2024 Dec 30 New streamlit tutorial

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

app.py

@@ -1,161 +1,214 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-import plotly.graph_objects as go
-from datetime import datetime, timedelta
 
-st.set_page_config(layout="wide", page_title="Business Analytics Dashboard Tutorial")
+st.set_page_config(page_title="Learn Streamlit", layout="wide")
+
+# Main navigation sidebar
+with st.sidebar:
+    st.title("Streamlit Tutorial")
+    selected_topic = st.radio(
+        "Choose a Topic:",
+        [
+            "1. Basic Text Elements",
+            "2. Input Widgets",
+            "3. Layouts & Containers", 
+            "4. Data Display",
+            "5. Charts & Plots",
+            "6. Interactive Components",
+            "Try It Yourself"
+        ]
+    )
 
-# 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)
+if selected_topic == "1. Basic Text Elements":
+    st.title("Basic Text Elements in Streamlit")
     
-    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
-
-# Main Navigation
-st.title("📊 Business Analytics & Data Analysis Tutorial")
-
-# Generate sample data
-df_sales = generate_sales_data()
-
-# 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())
-    )
+    # Example & Output side by side
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.header("Code Examples")
+        st.code("""
+# Title
+st.title('Main Title')
 
-# 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]))
+# Header
+st.header('Header')
 
-if product_type != 'All':
-    mask &= (df_sales['Product'] == product_type)
+# Subheader
+st.subheader('Subheader')
 
-filtered_df = df_sales[mask]
+# Normal text
+st.write('Normal text')
 
-# KPI Metrics
-st.subheader("Key Performance Indicators")
-kpi1, kpi2, kpi3, kpi4 = st.columns(4)
+# Markdown text
+st.markdown('**Bold** and *italic*')
 
-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}%"
-    )
+# Colored text
+st.markdown(':blue[Blue text]')
+        """)
+    
+    with col2:
+        st.header("Live Output")
+        st.title('Main Title')
+        st.header('Header')
+        st.subheader('Subheader')
+        st.write('Normal text')
+        st.markdown('**Bold** and *italic*')
+        st.markdown(':blue[Blue text]')
+
+elif selected_topic == "2. Input Widgets":
+    st.title("Input Widgets")
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.header("Code")
+        st.code("""
+# Text input
+name = st.text_input('Enter your name')
+
+# Number input
+age = st.number_input('Enter age', 
+    min_value=0, max_value=120)
+
+# Slider
+value = st.slider('Select value', 
+    0, 100)
+
+# Checkbox
+agree = st.checkbox('I agree')
+
+# Selectbox
+option = st.selectbox(
+    'Choose option',
+    ['A', 'B', 'C'])
+        """)
+    
+    with col2:
+        st.header("Try these widgets")
+        name = st.text_input('Enter your name')
+        if name:
+            st.write(f'Hello {name}!')
+            
+        age = st.number_input('Enter age', min_value=0, max_value=120)
+        value = st.slider('Select value', 0, 100)
+        agree = st.checkbox('I agree')
+        option = st.selectbox('Choose option', ['A', 'B', 'C'])
+
+elif selected_topic == "3. Layouts & Containers":
+    st.title("Layouts & Containers")
+    
+    st.header("1. Columns")
+    st.code("col1, col2 = st.columns(2)")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.write("This is column 1")
+    with col2:
+        st.write("This is column 2")
+    
+    st.header("2. Tabs")
+    st.code("tab1, tab2 = st.tabs(['Tab 1', 'Tab 2'])")
+    tab1, tab2 = st.tabs(['Tab 1', 'Tab 2'])
+    with tab1:
+        st.write("Content of tab 1")
+    with tab2:
+        st.write("Content of tab 2")
+    
+    st.header("3. Expander")
+    with st.expander("Click to expand"):
+        st.write("Hidden content revealed!")
 
-# Sales Trends
-st.subheader("Sales Trends Analysis")
-daily_sales = filtered_df.groupby('Date')[['Sales', 'Profit']].sum().reset_index()
-
-# Create the figure ensuring dates are in datetime format
-fig = go.Figure()
-
-fig.add_trace(go.Scatter(
-    x=daily_sales['Date'].dt.strftime('%Y-%m-%d'),  # Convert to string format
-    y=daily_sales['Sales'],
-    name='Sales',
-    line=dict(color='blue')
-))
-
-fig.add_trace(go.Scatter(
-    x=daily_sales['Date'].dt.strftime('%Y-%m-%d'),  # Convert to string format
-    y=daily_sales['Profit'],
-    name='Profit',
-    line=dict(color='green')
-))
-
-fig.update_layout(
-    title='Daily Sales and Profit Trends',
-    xaxis_title='Date',
-    yaxis_title='Amount ($)',
-    xaxis=dict(
-        type='category',  # Use category type for x-axis
-        tickangle=45
-    )
-)
-
-st.plotly_chart(fig, use_container_width=True)
-
-# Regional Analysis
-st.subheader("Regional Performance")
-regional_data = filtered_df.groupby('Region').agg({
-    'Sales': 'sum',
-    'Profit': 'sum'
-}).reset_index()
-
-fig_region = go.Figure(data=[
-    go.Bar(name='Sales', x=regional_data['Region'], y=regional_data['Sales']),
-    go.Bar(name='Profit', x=regional_data['Region'], y=regional_data['Profit'])
-])
-
-fig_region.update_layout(
-    barmode='group',
-    title='Sales and Profit by Region'
-)
-
-st.plotly_chart(fig_region, use_container_width=True)
-
-# Product Analysis
-if product_type == 'All':
-    st.subheader("Product Performance")
-    product_data = filtered_df.groupby('Product').agg({
-        'Sales': 'sum',
-        'Profit': 'sum'
-    }).reset_index()
-
-    fig_product = go.Figure(data=[
-        go.Bar(name='Sales', x=product_data['Product'], y=product_data['Sales']),
-        go.Bar(name='Profit', x=product_data['Product'], y=product_data['Profit'])
-    ])
-
-    fig_product.update_layout(
-        barmode='group',
-        title='Sales and Profit by Product'
+elif selected_topic == "4. Data Display":
+    st.title("Working with Data")
+    
+    # Create sample dataframe
+    df = pd.DataFrame({
+        'Name': ['John', 'Anna', 'Peter'],
+        'Age': [25, 30, 35],
+        'City': ['New York', 'Paris', 'London']
+    })
+    
+    st.header("Display DataFrame")
+    st.code("""
+# Create DataFrame
+df = pd.DataFrame({
+    'Name': ['John', 'Anna', 'Peter'],
+    'Age': [25, 30, 35],
+    'City': ['New York', 'Paris', 'London']
+})
+
+# Display as table
+st.dataframe(df)
+
+# Display as static table
+st.table(df)
+    """)
+    
+    st.dataframe(df)
+    st.table(df)
+
+elif selected_topic == "5. Charts & Plots":
+    st.title("Creating Charts")
+    
+    # Generate sample data
+    chart_data = pd.DataFrame(
+        np.random.randn(20, 3),
+        columns=['A', 'B', 'C']
     )
+    
+    st.header("Line Chart")
+    st.code("st.line_chart(chart_data)")
+    st.line_chart(chart_data)
+    
+    st.header("Bar Chart")
+    st.code("st.bar_chart(chart_data)")
+    st.bar_chart(chart_data)
+    
+    st.header("Area Chart")
+    st.code("st.area_chart(chart_data)")
+    st.area_chart(chart_data)
+
+elif selected_topic == "6. Interactive Components":
+    st.title("Interactive Components")
+    
+    st.header("Form Example")
+    with st.form("my_form"):
+        st.write("Inside the form")
+        name = st.text_input("Name")
+        age = st.slider("Age", 0, 100, 25)
+        submitted = st.form_submit_button("Submit")
+        if submitted:
+            st.write(f"Name: {name}, Age: {age}")
+    
+    st.header("File Uploader")
+    uploaded_file = st.file_uploader("Choose a file")
+    if uploaded_file:
+        st.write("File uploaded!")
 
-    st.plotly_chart(fig_product, use_container_width=True)
+elif selected_topic == "Try It Yourself":
+    st.title("Practice Zone")
+    
+    st.write("Try writing some Streamlit code below!")
+    
+    code = st.text_area("Your code:", height=200,
+        placeholder="Example:\nst.write('Hello World!')")
+    
+    if st.button("Run Code"):
+        try:
+            exec(code)
+        except Exception as e:
+            st.error(f"Error: {str(e)}")
+
+# Footer with helpful tips
+with st.expander("💡 Tips & Tricks"):
+    st.markdown("""
+    - Use `st.write()` for quick output
+    - Columns help organize content
+    - Forms batch multiple inputs
+    - Always handle errors in user inputs
+    - Use expanders for additional info
+    """)
 
-# Footer
 st.markdown("---")
-st.markdown(f"Dashboard last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+st.caption("Learn more at streamlit.io/docs")

datanalysis.py

@@ -0,0 +1,161 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+from datetime import datetime, timedelta
+
+st.set_page_config(layout="wide", page_title="Business Analytics Dashboard Tutorial")
+
+# 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
+
+# Main Navigation
+st.title("📊 Business Analytics & Data Analysis Tutorial")
+
+# Generate sample data
+df_sales = generate_sales_data()
+
+# 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())
+    )
+
+# 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]))
+
+if product_type != 'All':
+    mask &= (df_sales['Product'] == product_type)
+
+filtered_df = df_sales[mask]
+
+# 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"${filtered_df['Profit'].sum():,.0f}",
+        f"{((filtered_df['Profit'].sum() / df_sales['Profit'].sum()) - 1) * 100:.1f}%"
+    )
+with kpi4:
+    st.metric(
+        "Profit Margin",
+        f"{(filtered_df['Profit'].sum() / filtered_df['Sales'].sum() * 100):.1f}%"
+    )
+
+# Sales Trends
+st.subheader("Sales Trends Analysis")
+daily_sales = filtered_df.groupby('Date')[['Sales', 'Profit']].sum().reset_index()
+
+# Create the figure ensuring dates are in datetime format
+fig = go.Figure()
+
+fig.add_trace(go.Scatter(
+    x=daily_sales['Date'].dt.strftime('%Y-%m-%d'),  # Convert to string format
+    y=daily_sales['Sales'],
+    name='Sales',
+    line=dict(color='blue')
+))
+
+fig.add_trace(go.Scatter(
+    x=daily_sales['Date'].dt.strftime('%Y-%m-%d'),  # Convert to string format
+    y=daily_sales['Profit'],
+    name='Profit',
+    line=dict(color='green')
+))
+
+fig.update_layout(
+    title='Daily Sales and Profit Trends',
+    xaxis_title='Date',
+    yaxis_title='Amount ($)',
+    xaxis=dict(
+        type='category',  # Use category type for x-axis
+        tickangle=45
+    )
+)
+
+st.plotly_chart(fig, use_container_width=True)
+
+# Regional Analysis
+st.subheader("Regional Performance")
+regional_data = filtered_df.groupby('Region').agg({
+    'Sales': 'sum',
+    'Profit': 'sum'
+}).reset_index()
+
+fig_region = go.Figure(data=[
+    go.Bar(name='Sales', x=regional_data['Region'], y=regional_data['Sales']),
+    go.Bar(name='Profit', x=regional_data['Region'], y=regional_data['Profit'])
+])
+
+fig_region.update_layout(
+    barmode='group',
+    title='Sales and Profit by Region'
+)
+
+st.plotly_chart(fig_region, use_container_width=True)
+
+# Product Analysis
+if product_type == 'All':
+    st.subheader("Product Performance")
+    product_data = filtered_df.groupby('Product').agg({
+        'Sales': 'sum',
+        'Profit': 'sum'
+    }).reset_index()
+
+    fig_product = go.Figure(data=[
+        go.Bar(name='Sales', x=product_data['Product'], y=product_data['Sales']),
+        go.Bar(name='Profit', x=product_data['Product'], y=product_data['Profit'])
+    ])
+
+    fig_product.update_layout(
+        barmode='group',
+        title='Sales and Profit by Product'
+    )
+
+    st.plotly_chart(fig_product, use_container_width=True)
+
+# Footer
+st.markdown("---")
+st.markdown(f"Dashboard last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")