pip install hvplot 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
# DO import panel if working in .py files
import panel as pn
# Do importing hvplot.pandas to add .hvplot namespace to Pandas DataFrames and Series
import hvplot.pandas  # noqa: F401

# DO always run pn.extension() to load panel javascript extensions
pn.extension()

# Do keep the extraction, transformation and plotting of data clearly separate
# Extract: earthquakes sample data
data = hvsampledata.earthquakes("pandas")

# Transform: Group by mag_class and count occurrences
mag_class_counts = data.groupby('mag_class').size().reset_index(name='counts')

# Plot: counts by mag_class
plot = mag_class_counts.hvplot.bar(x='mag_class', y='counts', title='Earthquake Counts by Magnitude Class')
# If working in notebook DO output to plot:
plot
# Else if working in .py file DO:
# DO provide a method to serve the app with `panel serve`
if pn.state.served:
    # DO remember to add .servable to the panel components you want to serve with the app
    pn.panel(plot, sizing_mode="stretch_both").servable()
# DON'T provide a `if __name__ == "__main__":` method to serve the app with `python`

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

import hvplot.pandas # will add .hvplot namespace to Pandas dataframes
import hvplot.polars # will add .hvplot namespace to Polars dataframes
...

from bokeh.models.formatters import NumeralTickFormatter

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

import pandas as pd
import hvplot.pandas  # noqa
import panel as pn

import numpy as np

np.random.seed(42)
dates = pd.date_range("2022-08-01", periods=30, freq="B")
open_prices = np.cumsum(np.random.normal(100, 2, size=len(dates)))
high_prices = open_prices + np.random.uniform(1, 5, size=len(dates))
low_prices = open_prices - np.random.uniform(1, 5, size=len(dates))
close_prices = open_prices + np.random.uniform(-3, 3, size=len(dates))

data = pd.DataFrame({
    "open": open_prices.round(2),
    "high": high_prices.round(2),
    "low": low_prices.round(2),
    "close": close_prices.round(2),
}, index=dates)


# Create a scatter plot of date vs close price
scatter_plot = data.hvplot.scatter(x="index", y="close", grid=True, title="Close Price Scatter Plot", xlabel="Date", ylabel="Close Price")


# Create a Panel app
app = pn.Column("# Close Price Scatter Plot", scatter_plot)

if pn.state.served:
    app.servable()

panel serve plot.py --dev