ggplotly¶

A data visualization library for Python that combines the grammar of graphics from ggplot2 with the interactivity of Plotly.

Why ggplotly?¶

Familiar syntax - If you know ggplot2 from R, you'll feel right at home
Interactive plots - Powered by Plotly for zooming, panning, and hover tooltips
Jupyter-friendly - Plots render automatically in notebooks
Comprehensive - 113+ ggplot2-equivalent functions

Quick Example¶

In [1]:

Copied!





from ggplotly import *
import pandas as pd

df = pd.DataFrame({
    'x': [1, 2, 3, 4, 5],
    'y': [1, 4, 9, 16, 25],
    'group': ['A', 'A', 'B', 'B', 'B']
})

# Simple scatter plot with color mapping
(
    ggplot(df, aes(x='x', y='y', color='group'))
    + geom_point(size=10)
    + theme_minimal()
)
from ggplotly import *
import pandas as pd

df = pd.DataFrame({
    'x': [1, 2, 3, 4, 5],
    'y': [1, 4, 9, 16, 25],
    'group': ['A', 'A', 'B', 'B', 'B']
})

# Simple scatter plot with color mapping
(
    ggplot(df, aes(x='x', y='y', color='group'))
    + geom_point(size=10)
    + theme_minimal()
)

Out[1]:

Installation¶

pip install ggplotly

What's Included¶

Category	Count	Examples
Geoms	46	`geom_point`, `geom_line`, `geom_bar`, `geom_boxplot`, `geom_map`
Scales	17	`scale_color_manual`, `scale_fill_gradient`, `scale_x_log10`
Themes	9	`theme_minimal`, `theme_dark`, `theme_bbc`, `theme_nytimes`
Stats	13	`stat_smooth`, `stat_count`, `stat_density`
Coords	4	`coord_flip`, `coord_polar`, `coord_sf`
Facets	2	`facet_wrap`, `facet_grid`

Gallery¶

Explore examples organized by visualization type:

Basic Charts - Scatter, line, bar, histograms, box plots
Statistical - Smoothing, density, error bars, summaries
Time Series - Date axes, range plots, candlesticks, OHLC
Geographic Maps - Choropleths, projections, point maps
3D Visualizations - Scatter, surfaces, wireframes
Network Graphs - Edge bundling, sea routes
Multi-Panel (Facets) - Small multiples, facet_wrap, facet_grid
Theming - Custom themes, publication-ready styling

Coming from R?¶

ggplotly aims for API compatibility with ggplot2. Most code translates directly:

R (ggplot2):

ggplot(mpg, aes(x = displ, y = hwy, color = class)) +
  geom_point() +
  theme_minimal() +
  labs(title = "Fuel Efficiency")

Python (ggplotly):

(
  ggplot(mpg, aes(x='displ', y='hwy', color='class'))
  + geom_point() 
  + theme_minimal()
  + labs(title='Fuel Efficiency')
)

# OR

ggplot(mpg, aes(x='displ', y='hwy', color='class')) + \
  geom_point() + \
  theme_minimal() + \
  labs(title='Fuel Efficiency')

The main differences:

Column names are strings: x='column' not x = column
Use \ or parentheses for line continuation
Import with from ggplotly import *