Basic Charts¶

Fundamental chart types for everyday data visualization.

Scatter Plots¶

Basic Scatter¶

In [1]:

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

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

(ggplot(df, aes(x='x', y='y')) + geom_point())

import pandas as pd import numpy as np from ggplotly import * df = pd.DataFrame({ 'x': [1, 2, 3, 4, 5], 'y': [2, 4, 3, 5, 4] }) (ggplot(df, aes(x='x', y='y')) + geom_point())

Out[1]:

Scatter with Color Mapping¶

In [2]:

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

(ggplot(df, aes(x='x', y='y', color='category')) + geom_point())

df = pd.DataFrame({ 'x': np.random.randn(100), 'y': np.random.randn(100), 'category': np.random.choice(['A', 'B', 'C'], 100) }) (ggplot(df, aes(x='x', y='y', color='category')) + geom_point())

Out[2]:

Scatter with Size Mapping¶

In [3]:

df = pd.DataFrame({
    'x': np.random.randn(100),
    'y': np.random.randn(100),
    'size_var': np.random.rand(100) * 50
})

(ggplot(df, aes(x='x', y='y', size='size_var')) + geom_point(color='steelblue', alpha=0.6))

df = pd.DataFrame({ 'x': np.random.randn(100), 'y': np.random.randn(100), 'size_var': np.random.rand(100) * 50 }) (ggplot(df, aes(x='x', y='y', size='size_var')) + geom_point(color='steelblue', alpha=0.6))

Out[3]:

Multiple Aesthetics¶

In [4]:

df = pd.DataFrame({
    'x': np.random.randn(100),
    'y': np.random.randn(100),
    'category': np.random.choice(['A', 'B', 'C'], 100),
    'size_var': np.random.rand(100) * 50
})

(ggplot(df, aes(x='x', y='y', color='category', size='size_var')) + geom_point(alpha=0.7))

df = pd.DataFrame({ 'x': np.random.randn(100), 'y': np.random.randn(100), 'category': np.random.choice(['A', 'B', 'C'], 100), 'size_var': np.random.rand(100) * 50 }) (ggplot(df, aes(x='x', y='y', color='category', size='size_var')) + geom_point(alpha=0.7))

Out[4]:

Custom Point Styles¶

In [5]:

(ggplot(df, aes(x='x', y='y')) + geom_point(size=10, color='red', shape='diamond'))

(ggplot(df, aes(x='x', y='y')) + geom_point(size=10, color='red', shape='diamond'))

Out[5]:

Available shapes: 'circle', 'square', 'diamond', 'cross', 'x', 'triangle-up', 'triangle-down', 'star'

Line Charts¶

Basic Line¶

In [6]:

x = np.linspace(0, 10, 100)
df = pd.DataFrame({'x': x, 'y': np.sin(x)})

(ggplot(df, aes(x='x', y='y')) + geom_line(color='steelblue', size=2))

x = np.linspace(0, 10, 100) df = pd.DataFrame({'x': x, 'y': np.sin(x)}) (ggplot(df, aes(x='x', y='y')) + geom_line(color='steelblue', size=2))

Out[6]:

Multiple Lines¶

In [7]:

df = pd.DataFrame({
    'x': np.tile(np.linspace(0, 10, 50), 3),
    'y': np.concatenate([
        np.sin(np.linspace(0, 10, 50)),
        np.cos(np.linspace(0, 10, 50)),
        np.sin(np.linspace(0, 10, 50)) + np.cos(np.linspace(0, 10, 50))
    ]),
    'group': np.repeat(['sin', 'cos', 'sin+cos'], 50)
})

(ggplot(df, aes(x='x', y='y', color='group')) + geom_line(size=2))

df = pd.DataFrame({ 'x': np.tile(np.linspace(0, 10, 50), 3), 'y': np.concatenate([ np.sin(np.linspace(0, 10, 50)), np.cos(np.linspace(0, 10, 50)), np.sin(np.linspace(0, 10, 50)) + np.cos(np.linspace(0, 10, 50)) ]), 'group': np.repeat(['sin', 'cos', 'sin+cos'], 50) }) (ggplot(df, aes(x='x', y='y', color='group')) + geom_line(size=2))

Out[7]:

Line with Points¶

In [8]:

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))

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))

Out[8]:

Path Charts¶

geom_path connects points in data order (not sorted by x):

Spiral¶

In [9]:

import math

t_vals = [i * 4 * math.pi / 100 for i in range(100)]
spiral = pd.DataFrame({
    'x': [t * math.cos(t) for t in t_vals],
    'y': [t * math.sin(t) for t in t_vals],
})

(ggplot(spiral, aes(x='x', y='y'))
 + geom_path(color='steelblue', size=2)
 + labs(title='Spiral with geom_path'))

import math t_vals = [i * 4 * math.pi / 100 for i in range(100)] spiral = pd.DataFrame({ 'x': [t * math.cos(t) for t in t_vals], 'y': [t * math.sin(t) for t in t_vals], }) (ggplot(spiral, aes(x='x', y='y')) + geom_path(color='steelblue', size=2) + labs(title='Spiral with geom_path'))

Out[9]:

Star Shape¶

In [10]:

points = 5
outer_r, inner_r = 1, 0.4
star_x, star_y = [], []
for i in range(points * 2 + 1):
    angle = i * math.pi / points - math.pi / 2
    r = outer_r if i % 2 == 0 else inner_r
    star_x.append(r * math.cos(angle))
    star_y.append(r * math.sin(angle))

star = pd.DataFrame({'x': star_x, 'y': star_y})
(ggplot(star, aes(x='x', y='y')) + geom_path(color='gold', size=3))

points = 5 outer_r, inner_r = 1, 0.4 star_x, star_y = [], [] for i in range(points * 2 + 1): angle = i * math.pi / points - math.pi / 2 r = outer_r if i % 2 == 0 else inner_r star_x.append(r * math.cos(angle)) star_y.append(r * math.sin(angle)) star = pd.DataFrame({'x': star_x, 'y': star_y}) (ggplot(star, aes(x='x', y='y')) + geom_path(color='gold', size=3))

Out[10]:

Bar Charts¶

Count-Based Bar Chart¶

In [11]:

df = pd.DataFrame({'category': np.random.choice(['A', 'B', 'C', 'D', 'E'], 200)})
(ggplot(df, aes(x='category')) + geom_bar())

df = pd.DataFrame({'category': np.random.choice(['A', 'B', 'C', 'D', 'E'], 200)}) (ggplot(df, aes(x='category')) + geom_bar())

Out[11]:

Colored by Category¶

In [12]:

mpg = data('mpg')
(ggplot(mpg, aes(x='class', fill='class')) + geom_bar(alpha=0.8))

mpg = data('mpg') (ggplot(mpg, aes(x='class', fill='class')) + geom_bar(alpha=0.8))

Out[12]:

Stacked Bar Chart¶

In [13]:

(ggplot(mpg, aes(x='cyl', fill='drv')) + geom_bar())

(ggplot(mpg, aes(x='cyl', fill='drv')) + geom_bar())

Out[13]:

Dodged (Side-by-Side) Bar Chart¶

In [14]:

(ggplot(mpg, aes(x='cyl', fill='drv')) + geom_bar(position='dodge'))

(ggplot(mpg, aes(x='cyl', fill='drv')) + geom_bar(position='dodge'))

Out[14]:

Custom Dodge Width¶

In [15]:

(ggplot(mpg, aes(x='cyl', fill='drv'))
 + geom_bar(position=position_dodge(width=0.8)))

(ggplot(mpg, aes(x='cyl', fill='drv')) + geom_bar(position=position_dodge(width=0.8)))

Out[15]:

Column Charts¶

For pre-computed values, use geom_col:

In [16]:

df = pd.DataFrame({
    'category': ['A', 'B', 'C', 'D'],
    'value': [25, 40, 30, 55]
})

(ggplot(df, aes(x='category', y='value')) + geom_col(fill='steelblue'))

df = pd.DataFrame({ 'category': ['A', 'B', 'C', 'D'], 'value': [25, 40, 30, 55] }) (ggplot(df, aes(x='category', y='value')) + geom_col(fill='steelblue'))

Out[16]:

Grouped Column Chart¶

In [17]:

df = pd.DataFrame({
    'category': ['A', 'A', 'B', 'B', 'C', 'C'],
    'group': ['G1', 'G2'] * 3,
    'value': [10, 15, 20, 25, 15, 20]
})

(ggplot(df, aes(x='category', y='value', fill='group')) + geom_col(position='dodge'))

df = pd.DataFrame({ 'category': ['A', 'A', 'B', 'B', 'C', 'C'], 'group': ['G1', 'G2'] * 3, 'value': [10, 15, 20, 25, 15, 20] }) (ggplot(df, aes(x='category', y='value', fill='group')) + geom_col(position='dodge'))

Out[17]:

Histograms¶

Basic Histogram¶

In [18]:

df = pd.DataFrame({'x': np.random.randn(1000)})
(ggplot(df, aes(x='x')) + geom_histogram(fill='steelblue', alpha=0.7))

df = pd.DataFrame({'x': np.random.randn(1000)}) (ggplot(df, aes(x='x')) + geom_histogram(fill='steelblue', alpha=0.7))

Out[18]:

Custom Bins¶

In [19]:

(ggplot(df, aes(x='x')) + geom_histogram(bins=30, color='white', fill='#FF6B6B'))

(ggplot(df, aes(x='x')) + geom_histogram(bins=30, color='white', fill='#FF6B6B'))

Out[19]:

Overlapping Histograms¶

In [20]:

df = pd.DataFrame({
    'x': np.concatenate([np.random.normal(0, 1, 500), np.random.normal(2, 1.5, 500)]),
    'group': ['A'] * 500 + ['B'] * 500
})

(ggplot(df, aes(x='x', fill='group')) + geom_histogram(alpha=0.5, bins=30))

df = pd.DataFrame({ 'x': np.concatenate([np.random.normal(0, 1, 500), np.random.normal(2, 1.5, 500)]), 'group': ['A'] * 500 + ['B'] * 500 }) (ggplot(df, aes(x='x', fill='group')) + geom_histogram(alpha=0.5, bins=30))

Out[20]:

Box Plots¶

Basic Boxplot¶

In [21]:

df = pd.DataFrame({
    'category': np.repeat(['A', 'B', 'C', 'D'], 50),
    'value': np.random.randn(200) * np.tile([1, 2, 1.5, 0.8], 50) + np.tile([0, 1, -1, 2], 50)
})

(ggplot(df, aes(x='category', y='value')) + geom_boxplot())

df = pd.DataFrame({ 'category': np.repeat(['A', 'B', 'C', 'D'], 50), 'value': np.random.randn(200) * np.tile([1, 2, 1.5, 0.8], 50) + np.tile([0, 1, -1, 2], 50) }) (ggplot(df, aes(x='category', y='value')) + geom_boxplot())

Out[21]:

Colored Boxplot¶

In [22]:

(ggplot(df, aes(x='category', y='value', fill='category')) + geom_boxplot(alpha=0.7))

(ggplot(df, aes(x='category', y='value', fill='category')) + geom_boxplot(alpha=0.7))

Out[22]:

Violin Plots¶

In [23]:

(ggplot(df, aes(x='category', y='value', fill='category')) + geom_violin(alpha=0.6))

(ggplot(df, aes(x='category', y='value', fill='category')) + geom_violin(alpha=0.6))

Out[23]:

Area Charts¶

Simple Area¶

In [24]:

x = np.linspace(0, 10, 100)
df = pd.DataFrame({'x': x, 'y': np.sin(x) + 1.5})

(ggplot(df, aes(x='x', y='y')) + geom_area(fill='lightblue', alpha=0.7))

x = np.linspace(0, 10, 100) df = pd.DataFrame({'x': x, 'y': np.sin(x) + 1.5}) (ggplot(df, aes(x='x', y='y')) + geom_area(fill='lightblue', alpha=0.7))

Out[24]:

Stacked Area¶

In [25]:

df = pd.DataFrame({
    'x': np.tile(np.linspace(0, 10, 50), 3),
    'y': np.abs(np.concatenate([
        np.sin(np.linspace(0, 10, 50)),
        0.5 * np.cos(np.linspace(0, 10, 50)) + 0.5,
        0.3 * np.sin(2 * np.linspace(0, 10, 50)) + 0.3
    ])),
    'group': np.repeat(['A', 'B', 'C'], 50)
})

(ggplot(df, aes(x='x', y='y', fill='group')) + geom_area(alpha=0.6))

df = pd.DataFrame({ 'x': np.tile(np.linspace(0, 10, 50), 3), 'y': np.abs(np.concatenate([ np.sin(np.linspace(0, 10, 50)), 0.5 * np.cos(np.linspace(0, 10, 50)) + 0.5, 0.3 * np.sin(2 * np.linspace(0, 10, 50)) + 0.3 ])), 'group': np.repeat(['A', 'B', 'C'], 50) }) (ggplot(df, aes(x='x', y='y', fill='group')) + geom_area(alpha=0.6))

Out[25]:

Step Charts¶

In [26]:

x = np.linspace(0, 10, 20)
df = pd.DataFrame({'x': x, 'y': np.sin(x)})

(ggplot(df, aes(x='x', y='y')) + geom_step(color='blue', size=2))

x = np.linspace(0, 10, 20) df = pd.DataFrame({'x': x, 'y': np.sin(x)}) (ggplot(df, aes(x='x', y='y')) + geom_step(color='blue', size=2))

Out[26]:

Ribbon Charts¶

Confidence bands or ranges:

In [27]:

x = np.linspace(0, 10, 50)
y = np.sin(x)
df = pd.DataFrame({
    'x': x,
    'ymin': y - 0.3,
    'ymax': y + 0.3
})

(ggplot(df, aes(x='x', ymin='ymin', ymax='ymax')) + geom_ribbon(fill='steelblue', alpha=0.3))

x = np.linspace(0, 10, 50) y = np.sin(x) df = pd.DataFrame({ 'x': x, 'ymin': y - 0.3, 'ymax': y + 0.3 }) (ggplot(df, aes(x='x', ymin='ymin', ymax='ymax')) + geom_ribbon(fill='steelblue', alpha=0.3))

Out[27]:

Heatmaps¶

In [28]:

x = np.arange(10)
y = np.arange(10)
X, Y = np.meshgrid(x, y)
Z = np.sin(X / 2) * np.cos(Y / 2)

df = pd.DataFrame({
    'x': X.flatten(),
    'y': Y.flatten(),
    'z': Z.flatten()
})

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

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

Out[28]:

Text Labels¶

In [29]:

df = pd.DataFrame({
    'x': [1, 2, 3, 4],
    'y': [2, 4, 3, 5],
    'label': ['Point A', 'Point B', 'Point C', 'Point D']
})

(ggplot(df, aes(x='x', y='y'))
 + geom_point(size=10, color='steelblue')
 + geom_text(aes(label='label'), vjust=-1))

df = pd.DataFrame({ 'x': [1, 2, 3, 4], 'y': [2, 4, 3, 5], 'label': ['Point A', 'Point B', 'Point C', 'Point D'] }) (ggplot(df, aes(x='x', y='y')) + geom_point(size=10, color='steelblue') + geom_text(aes(label='label'), vjust=-1))

Out[29]:

Reference Lines¶

Horizontal and Vertical Lines¶

In [30]:

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

(ggplot(df, aes(x='x', y='y'))
 + geom_point()
 + geom_hline(data=0, color='red', linetype='dash')
 + geom_vline(data=0, color='blue', linetype='dash'))

df = pd.DataFrame({'x': np.random.randn(100), 'y': np.random.randn(100)}) (ggplot(df, aes(x='x', y='y')) + geom_point() + geom_hline(data=0, color='red', linetype='dash') + geom_vline(data=0, color='blue', linetype='dash'))

Out[30]:

Slope/Intercept Lines¶

In [31]:

df = pd.DataFrame({'x': range(10), 'y': [i * 2 + 1 for i in range(10)]})

(ggplot(df, aes(x='x', y='y'))
 + geom_point(size=8)
 + geom_abline(slope=2, intercept=1, color='red', linetype='dash')
 + geom_abline(slope=1.5, intercept=3, color='blue'))

df = pd.DataFrame({'x': range(10), 'y': [i * 2 + 1 for i in range(10)]}) (ggplot(df, aes(x='x', y='y')) + geom_point(size=8) + geom_abline(slope=2, intercept=1, color='red', linetype='dash') + geom_abline(slope=1.5, intercept=3, color='blue'))

Out[31]:

Jittered Points¶

Avoid overplotting for categorical x-axes:

In [32]:

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

# Without jitter - points overlap
(ggplot(df, aes(x='category', y='value'))
 + geom_point(alpha=0.5)
 + labs(title='Without Jitter'))

df = pd.DataFrame({ 'category': np.repeat(['A', 'B', 'C'], 50), 'value': np.random.randn(150) }) # Without jitter - points overlap (ggplot(df, aes(x='category', y='value')) + geom_point(alpha=0.5) + labs(title='Without Jitter'))

Out[32]:

In [33]:

# With jitter - points spread out
(ggplot(df, aes(x='category', y='value'))
 + geom_jitter(width=0.2, alpha=0.5)
 + labs(title='With Jitter'))

# With jitter - points spread out (ggplot(df, aes(x='category', y='value')) + geom_jitter(width=0.2, alpha=0.5) + labs(title='With Jitter'))

Out[33]:

Rug Plots¶

Add marginal tick marks:

In [34]:

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

(ggplot(df, aes(x='x', y='y'))
 + geom_point(alpha=0.5)
 + geom_rug(sides='bl', alpha=0.3)
 + labs(title='Scatter with Marginal Rugs'))

df = pd.DataFrame({'x': np.random.randn(100), 'y': np.random.randn(100)}) (ggplot(df, aes(x='x', y='y')) + geom_point(alpha=0.5) + geom_rug(sides='bl', alpha=0.3) + labs(title='Scatter with Marginal Rugs'))

Out[34]:

Point Borders (stroke)¶

Add borders to points with the stroke parameter:

In [35]:

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

# Points with colored borders
(ggplot(df, aes(x='x', y='y', color='category'))
 + geom_point(size=15, stroke=2)
 + labs(title='Points with Borders (stroke=2)'))

df = pd.DataFrame({ 'x': [1, 2, 3, 4, 5], 'y': [2, 4, 3, 5, 4], 'category': ['A', 'B', 'A', 'B', 'A'] }) # Points with colored borders (ggplot(df, aes(x='x', y='y', color='category')) + geom_point(size=15, stroke=2) + labs(title='Points with Borders (stroke=2)'))

Out[35]:

Rectangles (geom_rect)¶

Highlight regions with rectangles:

In [36]:

# Data for scatter plot
scatter_df = pd.DataFrame({
    'x': np.random.randn(100),
    'y': np.random.randn(100)
})

# Rectangle to highlight a region
rect_df = pd.DataFrame({
    'xmin': [-1], 'xmax': [1],
    'ymin': [-1], 'ymax': [1]
})

(ggplot(scatter_df, aes(x='x', y='y'))
 + geom_rect(data=rect_df, mapping=aes(xmin='xmin', xmax='xmax', ymin='ymin', ymax='ymax'),
             fill='yellow', alpha=0.3)
 + geom_point(alpha=0.6)
 + labs(title='Scatter with Highlighted Region'))

# Data for scatter plot scatter_df = pd.DataFrame({ 'x': np.random.randn(100), 'y': np.random.randn(100) }) # Rectangle to highlight a region rect_df = pd.DataFrame({ 'xmin': [-1], 'xmax': [1], 'ymin': [-1], 'ymax': [1] }) (ggplot(scatter_df, aes(x='x', y='y')) + geom_rect(data=rect_df, mapping=aes(xmin='xmin', xmax='xmax', ymin='ymin', ymax='ymax'), fill='yellow', alpha=0.3) + geom_point(alpha=0.6) + labs(title='Scatter with Highlighted Region'))

Out[36]:

Labels with Background (geom_label)¶

Text labels with a background box for better readability:

In [37]:

df = pd.DataFrame({
    'x': [1, 2, 3, 4],
    'y': [2, 4, 3, 5],
    'label': ['Alpha', 'Beta', 'Gamma', 'Delta']
})

(ggplot(df, aes(x='x', y='y'))
 + geom_point(size=10, color='steelblue')
 + geom_label(aes(label='label'), fill='white', alpha=0.8)
 + labs(title='Points with Background Labels'))

df = pd.DataFrame({ 'x': [1, 2, 3, 4], 'y': [2, 4, 3, 5], 'label': ['Alpha', 'Beta', 'Gamma', 'Delta'] }) (ggplot(df, aes(x='x', y='y')) + geom_point(size=10, color='steelblue') + geom_label(aes(label='label'), fill='white', alpha=0.8) + labs(title='Points with Background Labels'))

Out[37]:

Segments with Arrows¶

Add arrows to line segments:

In [38]:

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

(ggplot(df, aes(x='x', y='y', xend='xend', yend='yend'))
 + geom_segment(arrow=True, arrow_size=15, color='steelblue', size=2)
 + labs(title='Segments with Arrows'))

df = pd.DataFrame({ 'x': [1, 2, 3], 'y': [1, 2, 1], 'xend': [2, 3, 4], 'yend': [2, 1, 2] }) (ggplot(df, aes(x='x', y='y', xend='xend', yend='yend')) + geom_segment(arrow=True, arrow_size=15, color='steelblue', size=2) + labs(title='Segments with Arrows'))

Out[38]:

Fixed Aspect Ratio (coord_fixed)¶

Ensure equal scaling on both axes:

In [39]:

# Circle should appear as a circle, not an ellipse
theta = np.linspace(0, 2*np.pi, 100)
circle = pd.DataFrame({
    'x': np.cos(theta),
    'y': np.sin(theta)
})

(ggplot(circle, aes(x='x', y='y'))
 + geom_path(size=2)
 + coord_fixed(ratio=1)
 + labs(title='Circle with coord_fixed(ratio=1)'))

# Circle should appear as a circle, not an ellipse theta = np.linspace(0, 2*np.pi, 100) circle = pd.DataFrame({ 'x': np.cos(theta), 'y': np.sin(theta) }) (ggplot(circle, aes(x='x', y='y')) + geom_path(size=2) + coord_fixed(ratio=1) + labs(title='Circle with coord_fixed(ratio=1)'))

Out[39]:

Reversed Axes¶

Flip axis direction with scale_x_reverse() or scale_y_reverse():

In [40]:

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

# Reversed y-axis (useful for rankings, depth charts)
(ggplot(df, aes(x='x', y='y'))
 + geom_line(size=2)
 + geom_point(size=10)
 + scale_y_reverse()
 + labs(title='Reversed Y-Axis', y='Rank (1 = best)'))

df = pd.DataFrame({'x': range(1, 6), 'y': [2, 4, 3, 5, 4]}) # Reversed y-axis (useful for rankings, depth charts) (ggplot(df, aes(x='x', y='y')) + geom_line(size=2) + geom_point(size=10) + scale_y_reverse() + labs(title='Reversed Y-Axis', y='Rank (1 = best)'))

Out[40]:

LaTeX Labels (parse)¶

Render mathematical notation with parse=True:

In [41]:

df = pd.DataFrame({
    'x': [1, 2, 3],
    'y': [1, 4, 9],
    'label': [r'$x^2$', r'$\sqrt{x}$', r'$\frac{1}{x}$']
})

(ggplot(df, aes(x='x', y='y'))
 + geom_point(size=15)
 + geom_text(aes(label='label'), parse=True, vjust=-1.5, size=14)
 + labs(title='Mathematical Labels with LaTeX'))

df = pd.DataFrame({ 'x': [1, 2, 3], 'y': [1, 4, 9], 'label': [r'$x^2$', r'$\sqrt{x}$', r'$\frac{1}{x}$'] }) (ggplot(df, aes(x='x', y='y')) + geom_point(size=15) + geom_text(aes(label='label'), parse=True, vjust=-1.5, size=14) + labs(title='Mathematical Labels with LaTeX'))

Out[41]: