Create app.py

app.py

@@ -0,0 +1,45 @@
+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+
+def main():
+    st.title("Excel Column Analysis Dashboard")
+    
+    uploaded_file = st.file_uploader("Upload an Excel file", type=["xls", "xlsx"])
+    
+    if uploaded_file is not None:
+        df = pd.read_excel(uploaded_file)
+        st.write("Preview of Data:")
+        st.write(df.head())
+        
+        numeric_columns = df.select_dtypes(include=[np.number]).columns.tolist()
+        
+        if numeric_columns:
+            selected_column = st.selectbox("Select a column for analysis (as named in the Excel file)", numeric_columns)
+            
+            if selected_column:
+                data = df[selected_column].dropna()
+                std_dev = np.std(data, ddof=1)
+                
+                st.write(f"**Calculated Standard Deviation:** {std_dev:.4f}")
+                
+                fig, axes = plt.subplots(1, 2, figsize=(12, 5))
+                
+                sns.histplot(data, kde=True, ax=axes[0], bins=20, color='blue')
+                axes[0].set_title(f"Distribution Plot of {selected_column}")
+                
+                sns.lineplot(x=data.index, y=data, ax=axes[1], label='Data')
+                axes[1].axhline(y=np.mean(data), color='r', linestyle='--', label='Mean')
+                axes[1].axhline(y=np.mean(data) + std_dev, color='g', linestyle='--', label='+1 Std Dev')
+                axes[1].axhline(y=np.mean(data) - std_dev, color='g', linestyle='--', label='-1 Std Dev')
+                axes[1].legend()
+                axes[1].set_title(f"Standard Deviation Plot of {selected_column}")
+                
+                st.pyplot(fig)
+        else:
+            st.warning("No numeric columns found in the uploaded file.")
+
+if __name__ == "__main__":
+    main()