Update app.py

app.py

@@ -1,131 +1,149 @@
 import streamlit as st
 import pandas as pd
 import plotly.express as px
+import plotly.graph_objects as go
+import random
+from datetime import datetime, timedelta
 
 # 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"])
-
-if uploaded_file:
-    # Load Data
-    df = pd.read_excel(uploaded_file, sheet_name=0)
-
-    # Display the first few rows of the dataframe to understand its structure
-    st.write("Preview of the uploaded data:")
-    st.dataframe(df.head())
-
-    # Data Preprocessing
-    # Convert 'Date' to datetime
-    df['Date'] = pd.to_datetime(df['Date'])
-
-    # Extract year and month for further analysis
-    df['Year'] = df['Date'].dt.year
-    df['Month'] = df['Date'].dt.month
-
-    # 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['State'] = df['State'].str.strip().str.lower()
-    df['Country'] = df['Country'].str.strip().str.lower()
-
-    # Sidebar Filters
-    st.sidebar.header("Filter Options")
-
-    # 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()
-    state_query = st.sidebar.text_input('Enter State (partial or full):').strip().lower()
-    country_query = st.sidebar.text_input('Enter Country (partial or full):').strip().lower()
-
-    # Date Range Selection
-    start_date = st.sidebar.date_input('Start Date:', df['Date'].min())
-    end_date = st.sidebar.date_input('End Date:', df['Date'].max())
-
-    # Filter Data by Date Range
-    filtered_df = df[
-        (df['Date'] >= pd.to_datetime(start_date)) & 
-        (df['Date'] <= pd.to_datetime(end_date))
-    ]
-
-    # Filter Data by Customer Name
-    if customer_query:
-        filtered_df = filtered_df[filtered_df['Customer Name'].str.contains(customer_query, case=False, na=False)]
-
-    # Filter Data by Product Name
-    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 State
-    if state_query:
-        filtered_df = filtered_df[filtered_df['State'].str.contains(state_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)]
-
-    # Display Filtered Data
-    st.write(f"Filtered Data: {len(filtered_df)} records found.")
-    st.dataframe(filtered_df)
-
-    # Visualizations
-    if not filtered_df.empty:
-        st.subheader("Sales Trend Over Time")
-        trend = filtered_df.groupby('Date')['Net Sales Value'].sum().reset_index()
-        fig_trend = px.line(trend, x='Date', y='Net Sales Value', title='Sales Over Time')
-        st.plotly_chart(fig_trend)
-
-        st.subheader("Top 10 Products by Sales")
-        top_products = filtered_df.groupby('Product Name')['Net Sales Value'].sum().nlargest(10).reset_index()
-        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 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)
-    else:
-        st.write("No data available for the selected filters.")
+# Sample Data Generation (You can remove this part when uploading your own file)
+def generate_sample_data():
+    customers = ['Customer A', 'Customer B', 'Customer C', 'Customer D']
+    products = ['Product 1', 'Product 2', 'Product 3', 'Product 4']
+    cities = ['City 1', 'City 2', 'City 3', 'City 4']
+    states = ['State 1', 'State 2', 'State 3', 'State 4']
+    countries = ['Country 1', 'Country 2', 'Country 3', 'Country 4']
+    
+    # Generate sample sales data for 100 records
+    data = []
+    for i in range(100):
+        customer = random.choice(customers)
+        product = random.choice(products)
+        city = random.choice(cities)
+        state = random.choice(states)
+        country = random.choice(countries)
+        date = datetime.today() - timedelta(days=random.randint(1, 365))
+        nsv = random.randint(1000, 10000)  # Net Sales Value
+        cost = random.randint(500, 7000)  # Cost
+        data.append([customer, product, city, state, country, date, nsv, cost])
+    
+    columns = ['Customer Name', 'Product Name', 'City', 'State', 'Country', 'Date', 'Net Sales Value', 'Cost']
+    return pd.DataFrame(data, columns=columns)
+
+# Load sample data
+df = generate_sample_data()
+
+# Display the first few rows of the dataframe to understand its structure
+st.write("Preview of the sample data:")
+st.dataframe(df.head())
+
+# Data Preprocessing
+df['Date'] = pd.to_datetime(df['Date'])
+df['Year'] = df['Date'].dt.year
+df['Month'] = df['Date'].dt.month
+
+# Sidebar Filters
+st.sidebar.header("Filter Options")
+
+# 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()
+state_query = st.sidebar.text_input('Enter State (partial or full):').strip().lower()
+country_query = st.sidebar.text_input('Enter Country (partial or full):').strip().lower()
+
+# Date Range Selection
+start_date = st.sidebar.date_input('Start Date:', df['Date'].min())
+end_date = st.sidebar.date_input('End Date:', df['Date'].max())
+
+# Filter Data by Date Range
+filtered_df = df[
+    (df['Date'] >= pd.to_datetime(start_date)) & 
+    (df['Date'] <= pd.to_datetime(end_date))
+]
+
+# Filter Data by Customer Name
+if customer_query:
+    filtered_df = filtered_df[filtered_df['Customer Name'].str.contains(customer_query, case=False, na=False)]
+
+# Filter Data by Product Name
+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 State
+if state_query:
+    filtered_df = filtered_df[filtered_df['State'].str.contains(state_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)]
+
+# Display Filtered Data
+st.write(f"Filtered Data: {len(filtered_df)} records found.")
+st.dataframe(filtered_df)
+
+if not filtered_df.empty:
+    # KPI Metrics
+    st.subheader("Key Financial Metrics")
+
+    # Profit for the Year (Calculated as Net Sales Value - Cost)
+    profit_for_the_year = filtered_df['Net Sales Value'] - filtered_df['Cost']
+    st.metric("Profit for the Year", f"${profit_for_the_year.sum():,.2f}")
+
+    # Gross Margin (Net Sales Value - Cost)
+    gross_margin = filtered_df['Net Sales Value'] - filtered_df['Cost']
+    st.metric("Gross Margin", f"${gross_margin.sum():,.2f}")
+
+    # Total Sales
+    total_sales = filtered_df['Net Sales Value'].sum()
+    st.metric("Total Sales", f"${total_sales:,.2f}")
+
+    # Matrix View (like Power BI Matrix)
+    st.subheader("Matrix View of Financial Data")
+    matrix_data = filtered_df.pivot_table(
+        values='Net Sales Value', 
+        index=['Year', 'Customer Name'], 
+        columns=['Product Name'], 
+        aggfunc='sum', 
+        fill_value=0
+    )
+    st.dataframe(matrix_data)
+
+    # Visualization 1: Sales by Customer (Bar chart)
+    st.subheader("Sales by Customer")
+    sales_by_customer = filtered_df.groupby('Customer Name')['Net Sales Value'].sum().reset_index()
+    fig_customer = px.bar(sales_by_customer, x='Customer Name', y='Net Sales Value', title='Sales by Customer')
+    st.plotly_chart(fig_customer)
+
+    # Visualization 2: Sales by Product (Bar chart)
+    st.subheader("Sales by Product")
+    sales_by_product = filtered_df.groupby('Product Name')['Net Sales Value'].sum().reset_index()
+    fig_product = px.bar(sales_by_product, x='Product Name', y='Net Sales Value', title='Sales by Product')
+    st.plotly_chart(fig_product)
+
+    # Visualization 3: Year-over-Year Sales Trend (Line chart)
+    st.subheader("Year-over-Year Sales Trend")
+    year_trend = filtered_df.groupby(['Year'])['Net Sales Value'].sum().reset_index()
+    fig_year_trend = px.line(year_trend, x='Year', y='Net Sales Value', title='Year-over-Year Sales Trend')
+    st.plotly_chart(fig_year_trend)
+
+    # Visualization 4: Profit and Loss Overview (Simple Table)
+    st.subheader("Profit and Loss Overview")
+    pnl_data = filtered_df[['Customer Name', 'Product Name', 'Net Sales Value', 'Cost', 'Net Sales Value - Cost']]
+    pnl_data.columns = ['Customer', 'Product', 'Sales', 'Cost', 'Profit']
+    st.dataframe(pnl_data)
+    
+    # Visualization 5: Gross Margin Distribution (Pie chart)
+    st.subheader("Gross Margin Distribution")
+    margin_data = filtered_df.groupby('Product Name')['Net Sales Value'].sum().reset_index()
+    fig_margin = px.pie(margin_data, names='Product Name', values='Net Sales Value', title='Gross Margin by Product')
+    st.plotly_chart(fig_margin)
+
 else:
-    st.write("Upload a file to begin.")
+    st.write("No data available for the selected filters.")