Aesthetics¶

Aesthetics map columns in your data to visual properties of the plot. They are specified using the aes() function.

Basic Aesthetics¶

Aesthetic	Description	Example
x	X-axis position	aes(x='column')
y	Y-axis position	aes(y='column')
color	Point/line color	aes(color='category')
fill	Fill color (bars, areas)	aes(fill='category')
size	Point/line size	aes(size='value')
shape	Point shape	aes(shape='category')
alpha	Transparency	aes(alpha='value')
group	Grouping variable	aes(group='category')

In [1]:

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

# Sample data
np.random.seed(42)
df = pd.DataFrame({
    'x': np.random.randn(100),
    'y': np.random.randn(100),
    'species': np.random.choice(['A', 'B', 'C'], 100),
    'value': np.random.rand(100) * 10
})

# Time series data
ts_df = pd.DataFrame({
    'date': pd.date_range('2024-01-01', periods=50, freq='D'),
    'value': np.cumsum(np.random.randn(50)) + 50
})
ts_df = ts_df.set_index('date')  # Set date as index, removes it from columns

# Bar data
bar_df = pd.DataFrame({
    'category': ['A', 'B', 'C', 'D'],
    'count': [25, 40, 35, 30]
})

# Line data with groups
line_df = pd.DataFrame({
    'x': np.tile(np.arange(10), 3),
    'y': np.random.randn(30),
    'id': np.repeat(['A', 'B', 'C'], 10)
})

import pandas as pd import numpy as np from ggplotly import * # Sample data np.random.seed(42) df = pd.DataFrame({ 'x': np.random.randn(100), 'y': np.random.randn(100), 'species': np.random.choice(['A', 'B', 'C'], 100), 'value': np.random.rand(100) * 10 }) # Time series data ts_df = pd.DataFrame({ 'date': pd.date_range('2024-01-01', periods=50, freq='D'), 'value': np.cumsum(np.random.randn(50)) + 50 }) ts_df = ts_df.set_index('date') # Set date as index, removes it from columns # Bar data bar_df = pd.DataFrame({ 'category': ['A', 'B', 'C', 'D'], 'count': [25, 40, 35, 30] }) # Line data with groups line_df = pd.DataFrame({ 'x': np.tile(np.arange(10), 3), 'y': np.random.randn(30), 'id': np.repeat(['A', 'B', 'C'], 10) })

Mapping vs Setting¶

There's an important distinction between mapping an aesthetic (data-driven) and setting it (fixed value).

Mapping (inside aes())¶

Values come from your data:

In [2]:

# Color varies by 'species' column
ggplot(df, aes(x='x', y='y', color='species')) + geom_point()

# Color varies by 'species' column ggplot(df, aes(x='x', y='y', color='species')) + geom_point()

Out[2]:

Setting (inside geom)¶

Fixed value for all points:

In [3]:

# All points are blue
ggplot(df, aes(x='x', y='y')) + geom_point(color='blue')

# All points are blue ggplot(df, aes(x='x', y='y')) + geom_point(color='blue')

Out[3]:

Index as Aesthetic¶

You can use the DataFrame index as an aesthetic value:

In [4]:

# Auto-populate: if x is omitted, index is used
ggplot(ts_df, aes(y='value')) + geom_line()

# Auto-populate: if x is omitted, index is used ggplot(ts_df, aes(y='value')) + geom_line()

Out[4]:

Color and Fill¶

color affects lines and point outlines. fill affects filled areas.

In [5]:

# Points: color = outline
ggplot(df, aes(x='x', y='y', color='species')) + geom_point(size=5)

# Points: color = outline ggplot(df, aes(x='x', y='y', color='species')) + geom_point(size=5)

Out[5]:

In [6]:

# Bars: fill = bar color
ggplot(bar_df, aes(x='category', y='count', fill='category')) + geom_bar(stat='identity')

# Bars: fill = bar color ggplot(bar_df, aes(x='category', y='count', fill='category')) + geom_bar(stat='identity')

Out[6]:

Grouping¶

The group aesthetic controls how data is grouped for geoms like geom_line:

In [7]:

# With group: separate line per group
ggplot(line_df, aes(x='x', y='y', group='id')) + geom_line()

# With group: separate line per group ggplot(line_df, aes(x='x', y='y', group='id')) + geom_line()

Out[7]:

In [8]:

# color automatically sets group
ggplot(line_df, aes(x='x', y='y', color='id')) + geom_line()

# color automatically sets group ggplot(line_df, aes(x='x', y='y', color='id')) + geom_line()

Out[8]:

Inheriting Aesthetics¶

Aesthetics set in ggplot() are inherited by all geoms:

In [9]:

# Both geoms use x='x', y='y', color='species'
(
    ggplot(line_df, aes(x='x', y='y', color='id'))
    + geom_point()
    + geom_line()
)

# Both geoms use x='x', y='y', color='species' ( ggplot(line_df, aes(x='x', y='y', color='id')) + geom_point() + geom_line() )

Out[9]:

Override in individual geoms:

In [10]:

(
    ggplot(df, aes(x='x', y='y'))
    + geom_point(aes(color='species'))  # Points colored by species
    + geom_smooth(color='gray')  # Smooth line is gray
)

( ggplot(df, aes(x='x', y='y')) + geom_point(aes(color='species')) # Points colored by species + geom_smooth(color='gray') # Smooth line is gray )

Out[10]:

Common Aesthetic Values¶

Colors¶

In [11]:

# Named colors
ggplot(df, aes(x='x', y='y')) + geom_point(color='steelblue', size=5)

# Named colors ggplot(df, aes(x='x', y='y')) + geom_point(color='steelblue', size=5)

Out[11]:

In [12]:

# Hex colors
ggplot(df, aes(x='x', y='y')) + geom_point(color='#FF5733', size=5)

# Hex colors ggplot(df, aes(x='x', y='y')) + geom_point(color='#FF5733', size=5)

Out[12]:

Shapes¶

In [13]:

# Plotly marker symbols
ggplot(df, aes(x='x', y='y')) + geom_point(shape='diamond', size=8)

# Plotly marker symbols ggplot(df, aes(x='x', y='y')) + geom_point(shape='diamond', size=8)

Out[13]:

In [14]:

ggplot(df, aes(x='x', y='y')) + geom_point(shape='triangle-up', size=8)

ggplot(df, aes(x='x', y='y')) + geom_point(shape='triangle-up', size=8)

Out[14]:

Line Types¶

In [15]:

ggplot(ts_df, aes(y='value')) + geom_line(linetype='dash')

ggplot(ts_df, aes(y='value')) + geom_line(linetype='dash')

Out[15]:

In [16]:

ggplot(ts_df, aes(y='value')) + geom_line(linetype='dot')

ggplot(ts_df, aes(y='value')) + geom_line(linetype='dot')

Out[16]: