Master Plotly Express: Create Stunning Visualizations with One Line of Code

Environment Setup

The examples were tested with the following environment:

Python 3.7

Jupyter notebook

Pandas 1.1.3

Plotly_express 0.4.1

$ python3 -m pip install plotly_express

Tool Overview

Plotly_express is a high‑level wrapper around Plotly that provides a simplified API. It bundles modern templates and requires only a single function call to generate attractive, interactive visualizations. It is free, fully compatible with the Plotly ecosystem, and works seamlessly with Dash and Orca for exporting.

Getting Started with Plotting

Plotly_express ships with several built‑in datasets such as gapminder, tips, and iris. Below are examples of how to load these datasets and create different chart types.

Loading a Dataset

import pandas as pd
import numpy as np
import plotly_express as px  # or import plotly.express as px

gapminder = px.data.gapminder()
gapminder.head()

Line Chart

fig = px.line(
    gapminder,
    x="year",
    y="lifeExp",
    color="continent",
    line_group="continent",
    hover_name="country",
    line_shape="spline",
    render_mode="svg"
)
fig.show()

Area Chart

fig = px.area(
    gapminder,
    x="year",
    y="pop",
    color="continent",
    line_group="country"
)
fig.show()

Scatter Plot

fig = px.scatter(
    gapminder,
    x="gdpPercap",
    y="lifeExp",
    color="continent",
    size="pop",
    size_max=60,
    hover_name="country",
    facet_col="continent",
    animation_frame="year",
    log_x=True,
    range_x=[100, 100000],
    range_y=[25, 90],
    labels={"pop": "Populations", "gdpPercap": "GDP per Capital", "lifeExp": "Life Expectancy"}
)
fig.show()

Geographic Maps

fig = px.choropleth(
    gapminder,
    locations="iso_alpha",
    color="lifeExp",
    hover_name="country",
    animation_frame="year",
    color_continuous_scale=px.colors.sequential.Plasma,
    projection="natural earth"
)
fig.show()

Using the Iris Dataset

fig = px.scatter(
    iris,
    x="sepal_width",
    y="sepal_length",
    color="species"
)
fig.show()

Joint Distribution (Scatter + Marginals)

fig = px.scatter(
    iris,
    x="sepal_width",
    y="sepal_length",
    color="species",
    marginal_x="histogram",
    marginal_y="rug"
)
fig.show()

Violin Plot with Box and Trendline

fig = px.scatter(
    iris,
    x="sepal_width",
    y="sepal_length",
    color="species",
    marginal_y="violin",
    marginal_x="box",
    trendline="ols"
)
fig.show()

Scatter Matrix

fig = px.scatter_matrix(
    iris,
    dimensions=["sepal_width", "sepal_length", "petal_width", "petal_length"],
    color="species"
)
fig.show()

Parallel Coordinates

fig = px.parallel_coordinates(
    iris,
    color="species_id",
    labels={
        "species_id": "Species",
        "sepal_width": "Sepal Width",
        "sepal_length": "Sepal Length",
        "petal_length": "Petal Length",
        "petal_width": "Petal Width"
    },
    color_continuous_scale=px.colors.diverging.Tealrose,
    color_continuous_midpoint=2
)
fig.show()

Box Plot with Error Bars

iris["e"] = iris["sepal_width"] / 100
fig = px.scatter(
    iris,
    x="sepal_width",
    y="sepal_length",
    color="species",
    error_x="e",
    error_y="e"
)
fig.show()

Density Contour and Heatmap

fig = px.density_contour(
    iris,
    x="sepal_width",
    y="sepal_length",
    color="species"
)
fig.show()

fig = px.density_heatmap(
    iris,
    x="sepal_width",
    y="sepal_length",
    marginal_y="rug",
    marginal_x="histogram"
)
fig.show()

Parallel Categories (Tips Dataset)

fig = px.parallel_categories(
    tips,
    color="size",
    color_continuous_scale=px.colors.sequential.Inferno
)
fig.show()

Bar Chart with Facets

fig = px.bar(
    tips,
    x="sex",
    y="total_bill",
    color="smoker",
    barmode="group",
    facet_row="time",
    facet_col="day",
    category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], "time": ["Lunch", "Dinner"]}
)
fig.show()

Histogram

fig = px.histogram(
    tips,
    x="sex",
    y="tip",
    histfunc="avg",
    color="smoker",
    barmode="group",
    facet_row="time",
    facet_col="day",
    category_orders={"day": ["Thur", "Fri", "Sat", "Sun"], "time": ["Lunch", "Dinner"]}
)
fig.show()

Box and Violin Plots

px.box(tips, x="day", y="total_bill", color="smoker", notched=True)

px.violin(
    tips,
    x="smoker",
    y="tip",
    color="sex",
    box=True,
    points="all",
    hover_data=tips.columns
)

Polar Charts

fig = px.line_polar(
    wind,
    r="frequency",
    theta="direction",
    color="strength",
    line_close=True,
    color_discrete_sequence=px.colors.sequential.Plasma_r
)
fig.show()

fig = px.bar_polar(
    wind,
    r="frequency",
    theta="direction",
    color="strength",
    template="plotly_dark",
    color_discrete_sequence=px.colors.sequential.Plasma_r
)
fig.show()

Color Palettes

Plotly_express provides both qualitative and sequential color swatches.

px.colors.qualitative.swatches()

px.colors.sequential.swatches()

Themes

Three built‑in themes are available: plotly, plotly_white, and plotly_dark. They can be applied via the template argument.

px.scatter(
    iris,
    x="sepal_width",
    y="sepal_length",
    color="species",
    marginal_x="box",
    marginal_y="histogram",
    height=600,
    trendline="ols",
    template="plotly"
)

px.scatter(
    iris,
    x="sepal_width",
    y="sepal_length",
    color="species",
    marginal_x="box",
    marginal_y="histogram",
    height=600,
    trendline="ols",
    template="plotly_white"
)

px.scatter(
    iris,
    x="sepal_width",
    y="sepal_length",
    color="species",
    marginal_x="box",
    marginal_y="histogram",
    height=600,
    trendline="ols",
    template="plotly_dark"
)

Summary

Plotly_express enables rapid creation of a variety of charts—including bar, line, scatter, violin, and polar plots—with minimal code. Its advantages are:

Fast chart generation with just a few parameters.

Visually appealing, vibrant color schemes.

Interactive, dynamic graphics when used in Jupyter notebooks.

These features make Plotly_express an excellent tool for quickly mastering data visualization in Python.