Update app.py

app.py

@@ -5,6 +5,35 @@ import plotly.express as px
 # Title of the App
 st.title('Sales Data Visualization App')
 
+# Display Sample Data Format
+st.subheader('Sample Data Format:')
+st.write("""
+The data should be in the following format with the listed columns:
+
+| Customer Name | Date       | City         | Country     | State       | Product Name   | Product Attribute 1 | Product Attribute 2 | Product Attribute 3 | Product Attribute 4 | Net Sales Value | Margin Amount | Cost  |
+|----------------|------------|--------------|-------------|-------------|-----------------|---------------------|---------------------|---------------------|---------------------|------------------|---------------|-------|
+| John Doe       | 2024-01-01 | New York     | USA         | NY          | Product A       | Attribute 1A         | Attribute 2A         | Attribute 3A         | Attribute 4A         | 1000             | 300           | 700   |
+| Jane Smith     | 2024-01-02 | Los Angeles  | USA         | CA          | Product B       | Attribute 1B         | Attribute 2B         | Attribute 3B         | Attribute 4B         | 1500             | 400           | 1100  |
+| Bob Johnson    | 2024-02-15 | Chicago      | USA         | IL          | Product A       | Attribute 1A         | Attribute 2A         | Attribute 3A         | Attribute 4A         | 1200             | 350           | 850   |
+| Alice Williams | 2024-03-10 | Miami        | USA         | FL          | Product C       | Attribute 1C         | Attribute 2C         | Attribute 3C         | Attribute 4C         | 2000             | 500           | 1500  |
+| Charlie Brown  | 2024-04-05 | Houston      | USA         | TX          | Product B       | Attribute 1B         | Attribute 2B         | Attribute 3B         | Attribute 4B         | 1800             | 450           | 1350  |
+
+### Description of Columns:
+- **Customer Name**: The name of the customer (e.g., John Doe).
+- **Date**: The date of the sale, formatted as `YYYY-MM-DD` (e.g., `2024-01-01`).
+- **City**: The city where the sale took place (e.g., New York).
+- **Country**: The country where the sale took place (e.g., USA).
+- **State**: The state where the sale took place (e.g., NY).
+- **Product Name**: The name of the product (e.g., Product A).
+- **Product Attribute 1**: Additional product attribute (e.g., Attribute 1A).
+- **Product Attribute 2**: Additional product attribute (e.g., Attribute 2A).
+- **Product Attribute 3**: Additional product attribute (e.g., Attribute 3A).
+- **Product Attribute 4**: Additional product attribute (e.g., Attribute 4A).
+- **Net Sales Value**: The net sales value for the transaction (e.g., `1000`).
+- **Margin Amount**: The margin for the transaction (e.g., `300`).
+- **Cost**: The cost of the product sold (e.g., `700`).
+""")
+
 # Upload File
 uploaded_file = st.file_uploader("Upload your Excel file", type=["xlsx"])
 
@@ -27,19 +56,13 @@ if uploaded_file:
     # Clean whitespace and ensure consistent case in 'Customer Name' and 'Product Name'
     df['Customer Name'] = df['Customer Name'].str.strip().str.lower()
     df['Product Name'] = df['Product Name'].str.strip().str.lower()
-    df['City'] = df['City'].str.strip().str.lower()
-    df['Country'] = df['Country'].str.strip().str.lower()
-    df['State'] = df['State'].str.strip().str.lower()
 
     # Sidebar Filters
     st.sidebar.header("Filter Options")
 
-    # Text Input for Customer, Product, City, Country, and State
+    # Text Input for Customer and Product
     customer_query = st.sidebar.text_input('Enter Customer Name (partial or full):').strip().lower()
     product_query = st.sidebar.text_input('Enter Product Name (partial or full):').strip().lower()
-    city_query = st.sidebar.text_input('Enter City (partial or full):').strip().lower()
-    country_query = st.sidebar.text_input('Enter Country (partial or full):').strip().lower()
-    state_query = st.sidebar.text_input('Enter State (partial or full):').strip().lower()
 
     # Date Range Selection
     start_date = st.sidebar.date_input('Start Date:', df['Date'].min())
@@ -47,7 +70,7 @@ if uploaded_file:
 
     # Filter Data by Date Range
     filtered_df = df[
-        (df['Date'] >= pd.to_datetime(start_date)) &
+        (df['Date'] >= pd.to_datetime(start_date)) & 
         (df['Date'] <= pd.to_datetime(end_date))
     ]
 
@@ -59,18 +82,6 @@ if uploaded_file:
     if product_query:
         filtered_df = filtered_df[filtered_df['Product Name'].str.contains(product_query, case=False, na=False)]
 
-    # Filter Data by City
-    if city_query:
-        filtered_df = filtered_df[filtered_df['City'].str.contains(city_query, case=False, na=False)]
-
-    # Filter Data by Country
-    if country_query:
-        filtered_df = filtered_df[filtered_df['Country'].str.contains(country_query, case=False, na=False)]
-
-    # Filter Data by State
-    if state_query:
-        filtered_df = filtered_df[filtered_df['State'].str.contains(state_query, case=False, na=False)]
-
     # Display Filtered Data
     st.write(f"Filtered Data: {len(filtered_df)} records found.")
     st.dataframe(filtered_df)
@@ -87,25 +98,15 @@ if uploaded_file:
         fig_top_products = px.bar(top_products, x='Product Name', y='Net Sales Value', title='Top 10 Products')
         st.plotly_chart(fig_top_products)
 
-        st.subheader("Sales Distribution by Country")
-        country_sales = filtered_df.groupby('Country')['Net Sales Value'].sum().reset_index()
-        fig_country = px.pie(country_sales, names='Country', values='Net Sales Value', title='Sales by Country')
-        st.plotly_chart(fig_country)
-
-        st.subheader("Sales by City")
-        city_sales = filtered_df.groupby('City')['Net Sales Value'].sum().reset_index()
-        fig_city = px.bar(city_sales, x='City', y='Net Sales Value', title='Sales by City')
-        st.plotly_chart(fig_city)
+        st.subheader("Sales Distribution by Region")
+        region_sales = filtered_df.groupby('City')['Net Sales Value'].sum().reset_index()
+        fig_region = px.pie(region_sales, names='City', values='Net Sales Value', title='Sales by City')
+        st.plotly_chart(fig_region)
 
         st.subheader("Monthly Sales Heatmap")
         heatmap_data = filtered_df.pivot_table(values='Net Sales Value', index='Month', columns='Year', aggfunc='sum')
         fig_heatmap = px.imshow(heatmap_data, labels=dict(x="Year", y="Month", color="Sales"))
         st.plotly_chart(fig_heatmap)
-
-        st.subheader("Margin vs. Cost")
-        margin_cost = filtered_df.groupby('Product Name')[['Margin Amount', 'Cost']].sum().reset_index()
-        fig_margin_cost = px.scatter(margin_cost, x='Cost', y='Margin Amount', color='Product Name', title='Margin vs. Cost')
-        st.plotly_chart(fig_margin_cost)
     else:
         st.write("No data available for the selected filters.")
 else: