Update pages/comps_data.py

pages/comps_data.py

@@ -13,8 +13,8 @@ import seaborn as sns
 from datetime import datetime, timedelta
 
 def main():
-    df5 = pd.read_csv("market_underwriting_pred.csv", encoding='utf-8')
-    df5.insert(loc=0, column='Select rows', value=[False]*len(df5))
+    df6 = pd.read_csv("market_underwriting_pred.csv", encoding='utf-8')
+    df6.insert(loc=0, column='Select rows', value=[False]*len(df5))
 
     st.set_page_config(initial_sidebar_state="collapsed", layout="wide")
     
@@ -131,6 +131,32 @@ def main():
     if option == ":rainbow[On]":  
         col_3_1, col_3_2, col_3_3  = st.columns([5,1,5]) 
         with col_3_1: 
+            data = {
+                "favorite": [True, False, False, True, True, False, False, True],
+                'Address': ['Location_A', 'Location_B', 'Location_C', 'Location_D', 'Location_E',
+                        'Location_F', 'Location_G', 'Location_H'
+                        ],
+                'latitude': np.random.uniform(40.7, 40.8, size=8),  # Assuming latitude range between 40.7 and 40.8
+                'longitude': np.random.uniform(-74.0, -73.9, size=8),  # Assuming longitude range between -74.0 and -73.9
+                'Match score': [12, 12, 12, 12, 12, 12, 12, 12],
+                'Market': ["M2", "M2", "M2", "M2", "M2", "M2", "M2", "M2"],
+                'Sub-market': ["S2", "S2", "S2", "S2", "S2", "S2", "S2", "S2"],
+                'Lease Date': ["2024/1/1", "2024/1/1", "2024/1/1", "2024/1/1", "2024/1/1", "2024/1/1", "2024/1/1", "2024/1/1"],
+                'LSF': [20000, 30000, 20000, 30000, 20000, 30000, 50000, 35000],
+                'RSF': [20000, 30000, 20000, 30000, 20000, 30000, 50000, 35000],
+                'Rent (NNN)': [11, 11, 11, 12, 12, 12, 12, 15],
+                'Year Built': [2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019],
+                'Office %': [20, 20, 20, 20, 20, 20, 20, 20],
+                'Clear Height':[19, 18, 19, 18, 17, 19, 19, 18],
+                'Doors (drive in / Dock)': [2, 2, 2, 2, 2, 2, 2, 2],
+                'Lease Term ': [60, 60, 60, 60, 60, 60, 60, 60],
+                'Rent (Gross)': [11, 11, 11, 12, 12, 12, 12, 15],
+                'TIs ': [1, 1, 1, 1, 1, 1,  1, 1]
+            }
+            
+            # Create DataFrame
+            df5 =  pd.DataFrame(data)
+            df5.insert(loc=0, column='Select rows', value=[False]*len(df5))
             st.write('Additional comps')
             st.data_editor(
                 df5,