Plotly Express Quick Start: Create Stunning Interactive Visualizations with Minimal Code

Plotly Express Quick Introduction

Plotly Express is a high‑level wrapper for plotly.py that provides a simple API for creating beautiful, interactive visualizations with minimal code. It includes many built‑in datasets and color palettes.

Installation

Install the package via pip:

pip install plotly_express

Built‑in Datasets

Import the libraries and explore the datasets:

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

GDP Data

country

continent

year

lifeExp

pop

gdpPercap

iso_alpha

iso_num

Restaurant Tips Data

bill

tip

sex

smoker

day

time

size

Iris Data

sepal_length

sepal_width

petal_length

petal_width

species

species_id

Wind Data

direction

strength

frequency

Election Results

district

Coderre

Bergeron

Joly

total

winner

result

district_id

Car Sharing Availability

centroid_lat

centroid_lon

car_hours

peak_hour

Stock Data

date

GOOG

AAPL

AMZN

FB

NFLX

MSFT

Built‑in Color Palettes

Plotly Express provides many color palettes. Example calls:

px.colors.carto.swatches()

px.colors.cmocean.swatches()

px.colors.colorbrewer.swatches()

px.colors.cyclical.swatches()

px.colors.diverging.swatches()

px.colors.qualitative.swatches()

px.colors.sequential.swatches()

Plotting Examples

Bar Chart

# Select Switzerland data
gapminder = px.data.gapminder()
Switzerland = gapminder[gapminder["country"] == "Switzerland"]
px.bar(Switzerland, x="year", y="pop", color="pop")

Scatter Plot

gapminder_2002 = gapminder[gapminder["year"] == 2002]
px.scatter(gapminder_2002, x="gdpPercap", y="lifeExp", color="continent")

Bubble Scatter

px.scatter(gapminder_2002, x="gdpPercap", y="lifeExp", color="continent", size="pop", size_max=60)

Scatter Matrix

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

Area Chart

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

Line Chart (Stock)

px.line(stock, x='date', y="FB")

Pie Chart

px.pie(total_bill_byday, names="day", values="total_bill")

Sunburst

px.sunburst(gapminder_2002, path=['continent','country'], values='pop', color='lifeExp', hover_data=['iso_alpha'])

Funnel

data = dict(number=[1000,800,400,200,100,30], stage=["UV","搜索","搜藏","加购","下单","付款"])
px.funnel(data, x="number", y="stage")
px.funnel(data, x="number", y="stage", color="number")

3D Scatter

px.scatter_3d(election, x="Joly", y="Coderre", z="Bergeron", color="winner", size="total", hover_name="district_id", symbol="result", color_discrete_map={"Joly":"blue","Bergeron":"green","Coderre":"red"})

Map Visualizations

# Choropleth
px.choropleth(gapminder, locations="iso_alpha", color="lifeExp", hover_name="country", animation_frame="year", color_continuous_scale=px.colors.sequential.Plasma, projection="natural earth")
# Line Geo
px.line_geo(gapminder_2002, locations="iso_alpha", color="continent", projection="orthographic")

Built‑in Themes

Plotly Express offers three themes: plotly, plotly_white, and plotly_dark. Example:

px.scatter(gapminder_2002, x="gdpPercap", y="lifeExp", color="continent", template="plotly_dark")

Conclusion

This article provides a comprehensive overview of Plotly Express, highlighting its ease of use, extensive chart types, built‑in datasets, color palettes, and themes. With just a few lines of code, users can create sophisticated, interactive visualizations, making data exploration and presentation both efficient and visually appealing.