Scales¶

Scales control how data values are mapped to visual properties and how axes are displayed.

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.rand(100) * 100,
    'y': np.random.rand(100) * 100,
    'category': 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=100, freq='D'),
    'value': np.cumsum(np.random.randn(100)) + 50
})

# Log scale data
log_df = pd.DataFrame({
    'x': np.logspace(0, 3, 50),
    'y': np.logspace(0, 3, 50) * np.random.uniform(0.5, 2, 50)
})

import pandas as pd import numpy as np from ggplotly import * # Sample data np.random.seed(42) df = pd.DataFrame({ 'x': np.random.rand(100) * 100, 'y': np.random.rand(100) * 100, 'category': 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=100, freq='D'), 'value': np.cumsum(np.random.randn(100)) + 50 }) # Log scale data log_df = pd.DataFrame({ 'x': np.logspace(0, 3, 50), 'y': np.logspace(0, 3, 50) * np.random.uniform(0.5, 2, 50) })

Axis Scales¶

Continuous Axes¶

In [2]:

# Basic continuous scale with limits
ggplot(df, aes(x='x', y='y')) + geom_point() + scale_x_continuous(limits=(0, 100))

# Basic continuous scale with limits ggplot(df, aes(x='x', y='y')) + geom_point() + scale_x_continuous(limits=(0, 100))

Out[2]:

Log Scales¶

In [3]:

ggplot(log_df, aes(x='x', y='y')) + geom_point() + scale_x_log10() + scale_y_log10()

ggplot(log_df, aes(x='x', y='y')) + geom_point() + scale_x_log10() + scale_y_log10()

Out[3]:

Date/Time Axes¶

In [4]:

# Date axis
ggplot(ts_df, aes(x='date', y='value')) + geom_line() + scale_x_date()

# Date axis ggplot(ts_df, aes(x='date', y='value')) + geom_line() + scale_x_date()

Out[4]:

Interactive Scales¶

In [5]:

# Range slider for zooming
ggplot(ts_df, aes(x='date', y='value')) + geom_line() + scale_x_rangeslider()

# Range slider for zooming ggplot(ts_df, aes(x='date', y='value')) + geom_line() + scale_x_rangeslider()

Out[5]:

Color Scales¶

Manual Colors¶

In [6]:

ggplot(df, aes(x='x', y='y', color='category')) + geom_point(size=5) + \
    scale_color_manual(values=['red', 'blue', 'green'])

ggplot(df, aes(x='x', y='y', color='category')) + geom_point(size=5) + \ scale_color_manual(values=['red', 'blue', 'green'])

Out[6]:

In [7]:

# With named mapping
ggplot(df, aes(x='x', y='y', color='category')) + geom_point(size=5) + \
    scale_color_manual(values={'A': 'red', 'B': 'blue', 'C': 'green'})

# With named mapping ggplot(df, aes(x='x', y='y', color='category')) + geom_point(size=5) + \ scale_color_manual(values={'A': 'red', 'B': 'blue', 'C': 'green'})

Out[7]:

Color Gradients¶

In [8]:

# Two-color gradient
ggplot(df, aes(x='x', y='y', color='value')) + geom_point(size=5) + \
    scale_color_gradient(low='blue', high='red')

# Two-color gradient ggplot(df, aes(x='x', y='y', color='value')) + geom_point(size=5) + \ scale_color_gradient(low='blue', high='red')

Out[8]:

ColorBrewer Palettes¶

In [9]:

# Qualitative palette
ggplot(df, aes(x='x', y='y', color='category')) + geom_point(size=5) + \
    scale_color_brewer(palette='Set1')

# Qualitative palette ggplot(df, aes(x='x', y='y', color='category')) + geom_point(size=5) + \ scale_color_brewer(palette='Set1')

Out[9]:

Available palettes:

• Sequential: Blues, Greens, Oranges, Reds, Purples, Greys, BuGn, BuPu, GnBu, OrRd, PuBu, PuRd, RdPu, YlGn, YlGnBu, YlOrBr, YlOrRd
• Diverging: BrBG, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, Spectral
• Qualitative: Accent, Dark2, Paired, Pastel1, Pastel2, Set1, Set2, Set3

Viridis Palette¶

In [10]:

# Viridis for heatmaps
x = np.arange(10)
y = np.arange(10)
X, Y = np.meshgrid(x, y)
Z = np.sin(X / 2) * np.cos(Y / 2)
tile_df = pd.DataFrame({'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()})

ggplot(tile_df, aes(x='x', y='y', fill='z')) + geom_tile() + scale_fill_viridis_c()

# Viridis for heatmaps x = np.arange(10) y = np.arange(10) X, Y = np.meshgrid(x, y) Z = np.sin(X / 2) * np.cos(Y / 2) tile_df = pd.DataFrame({'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()}) ggplot(tile_df, aes(x='x', y='y', fill='z')) + geom_tile() + scale_fill_viridis_c()

Out[10]:

Fill Scales¶

In [11]:

# Fill scales for bars
bar_df = pd.DataFrame({'category': ['A', 'B', 'C'], 'value': [25, 40, 35]})
ggplot(bar_df, aes(x='category', y='value', fill='category')) + geom_col() + \
    scale_fill_brewer(palette='Set2')

# Fill scales for bars bar_df = pd.DataFrame({'category': ['A', 'B', 'C'], 'value': [25, 40, 35]}) ggplot(bar_df, aes(x='category', y='value', fill='category')) + geom_col() + \ scale_fill_brewer(palette='Set2')

Out[11]:

Shape Scale¶

In [12]:

ggplot(df, aes(x='x', y='y', shape='category')) + geom_point(size=8) + \
    scale_shape_manual(values=['circle', 'square', 'triangle-up'])

ggplot(df, aes(x='x', y='y', shape='category')) + geom_point(size=8) + \ scale_shape_manual(values=['circle', 'square', 'triangle-up'])

Out[12]:

Size Scale¶

In [13]:

ggplot(df, aes(x='x', y='y', size='value')) + geom_point() + \
    scale_size(range=(1, 15))

ggplot(df, aes(x='x', y='y', size='value')) + geom_point() + \ scale_size(range=(1, 15))

Out[13]:

Axis Limits¶

Quick ways to set axis limits:

In [14]:

# Using xlim/ylim
ggplot(df, aes(x='x', y='y')) + geom_point() + xlim(0, 50) + ylim(0, 50)

# Using xlim/ylim ggplot(df, aes(x='x', y='y')) + geom_point() + xlim(0, 50) + ylim(0, 50)

Out[14]:

In [15]:

# Using coord_cartesian (doesn't clip data, just zooms)
ggplot(df, aes(x='x', y='y')) + geom_point() + coord_cartesian(xlim=(20, 80), ylim=(20, 80))

# Using coord_cartesian (doesn't clip data, just zooms) ggplot(df, aes(x='x', y='y')) + geom_point() + coord_cartesian(xlim=(20, 80), ylim=(20, 80))

Out[15]:

Scale Reference¶

Scale	Description
scale_x_continuous	Continuous x-axis
scale_y_continuous	Continuous y-axis
scale_x_log10	Log10 x-axis
scale_y_log10	Log10 y-axis
scale_x_date	Date x-axis
scale_x_datetime	DateTime x-axis
scale_x_rangeslider	Interactive range slider
scale_x_rangeselector	Range selector buttons
scale_color_manual	Manual color mapping
scale_color_gradient	2-color gradient
scale_color_brewer	ColorBrewer palettes
scale_fill_manual	Manual fill mapping
scale_fill_gradient	Fill gradient
scale_fill_brewer	ColorBrewer fill
scale_fill_viridis_c	Viridis fill
scale_shape_manual	Manual shape mapping
scale_size	Size scaling