Plotly and Interactive Charts

Plotly is a Python library for creating interactive, publication-quality visualisations that can be displayed in Jupyter notebooks, embedded in web applications, or exported as standalone HTML files. Unlike Matplotlib and Seaborn (which produce static images), Plotly charts support hover tooltips, zoom, pan, click events, and animations out of the box.

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Installation

pip install plotly

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Plotly Express vs Graph Objects

Plotly has two main APIs:

API	Description	Code Complexity
Plotly Express (px)	High-level, concise, DataFrame-friendly	Low
Graph Objects (go)	Low-level, maximum customisation	Higher

Tip: Start with Plotly Express for most charts. Drop down to Graph Objects only when you need advanced customisation.

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Plotly Express Basics

Scatter Plot

import plotly.express as px

df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length",
                 color="species", size="petal_length",
                 hover_data=["petal_width"],
                 title="Iris Dataset — Sepal Dimensions")
fig.show()

Line Plot

import plotly.express as px

df = px.data.gapminder()
df_uk = df[df["country"] == "United Kingdom"]

fig = px.line(df_uk, x="year", y="gdpPercap",
              title="UK GDP Per Capita Over Time",
              labels={"gdpPercap": "GDP Per Capita (USD)",
                      "year": "Year"})
fig.show()

Bar Chart

import plotly.express as px

df = px.data.tips()

fig = px.bar(df, x="day", y="total_bill", color="sex",
             barmode="group",
             title="Total Bill by Day and Sex",
             labels={"total_bill": "Total Bill (USD)"})
fig.show()

Histogram

import plotly.express as px

df = px.data.tips()

fig = px.histogram(df, x="total_bill", nbins=30,
                   color="time", barmode="overlay",
                   opacity=0.7,
                   title="Distribution of Total Bill")
fig.show()

Box Plot

import plotly.express as px

df = px.data.tips()

fig = px.box(df, x="day", y="total_bill", color="smoker",
             title="Total Bill Distribution by Day",
             points="outliers")
fig.show()

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Heatmap

import plotly.express as px
import pandas as pd
import numpy as np

np.random.seed(42)
data = np.random.randn(10, 10)
labels = [f"Var {i}" for i in range(10)]

fig = px.imshow(data, x=labels, y=labels,
                color_continuous_scale="RdBu_r",
                title="Heatmap Example")
fig.show()

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Plotly Graph Objects

For advanced customisation, use the plotly.graph_objects module.

import plotly.graph_objects as go
import numpy as np

x = np.linspace(0, 10, 100)

fig = go.Figure()

fig.add_trace(go.Scatter(x=x, y=np.sin(x),
                         mode="lines", name="sin(x)",
                         line=dict(color="#4C72B0", width=2)))

fig.add_trace(go.Scatter(x=x, y=np.cos(x),
                         mode="lines+markers", name="cos(x)",
                         line=dict(color="#DD8452", width=2, dash="dash"),
                         marker=dict(size=4)))

fig.update_layout(
    title="Trigonometric Functions",
    xaxis_title="x",
    yaxis_title="y",
    template="plotly_white",
    legend=dict(x=0.01, y=0.99)
)

fig.show()

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Subplots in Plotly

from plotly.subplots import make_subplots
import plotly.graph_objects as go
import numpy as np

fig = make_subplots(rows=1, cols=2,
                    subplot_titles=("Sine", "Cosine"))

x = np.linspace(0, 10, 100)