Theming & Styling¶

Customize the appearance of your plots with built-in themes and custom styling options.

Built-in Themes¶

theme_default¶

The default Plotly styling:

In [1]:

import pandas as pd
import numpy as np
from ggplotly import *

df = pd.DataFrame({'x': range(1, 6), 'y': [2, 4, 3, 5, 4]})
base = ggplot(df, aes(x='x', y='y')) + geom_line() + geom_point(size=10)

base  # Default theme

import pandas as pd import numpy as np from ggplotly import * df = pd.DataFrame({'x': range(1, 6), 'y': [2, 4, 3, 5, 4]}) base = ggplot(df, aes(x='x', y='y')) + geom_line() + geom_point(size=10) base # Default theme

Out[1]:

theme_minimal¶

Clean, minimal design:

In [2]:

(base + theme_minimal() + labs(title='theme_minimal'))

(base + theme_minimal() + labs(title='theme_minimal'))

Out[2]:

theme_classic¶

Classic look with axis lines, no gridlines:

In [3]:

(base + theme_classic() + labs(title='theme_classic'))

(base + theme_classic() + labs(title='theme_classic'))

Out[3]:

theme_dark¶

Dark background for high contrast:

In [4]:

(base + theme_dark() + labs(title='theme_dark'))

(base + theme_dark() + labs(title='theme_dark'))

Out[4]:

theme_ggplot2¶

Mimic R's ggplot2 default appearance:

In [5]:

(base + theme_ggplot2() + labs(title='theme_ggplot2'))

(base + theme_ggplot2() + labs(title='theme_ggplot2'))

Out[5]:

theme_bbc¶

BBC-style news graphics:

In [6]:

(base + theme_bbc() + labs(title='theme_bbc'))

(base + theme_bbc() + labs(title='theme_bbc'))

Out[6]:

theme_nytimes¶

New York Times style:

In [7]:

(base + theme_nytimes() + labs(title='theme_nytimes'))

(base + theme_nytimes() + labs(title='theme_nytimes'))

Out[7]:

Custom Theme Elements¶

theme() Function¶

Customize individual elements:

In [8]:

(ggplot(df, aes(x='x', y='y'))
 + geom_point(size=10)
 + theme(
     plot_background=element_rect(fill='#f0f0f0'),
     panel_background=element_rect(fill='white'),
     axis_text=element_text(size=12, color='darkblue'),
     axis_title=element_text(size=14, color='darkblue')
 )
 + labs(title='Custom Theme'))

(ggplot(df, aes(x='x', y='y')) + geom_point(size=10) + theme( plot_background=element_rect(fill='#f0f0f0'), panel_background=element_rect(fill='white'), axis_text=element_text(size=12, color='darkblue'), axis_title=element_text(size=14, color='darkblue') ) + labs(title='Custom Theme'))

Out[8]:

Grid Customization¶

In [9]:

(ggplot(df, aes(x='x', y='y'))
 + geom_point(size=10)
 + theme(
     panel_grid_major=element_line(color='lightgray', width=1, dash='dash'),
     panel_grid_minor=element_line(color='#f0f0f0', width=0.5),
     axis_line=element_line(color='black', width=2)
 )
 + labs(title='Custom Grid'))

(ggplot(df, aes(x='x', y='y')) + geom_point(size=10) + theme( panel_grid_major=element_line(color='lightgray', width=1, dash='dash'), panel_grid_minor=element_line(color='#f0f0f0', width=0.5), axis_line=element_line(color='black', width=2) ) + labs(title='Custom Grid'))

Out[9]:

Legend Customization¶

Legend Position¶

In [10]:

df = pd.DataFrame({
    'x': np.random.randn(100),
    'y': np.random.randn(100),
    'group': np.random.choice(['A', 'B'], 100)
})

# Position: 'top', 'bottom', 'left', 'right', 'none'
(ggplot(df, aes(x='x', y='y', color='group'))
 + geom_point()
 + theme(legend_position='top'))

df = pd.DataFrame({ 'x': np.random.randn(100), 'y': np.random.randn(100), 'group': np.random.choice(['A', 'B'], 100) }) # Position: 'top', 'bottom', 'left', 'right', 'none' (ggplot(df, aes(x='x', y='y', color='group')) + geom_point() + theme(legend_position='top'))

Out[10]:

Hide Legend¶

In [11]:

(ggplot(df, aes(x='x', y='y', color='group'))
 + geom_point()
 + theme(legend_position='none'))

(ggplot(df, aes(x='x', y='y', color='group')) + geom_point() + theme(legend_position='none'))

Out[11]:

Using guides() to Hide¶

In [12]:

(ggplot(df, aes(x='x', y='y', color='group'))
 + geom_point()
 + guides(color='none'))

(ggplot(df, aes(x='x', y='y', color='group')) + geom_point() + guides(color='none'))

Out[12]:

guide_colorbar¶

Customize continuous color legends:

In [13]:

df = pd.DataFrame({
    'x': np.random.rand(100),
    'y': np.random.rand(100),
    'z': np.random.rand(100) * 100
})

(ggplot(df, aes(x='x', y='y', color='z'))
 + geom_point(size=8)
 + scale_color_gradient(low='blue', high='red')
 + guides(color=guide_colorbar(title='Value', barwidth=20)))

df = pd.DataFrame({ 'x': np.random.rand(100), 'y': np.random.rand(100), 'z': np.random.rand(100) * 100 }) (ggplot(df, aes(x='x', y='y', color='z')) + geom_point(size=8) + scale_color_gradient(low='blue', high='red') + guides(color=guide_colorbar(title='Value', barwidth=20)))

Out[13]:

Labels and Titles¶

labs() Function¶

In [14]:

df = pd.DataFrame({'x': range(1, 6), 'y': [2, 4, 3, 5, 4]})

(ggplot(df, aes(x='x', y='y'))
 + geom_line()
 + geom_point(size=10)
 + labs(
     title='Main Title',
     subtitle='This is a subtitle',
     x='X-Axis Label',
     y='Y-Axis Label',
     caption='Data source: Example'
 ))

df = pd.DataFrame({'x': range(1, 6), 'y': [2, 4, 3, 5, 4]}) (ggplot(df, aes(x='x', y='y')) + geom_line() + geom_point(size=10) + labs( title='Main Title', subtitle='This is a subtitle', x='X-Axis Label', y='Y-Axis Label', caption='Data source: Example' ))

Out[14]:

ggtitle()¶

Quick title addition:

In [15]:

(ggplot(df, aes(x='x', y='y'))
 + geom_point(size=10)
 + ggtitle('Quick Title'))

(ggplot(df, aes(x='x', y='y')) + geom_point(size=10) + ggtitle('Quick Title'))

Out[15]:

Annotations¶

Text Annotations¶

In [16]:

np.random.seed(42)
df = pd.DataFrame({
    'x': np.random.randn(50),
    'y': np.random.randn(50)
})
df.loc[50] = [3, 3]  # Add outlier

(ggplot(df, aes(x='x', y='y'))
 + geom_point(size=6, alpha=0.6)
 + annotate('text', x=3, y=3.5, label='Outlier!', size=14, color='red')
 + annotate('rect', xmin=-1, xmax=1, ymin=-1, ymax=1, fill='lightblue', alpha=0.3)
 + annotate('text', x=0, y=0, label='Main cluster', size=12, color='blue'))

np.random.seed(42) df = pd.DataFrame({ 'x': np.random.randn(50), 'y': np.random.randn(50) }) df.loc[50] = [3, 3] # Add outlier (ggplot(df, aes(x='x', y='y')) + geom_point(size=6, alpha=0.6) + annotate('text', x=3, y=3.5, label='Outlier!', size=14, color='red') + annotate('rect', xmin=-1, xmax=1, ymin=-1, ymax=1, fill='lightblue', alpha=0.3) + annotate('text', x=0, y=0, label='Main cluster', size=12, color='blue'))

Out[16]:

Arrow Annotations¶

In [17]:

df = pd.DataFrame({
    'x': range(10),
    'y': [1, 3, 2, 5, 8, 6, 4, 3, 2, 1]
})

(ggplot(df, aes(x='x', y='y'))
 + geom_line(size=2, color='steelblue')
 + geom_point(size=8)
 + annotate('segment', x=6, y=9, xend=4, yend=8.2, arrow=True, color='red', size=2)
 + annotate('text', x=6, y=9.5, label='Peak value', size=12, color='red'))

df = pd.DataFrame({ 'x': range(10), 'y': [1, 3, 2, 5, 8, 6, 4, 3, 2, 1] }) (ggplot(df, aes(x='x', y='y')) + geom_line(size=2, color='steelblue') + geom_point(size=8) + annotate('segment', x=6, y=9, xend=4, yend=8.2, arrow=True, color='red', size=2) + annotate('text', x=6, y=9.5, label='Peak value', size=12, color='red'))

Out[17]:

Figure Size¶

ggsize()¶

Control output dimensions:

In [18]:

(ggplot(df, aes(x='x', y='y'))
 + geom_line()
 + geom_point(size=10)
 + ggsize(width=1000, height=400)
 + labs(title='Wide Plot'))

(ggplot(df, aes(x='x', y='y')) + geom_line() + geom_point(size=10) + ggsize(width=1000, height=400) + labs(title='Wide Plot'))

Out[18]:

Publication-Ready Examples¶

BBC Style¶

In [19]:

gapminder = pd.DataFrame({
    'year': [1952, 1962, 1972, 1982, 1992, 2002, 2007] * 2,
    'lifeExp': [36.3, 40.0, 43.5, 48.1, 52.0, 52.7, 54.1,
                68.4, 70.0, 71.0, 74.0, 77.0, 78.8, 78.2],
    'country': ['Malawi'] * 7 + ['United States'] * 7
})

(ggplot(gapminder, aes(x='year', y='lifeExp', color='country'))
 + geom_line(size=3)
 + scale_x_continuous(format='d')
 + theme_bbc()
 + labs(title='Life Expectancy Over Time',
        subtitle='Malawi vs United States'))

gapminder = pd.DataFrame({ 'year': [1952, 1962, 1972, 1982, 1992, 2002, 2007] * 2, 'lifeExp': [36.3, 40.0, 43.5, 48.1, 52.0, 52.7, 54.1, 68.4, 70.0, 71.0, 74.0, 77.0, 78.8, 78.2], 'country': ['Malawi'] * 7 + ['United States'] * 7 }) (ggplot(gapminder, aes(x='year', y='lifeExp', color='country')) + geom_line(size=3) + scale_x_continuous(format='d') + theme_bbc() + labs(title='Life Expectancy Over Time', subtitle='Malawi vs United States'))

Out[19]:

Minimal Academic Style¶

In [20]:

df = pd.DataFrame({
    'x': np.random.randn(100),
    'y': np.random.randn(100)
})

(ggplot(df, aes(x='x', y='y'))
 + geom_point(size=6)
 + geom_smooth(method='lm', color='red')
 + theme_minimal()
 + theme(
     axis_title=element_text(size=12),
     plot_title=element_text(size=14)
 )
 + labs(title='Scatter with Linear Fit',
        x='Independent Variable',
        y='Dependent Variable'))

df = pd.DataFrame({ 'x': np.random.randn(100), 'y': np.random.randn(100) }) (ggplot(df, aes(x='x', y='y')) + geom_point(size=6) + geom_smooth(method='lm', color='red') + theme_minimal() + theme( axis_title=element_text(size=12), plot_title=element_text(size=14) ) + labs(title='Scatter with Linear Fit', x='Independent Variable', y='Dependent Variable'))

Out[20]:

Dark Dashboard¶

In [21]:

df = pd.DataFrame({
    'x': np.random.randn(100),
    'y': np.random.randn(100),
    'group': np.random.choice(['A', 'B'], 100)
})

(ggplot(df, aes(x='x', y='y', color='group'))
 + geom_point(size=8)
 + theme_dark()
 + scale_color_manual(values=['#00ff00', '#ff00ff'])
 + labs(title='Dashboard Metric'))

df = pd.DataFrame({ 'x': np.random.randn(100), 'y': np.random.randn(100), 'group': np.random.choice(['A', 'B'], 100) }) (ggplot(df, aes(x='x', y='y', color='group')) + geom_point(size=8) + theme_dark() + scale_color_manual(values=['#00ff00', '#ff00ff']) + labs(title='Dashboard Metric'))

Out[21]:

Color Scales with Themes¶

Sequential Palettes¶

In [22]:

df = pd.DataFrame({
    'x': np.tile(np.arange(10), 10),
    'y': np.repeat(np.arange(10), 10),
    'z': np.random.randn(100)
})

(ggplot(df, aes(x='x', y='y', fill='z'))
 + geom_tile()
 + scale_fill_gradient(low='blue', high='red')
 + theme_minimal()
 + labs(title='Heatmap with Custom Gradient'))

df = pd.DataFrame({ 'x': np.tile(np.arange(10), 10), 'y': np.repeat(np.arange(10), 10), 'z': np.random.randn(100) }) (ggplot(df, aes(x='x', y='y', fill='z')) + geom_tile() + scale_fill_gradient(low='blue', high='red') + theme_minimal() + labs(title='Heatmap with Custom Gradient'))

Out[22]:

Viridis for Accessibility¶

In [23]:

(ggplot(df, aes(x='x', y='y', fill='z'))
 + geom_tile()
 + scale_fill_viridis_c()
 + theme_minimal()
 + labs(title='Colorblind-Friendly Heatmap'))

(ggplot(df, aes(x='x', y='y', fill='z')) + geom_tile() + scale_fill_viridis_c() + theme_minimal() + labs(title='Colorblind-Friendly Heatmap'))

Out[23]:

ColorBrewer Palettes¶

In [24]:

df = pd.DataFrame({
    'x': np.random.randn(150),
    'y': np.random.randn(150),
    'group': np.repeat(['A', 'B', 'C'], 50)
})

(ggplot(df, aes(x='x', y='y', color='group'))
 + geom_point(size=8)
 + scale_color_brewer(palette='Set2')
 + theme_minimal())

df = pd.DataFrame({ 'x': np.random.randn(150), 'y': np.random.randn(150), 'group': np.repeat(['A', 'B', 'C'], 50) }) (ggplot(df, aes(x='x', y='y', color='group')) + geom_point(size=8) + scale_color_brewer(palette='Set2') + theme_minimal())

Out[24]:

Theme Reference¶

Theme Element	Description
plot_background	Overall plot background
panel_background	Panel (data area) background
panel_grid_major	Major gridlines
panel_grid_minor	Minor gridlines
axis_line	Axis lines
axis_text	Axis tick labels
axis_title	Axis titles
axis_text_x	X-axis tick labels only
axis_text_y	Y-axis tick labels only
legend_position	Legend placement
legend_title	Legend title text
legend_text	Legend item text
plot_title	Main title
plot_subtitle	Subtitle
plot_caption	Caption text