
     st.write(df_filtered)
     
-    # Create a second table summing the impact for each unique merchantid
-    df_sumif = df_filtered.groupby('merchantid')['impact'].sum().reset_index()
+    # Create a second table summing the impact for each unique organisation name
+    df_sumif = df_filtered.groupby('organisation name')['impact'].sum().reset_index()
     
     # Calculate the total impact
     total_impact = df_sumif['impact'].sum()
     
     # Add a total row to the DataFrame
-    total_row = pd.DataFrame({'merchantid': ['Total'], 'impact': [total_impact]})
+    total_row = pd.DataFrame({'organisation name': ['Total'], 'impact': [total_impact]})
     df_sumif = pd.concat([df_sumif, total_row], ignore_index=True)
     
     # Display the second DataFrame as a table
-    st.write("Impact Summary by Merchant")
+    st.write("Impact Summary by Organisation")
     st.write(df_sumif)