From 59ef9e8412d5dc414994654b036304c917edc01a Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:39:03 +0530
Subject: [PATCH 01/34] new: Add `bokeh-0.py` - `line-plot`

---
 marimo/_snippets/data/bokeh-0.py | 62 ++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)
 create mode 100644 marimo/_snippets/data/bokeh-0.py

diff --git a/marimo/_snippets/data/bokeh-0.py b/marimo/_snippets/data/bokeh-0.py
new file mode 100644
index 00000000000..174541936ec
--- /dev/null
+++ b/marimo/_snippets/data/bokeh-0.py
@@ -0,0 +1,62 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Bokeh: Interactive Line Plot
+
+        Create an interactive line plot with pan, zoom, and hover tools.
+        Common usage: `figure(tools='pan,box_zoom,reset,save')`.
+        Commonly used in: time series analysis, trend visualization.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource
+    import numpy as np
+
+    # Generate sample data
+    x = np.linspace(0, 10, 100)
+    y = np.sin(x) + np.random.normal(0, 0.1, 100)
+
+    # Create data source
+    source = ColumnDataSource(data=dict(x=x, y=y))
+
+    # Create figure with tools
+    p = figure(
+        height=400,
+        width=700,
+        tools='pan,box_zoom,wheel_zoom,reset,save,hover',
+        title='Interactive Line Plot'
+    )
+
+    # Add line with hover tooltips
+    p.line('x', 'y', line_width=2, source=source)
+    p.hover.tooltips = [
+        ('x', '@x{0.00}'),
+        ('y', '@y{0.00}')
+    ]
+
+    p
+    return ColumnDataSource, figure, np, p, source, x, y
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()

From 23cafedea66cf5786bca36f291bdaebd4f309527 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:39:54 +0530
Subject: [PATCH 02/34] new: Add `bokeh-1.py` - `scatter-plot`

---
 marimo/_snippets/data/bokeh-1.py | 87 ++++++++++++++++++++++++++++++++
 1 file changed, 87 insertions(+)
 create mode 100644 marimo/_snippets/data/bokeh-1.py

diff --git a/marimo/_snippets/data/bokeh-1.py b/marimo/_snippets/data/bokeh-1.py
new file mode 100644
index 00000000000..66e7dd0ff00
--- /dev/null
+++ b/marimo/_snippets/data/bokeh-1.py
@@ -0,0 +1,87 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Bokeh: Interactive Scatter Plot
+
+        Create a scatter plot with interactive tools and color mapping.
+        Common usage: `figure(tools='box_select,lasso_select,reset')`.
+        Commonly used in: data exploration, cluster analysis, outlier detection.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource, ColorBar
+    from bokeh.transform import linear_cmap
+    import numpy as np
+    
+    # Generate sample clustered data
+    np.random.seed(42)
+    N = 500
+    x = np.random.normal(size=N)
+    y = np.random.normal(size=N)
+    color_value = np.random.uniform(size=N)  # Value for color mapping
+    
+    # Create data source
+    source = ColumnDataSource(data=dict(
+        x=x,
+        y=y,
+        color=color_value,
+    ))
+    
+    # Create figure with selection tools
+    p = figure(
+        height=400,
+        width=700,
+        tools='box_select,lasso_select,wheel_zoom,pan,reset,save',
+        title='Interactive Scatter Plot'
+    )
+    
+    # Add points with color mapping
+    mapper = linear_cmap('color', 'Viridis256', 0, 1)
+    scatter = p.scatter('x', 'y', 
+                       size=8,
+                       source=source,
+                       fill_color=mapper,
+                       line_color='white',
+                       alpha=0.6,
+                       selection_color='red',
+                       nonselection_alpha=0.1)
+    
+    # Add colorbar
+    color_bar = ColorBar(color_mapper=mapper['transform'], 
+                        width=8,
+                        location=(0,0))
+    p.add_layout(color_bar, 'right')
+    
+    # Add hover tooltips
+    p.hover.tooltips = [
+        ('x', '@x{0.00}'),
+        ('y', '@y{0.00}'),
+        ('value', '@color{0.00}')
+    ]
+    
+    p
+    return (ColorBar, ColumnDataSource, color_bar, color_value, figure, linear_cmap,
+            mapper, np, p, scatter, source, x, y)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From dddda2412889376f3e61659a2ae15e72ba6b7433 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:40:46 +0530
Subject: [PATCH 03/34] new: Add `bokeh-2.py` - `layout`

---
 marimo/_snippets/data/bokeh-2.py | 111 +++++++++++++++++++++++++++++++
 1 file changed, 111 insertions(+)
 create mode 100644 marimo/_snippets/data/bokeh-2.py

diff --git a/marimo/_snippets/data/bokeh-2.py b/marimo/_snippets/data/bokeh-2.py
new file mode 100644
index 00000000000..d416598aab5
--- /dev/null
+++ b/marimo/_snippets/data/bokeh-2.py
@@ -0,0 +1,111 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Bokeh: Linked Brushing and Selection
+
+        Create linked plots with synchronized selection.
+        Common usage: `layout([p1, p2])` with shared `ColumnDataSource`.
+        Commonly used in: exploratory data analysis, correlation analysis, outlier detection.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource
+    from bokeh.layouts import row
+    import numpy as np
+    
+    # Generate sample data
+    np.random.seed(42)
+    N = 1000
+    x = np.random.normal(0, 1, N)
+    y = x * 0.5 + np.random.normal(0, 0.5, N)  # Positive correlation with x
+    z = x * -0.2 + np.random.normal(0, 0.2, N)  # Negative correlation with x
+    
+    # Create data source
+    source = ColumnDataSource(data=dict(x=x, y=y, z=z))
+    
+    # Common figure properties
+    plot_config = dict(
+        height=400,
+        width=400,
+        tools='box_select,lasso_select,wheel_zoom,pan,reset,hover',
+        background_fill_color='#f5f5f5'
+    )
+    
+    # Create first plot
+    p1 = figure(
+        title='Y vs X (Positive Correlation)',
+        **plot_config
+    )
+    p1.circle('x', 'y', 
+              source=source,
+              size=8,
+              fill_color='navy',
+              fill_alpha=0.5,
+              line_color='white',
+              selection_color='red',
+              nonselection_alpha=0.1)
+    
+    # Create second plot
+    p2 = figure(
+        title='Z vs X (Negative Correlation)',
+        **plot_config
+    )
+    p2.circle('x', 'z', 
+              source=source,
+              size=8,
+              fill_color='navy',
+              fill_alpha=0.5,
+              line_color='white',
+              selection_color='red',
+              nonselection_alpha=0.1)
+    
+    # Add hover tooltips
+    tooltip_config = [
+        ('x', '@x{0.00}'),
+        ('y', '@y{0.00}'),
+        ('z', '@z{0.00}')
+    ]
+    p1.hover.tooltips = tooltip_config
+    p2.hover.tooltips = tooltip_config
+    
+    # Add grid styling
+    for p in [p1, p2]:
+        p.grid.grid_line_color = 'white'
+        p.grid.grid_line_width = 2
+        p.axis.axis_label_text_font_size = '12pt'
+        p.axis.axis_label_text_font_style = 'bold'
+    
+    # Add axis labels
+    p1.xaxis.axis_label = 'X Value'
+    p1.yaxis.axis_label = 'Y Value'
+    p2.xaxis.axis_label = 'X Value'
+    p2.yaxis.axis_label = 'Z Value'
+    
+    # Layout plots side by side
+    layout = row(p1, p2)
+    
+    layout
+    return (ColumnDataSource, figure, layout, np, p1, p2, row, source, x, y, z)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From a71f75e47b4693919f2cba9a6236872fa1903786 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:41:54 +0530
Subject: [PATCH 04/34] new: Add `bokeh-3.py` - `RT-DS`

---
 marimo/_snippets/data/bokeh-3.py | 105 +++++++++++++++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 marimo/_snippets/data/bokeh-3.py

diff --git a/marimo/_snippets/data/bokeh-3.py b/marimo/_snippets/data/bokeh-3.py
new file mode 100644
index 00000000000..6de5e6cbb5a
--- /dev/null
+++ b/marimo/_snippets/data/bokeh-3.py
@@ -0,0 +1,105 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Bokeh: Real-time Data Streaming
+
+        Create a real-time streaming visualization with rolling window.
+        Common usage: `ColumnDataSource` with periodic updates.
+        Commonly used in: system monitoring, IoT dashboards, live analytics.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource, DatetimeTickFormatter
+    import numpy as np
+    from datetime import datetime, timedelta
+    import pandas as pd
+    
+    # Initialize data with rolling window
+    window_size = 100
+    now = datetime.now()
+    times = [(now + timedelta(seconds=i)) for i in range(-window_size, 0)]
+    
+    # Simulate metric data (e.g., system metrics)
+    values = np.random.normal(100, 10, window_size)
+    values_smooth = pd.Series(values).rolling(window=5).mean()
+    
+    # Create data source
+    source = ColumnDataSource(data=dict(
+        time=times,
+        value=values,
+        value_smooth=values_smooth
+    ))
+    
+    # Create figure
+    p = figure(
+        height=400,
+        width=800,
+        title='Real-time Metric Monitoring',
+        x_axis_type='datetime',
+        tools='pan,box_zoom,wheel_zoom,reset,save'
+    )
+    
+    # Add raw data line
+    p.line('time', 'value', 
+           line_color='lightgray',
+           line_alpha=0.5,
+           line_width=1,
+           legend_label='Raw Data',
+           source=source)
+    
+    # Add smoothed line
+    p.line('time', 'value_smooth',
+           line_color='navy',
+           line_width=2,
+           legend_label='Smoothed (5-point MA)',
+           source=source)
+    
+    # Style the plot
+    p.grid.grid_line_alpha = 0.3
+    p.xaxis.formatter = DatetimeTickFormatter(
+        seconds="%Y-%m-%d %H:%M:%S",
+        minsec="%Y-%m-%d %H:%M:%S",
+        minutes="%Y-%m-%d %H:%M:%S",
+        hourmin="%Y-%m-%d %H:%M"
+    )
+    p.xaxis.axis_label = 'Time'
+    p.yaxis.axis_label = 'Value'
+    
+    # Add hover tool
+    p.hover.tooltips = [
+        ('Time', '@time{%Y-%m-%d %H:%M:%S}'),
+        ('Value', '@value{0.00}'),
+        ('Smoothed', '@value_smooth{0.00}')
+    ]
+    p.hover.formatters = {'@time': 'datetime'}
+    
+    # Configure legend
+    p.legend.location = 'top_left'
+    p.legend.click_policy = 'hide'
+    
+    p
+    return (ColumnDataSource, DatetimeTickFormatter, datetime, figure, now, np, p,
+            pd, source, timedelta, times, values, values_smooth, window_size)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 7cba5e2028eb7d7cb802354c92d657fad786fc68 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:42:15 +0530
Subject: [PATCH 05/34] new: Add `bokeh-4.py` - `dashboard`

---
 marimo/_snippets/data/bokeh-4.py | 117 +++++++++++++++++++++++++++++++
 1 file changed, 117 insertions(+)
 create mode 100644 marimo/_snippets/data/bokeh-4.py

diff --git a/marimo/_snippets/data/bokeh-4.py b/marimo/_snippets/data/bokeh-4.py
new file mode 100644
index 00000000000..5e358c7c6fe
--- /dev/null
+++ b/marimo/_snippets/data/bokeh-4.py
@@ -0,0 +1,117 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Bokeh: Interactive Dashboard Layout
+
+        Create a multi-panel dashboard with different visualization types.
+        Common usage: `column(row(p1, p2), p3)` for complex layouts.
+        Commonly used in: business analytics, KPI monitoring, data reporting.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource
+    from bokeh.layouts import column, row
+    import numpy as np
+    import pandas as pd
+    
+    # Generate sample data
+    np.random.seed(42)
+    dates = pd.date_range('2023-01-01', '2023-12-31', freq='D')
+    
+    data = pd.DataFrame({
+        'date': dates,
+        'metric1': np.random.normal(100, 15, len(dates)).cumsum(),
+        'metric2': np.random.normal(50, 10, len(dates)).cumsum(),
+        'category': np.random.choice(['A', 'B', 'C'], len(dates)),
+        'value': np.random.uniform(20, 80, len(dates))
+    })
+    
+    # Create data sources
+    ts_source = ColumnDataSource(data)
+    cat_source = data.groupby('category')['value'].mean().reset_index()
+    cat_source = ColumnDataSource(cat_source)
+    
+    # Time series plot
+    p1 = figure(
+        height=300,
+        width=800,
+        title='Metrics Over Time',
+        x_axis_type='datetime',
+        tools='pan,box_zoom,wheel_zoom,reset'
+    )
+    
+    p1.line('date', 'metric1', line_color='navy', legend_label='Metric 1',
+            source=ts_source)
+    p1.line('date', 'metric2', line_color='crimson', legend_label='Metric 2',
+            source=ts_source)
+    p1.legend.click_policy = 'hide'
+    
+    # Bar chart
+    p2 = figure(
+        height=300,
+        width=400,
+        title='Average by Category',
+        x_range=list(data.category.unique()),
+        tools='pan,box_zoom,wheel_zoom,reset'
+    )
+    
+    p2.vbar(x='category', top='value', width=0.8, source=cat_source,
+            fill_color='navy', line_color=None)
+    
+    # Distribution plot
+    hist, edges = np.histogram(data.value, bins=20)
+    hist_data = pd.DataFrame({
+        'count': hist,
+        'left': edges[:-1],
+        'right': edges[1:]
+    })
+    hist_source = ColumnDataSource(hist_data)
+    
+    p3 = figure(
+        height=300,
+        width=400,
+        title='Value Distribution',
+        tools='pan,box_zoom,wheel_zoom,reset'
+    )
+    
+    p3.quad(top='count', bottom=0, left='left', right='right',
+            source=hist_source, fill_color='navy', line_color='white')
+    
+    # Style all plots
+    for p in [p1, p2, p3]:
+        p.grid.grid_line_alpha = 0.3
+        p.axis.axis_label_text_font_size = '12pt'
+    
+    # Create layout
+    layout = column(
+        p1,
+        row(p2, p3)
+    )
+    
+    layout
+    return (ColumnDataSource, cat_source, column, data, dates, edges, figure,
+            hist, hist_data, hist_source, layout, np, p1, p2, p3, pd, row,
+            ts_source)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 8cd45f88a44ada771e84382483b7d2a7084b6c06 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:43:27 +0530
Subject: [PATCH 06/34] new: Add `bokeh-5.py` - `stats-viz`

---
 marimo/_snippets/data/bokeh-5.py | 130 +++++++++++++++++++++++++++++++
 1 file changed, 130 insertions(+)
 create mode 100644 marimo/_snippets/data/bokeh-5.py

diff --git a/marimo/_snippets/data/bokeh-5.py b/marimo/_snippets/data/bokeh-5.py
new file mode 100644
index 00000000000..45feab4d2d7
--- /dev/null
+++ b/marimo/_snippets/data/bokeh-5.py
@@ -0,0 +1,130 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Bokeh: Statistical Visualization
+
+        Create box plots and violin plots for statistical analysis.
+        Common usage: Box plots for distribution comparison across categories.
+        Commonly used in: research analysis, quality control, performance comparison.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource, FactorRange
+    from bokeh.transform import factor_cmap
+    import numpy as np
+    import pandas as pd
+    
+    # Generate sample data for different groups
+    np.random.seed(42)
+    groups = ['Group A', 'Group B', 'Group C', 'Group D']
+    data = []
+    
+    for group in groups:
+        if group == 'Group A':
+            values = np.random.normal(100, 15, 100)
+        elif group == 'Group B':
+            values = np.random.normal(85, 10, 100)
+        elif group == 'Group C':
+            values = np.random.normal(110, 20, 100)
+        else:
+            values = np.random.normal(95, 12, 100)
+        
+        # Calculate statistics
+        q1 = np.percentile(values, 25)
+        q2 = np.percentile(values, 50)
+        q3 = np.percentile(values, 75)
+        iqr = q3 - q1
+        upper = min(q3 + 1.5*iqr, np.max(values))
+        lower = max(q1 - 1.5*iqr, np.min(values))
+        
+        # Store outliers
+        outliers = values[(values < lower) | (values > upper)]
+        
+        data.append({
+            'group': group,
+            'lower': lower,
+            'q1': q1,
+            'q2': q2,
+            'q3': q3,
+            'upper': upper,
+            'outliers': list(outliers)
+        })
+    
+    df = pd.DataFrame(data)
+    source = ColumnDataSource(df)
+    
+    # Create figure
+    p = figure(
+        height=400,
+        width=600,
+        title='Distribution Comparison Across Groups',
+        x_range=groups,
+        tools='pan,box_zoom,wheel_zoom,reset,save,hover'
+    )
+    
+    # Add box glyphs
+    p.vbar(x='group', top='q3', bottom='q1', width=0.7,
+           fill_color=factor_cmap('group', 'Blues4', groups),
+           line_color='black', source=source,
+           legend_field='group')
+    
+    # Add whiskers
+    p.segment(x0='group', y0='upper', x1='group', y1='q3',
+              line_color='black', source=source)
+    p.segment(x0='group', y0='lower', x1='group', y1='q1',
+              line_color='black', source=source)
+    
+    # Add outlier points
+    for i, group in enumerate(groups):
+        outliers = df.iloc[i]['outliers']
+        if len(outliers) > 0:
+            p.circle([group]*len(outliers), outliers,
+                    size=6, fill_alpha=0.6,
+                    line_color='black', fill_color='red')
+    
+    # Style the plot
+    p.xgrid.grid_line_color = None
+    p.ygrid.grid_line_alpha = 0.1
+    p.xaxis.axis_label = 'Groups'
+    p.yaxis.axis_label = 'Values'
+    
+    # Configure hover tooltips
+    p.hover.tooltips = [
+        ('Group', '@group'),
+        ('Median', '@q2{0.0}'),
+        ('Q1', '@q1{0.0}'),
+        ('Q3', '@q3{0.0}'),
+        ('Upper', '@upper{0.0}'),
+        ('Lower', '@lower{0.0}')
+    ]
+    
+    # Configure legend
+    p.legend.click_policy = 'hide'
+    p.legend.location = 'top_right'
+    
+    p
+    return (ColumnDataSource, FactorRange, data, df, factor_cmap, figure, group,
+            groups, np, p, pd, source, values)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 6be1dea12ea1d5c07055b8e92d85445882803dd6 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:44:06 +0530
Subject: [PATCH 07/34] new: Add `bokeh-6.py` - `TS`

---
 marimo/_snippets/data/bokeh-6.py | 121 +++++++++++++++++++++++++++++++
 1 file changed, 121 insertions(+)
 create mode 100644 marimo/_snippets/data/bokeh-6.py

diff --git a/marimo/_snippets/data/bokeh-6.py b/marimo/_snippets/data/bokeh-6.py
new file mode 100644
index 00000000000..07f05c29467
--- /dev/null
+++ b/marimo/_snippets/data/bokeh-6.py
@@ -0,0 +1,121 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Bokeh: Time Series with Range Tool
+
+        Create a time series plot with range selection tool.
+        Common usage: Financial analysis, trend analysis with zoom capability.
+        Commonly used in: stock analysis, sensor data analysis, trend investigation.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource, RangeTool
+    import numpy as np
+    import pandas as pd
+    
+    # Generate sample time series data
+    np.random.seed(42)
+    dates = pd.date_range('2023-01-01', '2023-12-31', freq='D')
+    
+    # Simulate stock price with trend and volatility
+    base = 100
+    trend = np.linspace(0, 20, len(dates))
+    volatility = np.random.normal(0, 1, len(dates)).cumsum()
+    price = base + trend + volatility
+    volume = np.random.uniform(50000, 200000, len(dates))
+    
+    # Create DataFrame
+    df = pd.DataFrame({
+        'date': dates,
+        'price': price,
+        'volume': volume,
+        'ma20': pd.Series(price).rolling(window=20).mean(),
+        'ma50': pd.Series(price).rolling(window=50).mean()
+    })
+    
+    source = ColumnDataSource(df)
+    
+    # Create main figure
+    p = figure(
+        height=400,
+        width=800,
+        title='Interactive Time Series Analysis',
+        x_axis_type='datetime',
+        tools='pan,wheel_zoom,box_zoom,reset,save'
+    )
+    
+    # Add price and moving averages
+    p.line('date', 'price', line_color='gray', line_alpha=0.6,
+           legend_label='Price', source=source)
+    p.line('date', 'ma20', line_color='blue', line_width=2,
+           legend_label='20-day MA', source=source)
+    p.line('date', 'ma50', line_color='red', line_width=2,
+           legend_label='50-day MA', source=source)
+    
+    # Create range tool figure
+    select = figure(
+        height=150,
+        width=800,
+        y_axis_type=None,
+        x_axis_type='datetime',
+        tools='',
+        toolbar_location=None
+    )
+    
+    range_tool = RangeTool(x_range=p.x_range)
+    range_tool.overlay.fill_color = 'navy'
+    range_tool.overlay.fill_alpha = 0.2
+    
+    select.line('date', 'price', source=source)
+    select.ygrid.grid_line_color = None
+    select.add_tools(range_tool)
+    
+    # Style the plots
+    p.grid.grid_line_alpha = 0.3
+    p.xaxis.axis_label = 'Date'
+    p.yaxis.axis_label = 'Price'
+    
+    # Add hover tooltips
+    p.hover.tooltips = [
+        ('Date', '@date{%F}'),
+        ('Price', '@price{0.00}'),
+        ('MA20', '@ma20{0.00}'),
+        ('MA50', '@ma50{0.00}'),
+        ('Volume', '@volume{0,0}')
+    ]
+    p.hover.formatters = {'@date': 'datetime'}
+    
+    # Configure legend
+    p.legend.location = 'top_left'
+    p.legend.click_policy = 'hide'
+    
+    from bokeh.layouts import column
+    layout = column(p, select)
+    
+    layout
+    return (ColumnDataSource, RangeTool, base, column, dates, df, figure, layout,
+            np, p, pd, price, range_tool, select, source, trend, volatility,
+            volume)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 5de0e3ff459c37915cd244ff0f9c0b6d2d59367e Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:44:38 +0530
Subject: [PATCH 08/34] new: Add `bokeh-7.py` - `categorical data`

---
 marimo/_snippets/data/bokeh-7.py | 93 ++++++++++++++++++++++++++++++++
 1 file changed, 93 insertions(+)
 create mode 100644 marimo/_snippets/data/bokeh-7.py

diff --git a/marimo/_snippets/data/bokeh-7.py b/marimo/_snippets/data/bokeh-7.py
new file mode 100644
index 00000000000..7084768ea64
--- /dev/null
+++ b/marimo/_snippets/data/bokeh-7.py
@@ -0,0 +1,93 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Bokeh: Categorical Data Analysis
+
+        Create interactive bar charts and heatmaps for categorical data.
+        Common usage: Comparing metrics across categories and subcategories.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource, FactorRange
+    from bokeh.transform import factor_cmap
+    import numpy as np
+    import pandas as pd
+    
+    # Sample data
+    categories = ['A', 'B', 'C']
+    regions = ['North', 'South', 'East']
+    
+    data = []
+    np.random.seed(42)
+    
+    for cat in categories:
+        for region in regions:
+            data.append({
+                'category': cat,
+                'region': region,
+                'sales': np.random.randint(50, 150)
+            })
+    
+    df = pd.DataFrame(data)
+    factors = [(cat, region) for cat in categories for region in regions]
+    
+    # Create bar chart
+    p = figure(
+        height=400,
+        width=600,
+        title='Sales by Category and Region',
+        x_range=FactorRange(*factors),
+        tools='pan,box_zoom,reset,hover'
+    )
+    
+    # Add bars
+    p.vbar(
+        x='category_region',
+        top='sales',
+        width=0.9,
+        source=ColumnDataSource(df.assign(
+            category_region=list(zip(df.category, df.region))
+        )),
+        fill_color=factor_cmap(
+            'category_region',
+            ['#1f77b4', '#ff7f0e', '#2ca02c']*3,
+            factors
+        )
+    )
+    
+    # Style
+    p.xgrid.grid_line_color = None
+    p.xaxis.major_label_orientation = 0.7
+    
+    # Tooltips
+    p.hover.tooltips = [
+        ('Category', '@category'),
+        ('Region', '@region'),
+        ('Sales', '@sales')
+    ]
+    
+    p
+    return ColumnDataSource, FactorRange, df, factor_cmap, factors, figure, p
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 785e19cbddc029cb8e61d404281057830b1c1d3d Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:45:38 +0530
Subject: [PATCH 09/34] new: Add `bokeh-8.py` - `heatmap`

---
 marimo/_snippets/data/bokeh-8.py | 131 +++++++++++++++++++++++++++++++
 1 file changed, 131 insertions(+)
 create mode 100644 marimo/_snippets/data/bokeh-8.py

diff --git a/marimo/_snippets/data/bokeh-8.py b/marimo/_snippets/data/bokeh-8.py
new file mode 100644
index 00000000000..34fe861bf7b
--- /dev/null
+++ b/marimo/_snippets/data/bokeh-8.py
@@ -0,0 +1,131 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Bokeh: Interactive Heatmap with Clustering
+
+        Create a heatmap with hierarchical clustering for correlation analysis.
+        Common usage: Feature correlation analysis, pattern detection.
+        Commonly used in: data science, bioinformatics, financial analysis.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource, LinearColorMapper, ColorBar
+    from bokeh.transform import transform
+    import numpy as np
+    import pandas as pd
+    from scipy.cluster import hierarchy
+    from scipy.spatial.distance import pdist
+    
+    # Generate correlated data
+    np.random.seed(42)
+    n_features = 10
+    n_samples = 1000
+    
+    # Create feature names
+    features = [f'Feature_{i+1}' for i in range(n_features)]
+    
+    # Generate data with some correlations
+    data = np.random.randn(n_samples, n_features)
+    data[:, 1] = data[:, 0] + np.random.randn(n_samples) * 0.3
+    data[:, 4] = data[:, 3] - np.random.randn(n_samples) * 0.2
+    
+    df = pd.DataFrame(data, columns=features)
+    
+    # Compute correlation matrix
+    corr = df.corr()
+    
+    # Perform hierarchical clustering
+    linkage = hierarchy.linkage(pdist(corr), method='ward')
+    order = hierarchy.leaves_list(linkage)
+    
+    # Reorder correlation matrix
+    corr = corr.iloc[order, order]
+    
+    # Prepare data for heatmap
+    source = ColumnDataSource(data=dict(
+        x=[(i+0.5) for i in range(n_features) for _ in range(n_features)],
+        y=[(i+0.5) for _ in range(n_features) for i in range(n_features)],
+        correlation=corr.values.flatten(),
+        xname=[corr.index[i] for i in range(n_features) for _ in range(n_features)],
+        yname=[corr.columns[i] for _ in range(n_features) for i in range(n_features)]
+    ))
+    
+    # Create color mapper
+    mapper = LinearColorMapper(
+        palette='RdBu11',
+        low=-1,
+        high=1
+    )
+    
+    # Create figure
+    p = figure(
+        width=600,
+        height=600,
+        title='Feature Correlation Heatmap',
+        x_range=list(corr.index),
+        y_range=list(reversed(corr.columns)),
+        tools='pan,box_zoom,wheel_zoom,reset,save,hover',
+        x_axis_location='above'
+    )
+    
+    # Add heatmap rectangles
+    p.rect(
+        x='x',
+        y='y',
+        width=1,
+        height=1,
+        source=source,
+        fill_color=transform('correlation', mapper),
+        line_color=None
+    )
+    
+    # Add color bar
+    color_bar = ColorBar(
+        color_mapper=mapper,
+        location=(0, 0),
+        title='Correlation',
+        orientation='horizontal'
+    )
+    
+    p.add_layout(color_bar, 'below')
+    
+    # Style the plot
+    p.grid.grid_line_color = None
+    p.axis.axis_line_color = None
+    p.axis.major_tick_line_color = None
+    p.xaxis.major_label_orientation = 0.7
+    p.yaxis.major_label_orientation = 0
+    
+    # Add hover tooltips
+    p.hover.tooltips = [
+        ('Features', '@xname vs @yname'),
+        ('Correlation', '@correlation{0.00}')
+    ]
+    
+    p
+    return (ColorBar, ColumnDataSource, LinearColorMapper, corr, data, df,
+            features, figure, hierarchy, linkage, mapper, n_features, n_samples,
+            np, order, p, pd, pdist, source, transform)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 08bb7d30ea47b4be3a1147a1574f692015000a86 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:47:17 +0530
Subject: [PATCH 10/34] new: Add `bokeh-9.py` - `networkx x bokeh`

---
 marimo/_snippets/data/bokeh-9.py | 120 +++++++++++++++++++++++++++++++
 1 file changed, 120 insertions(+)
 create mode 100644 marimo/_snippets/data/bokeh-9.py

diff --git a/marimo/_snippets/data/bokeh-9.py b/marimo/_snippets/data/bokeh-9.py
new file mode 100644
index 00000000000..a4206aa07e7
--- /dev/null
+++ b/marimo/_snippets/data/bokeh-9.py
@@ -0,0 +1,120 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Bokeh: Network Graph Visualization
+
+        Create interactive network graphs with hover information.
+        Common usage: Relationship analysis, dependency graphs, social networks.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    from bokeh.plotting import figure
+    from bokeh.models import ColumnDataSource, NodesAndLinkedEdges
+    from bokeh.layouts import column
+    import networkx as nx
+    import numpy as np
+
+    # Create sample network
+    G = nx.random_geometric_graph(20, 0.3, seed=42)
+
+    # Get node positions
+    pos = nx.spring_layout(G, seed=42)
+
+    # Prepare node data
+    node_x = [pos[node][0] for node in G.nodes()]
+    node_y = [pos[node][1] for node in G.nodes()]
+    node_size = [3 + 2*len(list(G.neighbors(node))) for node in G.nodes()]
+
+    # Prepare edge data
+    edge_x = []
+    edge_y = []
+    for edge in G.edges():
+        x0, y0 = pos[edge[0]]
+        x1, y1 = pos[edge[1]]
+        edge_x.extend([x0, x1, None])
+        edge_y.extend([y0, y1, None])
+
+    # Create figure
+    p = figure(
+        width=500,
+        height=500,
+        title='Network Graph',
+        tools='pan,wheel_zoom,box_zoom,reset,hover',
+        active_scroll='wheel_zoom'
+    )
+
+    # Add edges
+    p.line(edge_x, edge_y, line_color='gray', line_alpha=0.5)
+
+    # Add nodes
+    node_source = ColumnDataSource({
+        'x': node_x,
+        'y': node_y,
+        'size': node_size,
+        'connections': [len(list(G.neighbors(node))) for node in G.nodes()]
+    })
+
+    p.scatter('x', 'y',
+             size='size',
+             source=node_source,
+             fill_color='navy',
+             fill_alpha=0.6,
+             hover_fill_color='red',
+             line_color='white')
+
+    # Add hover tooltips
+    p.hover.tooltips = [
+        ('Node ID', '$index'),
+        ('Connections', '@connections')
+    ]
+
+    # Style
+    p.grid.visible = False
+    p.axis.visible = False
+
+    p
+    return (
+        ColumnDataSource,
+        G,
+        NodesAndLinkedEdges,
+        column,
+        edge,
+        edge_x,
+        edge_y,
+        figure,
+        node_size,
+        node_source,
+        node_x,
+        node_y,
+        np,
+        nx,
+        p,
+        pos,
+        x0,
+        x1,
+        y0,
+        y1,
+    )
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()

From 38afa56e52b6974cbbd098facb322f754bf29407 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:47:49 +0530
Subject: [PATCH 11/34] new: Add `holoviews-0.py` - `hist + scatter`

---
 marimo/_snippets/data/holoviews-0.py | 80 ++++++++++++++++++++++++++++
 1 file changed, 80 insertions(+)
 create mode 100644 marimo/_snippets/data/holoviews-0.py

diff --git a/marimo/_snippets/data/holoviews-0.py b/marimo/_snippets/data/holoviews-0.py
new file mode 100644
index 00000000000..3ac82e71f99
--- /dev/null
+++ b/marimo/_snippets/data/holoviews-0.py
@@ -0,0 +1,80 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # HoloViews: Interactive Data Exploration
+
+        Create interactive visualizations with linked views.
+        Common usage: Exploratory data analysis with linked selections.
+        Commonly used in: Data science workflows, interactive dashboards.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import holoviews as hv
+    import numpy as np
+    import pandas as pd
+    
+    # Enable Bokeh backend
+    hv.extension('bokeh')
+    
+    # Generate sample data
+    np.random.seed(42)
+    n_points = 1000
+    
+    df = pd.DataFrame({
+        'x': np.random.normal(0, 1, n_points),
+        'y': np.random.normal(0, 1, n_points),
+        'category': np.random.choice(['A', 'B', 'C'], n_points),
+        'value': np.random.uniform(0, 100, n_points)
+    })
+    
+    # Create scatter plot with hover
+    scatter = hv.Points(df, kdims=['x', 'y'], vdims=['category', 'value'])
+    scatter.opts(
+        tools=['hover', 'box_select'],
+        size=8,
+        color='category',
+        cmap='Category10',
+        width=400,
+        height=400,
+        title='Interactive Scatter Plot'
+    )
+    
+    # Create linked histogram
+    hist = hv.operation.histogram(scatter, dimension='value', normed=False)
+    hist.opts(
+        tools=['hover'],
+        width=400,
+        height=200,
+        title='Value Distribution'
+    )
+    
+    # Layout plots
+    layout = (scatter + hist).cols(1)
+    layout.opts(
+        title='Data Exploration Dashboard'
+    )
+    
+    layout
+    return df, hist, hv, layout, n_points, np, pd, scatter
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 0ff35bec43c264c98859721469d4e8dd47128cb6 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:50:11 +0530
Subject: [PATCH 12/34] new: Add `holoviews-1.py` - `TS analysis`

---
 marimo/_snippets/data/holoviews-1.py | 104 +++++++++++++++++++++++++++
 1 file changed, 104 insertions(+)
 create mode 100644 marimo/_snippets/data/holoviews-1.py

diff --git a/marimo/_snippets/data/holoviews-1.py b/marimo/_snippets/data/holoviews-1.py
new file mode 100644
index 00000000000..df9f369b30f
--- /dev/null
+++ b/marimo/_snippets/data/holoviews-1.py
@@ -0,0 +1,104 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # HoloViews: Time Series Analysis
+
+        Create interactive time series visualizations with linked views.
+        Common usage: Financial analysis, sensor data monitoring.
+        Commonly used in: Trading analysis, IoT monitoring, performance tracking.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import holoviews as hv
+    import numpy as np
+    import pandas as pd
+    
+    # Enable Bokeh backend
+    hv.extension('bokeh')
+    
+    # Generate sample time series data
+    np.random.seed(42)
+    dates = pd.date_range('2023-01-01', '2023-12-31', freq='D')
+    n_points = len(dates)
+    
+    # Create multiple metrics (e.g., stock data)
+    df = pd.DataFrame({
+        'date': dates,
+        'price': 100 + np.random.randn(n_points).cumsum(),
+        'volume': np.random.randint(1000, 5000, n_points),
+        'volatility': np.abs(np.random.randn(n_points))
+    })
+    
+    # Calculate moving averages
+    df['MA20'] = df['price'].rolling(window=20).mean()
+    df['MA50'] = df['price'].rolling(window=50).mean()
+    
+    # Create main price plot
+    price_plot = hv.Curve(df, 'date', 'price', label='Price')
+    ma20_plot = hv.Curve(df, 'date', 'MA20', label='20-day MA')
+    ma50_plot = hv.Curve(df, 'date', 'MA50', label='50-day MA')
+    
+    # Combine price plots
+    price_overlay = (price_plot * ma20_plot * ma50_plot).opts(
+        width=800,
+        height=300,
+        tools=['hover', 'box_zoom', 'wheel_zoom', 'pan', 'reset'],
+        title='Price and Moving Averages',
+        legend_position='top_left',
+        show_grid=True
+    )
+    
+    # Create volume bars
+    volume_plot = hv.Bars(df, 'date', 'volume').opts(
+        width=800,
+        height=150,
+        tools=['hover'],
+        title='Trading Volume',
+        color='navy',
+        alpha=0.5
+    )
+    
+    # Create volatility heatmap
+    volatility_plot = hv.HeatMap(
+        df.assign(month=df.date.dt.month, 
+                 day=df.date.dt.day)[['month', 'day', 'volatility']]
+    ).opts(
+        width=400,
+        height=300,
+        tools=['hover'],
+        title='Volatility Heatmap',
+        colorbar=True,
+        cmap='RdYlBu_r'
+    )
+    
+    # Layout all plots
+    layout = (price_overlay + volume_plot + volatility_plot).cols(1)
+    layout.opts(
+        title='Interactive Time Series Analysis'
+    )
+    
+    layout
+    return (df, dates, hv, layout, ma20_plot, ma50_plot, n_points, np, pd,
+            price_overlay, price_plot, volume_plot, volatility_plot)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From cfbf036da99d85d9e82e1e1d4573c599f5b39a1e Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:51:32 +0530
Subject: [PATCH 13/34] new: Add `holoviews-2.py` - `overlapping analysis`

---
 marimo/_snippets/data/holoviews-2.py | 78 ++++++++++++++++++++++++++++
 1 file changed, 78 insertions(+)
 create mode 100644 marimo/_snippets/data/holoviews-2.py

diff --git a/marimo/_snippets/data/holoviews-2.py b/marimo/_snippets/data/holoviews-2.py
new file mode 100644
index 00000000000..faaa1d56d01
--- /dev/null
+++ b/marimo/_snippets/data/holoviews-2.py
@@ -0,0 +1,78 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # HoloViews: Dynamic Selection Analysis
+
+        Create interactive plots with dynamic statistics.
+        Common usage: Real-time analysis of selected data points.
+        Commonly used in: Exploratory data analysis, outlier detection.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import holoviews as hv
+    import numpy as np
+    from holoviews import streams
+    
+    hv.extension('bokeh')
+    
+    # Generate sample data
+    np.random.seed(42)
+    points = hv.Points(np.random.multivariate_normal(
+        (0, 0), [[1, 0.1], [0.1, 1]], (1000,)
+    ))
+    
+    # Create selection stream
+    selection = streams.Selection1D(source=points)
+    
+    # Dynamic computation of statistics for selected points
+    def selected_info(index):
+        selected = points.iloc[index]
+        if index:
+            mean_vals = selected.array().mean(axis=0)
+            label = f'Selection Mean: ({mean_vals[0]:.2f}, {mean_vals[1]:.2f})'
+            return selected.relabel(label).opts(
+                color='red',
+                size=8,
+                alpha=0.6
+            )
+        return hv.Points([]).relabel('No Selection')
+    
+    # Create dynamic map for selection
+    dmap = hv.DynamicMap(selected_info, streams=[selection])
+    
+    # Combine original points with selection
+    plot = (points * dmap).opts(
+        hv.opts.Points(
+            tools=['box_select', 'lasso_select'],
+            size=4,
+            alpha=0.6,
+            width=500,
+            height=400,
+            title='Interactive Point Selection'
+        )
+    )
+    
+    plot
+    return dmap, plot, points, selection
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 244dd8ab7c3521a54c8c83bc264ade012f91e97c Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:51:53 +0530
Subject: [PATCH 14/34] new: Add `holoviews-3.py` - `geodata`

---
 marimo/_snippets/data/holoviews-3.py | 83 ++++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)
 create mode 100644 marimo/_snippets/data/holoviews-3.py

diff --git a/marimo/_snippets/data/holoviews-3.py b/marimo/_snippets/data/holoviews-3.py
new file mode 100644
index 00000000000..9048ccefcfd
--- /dev/null
+++ b/marimo/_snippets/data/holoviews-3.py
@@ -0,0 +1,83 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # HoloViews: Geographic Data Visualization
+
+        Create interactive maps with dynamic overlays.
+        Common usage: Geospatial analysis, location-based analytics.
+        Commonly used in: GIS analysis, location intelligence.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import holoviews as hv
+    import numpy as np
+    import pandas as pd
+    
+    hv.extension('bokeh')
+    
+    # Generate sample location data
+    np.random.seed(42)
+    n_points = 100
+    
+    # Sample data for US locations
+    data = pd.DataFrame({
+        'latitude': np.random.uniform(25, 48, n_points),
+        'longitude': np.random.uniform(-125, -70, n_points),
+        'value': np.random.normal(100, 15, n_points),
+        'category': np.random.choice(['A', 'B', 'C'], n_points)
+    })
+    
+    # Create tile source and points
+    tiles = hv.element.tiles.OSM()
+    points = hv.Points(
+        data, 
+        ['longitude', 'latitude'], 
+        ['value', 'category']
+    )
+    
+    # Create map view with options
+    map_view = (tiles * points).options(
+        width=700,
+        height=500,
+        title='Geographic Distribution'
+    )
+    
+    # Style the points with tooltips
+    points.options(
+        color='value',
+        cmap='viridis',
+        size=8,
+        tools=['hover', 'box_select'],
+        colorbar=True,
+        alpha=0.6,
+        hover_tooltips=[
+            ('Category', '@category'),
+            ('Value', '@value{0.00}'),
+            ('Location', '(@longitude, @latitude)')
+        ]
+    )
+    
+    map_view
+    return data, hv, map_view, n_points, np, pd, points, tiles
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From be5d00792fe649bfbff4541398af859ccb99656d Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:52:12 +0530
Subject: [PATCH 15/34] new: Add `holoviews-4.py` - `linked`

---
 marimo/_snippets/data/holoviews-4.py | 101 +++++++++++++++++++++++++++
 1 file changed, 101 insertions(+)
 create mode 100644 marimo/_snippets/data/holoviews-4.py

diff --git a/marimo/_snippets/data/holoviews-4.py b/marimo/_snippets/data/holoviews-4.py
new file mode 100644
index 00000000000..4b41af8e7bc
--- /dev/null
+++ b/marimo/_snippets/data/holoviews-4.py
@@ -0,0 +1,101 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # HoloViews: Linked Selection Analysis
+
+        Create linked views with dynamic statistics.
+        Common usage: Multi-dimensional data exploration.
+        Commonly used in: Feature analysis, correlation studies.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import holoviews as hv
+    import numpy as np
+    import pandas as pd
+    from holoviews import streams
+    
+    hv.extension('bokeh')
+    
+    # Generate sample data
+    np.random.seed(42)
+    n_points = 1000
+    
+    # Create correlated features
+    x = np.random.normal(0, 1, n_points)
+    y = x * 0.5 + np.random.normal(0, 0.5, n_points)
+    z = x * -0.2 + y * 0.3 + np.random.normal(0, 0.3, n_points)
+    
+    data = pd.DataFrame({
+        'x': x, 
+        'y': y, 
+        'z': z
+    })
+    
+    # Create scatter plot with selection stream
+    scatter = hv.Points(data, ['x', 'y'])
+    selection = streams.Selection1D(source=scatter)
+    
+    # Create initial histogram
+    hist = hv.Histogram(np.histogram(data['z'], bins=20))
+    
+    # Dynamic histogram of selected points
+    def selected_hist(index):
+        if len(index):
+            selected = data.iloc[index]['z']
+            return hv.Histogram(np.histogram(selected, bins=20)).options(
+                fill_color='navy'
+            )
+        return hist.options(fill_color='gray')
+    
+    dmap = hv.DynamicMap(selected_hist, streams=[selection])
+    
+    # Layout with styling
+    layout = (scatter + dmap).cols(2)
+    
+    # Style scatter plot
+    scatter.options(
+        tools=['box_select', 'lasso_select', 'hover'],
+        width=400,
+        height=400,
+        color='navy',
+        alpha=0.6,
+        title='Select Points',
+        hover_tooltips=[
+            ('x', '@x{0.2f}'),
+            ('y', '@y{0.2f}')
+        ]
+    )
+    
+    # Style histogram
+    dmap.options(
+        width=400,
+        height=400,
+        title='Distribution (Selected vs All)',
+        xlabel='z value',
+        ylabel='Count'
+    )
+    
+    layout
+    return data, dmap, hist, hv, layout, n_points, np, pd, scatter, selection, streams
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 490d29a864334be3cc3e8a449129988adeb2b926 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:52:28 +0530
Subject: [PATCH 16/34] new: Add `holoviews-5.py` - `TS`

---
 marimo/_snippets/data/holoviews-5.py | 89 ++++++++++++++++++++++++++++
 1 file changed, 89 insertions(+)
 create mode 100644 marimo/_snippets/data/holoviews-5.py

diff --git a/marimo/_snippets/data/holoviews-5.py b/marimo/_snippets/data/holoviews-5.py
new file mode 100644
index 00000000000..b1a501146fa
--- /dev/null
+++ b/marimo/_snippets/data/holoviews-5.py
@@ -0,0 +1,89 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # HoloViews: Time Series Analysis
+
+        Create interactive time series with dynamic updates.
+        Common usage: Real-time monitoring, financial analysis.
+        Commonly used in: Trading systems, IoT monitoring, performance tracking.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import holoviews as hv
+    import numpy as np
+    import pandas as pd
+    from datetime import datetime, timedelta
+    
+    hv.extension('bokeh')
+    
+    # Generate time series data
+    np.random.seed(42)
+    now = datetime.now()
+    dates = [now - timedelta(minutes=i) for i in range(100, 0, -1)]
+    
+    # Simulate multiple metrics (e.g., system metrics)
+    data = pd.DataFrame({
+        'time': dates,
+        'value1': np.random.normal(100, 10, 100).cumsum(),
+        'value2': np.random.normal(50, 5, 100).cumsum(),
+        'anomaly': np.random.choice([0, 1], 100, p=[0.95, 0.05])
+    })
+    
+    # Create main time series plot
+    curve1 = hv.Curve(
+        data, kdims=['time'], vdims=['value1'], 
+        label='Metric 1'
+    )
+    curve2 = hv.Curve(
+        data, kdims=['time'], vdims=['value2'], 
+        label='Metric 2'
+    )
+    
+    # Add anomaly points
+    anomalies = data[data['anomaly'] == 1]
+    points = hv.Points(
+        anomalies, kdims=['time', 'value1'],
+        label='Anomalies'
+    )
+    
+    # Style the plots
+    curve1 = curve1.options(color='navy', line_width=2)
+    curve2 = curve2.options(color='green', line_width=2)
+    points = points.options(color='red', size=10)
+    
+    # Combine plots with styling
+    plot = (curve1 * curve2 * points).options(
+        width=800,
+        height=400,
+        title='Real-time Metrics with Anomaly Detection',
+        tools=['hover', 'box_zoom', 'wheel_zoom', 'reset'],
+        legend_position='top_right',
+        xlabel='Time',
+        ylabel='Value'
+    )
+    
+    plot
+    return (anomalies, curve1, curve2, data, dates, datetime, hv, now, np, pd,
+            plot, points, timedelta)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run() 
\ No newline at end of file

From a4e495eb0338c19a7f997ab4a0cd7e1cd720543b Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:52:44 +0530
Subject: [PATCH 17/34] new: Add `holoviews-6.py` - `stat`

---
 marimo/_snippets/data/holoviews-6.py | 96 ++++++++++++++++++++++++++++
 1 file changed, 96 insertions(+)
 create mode 100644 marimo/_snippets/data/holoviews-6.py

diff --git a/marimo/_snippets/data/holoviews-6.py b/marimo/_snippets/data/holoviews-6.py
new file mode 100644
index 00000000000..db9ac34aeb6
--- /dev/null
+++ b/marimo/_snippets/data/holoviews-6.py
@@ -0,0 +1,96 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # HoloViews: Statistical Analysis with Faceting
+
+        Create comparative statistical visualizations.
+        Common usage: A/B testing, multi-group analysis.
+        Commonly used in: Research analysis, experiment evaluation.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import holoviews as hv
+    import numpy as np
+    import pandas as pd
+    
+    hv.extension('bokeh')
+    
+    # Generate experimental data
+    np.random.seed(42)
+    n_samples = 200
+    
+    # Simulate A/B test results across different segments
+    segments = ['Segment A', 'Segment B', 'Segment C']
+    variants = ['Control', 'Treatment']
+    
+    data = []
+    for segment in segments:
+        # Control group
+        control_mean = np.random.uniform(20, 30)
+        control_data = np.random.normal(control_mean, 5, n_samples)
+        
+        # Treatment group (with effect)
+        effect = np.random.uniform(2, 5)
+        treatment_data = np.random.normal(control_mean + effect, 5, n_samples)
+        
+        data.extend([
+            {'segment': segment, 'variant': 'Control', 'value': v}
+            for v in control_data
+        ])
+        data.extend([
+            {'segment': segment, 'variant': 'Treatment', 'value': v}
+            for v in treatment_data
+        ])
+    
+    df = pd.DataFrame(data)
+    
+    # Create violin plots by segment
+    violin = hv.Violin(df, ['segment', 'variant'], 'value').options(
+        alpha=0.6,
+        violin_fill_color='variant',
+        cmap=['#1f77b4', '#ff7f0e']
+    )
+    
+    # Add box plots overlay
+    box = hv.BoxWhisker(df, ['segment', 'variant'], 'value').options(
+        box_fill_alpha=0.4,
+        box_color='white',
+        whisker_color='black'
+    )
+    
+    # Combine and style
+    plot = (violin * box).options(
+        width=800,
+        height=400,
+        title='Treatment Effect by Segment',
+        tools=['hover'],
+        xlabel='Segment',
+        ylabel='Value',
+        legend_position='top_right',
+        show_grid=True
+    )
+    
+    plot
+    return box, df, hv, n_samples, np, pd, plot, segments, variants, violin
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run() 
\ No newline at end of file

From 2088992fcdbf4ab70a99a7690c709c77b01885ab Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:53:04 +0530
Subject: [PATCH 18/34] new: Add `holoviews-7.py` - `adv data analysis`

---
 marimo/_snippets/data/holoviews-7.py | 115 +++++++++++++++++++++++++++
 1 file changed, 115 insertions(+)
 create mode 100644 marimo/_snippets/data/holoviews-7.py

diff --git a/marimo/_snippets/data/holoviews-7.py b/marimo/_snippets/data/holoviews-7.py
new file mode 100644
index 00000000000..9b6cb178ca8
--- /dev/null
+++ b/marimo/_snippets/data/holoviews-7.py
@@ -0,0 +1,115 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # HoloViews: Advanced Data Analysis Suite
+
+        Create interactive visualizations for comprehensive data analysis.
+        Common usage: Multi-dimensional analysis, pattern discovery.
+        Domains: Data Science, Research, Business Intelligence.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import holoviews as hv
+    import numpy as np
+    import pandas as pd
+    from holoviews import opts
+    from sklearn.preprocessing import StandardScaler
+    from sklearn.cluster import KMeans
+    
+    hv.extension('bokeh')
+    
+    # Generate sample dataset
+    np.random.seed(42)
+    n_samples = 1000
+    
+    # Create multi-dimensional dataset
+    data = pd.DataFrame({
+        'feature1': np.random.normal(0, 1, n_samples),
+        'feature2': np.random.normal(0, 1, n_samples),
+        'feature3': np.random.normal(0, 1, n_samples),
+        'target': np.random.normal(0, 1, n_samples)
+    })
+    
+    # Add correlations
+    data['feature2'] += data['feature1'] * 0.5
+    data['feature3'] += data['feature2'] * 0.3
+    data['target'] += data['feature1'] * 0.6 + data['feature2'] * 0.3
+    
+    # Create bivariate distribution plot
+    bivariate = hv.Bivariate(
+        (data['feature1'], data['feature2'])
+    ).options(
+        width=300,
+        height=300,
+        cmap='viridis',
+        title='Feature Distribution'
+    )
+    
+    # Create scatter with marginal distributions
+    scatter = hv.Points((data['feature1'], data['target'])).options(
+        color='navy',
+        alpha=0.6,
+        width=300,
+        height=300,
+        tools=['box_select', 'lasso_select']
+    )
+    
+    marginal_x = hv.Histogram(np.histogram(data['feature1'], bins=30))
+    marginal_y = hv.Histogram(np.histogram(data['target'], bins=30))
+    
+    # Cluster the data
+    scaler = StandardScaler()
+    scaled_data = scaler.fit_transform(data)
+    kmeans = KMeans(n_clusters=3, random_state=42)
+    clusters = kmeans.fit_predict(scaled_data)
+    
+    # Create cluster visualization
+    cluster_viz = hv.Points(
+        pd.DataFrame({
+            'x': data['feature1'],
+            'y': data['feature2'],
+            'Cluster': clusters.astype(str)
+        }),
+        kdims=['x', 'y'],
+        vdims=['Cluster']
+    ).options(
+        color='Cluster',
+        cmap='Category10',
+        width=300,
+        height=300,
+        title='Cluster Analysis',
+        tools=['hover']
+    )
+    
+    # Combine visualizations
+    layout = (
+        bivariate + 
+        (scatter << marginal_x << marginal_y) + 
+        cluster_viz
+    ).cols(3)
+    
+    layout
+    return (bivariate, cluster_viz, clusters, data, hv, kmeans, layout, marginal_x,
+            marginal_y, n_samples, np, opts, pd, scaler, scaled_data, scatter)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run() 
\ No newline at end of file

From 9dbcecb88c6e0b4c5c065943142c4aa4fe9b6679 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:53:34 +0530
Subject: [PATCH 19/34] new: Add `holoviews-8.py` - `mixed`

---
 marimo/_snippets/data/holoviews-8.py | 103 +++++++++++++++++++++++++++
 1 file changed, 103 insertions(+)
 create mode 100644 marimo/_snippets/data/holoviews-8.py

diff --git a/marimo/_snippets/data/holoviews-8.py b/marimo/_snippets/data/holoviews-8.py
new file mode 100644
index 00000000000..7c967f62691
--- /dev/null
+++ b/marimo/_snippets/data/holoviews-8.py
@@ -0,0 +1,103 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # HoloViews: Core Plotting with `holoviews`
+
+        Create essential visualizations using the `holoviews` library (imported as `hv`).
+        Common usage: Data visualization, exploratory analysis, statistical plotting.
+        Commonly used in: Data science, research, interactive dashboards.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import holoviews as hv
+    import numpy as np
+    import pandas as pd
+    
+    hv.extension('bokeh')
+    
+    # Generate sample data
+    np.random.seed(42)
+    
+    # 1. Basic Scatter with Path overlay
+    x = np.linspace(0, 10, 100)
+    y = np.sin(x) + np.random.normal(0, 0.1, 100)
+    scatter = hv.Scatter((x, y)).options(
+        color='navy', size=8, tools=['hover']
+    )
+    path = hv.Path((x, y)).options(
+        line_color='red', line_width=2
+    )
+    
+    # 2. QuadMesh plot (instead of Surface)
+    x_range = np.linspace(-2, 2, 20)
+    y_range = np.linspace(-2, 2, 20)
+    xx, yy = np.meshgrid(x_range, y_range)
+    zz = np.sin(xx) * np.cos(yy)
+    quadmesh = hv.QuadMesh((x_range, y_range, zz)).options(
+        cmap='viridis',
+        width=300,
+        height=300,
+        tools=['hover'],
+        title='QuadMesh Plot'
+    )
+    
+    # 3. Contour plot
+    contours = hv.Contours((x_range, y_range, zz)).options(
+        width=300,
+        height=300,
+        tools=['hover'],
+        title='Contour Plot',
+        cmap='viridis'
+    )
+    
+    # 4. Points with color mapping
+    points_data = pd.DataFrame({
+        'x': np.random.normal(0, 1, 100),
+        'y': np.random.normal(0, 1, 100),
+        'color': np.random.uniform(0, 1, 100)
+    })
+    points = hv.Points(points_data, ['x', 'y'], 'color').options(
+        color='color',
+        cmap='RdYlBu',
+        size=8,
+        tools=['hover'],
+        width=300,
+        height=300,
+        title='Colored Points'
+    )
+    
+    # Combine plots in layout
+    layout = (
+        (scatter * path) + 
+        quadmesh +
+        contours + 
+        points
+    ).cols(4).options(
+        title='HoloViews Plot Gallery'
+    )
+    
+    layout
+    return (contours, hv, layout, np, path, pd, points, points_data, quadmesh,
+            scatter, x, x_range, xx, y, y_range, yy, zz)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 20b83e54a7d2bf0cce392c3e218731fb40da420f Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:53:58 +0530
Subject: [PATCH 20/34] new: Add `holoviews-9.py` - `stats layout`

---
 marimo/_snippets/data/holoviews-9.py | 75 ++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)
 create mode 100644 marimo/_snippets/data/holoviews-9.py

diff --git a/marimo/_snippets/data/holoviews-9.py b/marimo/_snippets/data/holoviews-9.py
new file mode 100644
index 00000000000..8e77678f6be
--- /dev/null
+++ b/marimo/_snippets/data/holoviews-9.py
@@ -0,0 +1,75 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # HoloViews: Essential Plot Types with `holoviews`
+
+        Create fundamental visualizations using the `holoviews` library.
+        Common usage: Basic data visualization, statistical plotting.
+        Commonly used in: Data analysis, research, reporting.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import holoviews as hv
+    import numpy as np
+    import pandas as pd
+    
+    hv.extension('bokeh')
+    
+    # Generate sample data
+    np.random.seed(42)
+    x = np.linspace(0, 10, 50)
+    y = np.sin(x) + np.random.normal(0, 0.1, 50)
+    categories = ['A', 'B', 'C', 'D']
+    values = np.random.normal(10, 2, len(categories))
+    
+    # Create basic plots
+    curve = hv.Curve((x, y), 'x', 'y').options(
+        width=300, height=200, color='navy',
+        title='Curve Plot'
+    )
+    
+    bars = hv.Bars(({'category': categories, 'value': values}), 
+                   'category', 'value').options(
+        width=300, height=200, color='green',
+        title='Bar Plot'
+    )
+    
+    area = hv.Area((x, y)).options(
+        width=300, height=200, color='purple', alpha=0.5,
+        title='Area Plot'
+    )
+    
+    spikes = hv.Spikes((x, np.abs(y))).options(
+        width=300, height=200, color='red',
+        title='Spike Plot'
+    )
+    
+    # Combine in layout
+    layout = (curve + bars + area + spikes).cols(2).options(
+        title='Basic Plot Types'
+    )
+    
+    layout
+    return area, bars, categories, curve, hv, layout, np, pd, spikes, values, x, y
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From f817858835d74a925a63707231aa58a171888682 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:55:06 +0530
Subject: [PATCH 21/34] new: Add `holoviews-10.py` - `error`

---
 marimo/_snippets/data/holoviews-10.py | 78 +++++++++++++++++++++++++++
 1 file changed, 78 insertions(+)
 create mode 100644 marimo/_snippets/data/holoviews-10.py

diff --git a/marimo/_snippets/data/holoviews-10.py b/marimo/_snippets/data/holoviews-10.py
new file mode 100644
index 00000000000..e7d47995083
--- /dev/null
+++ b/marimo/_snippets/data/holoviews-10.py
@@ -0,0 +1,78 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # HoloViews: Error Analysis with `holoviews`
+
+        Create visualizations with error bars and range overlays using `hv.ErrorBars` and `hv.VSpan`.
+        Common usage: Uncertainty visualization, confidence intervals, data ranges.
+        Commonly used in: Scientific research, statistical analysis, experimental data.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import holoviews as hv
+    import numpy as np
+    import pandas as pd
+    
+    hv.extension('bokeh')
+    
+    # Generate sample data with uncertainty
+    x = np.linspace(0, 10, 30)
+    y = np.sin(x) + 0.1
+    yerr = np.random.uniform(0.1, 0.2, len(x))
+    
+    # Create error bars plot
+    errorbars = hv.ErrorBars((x, y, yerr)).options(
+        width=400, height=300,
+        title='Measurement with Uncertainty'
+    )
+    
+    curve = hv.Curve((x, y)).options(
+        color='navy',
+        xlabel='X',
+        ylabel='Value'
+    )
+    error_plot = (curve * errorbars)
+    
+    # Create range visualization
+    spans = []
+    for i in range(3):
+        x1, x2 = np.random.uniform(0, 10, 2)
+        x1, x2 = min(x1, x2), max(x1, x2)
+        spans.append(hv.VSpan(x1, x2).options(
+            alpha=0.2, 
+            fill_color=['blue', 'green', 'red'][i]
+        ))
+    
+    range_plot = hv.Curve((x, y)) * hv.Overlay(spans).options(
+        width=400, height=300,
+        title='Regions of Interest'
+    )
+    
+    # Combine visualizations
+    layout = (error_plot + range_plot).cols(2)
+    
+    layout
+    return (curve, error_plot, errorbars, hv, layout, np, pd, range_plot, spans,
+            x, y, yerr)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 33a6ed8060395af1f32d971525ec6c27b4053e14 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:55:28 +0530
Subject: [PATCH 22/34] new: Add `holoviews-11.py` - `curve ranges`

---
 marimo/_snippets/data/holoviews-11.py | 73 +++++++++++++++++++++++++++
 1 file changed, 73 insertions(+)
 create mode 100644 marimo/_snippets/data/holoviews-11.py

diff --git a/marimo/_snippets/data/holoviews-11.py b/marimo/_snippets/data/holoviews-11.py
new file mode 100644
index 00000000000..603a92aed94
--- /dev/null
+++ b/marimo/_snippets/data/holoviews-11.py
@@ -0,0 +1,73 @@
+# Copyright 2024 Marimo. All rights reserved.
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # `holoviews`: Scientific Data Visualization
+
+        Create interactive scientific plots with error bars and ranges using `holoviews`.
+        Common usage: Error analysis, uncertainty visualization, region highlighting.
+        Commonly used in: Research, data science, scientific computing.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import holoviews as hv
+    import numpy as np
+    import pandas as pd
+    
+    hv.extension('bokeh')
+    
+    # Generate sample data with uncertainty
+    x = np.linspace(0, 10, 30)
+    y = np.sin(x) + 0.1
+    yerr = np.random.uniform(0.1, 0.2, len(x))
+    
+    # Create error bars plot
+    errorbars = hv.ErrorBars((x, y, yerr)).options(
+        width=400, height=300,
+        title='Measurement with Uncertainty'
+    )
+    
+    curve = hv.Curve((x, y), 'X', 'Value').options(color='navy')
+    error_plot = (curve * errorbars)
+    
+    # Create range visualization
+    spans = []
+    for i in range(3):
+        x1, x2 = np.random.uniform(0, 10, 2)
+        x1, x2 = min(x1, x2), max(x1, x2)
+        spans.append(hv.VSpan(x1, x2).options(
+            alpha=0.2, color=['blue', 'green', 'red'][i]
+        ))
+    
+    range_plot = hv.Curve((x, y)) * hv.Overlay(spans).options(
+        width=400, height=300,
+        title='Regions of Interest'
+    )
+    
+    # Combine visualizations
+    layout = (error_plot + range_plot).cols(2)
+    
+    layout
+    return (curve, error_plot, errorbars, hv, layout, np, pd, range_plot, spans,
+            x, y, yerr)
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 5706a702f9226c1c9d99114bd2c6d931f6bc5dab Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:55:45 +0530
Subject: [PATCH 23/34] new: Add `plotly-0.py` - `line plot`

---
 marimo/_snippets/data/plotly-0.py | 56 +++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 marimo/_snippets/data/plotly-0.py

diff --git a/marimo/_snippets/data/plotly-0.py b/marimo/_snippets/data/plotly-0.py
new file mode 100644
index 00000000000..d3862994624
--- /dev/null
+++ b/marimo/_snippets/data/plotly-0.py
@@ -0,0 +1,56 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Plotly: Interactive Line Plot
+
+        Create an interactive line plot with hover tooltips and zoom capabilities. 
+        Common usage: `fig = px.line(df, x="x_col", y="y_col")`.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import plotly.express as px
+    import numpy as np
+
+    # Sample data
+    x = np.linspace(0, 10, 100)
+    y = np.sin(x)
+
+    # Create interactive plot
+    fig = px.line(
+        x=x, 
+        y=y, 
+        title="Interactive Sine Wave",
+        labels={"x": "X Axis", "y": "Sin(x)"}
+    )
+
+    # Update layout for better appearance
+    fig.update_layout(
+        showlegend=False,
+        hovermode="x unified"
+    )
+
+    fig
+    return fig, np, px
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From be80af11581dbf6265b8e0e7d0ea0a593487ff29 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:56:01 +0530
Subject: [PATCH 24/34] new: Add `plotly-1.py` - `scatter plot`

---
 marimo/_snippets/data/plotly-1.py | 66 +++++++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)
 create mode 100644 marimo/_snippets/data/plotly-1.py

diff --git a/marimo/_snippets/data/plotly-1.py b/marimo/_snippets/data/plotly-1.py
new file mode 100644
index 00000000000..851c75552a1
--- /dev/null
+++ b/marimo/_snippets/data/plotly-1.py
@@ -0,0 +1,66 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Plotly: Interactive Scatter Plot with Categories
+
+        Create a scatter plot with categorical coloring and size mapping.
+        Common usage: `fig = px.scatter(df, x="col1", y="col2", color="category", size="values")`.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    import plotly.express as px
+    import numpy as np
+
+    # Sample data
+    np.random.seed(42)
+    n_points = 50
+
+    data = {
+        'x': np.random.normal(0, 1, n_points),
+        'y': np.random.normal(0, 1, n_points),
+        'size': np.random.uniform(5, 25, n_points),
+        'group': [f"Group {i}" for i in np.random.randint(1, 4, n_points)]
+    }
+
+    # Create interactive scatter plot
+    fig = px.scatter(
+        data,
+        x='x',
+        y='y',
+        size='size',
+        color='group',
+        title="Interactive Grouped Scatter",
+        labels={'x': 'X Value', 'y': 'Y Value', 'size': 'Size Value'},
+        hover_data=['group', 'size']
+    )
+
+    # Update layout
+    fig.update_layout(
+        hovermode='closest'
+    )
+
+    fig
+    return data, fig, n_points, np, px
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()

From 43ff07988f9b9356775fa1ac26d180d053a89cdf Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:56:13 +0530
Subject: [PATCH 25/34] new: Add `plotly-2.py` - `TS`

---
 marimo/_snippets/data/plotly-2.py | 71 +++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100644 marimo/_snippets/data/plotly-2.py

diff --git a/marimo/_snippets/data/plotly-2.py b/marimo/_snippets/data/plotly-2.py
new file mode 100644
index 00000000000..4ecf52854c2
--- /dev/null
+++ b/marimo/_snippets/data/plotly-2.py
@@ -0,0 +1,71 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Plotly: Time Series with Range Selector
+
+        Create an interactive time series plot with built-in range selector and slider.
+        Common usage: `fig = px.line(df, x="date_column", y="value_column")`.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    import plotly.express as px
+    import pandas as pd
+    import numpy as np
+
+    # Generate sample time series data
+    dates = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')
+    values = np.cumsum(np.random.randn(len(dates))) + 100
+
+    df = pd.DataFrame({
+        'date': dates,
+        'value': values
+    })
+
+    # Create interactive time series
+    fig = px.line(
+        df,
+        x='date',
+        y='value',
+        title='Interactive Time Series'
+    )
+
+    # Add range selector and slider
+    fig.update_layout(
+        xaxis=dict(
+            rangeselector=dict(
+                buttons=[
+                    dict(count=1, label="1m", step="month", stepmode="backward"),
+                    dict(count=3, label="3m", step="month", stepmode="backward"),
+                    dict(count=6, label="6m", step="month", stepmode="backward"),
+                    dict(step="all", label="All")
+                ]
+            ),
+            rangeslider=dict(visible=True)
+        )
+    )
+
+    fig
+    return dates, df, fig, np, pd, px, values
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()

From 050f36760c3ef960ce5c17461bd0bffe8e5bb5ae Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:56:28 +0530
Subject: [PATCH 26/34] new: Add `plotly-3.py` - `heatmap`

---
 marimo/_snippets/data/plotly-3.py | 69 +++++++++++++++++++++++++++++++
 1 file changed, 69 insertions(+)
 create mode 100644 marimo/_snippets/data/plotly-3.py

diff --git a/marimo/_snippets/data/plotly-3.py b/marimo/_snippets/data/plotly-3.py
new file mode 100644
index 00000000000..bddd2f9fef9
--- /dev/null
+++ b/marimo/_snippets/data/plotly-3.py
@@ -0,0 +1,69 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Plotly: Correlation Heatmap
+
+        Create an interactive correlation heatmap with customizable color scale.
+        Common usage: `fig = px.imshow(df.corr(), color_continuous_scale='RdBu_r')`.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    import plotly.express as px
+    import pandas as pd
+    import numpy as np
+
+    # Generate sample correlated data
+    np.random.seed(42)
+    n_samples = 100
+    df = pd.DataFrame({
+        'A': np.random.normal(0, 1, n_samples),
+        'B': np.random.normal(0, 1, n_samples),
+        'C': np.random.normal(0, 1, n_samples),
+        'D': np.random.normal(0, 1, n_samples)
+    })
+    df['B'] = df['A'] * 0.8 + np.random.normal(0, 0.2, n_samples)
+    df['D'] = -df['C'] * 0.6 + np.random.normal(0, 0.3, n_samples)
+
+    # Create correlation matrix
+    corr_matrix = df.corr()
+
+    # Create heatmap
+    fig = px.imshow(
+        corr_matrix,
+        color_continuous_scale='RdBu_r',
+        aspect='auto',
+        title='Correlation Matrix Heatmap'
+    )
+
+    # Update layout
+    fig.update_layout(
+        xaxis_title="Features",
+        yaxis_title="Features",
+        coloraxis_colorbar_title="Correlation"
+    )
+
+    fig
+    return corr_matrix, df, fig, n_samples, np, pd, px
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()

From c95ba42e5244b20c58b11e2b8e6d953bae7299c1 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:56:41 +0530
Subject: [PATCH 27/34] new: Add `plotly-4.py` - `dashboard`

---
 marimo/_snippets/data/plotly-4.py | 83 +++++++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)
 create mode 100644 marimo/_snippets/data/plotly-4.py

diff --git a/marimo/_snippets/data/plotly-4.py b/marimo/_snippets/data/plotly-4.py
new file mode 100644
index 00000000000..d1805b9d983
--- /dev/null
+++ b/marimo/_snippets/data/plotly-4.py
@@ -0,0 +1,83 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Plotly: Multiple Metrics Dashboard
+
+        Create a dashboard-style layout with multiple plots using subplots.
+        Common usage: `make_subplots(rows=2, cols=2)` followed by `add_trace()` for each plot.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    import plotly.graph_objects as go
+    from plotly.subplots import make_subplots
+    import numpy as np
+    import pandas as pd
+
+    # Generate sample data
+    np.random.seed(42)
+    x = np.linspace(0, 10, 100)
+    y1 = np.sin(x) + np.random.normal(0, 0.1, 100)
+    y2 = np.cumsum(np.random.randn(100))
+    y3 = np.random.normal(0, 1, 100)
+
+    # Create subplots
+    fig = make_subplots(
+        rows=2, cols=2,
+        subplot_titles=('Time Series', 'Cumulative Sum', 
+                       'Distribution', 'Moving Average')
+    )
+
+    # Add traces
+    fig.add_trace(
+        go.Scatter(x=x, y=y1, name="Time Series"),
+        row=1, col=1
+    )
+
+    fig.add_trace(
+        go.Scatter(x=x, y=y2, name="Cumulative"),
+        row=1, col=2
+    )
+
+    fig.add_trace(
+        go.Histogram(x=y3, name="Distribution"),
+        row=2, col=1
+    )
+
+    fig.add_trace(
+        go.Scatter(x=x, y=pd.Series(y1).rolling(10).mean(), 
+                  name="Moving Avg"),
+        row=2, col=2
+    )
+
+    # Update layout
+    fig.update_layout(
+        height=800,
+        showlegend=False,
+        title_text="Multiple Metrics Dashboard"
+    )
+
+    fig
+    return fig, go, make_subplots, np, pd, x, y1, y2, y3
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()

From 58b0a59136ffb862e69ea6ae2190876c84ed7e20 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:56:54 +0530
Subject: [PATCH 28/34] new: Add `plotly-5.py` - `finance`

---
 marimo/_snippets/data/plotly-5.py | 91 +++++++++++++++++++++++++++++++
 1 file changed, 91 insertions(+)
 create mode 100644 marimo/_snippets/data/plotly-5.py

diff --git a/marimo/_snippets/data/plotly-5.py b/marimo/_snippets/data/plotly-5.py
new file mode 100644
index 00000000000..d5741d43c11
--- /dev/null
+++ b/marimo/_snippets/data/plotly-5.py
@@ -0,0 +1,91 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def __(mo):
+    mo.md(
+        r"""
+        # Plotly: Financial Chart with Volume
+
+        Create an interactive financial chart combining candlesticks and volume bars.
+        Common usage: `go.Figure(data=[go.Candlestick(x=df.index, open=df.Open)])`.
+        """
+    )
+    return
+
+
+@app.cell
+def __():
+    import plotly.graph_objects as go
+    from plotly.subplots import make_subplots
+    import pandas as pd
+    import numpy as np
+    
+    # Generate sample OHLCV data
+    np.random.seed(42)
+    dates = pd.date_range('2023-01-01', '2023-12-31', freq='D')
+    price = 100 + np.random.randn(len(dates)).cumsum()
+    
+    df = pd.DataFrame({
+        'Open': price + np.random.randn(len(dates)),
+        'High': price + abs(np.random.randn(len(dates))*2),
+        'Low': price - abs(np.random.randn(len(dates))*2),
+        'Close': price + np.random.randn(len(dates)),
+        'Volume': np.random.randint(1000, 10000, len(dates))
+    }, index=dates)
+    
+    # Create figure with secondary y-axis
+    fig = make_subplots(
+        rows=2, cols=1,
+        row_heights=[0.7, 0.3],
+        vertical_spacing=0.05,
+        shared_xaxes=True
+    )
+    
+    # Add candlestick
+    fig.add_trace(
+        go.Candlestick(
+            x=df.index,
+            open=df.Open,
+            high=df.High,
+            low=df.Low,
+            close=df.Close,
+            name="OHLC"
+        ),
+        row=1, col=1
+    )
+    
+    # Add volume bars
+    fig.add_trace(
+        go.Bar(
+            x=df.index,
+            y=df.Volume,
+            name="Volume"
+        ),
+        row=2, col=1
+    )
+    
+    # Update layout
+    fig.update_layout(
+        title="Stock Price and Volume Analysis",
+        xaxis_rangeslider_visible=False,
+        height=800
+    )
+    
+    fig
+    return fig, go, make_subplots, pd, np
+
+
+@app.cell
+def __():
+    import marimo as mo
+    return mo,
+
+
+if __name__ == "__main__":
+    app.run()

From 0f38902b51db4b129dc4088094f1c5d6cbb1b931 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:57:24 +0530
Subject: [PATCH 29/34] new: Add `plotly-6.py` - `stats distr`

---
 marimo/_snippets/data/plotly-6.py | 72 +++++++++++++++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100644 marimo/_snippets/data/plotly-6.py

diff --git a/marimo/_snippets/data/plotly-6.py b/marimo/_snippets/data/plotly-6.py
new file mode 100644
index 00000000000..43474863db3
--- /dev/null
+++ b/marimo/_snippets/data/plotly-6.py
@@ -0,0 +1,72 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Plotly: Statistical Distribution Comparison
+
+        Create violin plots with box plots overlay for distribution comparison.
+        Common usage: `fig = go.Figure(data=[go.Violin(x=groups, y=values)])`.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    import plotly.graph_objects as go
+    import numpy as np
+
+    # Generate sample data from different distributions
+    np.random.seed(42)
+
+    groups = ['A', 'B', 'C']
+    data = {
+        'A': np.random.normal(0, 1, 200),
+        'B': np.random.normal(2, 1.5, 200),
+        'C': np.random.exponential(2, 200)
+    }
+
+    # Create figure
+    fig = go.Figure()
+
+    # Add violin plots
+    for group in groups:
+        fig.add_trace(
+            go.Violin(
+                x=[group] * len(data[group]),
+                y=data[group],
+                name=group,
+                box_visible=True,
+                meanline_visible=True,
+                points="outliers"
+            )
+        )
+
+    # Update layout
+    fig.update_layout(
+        title="Distribution Comparison",
+        yaxis_title="Values",
+        xaxis_title="Groups",
+        violinmode='group'
+    )
+
+    fig
+    return data, fig, go, group, groups, np
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()

From 6ffee00518747e4131c34f10aef0b819c6a2ca98 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:58:24 +0530
Subject: [PATCH 30/34] new: Add `plotly-7.py` - `3D scatter`

---
 marimo/_snippets/data/plotly-7.py | 80 +++++++++++++++++++++++++++++++
 1 file changed, 80 insertions(+)
 create mode 100644 marimo/_snippets/data/plotly-7.py

diff --git a/marimo/_snippets/data/plotly-7.py b/marimo/_snippets/data/plotly-7.py
new file mode 100644
index 00000000000..155a276807b
--- /dev/null
+++ b/marimo/_snippets/data/plotly-7.py
@@ -0,0 +1,80 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Plotly: 3D Scatter Plot with Clusters
+
+        Create an interactive 3D scatter plot for dimensional analysis.
+        Common usage: `fig = px.scatter_3d(df, x='x', y='y', z='z', color='cluster')`.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    import plotly.express as px
+    import numpy as np
+    import pandas as pd
+    from sklearn.datasets import make_blobs
+
+    # Generate clustered 3D data
+    n_samples = 300
+    n_clusters = 4
+
+    X, labels = make_blobs(
+        n_samples=n_samples,
+        n_features=3,
+        centers=n_clusters,
+        random_state=42
+    )
+
+    df = pd.DataFrame(
+        X,
+        columns=['x', 'y', 'z']
+    )
+    df['cluster'] = labels
+
+    # Create 3D scatter plot
+    fig = px.scatter_3d(
+        df,
+        x='x',
+        y='y',
+        z='z',
+        color='cluster',
+        title='3D Cluster Visualization',
+        labels={'cluster': 'Cluster'},
+        opacity=0.7
+    )
+
+    # Update layout for better interactivity
+    fig.update_layout(
+        scene=dict(
+            camera=dict(
+                up=dict(x=0, y=0, z=1),
+                center=dict(x=0, y=0, z=0),
+                eye=dict(x=1.5, y=1.5, z=1.5)
+            )
+        )
+    )
+
+    fig
+    return X, df, fig, labels, make_blobs, n_clusters, n_samples, np, pd, px
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()

From 217ecb17953c59ae8866a99d193204dc675f1a4e Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:58:40 +0530
Subject: [PATCH 31/34] new: Add `plotly-8.py` - `geo map`

---
 marimo/_snippets/data/plotly-8.py | 68 +++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)
 create mode 100644 marimo/_snippets/data/plotly-8.py

diff --git a/marimo/_snippets/data/plotly-8.py b/marimo/_snippets/data/plotly-8.py
new file mode 100644
index 00000000000..fd14ff28a46
--- /dev/null
+++ b/marimo/_snippets/data/plotly-8.py
@@ -0,0 +1,68 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Plotly: Geographic Choropleth Map
+
+        Create an interactive choropleth map for geographic data visualization.
+        Common usage: `fig = px.choropleth(df, locations='iso_code', color='values')`.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    import plotly.express as px
+    import pandas as pd
+
+    # Sample data for countries
+    data = {
+        'country_code': ['USA', 'GBR', 'FRA', 'DEU', 'JPN', 'IND', 'CHN'],
+        'value': [100, 30, 50, 76, 61, 89, 95],
+        'text': ['United States', 'United Kingdom', 'France', 
+                'Germany', 'Japan', 'India', 'China']
+    }
+    df = pd.DataFrame(data)
+
+    # Create choropleth map
+    fig = px.choropleth(
+        df,
+        locations='country_code',
+        color='value',
+        hover_name='text',
+        color_continuous_scale='Viridis',
+        title='Global Distribution Map',
+        locationmode='ISO-3'
+    )
+
+    # Update layout
+    fig.update_layout(
+        geo=dict(
+            showframe=False,
+            showcoastlines=True,
+            projection_type='equirectangular'
+        ),
+        height=600
+    )
+
+    fig
+    return data, df, fig, pd, px
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()

From 6a3ba4925c4c9e5aa8cc371fd2152ed0d1e9ad65 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:59:03 +0530
Subject: [PATCH 32/34] new: Add `plotly-9.py` - `analyze plot`

---
 marimo/_snippets/data/plotly-9.py | 64 +++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 marimo/_snippets/data/plotly-9.py

diff --git a/marimo/_snippets/data/plotly-9.py b/marimo/_snippets/data/plotly-9.py
new file mode 100644
index 00000000000..eb31c3c6c45
--- /dev/null
+++ b/marimo/_snippets/data/plotly-9.py
@@ -0,0 +1,64 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Plotly: Conversion Funnel Analysis
+
+        Create an interactive funnel chart for analyzing conversion steps.
+        Common usage: `fig = go.Figure(go.Funnel(y=stages, x=values))`.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    import plotly.graph_objects as go
+
+    # Sample conversion data
+    stages = ['Visitors', 'Cart', 'Checkout', 'Purchase']
+    values = [1000, 600, 300, 150]
+
+    # Calculate conversion rates
+    rates = [f"{100*v2/v1:.1f}%" 
+            for v1, v2 in zip(values[:-1], values[1:])]
+    rates = [""] + rates
+
+    # Create funnel chart
+    fig = go.Figure(go.Funnel(
+        y=stages,
+        x=values,
+        textinfo="value+percent initial",
+        textposition="auto",
+        texttemplate="%{value}<br>%{text}",
+        text=rates,
+        connector={"line": {"color": "royalblue", "dash": "dot"}}
+    ))
+
+    # Update layout
+    fig.update_layout(
+        title="Conversion Funnel Analysis",
+        showlegend=False,
+        height=500
+    )
+
+    fig
+    return fig, go, rates, stages, values
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()

From 776f1338538e27bba99ed27d1c0f893a1bb2718b Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:59:17 +0530
Subject: [PATCH 33/34] new: Add `plotly-10.py` - `bubble chart`

---
 marimo/_snippets/data/plotly-10.py | 85 ++++++++++++++++++++++++++++++
 1 file changed, 85 insertions(+)
 create mode 100644 marimo/_snippets/data/plotly-10.py

diff --git a/marimo/_snippets/data/plotly-10.py b/marimo/_snippets/data/plotly-10.py
new file mode 100644
index 00000000000..65e76e8c3a7
--- /dev/null
+++ b/marimo/_snippets/data/plotly-10.py
@@ -0,0 +1,85 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Plotly: Animated Bubble Chart
+
+        Create an animated bubble chart for time-series multi-dimensional data.
+        Common usage: `px.scatter(df, x='x', y='y', animation_frame='year', size='size')`.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    import plotly.express as px
+    import pandas as pd
+    import numpy as np
+
+    # Generate sample time-series data
+    np.random.seed(42)
+    years = range(2015, 2024)
+    categories = ['A', 'B', 'C', 'D']
+
+    data = []
+    for year in years:
+        for cat in categories:
+            data.append({
+                'year': year,
+                'category': cat,
+                'value_x': np.random.normal(loc=year-2015, scale=1),
+                'value_y': np.random.normal(loc=5, scale=2),
+                'size': np.random.randint(20, 100),
+                'growth': np.random.uniform(-10, 20)
+            })
+
+    df = pd.DataFrame(data)
+
+    # Create animated bubble chart
+    fig = px.scatter(
+        df,
+        x='value_x',
+        y='value_y',
+        animation_frame='year',
+        size='size',
+        color='category',
+        hover_name='category',
+        text='category',
+        size_max=60,
+        range_x=[-1, 10],
+        range_y=[-5, 15],
+        title='Metric Evolution Over Time'
+    )
+
+    # Update layout
+    fig.update_traces(
+        textposition='top center',
+        marker=dict(sizemin=10)
+    )
+
+    fig.update_layout(
+        height=600,
+        showlegend=True
+    )
+
+    fig
+    return cat, categories, data, df, fig, np, pd, px, year, years
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()

From b1c46da47e87d57747d33d36e50087dededd5345 Mon Sep 17 00:00:00 2001
From: Srihari Thyagarajan <hari.leo03@gmail.com>
Date: Mon, 10 Feb 2025 22:59:46 +0530
Subject: [PATCH 34/34] new: Add `plotly-11.py` - `dendrograms`

---
 marimo/_snippets/data/plotly-11.py | 107 +++++++++++++++++++++++++++++
 1 file changed, 107 insertions(+)
 create mode 100644 marimo/_snippets/data/plotly-11.py

diff --git a/marimo/_snippets/data/plotly-11.py b/marimo/_snippets/data/plotly-11.py
new file mode 100644
index 00000000000..fe6256274ce
--- /dev/null
+++ b/marimo/_snippets/data/plotly-11.py
@@ -0,0 +1,107 @@
+# Copyright 2024 Marimo. All rights reserved.
+
+import marimo
+
+__generated_with = "0.11.0"
+app = marimo.App()
+
+
+@app.cell
+def _(mo):
+    mo.md(
+        r"""
+        # Plotly: Clustering Analysis Visualization
+
+        Create an interactive heatmap with dendrograms for hierarchical clustering analysis.
+        Common usage: `fig = ff.create_dendrogram(X)` and `px.imshow(correlation_matrix)`.
+        Commonly used in: feature correlation analysis, gene expression analysis, customer segmentation.
+        """
+    )
+    return
+
+
+@app.cell
+def _():
+    import plotly.figure_factory as ff
+    import plotly.express as px
+    from scipy.cluster.hierarchy import linkage
+    import numpy as np
+    import pandas as pd
+
+    # Generate sample data
+    np.random.seed(42)
+    n_features = 10
+    n_samples = 100
+
+    # Create feature names
+    feature_names = [f'Feature_{i}' for i in range(n_features)]
+
+    # Generate correlated data
+    data = np.random.randn(n_samples, n_features)
+    data[:, 1] = data[:, 0] + np.random.randn(n_samples) * 0.3  # Correlate features 0 and 1
+    data[:, 4] = data[:, 3] - np.random.randn(n_samples) * 0.2  # Correlate features 3 and 4
+
+    df = pd.DataFrame(data, columns=feature_names)
+
+    # Compute correlation matrix
+    corr_matrix = df.corr()
+
+    # Compute linkage for dendrogram
+    linkage_matrix = linkage(corr_matrix, 'ward')
+
+    # Create dendrogram
+    fig_dendrogram = ff.create_dendrogram(
+        corr_matrix,
+        labels=feature_names,
+        color_threshold=1.5
+    )
+    fig_dendrogram.update_layout(
+        title='Feature Clustering Dendrogram',
+        width=800,
+        height=400
+    )
+
+    # Create heatmap
+    fig_heatmap = px.imshow(
+        corr_matrix,
+        labels=dict(x="Features", y="Features", color="Correlation"),
+        x=feature_names,
+        y=feature_names,
+        color_continuous_scale="RdBu_r",
+        aspect="auto"
+    )
+    fig_heatmap.update_layout(
+        title='Feature Correlation Heatmap',
+        width=800,
+        height=800
+    )
+
+    # Display both visualizations
+    fig_dendrogram
+    fig_heatmap
+    return (
+        corr_matrix,
+        data,
+        df,
+        feature_names,
+        ff,
+        fig_dendrogram,
+        fig_heatmap,
+        linkage,
+        linkage_matrix,
+        n_features,
+        n_samples,
+        np,
+        pd,
+        px,
+    )
+
+
+@app.cell
+def _():
+    import marimo as mo
+    return (mo,)
+
+
+if __name__ == "__main__":
+    app.run()