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| import streamlit as st | |
| import pandas as pd | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| # 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 | | |
| """) | |
| # File upload functionality | |
| st.sidebar.header("Upload Data") | |
| uploaded_file = st.sidebar.file_uploader("Choose a CSV file", type=["csv"]) | |
| # Check if file is uploaded | |
| if uploaded_file is not None: | |
| # Load the data | |
| df = pd.read_csv(uploaded_file) | |
| # Convert Date to datetime format if it's in string format | |
| if 'Date' in df.columns: | |
| df['Date'] = pd.to_datetime(df['Date']) | |
| # Add 'Year' and 'Month' columns for easy filtering and analysis | |
| 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)] | |
| # Add 'Net Sales Value - Cost' as a new column | |
| filtered_df['Net Sales Value - Cost'] = filtered_df['Net Sales Value'] - filtered_df['Cost'] | |
| # 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}") | |
| # Visualization 1: Profit and Loss Overview (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 2: 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 3: Sales Trend over Time | |
| st.subheader("Sales Trend over Time") | |
| sales_trend = filtered_df.groupby(['Year', 'Month'])['Net Sales Value'].sum().reset_index() | |
| fig = px.line(sales_trend, x='Month', y='Net Sales Value', color='Year', title="Sales Trend over Time") | |
| st.plotly_chart(fig) | |
| # Visualization 4: Profit Margin Visualization (Bar Chart) | |
| st.subheader("Profit Margin per Product") | |
| profit_margin_data = filtered_df.groupby('Product Name').apply( | |
| lambda x: (x['Net Sales Value'] - x['Cost']).sum() / x['Net Sales Value'].sum() | |
| ).reset_index(name="Profit Margin") | |
| fig = px.bar(profit_margin_data, x='Product Name', y='Profit Margin', title="Profit Margin per Product") | |
| st.plotly_chart(fig) | |
| else: | |
| st.write("No data available for the selected filters.") | |
| else: | |
| st.write("Please upload a CSV file to get started.") | |