pip install holoviews hvsampledata panel watchfiles

import hvsampledata

hvsampledata.earthquakes("pandas")

Tabular record of earthquake events from the USGS Earthquake Catalog that provides detailed
information including parameters such as time, location as latitude/longitude coordinates
and place name, depth, and magnitude. The dataset contains 596 events.

Note: The columns `depth_class` and `mag_class` were created by categorizing numerical values from
the `depth` and `mag` columns in the original dataset using custom-defined binning:

Depth Classification

| depth     | depth_class  |
|-----------|--------------|
| Below 70  | Shallow      |
| 70 - 300  | Intermediate |
| Above 300 | Deep         |

Magnitude Classification

| mag         | mag_class |
|-------------|-----------|
| 3.9 - <4.9  | Light     |
| 4.9 - <5.9  | Moderate  |
| 5.9 - <6.9  | Strong    |
| 6.9 - <7.9  | Major     |


Schema
------
| name        | type       | description                                                         |
|:------------|:-----------|:--------------------------------------------------------------------|
| time        | datetime   | UTC Time when the event occurred.                                   |
| lat         | float      | Decimal degrees latitude. Negative values for southern latitudes.   |
| lon         | float      | Decimal degrees longitude. Negative values for western longitudes   |
| depth       | float      | Depth of the event in kilometers.                                   |
| depth_class | category   | The depth category derived from the depth column.                   |
| mag         | float      | The magnitude for the event.                                        |
| mag_class   | category   | The magnitude category derived from the mag column.                 |
| place       | string     | Textual description of named geographic region near to the event.   |

import hvsampledata
import holoviews as hv

If working in a .py file DO serve the plot with hotreload:

```bash
panel serve path/to/file.py --dev --show

import hvsampledata
import holoviews as hv

hv.extension("bokeh")

data = hvsampledata.earthquakes("pandas")

mag_class_counts = data.groupby(['mag_class', 'depth_class']).size().reset_index(name='counts')
print(mag_class_counts)

plot = hv.Bars(
    data = mag_class_counts,
    kdims=['mag_class','depth_class'],
    vdims='counts',
).groupby(
    "depth_class"
).opts(
    # DO specify optional styling options using .opts()
    line_color=None,
    # DO specify optional plot options using .opts()
    title='Earthquake Counts by Magnitude Class and Depth Class',
    width=800,
)

If we add `.layout` the data will be visualized as 3 individual plots (one per depth_class):

```python
import hvsampledata
import holoviews as hv

hv.extension("bokeh")

data = hvsampledata.earthquakes("pandas")

mag_class_counts = data.groupby(['mag_class', 'depth_class']).size().reset_index(name='counts')
print(mag_class_counts)

plot = hv.Bars(
    data = mag_class_counts,
    kdims=['mag_class','depth_class'],
    vdims='counts',
).groupby(
    "depth_class"
).opts(
    # DO specify optional styling options using .opts()
    line_color=None,
    width=800,
).layout()

If instead of `.layout()` we add `.overlay()`, one plot will be created, but the depth_class'es will be visualized by different colors.

```python
import hvsampledata
import holoviews as hv

hv.extension("bokeh")

data = hvsampledata.earthquakes("pandas")

mag_class_counts = data.groupby(['mag_class', 'depth_class']).size().reset_index(name='counts')
print(mag_class_counts)

plot = hv.Bars(
    data = mag_class_counts,
    kdims=['mag_class','depth_class'],
    vdims='counts',
).groupby(
    "depth_class"
).opts(
    # DO specify optional styling options using .opts()
    line_color=None,
    width=800,
).overlay()

Note: This works better for Curve or Scatter plots

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        }
    }
})

# Add percentage column for tooltips
aggregated['percentage'] = (
    aggregated['event_count'] / aggregated['event_count'].sum() * 100
)

return aggregated

# Main bar chart with professional styling
bar_chart = hv.Bars(aggregated_data, kdims='mag_class', vdims=['event_count', 'percentage', 'avg_depth']).opts(
    # Titles and labels
    title='Earthquake Distribution by Magnitude',
    xlabel='Magnitude',
    ylabel='Number of Events',

    # Interactivity
    # hover_cols = ["mag_class", "event_count", "percentage", "avg_depth"],
    hover_tooltips=[
        ('Magnitude', '@mag_class'),
        ('Events', '@event_count{0,0}'),      # Format: 1,234
        ('Percentage', '@percentage{0 a}%'), # Format: 45%
        ('Avg Depth', '@avg_depth{0f} km')  # Format: 99 km
    ],
    default_tools=default_tools
)

# Add text labels above bars
labels_data = aggregated_data.copy()
labels_data['label_y'] = labels_data['event_count'] + 20  # Offset above bars

text_labels = hv.Labels(labels_data, kdims=['mag_class', 'label_y'], vdims=['event_count', 'percentage', 'avg_depth']).opts(
    hover_tooltips=[
        ('Magnitude', '@mag_class'),
        ('Events', '@event_count{0,0}'),      # Format: 1,234
        # tooltips below do currently not work on Labels
        # ('Percentage', '@percentage{0 a}%'), # Format: 45%
        # ('Avg Depth', '@avg_depth{0f} km'),  # Format: 99 km
    ],
    default_tools=default_tools
)

# Overlay: bar chart * text labels
return bar_chart * text_labels

# Transform: Aggregate and calculate statistics
aggregated = aggregate_by_magnitude(earthquake_data)

# Visualize: Create publication-quality chart
chart = create_bar_chart(aggregated)

return chart

# Apply custom Bokeh theme (override the global theme)
# Create and serve the chart
plot = create_plot()
pn.panel(plot, sizing_mode="stretch_both", margin=25).servable()

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import holoviews as hv

hv.extension()
...

from bokeh.models.formatters import NumeralTickFormatter

plot.opts(
    yformatter=NumeralTickFormatter(format='0.00a'),  # Format as 1.00M, 2.50M, etc.
)

hv.save(some_plot, 'some_plot.html')