2024 DEC 30 : Ver 20 More exmaples

Signed-off-by: Milind Kamat <36366961+milindkamat0507@users.noreply.github.com>

app.py

@@ -1,169 +1,267 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from datetime import datetime, timedelta
 
-st.set_page_config(page_title="Interactive Streamlit Tutorial", layout="wide")
+st.set_page_config(layout="wide", page_title="Business Analytics Dashboard Tutorial")
 
-st.title("Welcome to Streamlit Development Journey 🚀")
-st.markdown("*An interactive guide for MBA students - Practice as you learn!*")
+# Sample business data generation
+def generate_sales_data():
+    dates = pd.date_range(start='2024-01-01', end='2024-12-31', freq='D')
+    np.random.seed(42)
+    
+    df = pd.DataFrame({
+        'Date': dates,
+        'Sales': np.random.normal(1000, 200, len(dates)),
+        'Region': np.random.choice(['North', 'South', 'East', 'West'], len(dates)),
+        'Product': np.random.choice(['Electronics', 'Clothing', 'Food', 'Books'], len(dates)),
+        'Customer_Type': np.random.choice(['Retail', 'Wholesale'], len(dates)),
+    })
+    df['Profit'] = df['Sales'] * np.random.uniform(0.15, 0.25, len(df))
+    return df
 
-with st.sidebar:
-    st.header("Navigation Panel")
-    page = st.radio(
-        "Choose your learning path:",
-        ["Text Elements", "Input Widgets", "Layout & Containers", "Data Display", "Interactive Charts"]
-    )
+# Main Navigation
+st.title("📊 Business Analytics & Data Analysis Tutorial")
 
-if page == "Text Elements":
-    st.header("1. Text Elements Playground")
-    
-    # Learning Section
-    st.subheader("📚 Learn")
-    with st.expander("See text formatting examples"):
-        st.code("""
-# Different ways to display text:
-st.write('Regular text')
-st.markdown('**Bold** and *italic*')
-st.title('Main Title')
-st.header('Header')
-st.subheader('Subheader')
-st.code('print("Hello World")')
-        """)
+tabs = st.tabs([
+    "Business Dashboard",
+    "Data Analysis",
+    "Financial Metrics",
+    "Practice Zone"
+])
+
+# Tab 1: Business Dashboard
+with tabs[0]:
+    st.header("Creating Business Dashboards")
+    st.write("Learn how to build interactive business dashboards with Streamlit")
     
-    # Practice Section
-    st.subheader("🔨 Practice")
-    st.write("Try these text elements yourself!")
+    # Generate sample data
+    df_sales = generate_sales_data()
     
-    code_input = st.text_area(
-        "Type your Streamlit code here:",
-        height=100,
-        placeholder="Example: st.write('Hello World')"
-    )
+    # Dashboard filters
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        selected_region = st.multiselect(
+            "Select Region",
+            df_sales['Region'].unique(),
+            default=df_sales['Region'].unique()[0]
+        )
+    with col2:
+        date_range = st.date_input(
+            "Select Date Range",
+            value=(df_sales['Date'].min(), df_sales['Date'].max())
+        )
+    with col3:
+        product_type = st.selectbox(
+            "Select Product",
+            ['All'] + list(df_sales['Product'].unique())
+        )
     
-    if st.button("Run Code"):
-        try:
-            with st.container():
-                st.write("Your Output:")
-                exec(code_input)
-        except Exception as e:
-            st.error(f"Error: {str(e)}")
-
-elif page == "Input Widgets":
-    st.header("2. Input Widgets Laboratory")
+    # Filter data based on selections
+    mask = (df_sales['Region'].isin(selected_region)) & \
+           (df_sales['Date'] >= pd.Timestamp(date_range[0])) & \
+           (df_sales['Date'] <= pd.Timestamp(date_range[1]))
     
-    col1, col2 = st.columns(2)
+    if product_type != 'All':
+        mask &= (df_sales['Product'] == product_type)
     
-    with col1:
-        st.subheader("Widget Examples")
-        st.write("Try these interactive widgets:")
-        
-        # Text input
-        name = st.text_input("Enter your name:")
-        if name:
-            st.write(f"Hello, {name}!")
-            
-        # Number input
-        number = st.number_input("Pick a number", 0, 100)
-        st.write(f"Your number squared is: {number**2}")
-        
-        # Slider
-        age = st.slider("Select your age", 0, 100, 25)
-        st.write(f"You selected: {age} years")
+    filtered_df = df_sales[mask]
     
-    with col2:
-        st.subheader("Code Reference")
-        st.code("""
-# Text input
-name = st.text_input("Enter your name:")
-if name:
-    st.write(f"Hello, {name}!")
+    # KPI Metrics
+    st.subheader("Key Performance Indicators")
+    kpi1, kpi2, kpi3, kpi4 = st.columns(4)
+    
+    with kpi1:
+        st.metric(
+            "Total Sales",
+            f"${filtered_df['Sales'].sum():,.0f}",
+            f"{((filtered_df['Sales'].sum() / df_sales['Sales'].sum()) - 1) * 100:.1f}%"
+        )
+    with kpi2:
+        st.metric(
+            "Average Daily Sales",
+            f"${filtered_df['Sales'].mean():,.0f}",
+            f"{((filtered_df['Sales'].mean() / df_sales['Sales'].mean()) - 1) * 100:.1f}%"
+        )
+    with kpi3:
+        st.metric(
+            "Total Profit",
+            f"${filtered_df['Profit'].sum():,.0f}",
+            f"{((filtered_df['Profit'].sum() / df_sales['Profit'].sum()) - 1) * 100:.1f}%"
+        )
+    with kpi4:
+        st.metric(
+            "Profit Margin",
+            f"{(filtered_df['Profit'].sum() / filtered_df['Sales'].sum() * 100):.1f}%"
+        )
 
-# Number input
-number = st.number_input("Pick a number", 0, 100)
+    # Sales Trends
+    st.subheader("Sales Trends Analysis")
+    daily_sales = filtered_df.groupby('Date')[['Sales', 'Profit']].sum().reset_index()
+    
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=daily_sales['Date'],
+        y=daily_sales['Sales'],
+        name='Sales',
+        line=dict(color='blue')
+    ))
+    fig.add_trace(go.Scatter(
+        x=daily_sales['Date'],
+        y=daily_sales['Profit'],
+        name='Profit',
+        line=dict(color='green')
+    ))
+    fig.update_layout(title='Daily Sales and Profit Trends')
+    st.plotly_chart(fig, use_container_width=True)
 
-# Slider
-age = st.slider("Select your age", 0, 100, 25)
-        """)
+# Tab 2: Data Analysis
+with tabs[1]:
+    st.header("Advanced Data Analysis")
+    
+    analysis_type = st.selectbox(
+        "Choose Analysis Type",
+        ["Time Series Analysis", "Regional Performance", "Product Analysis"]
+    )
+    
+    if analysis_type == "Time Series Analysis":
+        st.subheader("Time Series Decomposition")
+        # Monthly aggregation
+        monthly_sales = df_sales.groupby(df_sales['Date'].dt.to_period('M')).agg({
+            'Sales': 'sum',
+            'Profit': 'sum'
+        }).reset_index()
+        
+        # Rolling averages
+        window_size = st.slider("Moving Average Window (months)", 1, 12, 3)
+        monthly_sales['Sales_MA'] = monthly_sales['Sales'].rolling(window=window_size).mean()
+        
+        fig = px.line(monthly_sales, 
+                     x='Date', 
+                     y=['Sales', 'Sales_MA'],
+                     title=f'Monthly Sales with {window_size}-Month Moving Average')
+        st.plotly_chart(fig, use_container_width=True)
 
-elif page == "Layout & Containers":
-    st.header("3. Layout Workshop")
-    
-    st.write("Experiment with different layouts:")
-    
-    tab1, tab2, tab3 = st.tabs(["Columns", "Expander", "Container"])
-    
-    with tab1:
-        st.write("Create two columns:")
-        col1, col2 = st.columns(2)
-        with col1:
-            st.write("This is column 1")
-            st.button("Column 1 Button")
-        with col2:
-            st.write("This is column 2")
-            st.button("Column 2 Button")
-            
-    with tab2:
-        with st.expander("Click to expand"):
-            st.write("This content is hidden until expanded!")
-            st.slider("Hidden slider", 0, 100)
-            
-    with tab3:
-        with st.container():
-            st.write("This is a container")
-            st.metric(label="Temperature", value="24 °C", delta="1.2 °C")
+    elif analysis_type == "Regional Performance":
+        st.subheader("Regional Sales Distribution")
+        
+        regional_sales = df_sales.groupby('Region').agg({
+            'Sales': 'sum',
+            'Profit': 'sum'
+        }).reset_index()
+        
+        fig = px.scatter(regional_sales, 
+                        x='Sales', 
+                        y='Profit',
+                        size='Sales',
+                        color='Region',
+                        title='Regional Sales vs Profit')
+        st.plotly_chart(fig, use_container_width=True)
 
-elif page == "Data Display":
-    st.header("4. Data Display Workshop")
+# Tab 3: Financial Metrics
+with tabs[2]:
+    st.header("Financial Analysis Tools")
     
-    # Create sample data
-    df = pd.DataFrame({
-        'Name': ['John', 'Emma', 'Alex', 'Sarah'],
-        'Age': [28, 24, 32, 27],
-        'City': ['New York', 'London', 'Paris', 'Tokyo']
-    })
+    # ROI Calculator
+    st.subheader("ROI Calculator")
+    col1, col2 = st.columns(2)
+    with col1:
+        investment = st.number_input("Initial Investment ($)", min_value=0, value=10000)
+        revenue = st.number_input("Expected Revenue ($)", min_value=0, value=15000)
+    with col2:
+        costs = st.number_input("Operating Costs ($)", min_value=0, value=5000)
+        time_period = st.number_input("Time Period (years)", min_value=1, value=1)
     
-    st.subheader("Practice with DataFrames")
+    roi = ((revenue - costs - investment) / investment) * 100
+    st.metric("ROI (%)", f"{roi:.1f}%")
     
-    # Show data manipulation options
-    st.write("Original DataFrame:")
-    st.dataframe(df)
+    # Break-even Analysis
+    st.subheader("Break-even Analysis")
+    fixed_costs = st.number_input("Fixed Costs ($)", min_value=0, value=50000)
+    unit_price = st.number_input("Unit Price ($)", min_value=0, value=100)
+    unit_cost = st.number_input("Unit Variable Cost ($)", min_value=0, value=60)
     
-    # Let users try filtering
-    city_filter = st.selectbox("Filter by city:", ['All'] + list(df['City'].unique()))
-    if city_filter != 'All':
-        filtered_df = df[df['City'] == city_filter]
-        st.write("Filtered DataFrame:")
-        st.dataframe(filtered_df)
+    break_even_units = fixed_costs / (unit_price - unit_cost)
+    st.metric("Break-even Point (units)", f"{break_even_units:,.0f}")
 
-elif page == "Interactive Charts":
-    st.header("5. Visualization Lab")
+# Tab 4: Practice Zone
+with tabs[3]:
+    st.header("Practice Exercises")
     
-    # Generate random data
-    chart_data = pd.DataFrame(
-        np.random.randn(20, 3),
-        columns=['A', 'B', 'C']
+    exercise = st.selectbox(
+        "Select Exercise",
+        ["Sales Dashboard", "Financial Analysis", "Customer Segmentation"]
     )
     
-    # Let users choose chart type
-    chart_type = st.selectbox(
-        "Select chart type:",
-        ["Line Chart", "Bar Chart", "Area Chart"]
-    )
+    if exercise == "Sales Dashboard":
+        st.markdown("""
+        ### Exercise: Create a Sales Dashboard
+        
+        Create a dashboard that shows:
+        1. Total sales by region
+        2. Monthly sales trend
+        3. Top-selling products
+        
+        ```python
+        # Sample code to get started
+        import streamlit as st
+        import pandas as pd
+        
+        # Load your data
+        df = pd.DataFrame({
+            'Date': dates,
+            'Sales': values,
+            'Region': regions
+        })
+        
+        # Create visualizations
+        st.line_chart(df.set_index('Date')['Sales'])
+        ```
+        """)
+    
+    # Code input and execution area
+    st.subheader("Your Code")
+    user_code = st.text_area("Write your code here:", height=200)
+    if st.button("Run Analysis"):
+        try:
+            with st.container(border=True):
+                exec(user_code)
+        except Exception as e:
+            st.error(f"Error: {str(e)}")
+
+# Documentation
+with st.expander("📚 Documentation & Tips"):
+    st.markdown("""
+    ### Business Analytics Best Practices
+    
+    1. **Data Preparation**
+    - Always clean and validate your data
+    - Handle missing values appropriately
+    - Consider seasonality in time series data
+    
+    2. **Visualization Guidelines**
+    - Choose appropriate chart types
+    - Use consistent color schemes
+    - Include clear labels and titles
+    
+    3. **Financial Analysis**
+    - Document all assumptions
+    - Include sensitivity analysis
+    - Provide context for metrics
     
-    if chart_type == "Line Chart":
-        st.line_chart(chart_data)
-    elif chart_type == "Bar Chart":
-        st.bar_chart(chart_data)
-    else:
-        st.area_chart(chart_data)
-    
-    st.code("""
-# Create charts
-st.line_chart(chart_data)
-st.bar_chart(chart_data)
-st.area_chart(chart_data)
+    4. **Dashboard Design**
+    - Keep it simple and focused
+    - Provide interactive filters
+    - Update in real-time
     """)
 
-# Footer
+# Footer with additional resources
 st.markdown("---")
-st.markdown("💡 **Pro Tip:** Try modifying the code examples to see how they change the output!")
+st.markdown("""
+<div style='text-align: center'>
+    <p>📈 For more business analytics resources, visit our documentation</p>
+    <p>Last updated: {}</p>
+</div>
+""".format(datetime.now().strftime("%Y-%m-%d")), unsafe_allow_html=True)

app01.py

@@ -0,0 +1,169 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+st.set_page_config(page_title="Interactive Streamlit Tutorial", layout="wide")
+
+st.title("Welcome to Streamlit Development Journey 🚀")
+st.markdown("*An interactive guide for MBA students - Practice as you learn!*")
+
+with st.sidebar:
+    st.header("Navigation Panel")
+    page = st.radio(
+        "Choose your learning path:",
+        ["Text Elements", "Input Widgets", "Layout & Containers", "Data Display", "Interactive Charts"]
+    )
+
+if page == "Text Elements":
+    st.header("1. Text Elements Playground")
+    
+    # Learning Section
+    st.subheader("📚 Learn")
+    with st.expander("See text formatting examples"):
+        st.code("""
+# Different ways to display text:
+st.write('Regular text')
+st.markdown('**Bold** and *italic*')
+st.title('Main Title')
+st.header('Header')
+st.subheader('Subheader')
+st.code('print("Hello World")')
+        """)
+    
+    # Practice Section
+    st.subheader("🔨 Practice")
+    st.write("Try these text elements yourself!")
+    
+    code_input = st.text_area(
+        "Type your Streamlit code here:",
+        height=100,
+        placeholder="Example: st.write('Hello World')"
+    )
+    
+    if st.button("Run Code"):
+        try:
+            with st.container():
+                st.write("Your Output:")
+                exec(code_input)
+        except Exception as e:
+            st.error(f"Error: {str(e)}")
+
+elif page == "Input Widgets":
+    st.header("2. Input Widgets Laboratory")
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.subheader("Widget Examples")
+        st.write("Try these interactive widgets:")
+        
+        # Text input
+        name = st.text_input("Enter your name:")
+        if name:
+            st.write(f"Hello, {name}!")
+            
+        # Number input
+        number = st.number_input("Pick a number", 0, 100)
+        st.write(f"Your number squared is: {number**2}")
+        
+        # Slider
+        age = st.slider("Select your age", 0, 100, 25)
+        st.write(f"You selected: {age} years")
+    
+    with col2:
+        st.subheader("Code Reference")
+        st.code("""
+# Text input
+name = st.text_input("Enter your name:")
+if name:
+    st.write(f"Hello, {name}!")
+
+# Number input
+number = st.number_input("Pick a number", 0, 100)
+
+# Slider
+age = st.slider("Select your age", 0, 100, 25)
+        """)
+
+elif page == "Layout & Containers":
+    st.header("3. Layout Workshop")
+    
+    st.write("Experiment with different layouts:")
+    
+    tab1, tab2, tab3 = st.tabs(["Columns", "Expander", "Container"])
+    
+    with tab1:
+        st.write("Create two columns:")
+        col1, col2 = st.columns(2)
+        with col1:
+            st.write("This is column 1")
+            st.button("Column 1 Button")
+        with col2:
+            st.write("This is column 2")
+            st.button("Column 2 Button")
+            
+    with tab2:
+        with st.expander("Click to expand"):
+            st.write("This content is hidden until expanded!")
+            st.slider("Hidden slider", 0, 100)
+            
+    with tab3:
+        with st.container():
+            st.write("This is a container")
+            st.metric(label="Temperature", value="24 °C", delta="1.2 °C")
+
+elif page == "Data Display":
+    st.header("4. Data Display Workshop")
+    
+    # Create sample data
+    df = pd.DataFrame({
+        'Name': ['John', 'Emma', 'Alex', 'Sarah'],
+        'Age': [28, 24, 32, 27],
+        'City': ['New York', 'London', 'Paris', 'Tokyo']
+    })
+    
+    st.subheader("Practice with DataFrames")
+    
+    # Show data manipulation options
+    st.write("Original DataFrame:")
+    st.dataframe(df)
+    
+    # Let users try filtering
+    city_filter = st.selectbox("Filter by city:", ['All'] + list(df['City'].unique()))
+    if city_filter != 'All':
+        filtered_df = df[df['City'] == city_filter]
+        st.write("Filtered DataFrame:")
+        st.dataframe(filtered_df)
+
+elif page == "Interactive Charts":
+    st.header("5. Visualization Lab")
+    
+    # Generate random data
+    chart_data = pd.DataFrame(
+        np.random.randn(20, 3),
+        columns=['A', 'B', 'C']
+    )
+    
+    # Let users choose chart type
+    chart_type = st.selectbox(
+        "Select chart type:",
+        ["Line Chart", "Bar Chart", "Area Chart"]
+    )
+    
+    if chart_type == "Line Chart":
+        st.line_chart(chart_data)
+    elif chart_type == "Bar Chart":
+        st.bar_chart(chart_data)
+    else:
+        st.area_chart(chart_data)
+    
+    st.code("""
+# Create charts
+st.line_chart(chart_data)
+st.bar_chart(chart_data)
+st.area_chart(chart_data)
+    """)
+
+# Footer
+st.markdown("---")
+st.markdown("💡 **Pro Tip:** Try modifying the code examples to see how they change the output!")