Update app.py

app.py

@@ -480,7 +480,8 @@ df.drop(columns={col_to_delete}, inplace=True)
                 ax.set_title('Feature Importance')
                 st.pyplot(fig)
                 new_line()
-
+        
+        new_line()
         if st.checkbox("Identify Outliers", value=False):
             numeric_cols = df.select_dtypes(include=np.number).columns.tolist()
             col_for_outliers = st.selectbox("Select Column to Check Outliers", options=numeric_cols)
@@ -490,12 +491,7 @@ df.drop(columns={col_to_delete}, inplace=True)
             st.pyplot(fig)
             new_line()
 
-        if st.checkbox("Show Pairwise Scatter Plots", value=False):
-            selected_cols = st.multiselect("Select Columns", options=df.columns, default=df.columns[:2])
-            sns.pairplot(df[selected_cols])
-            st.pyplot()
-            new_line()
-
+        new_line()
         if st.checkbox("Show Cross-tabulations", value=False):
             categorical_cols = df.select_dtypes(include=['object', 'category']).columns.tolist()
             x_col = st.selectbox("Select X-axis Column for Cross-tab", options=categorical_cols, index=0)
@@ -504,6 +500,7 @@ df.drop(columns={col_to_delete}, inplace=True)
             st.write(cross_tab)
             new_line()
 
+        new_line()
         if st.checkbox("Segmented Analysis", value=False):
             segments = st.selectbox("Select Segment", options=df.columns)
             segment_values = df[segments].dropna().unique()
@@ -512,15 +509,25 @@ df.drop(columns={col_to_delete}, inplace=True)
             st.write(segmented_data)
             new_line()
 
-        # Assuming 'date_column' is the name of your datetime column
+        new_line()
         if st.checkbox("Temporal Analysis", value=False):
-            fig, ax = plt.subplots()
-            df.set_index('date_column')['some_value'].plot(ax=ax)
-            ax.set_title('Trend Over Time')
-            st.pyplot(fig)
-            new_line()
+            date_col_options = df.select_dtypes(include=[np.datetime64]).columns.tolist()
+            value_col_options = df.select_dtypes(include=np.number).columns.tolist()
+            
+            if not date_col_options:
+                st.error("No datetime columns found in the DataFrame.")
+            elif not value_col_options:
+                st.error("No numeric columns found in the DataFrame.")
+            else:
+                date_col = st.selectbox("Select Date Column", options=date_col_options)
+                value_col = st.selectbox("Select Value Column", options=value_col_options)
+                
+                fig, ax = plt.subplots()
+                df.set_index(date_col)[value_col].plot(ax=ax)
+                ax.set_title(f'Trend Over Time - {value_col}')
+                st.pyplot(fig)
 
-        
+        new_line()
         if st.checkbox("Show Word Cloud", value=False):
             text_col = st.selectbox("Select Text Column for Word Cloud", options=df.select_dtypes(include=[np.object]).columns.tolist())
             text_data = ' '.join(df[text_col].dropna())
@@ -531,6 +538,7 @@ df.drop(columns={col_to_delete}, inplace=True)
             st.pyplot(fig)
             new_line()
 
+        new_line()
         if st.checkbox("Show Text Statistics", value=False):
             text_col = st.selectbox("Select Text Column for Statistics", options=df.select_dtypes(include=[np.object]).columns.tolist())
             text_stats = df[text_col].dropna().apply(lambda x: {'length': len(x), 'word_count': len(x.split())})
@@ -539,7 +547,7 @@ df.drop(columns={col_to_delete}, inplace=True)
             new_line()
 
 
-            
+        new_line()    
         # Interactive Data Tables
         interactive_table = st.checkbox("Show Interactive Data Table", value=False)
         new_line()