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Examples

Copy-paste snippets for the most common visualization types. All examples use the default jupyter method — the last expression is displayed automatically, just like a notebook cell.

Use the panel method (with .servable()) for multi-component dashboards; those examples are in the Panel dashboard and DataFrame table sections below.

Install the full PyData stack

pip install "panel-live-server[pydata]"
This installs hvplot, plotly, altair, matplotlib, seaborn, polars, and more alongside Panel Live Server.

Plotly

Interactive charts with hover tooltips, zoom, and pan out of the box.

Bar chart

import plotly.express as px

df = px.data.medals_long()
px.bar(df, x="nation", y="count", color="medal", barmode="group",
       title="Olympic medals by nation")

Scatter plot

import plotly.express as px

df = px.data.iris()
px.scatter(df, x="sepal_width", y="sepal_length", color="species",
           size="petal_length", hover_data=["petal_width"],
           title="Iris sepal dimensions")

Time series

import plotly.express as px

df = px.data.stocks()
px.line(df, x="date", y=["GOOG", "AAPL", "AMZN"],
        title="Stock prices over time")

hvPlot / HoloViews

Quick .hvplot() on any pandas or polars DataFrame. Produces interactive Bokeh charts.

Bar chart

import pandas as pd
import hvplot.pandas

df = pd.DataFrame({
    "species":  ["Adelie", "Chinstrap", "Gentoo"],
    "count":    [152, 68, 124],
})
df.hvplot.bar(x="species", y="count", title="Penguin species counts",
              color="species", legend=False)

Scatter plot

import pandas as pd
import numpy as np
import hvplot.pandas

rng = np.random.default_rng(0)
df = pd.DataFrame({
    "x": rng.normal(size=200),
    "y": rng.normal(size=200),
    "group": rng.choice(["A", "B", "C"], size=200),
})
df.hvplot.scatter(x="x", y="y", by="group", title="Scatter by group")

Line chart

import pandas as pd
import numpy as np
import hvplot.pandas

dates = pd.date_range("2024-01-01", periods=90, freq="D")
df = pd.DataFrame({
    "date":  dates,
    "value": np.cumsum(np.random.default_rng(1).normal(size=90)),
})
df.hvplot.line(x="date", y="value", title="90-day cumulative return")

Matplotlib / Seaborn

Static figures rendered as images. Seaborn's statistical plots are especially useful.

Histogram (matplotlib)

import matplotlib.pyplot as plt
import numpy as np

rng = np.random.default_rng(42)
data = rng.normal(loc=0, scale=1, size=1000)

fig, ax = plt.subplots()
ax.hist(data, bins=30, edgecolor="white")
ax.set(title="Normal distribution", xlabel="Value", ylabel="Count")
fig

Distribution plot (seaborn)

import seaborn as sns
import matplotlib.pyplot as plt

penguins = sns.load_dataset("penguins").dropna()

fig, ax = plt.subplots(figsize=(8, 4))
sns.histplot(penguins, x="flipper_length_mm", hue="species", kde=True, ax=ax)
ax.set_title("Flipper length distribution by species")
fig

Heatmap (seaborn)

import seaborn as sns
import matplotlib.pyplot as plt

flights = sns.load_dataset("flights").pivot(
    index="month", columns="year", values="passengers"
)
fig, ax = plt.subplots(figsize=(10, 6))
sns.heatmap(flights, annot=True, fmt="d", cmap="YlOrRd", ax=ax)
ax.set_title("Monthly airline passengers")
fig

Panel widgets

Interactive widgets with reactive bindings. Uses the jupyter method — Panel's pn.panel() wraps the bound function output automatically.

Slider with live output

import panel as pn

x_slider = pn.widgets.IntSlider(name="x", start=0, end=100, value=10)

def square(x):
    return f"{x} squared is **{x ** 2}**"

pn.Column(x_slider, pn.bind(pn.pane.Markdown, pn.bind(square, x_slider)))

Select + plot

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

df = pd.DataFrame({
    "month":   ["Jan", "Feb", "Mar", "Apr", "May", "Jun"],
    "revenue": [120, 95, 180, 150, 210, 175],
    "cost":    [80,  70, 100, 110, 130, 120],
})

metric = pn.widgets.Select(name="Metric", options=["revenue", "cost"])

def plot(m):
    return df.hvplot.bar(x="month", y=m, title=f"Monthly {m}")

pn.Column(metric, pn.bind(plot, metric))

Panel dashboard

A multi-component layout with a sidebar and reactive widgets. Use method="panel" and call .servable() on the objects you want displayed.

import panel as pn
import pandas as pd
import numpy as np
import hvplot.pandas

pn.extension()

rng = np.random.default_rng(0)
N = 200
df = pd.DataFrame({
    "x":     rng.normal(size=N),
    "y":     rng.normal(size=N),
    "group": rng.choice(["A", "B", "C"], size=N),
})

group_select = pn.widgets.CheckBoxGroup(
    name="Groups", value=["A", "B", "C"], options=["A", "B", "C"]
)
alpha_slider = pn.widgets.FloatSlider(name="Point opacity", start=0.1, end=1.0, value=0.7)

def scatter(groups, alpha):
    filtered = df[df["group"].isin(groups)] if groups else df.iloc[:0]
    return filtered.hvplot.scatter(x="x", y="y", by="group", alpha=alpha,
                                   width=500, height=400)

pn.template.FastListTemplate(
    title="Scatter Explorer",
    sidebar=[group_select, alpha_slider],
    main=[pn.bind(scatter, group_select, alpha_slider)],
).servable()

DataFrame table

An interactive, sortable and filterable table using pn.widgets.Tabulator. Use method="panel".

import panel as pn
import pandas as pd

pn.extension("tabulator")

df = pd.DataFrame({
    "name":    ["Alice", "Bob", "Carol", "Dave", "Eve"],
    "dept":    ["Eng", "Eng", "HR", "Finance", "HR"],
    "salary":  [95000, 88000, 72000, 81000, 69000],
    "rating":  [4.5, 3.8, 4.2, 4.0, 4.7],
})

pn.widgets.Tabulator(
    df,
    pagination="local",
    page_size=10,
    header_filters=True,
    sizing_mode="stretch_width",
).servable()