Example Gallery

Making accessible data representation with maidr is easy and straightforward. If you already have data visualization code using matplotlib or seaborn, you can make your plots accessible with maidr in just a few lines of code.

Simply import the maidr package and use the maidr.show() function to display your plots. maidr will automatically generate accessible versions of your plots in your default browser. You can then interact with the accessible versions using keyboard shortcuts (refer to Table 1).

Bar Plots

Simple Bar Plot

import matplotlib.pyplot as plt
import seaborn as sns

# Just import maidr package 
import maidr  


# Load the penguins dataset
penguins = sns.load_dataset("penguins")

# Create a bar plot showing the average body mass of penguins by species
plt.figure(figsize=(6, 6))

# Assign the plot to a variable 
bar_plot = sns.barplot(  
    x="species", y="body_mass_g", data=penguins, errorbar="sd", palette="Blues_d"
)
plt.title("Average Body Mass of Penguins by Species")
plt.xlabel("Species")
plt.ylabel("Body Mass (g)")

# plt.show()

# Use maidr.show() to display your plot 
maidr.show(bar_plot)

Count Plots

import matplotlib.pyplot as plt
import seaborn as sns

import maidr 

# Load the Titanic dataset
titanic = sns.load_dataset("titanic")

# Create a count plot
plt.figure(figsize=(6, 6))
count_plot = sns.countplot(x="class", data=titanic, palette="viridis") 

plt.title("Passenger Class Distribution on the Titanic")
plt.xlabel("Passenger Class")
plt.ylabel("Count")

# plt.show()
maidr.show(count_plot)

Vertical Stacked Bar Plot

import matplotlib.pyplot as plt
import numpy as np

import maidr 

species = (
    "Adelie",
    "Chinstrap",
    "Gentoo",
)
weight_counts = {
    "Below": np.array([70, 31, 58]),
    "Above": np.array([82, 37, 66]),
}
width = 0.5

fig, ax = plt.subplots()

bottom = np.zeros(3)

for boolean, weight_count in weight_counts.items():
    p = ax.bar(species, weight_count, width, label=boolean, bottom=bottom) 
    bottom += weight_count

ax.set_xlabel("Species of Penguins")
ax.set_ylabel("Average Body Mass")

ax.set_title("Number of penguins with above average body mass")
ax.legend(loc="upper right")

maidr.show(p)

Side-By-Side Dodged Bar Plot

import matplotlib.pyplot as plt
import numpy as np

import maidr 

species: tuple[str, str, str] = (
    "Adelie",
    "Chinstrap",
    "Gentoo",
)
weight_counts: dict[str, np.ndarray] = {
    "Below": np.array([70, 31, 58]),
    "Above": np.array([82, 37, 66]),
}

x: np.ndarray = np.arange(len(species))
total_groups: int = len(weight_counts)
width: float = 0.35

fig, ax = plt.subplots() 

offsets: list[float] = [(-width / 2) + i * width for i in range(total_groups)]

for offset, (category, counts) in zip(offsets, weight_counts.items()):
    positions = x + offset
    p = ax.bar(positions, counts, width, label=category) 

# Set x-axis labels and title
ax.set_xticks(x)
ax.set_xticklabels(species)
ax.set_xlabel("Species")
ax.set_title("Dodged Bar Plot: Penguin Weight Counts")
ax.legend(loc="upper right")

# Show plot using maidr.show
maidr.show(p)

Histogram

import matplotlib.pyplot as plt
import seaborn as sns

import maidr 


# Load the Iris dataset
iris = sns.load_dataset("iris")

# Select the petal lengths
petal_lengths = iris["petal_length"]

# Plot a histogram of the petal lengths
plt.figure(figsize=(6, 6))

hist_plot = sns.histplot(petal_lengths, kde=True, color="blue", binwidth=0.5) 

plt.title("Petal Lengths in Iris Dataset")
plt.xlabel("Petal Length (cm)")
plt.ylabel("Frequency")

# plt.show()
maidr.show(hist_plot)

KDE (Kernel Density Estimation) Plots

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

import maidr 

# Generate sample data
np.random.seed(42)
data = np.random.randn(500)

# Create a KDE plot
plt.figure(figsize=(6, 6))
kde_plot = sns.kdeplot(data, fill=True, color="blue") 

plt.title("KDE Plot of Random Data")
plt.xlabel("Value")
plt.ylabel("Density")

# plt.show()
maidr.show(kde_plot)

Line Plots

Single Line Plot

import matplotlib.pyplot as plt
import seaborn as sns

import maidr 


# Load the 'tips' dataset from seaborn
tips = sns.load_dataset("tips") 

# Choose a specific subset of the dataset (e.g., data for 'Thursday')
subset_data = tips[tips["day"] == "Thur"]

# Create a line plot
plt.figure(figsize=(6, 6))
line_plot = sns.lineplot( 
    data=subset_data,
    x="total_bill",
    y="tip",
    markers=True,
    style="day",
    legend=False,
)
plt.title("Tips vs Total Bill (Thursday)")
plt.xlabel("Total Bill")
plt.ylabel("Tip")

# plt.show()
maidr.show(line_plot)

Multiline Plot

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

import maidr 
# Create sample data points
x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
y1 = np.array([2, 4, 1, 5, 3, 7, 6, 8])
y2 = np.array([1, 3, 5, 2, 4, 6, 8, 7])
y3 = np.array([3, 1, 4, 6, 5, 2, 4, 5])

# Convert to pandas DataFrame for seaborn
data = pd.DataFrame(
    {
        "x": np.tile(x, 3),
        "y": np.concatenate([y1, y2, y3]),
        "series": np.repeat(["Series 1", "Series 2", "Series 3"], len(x)),
    }
)

# Create the plot
plt.figure(figsize=(6, 6))

# Use seaborn lineplot for multiple lines
lineplot = sns.lineplot(
    x="x", y="y", hue="series", style="series", markers=True, dashes=True, data=data
)

# Customize the plot
plt.title("Seaborn Multiline Plot")
plt.xlabel("X values")
plt.ylabel("Y values")

# Display the plot using maidr
maidr.show(lineplot)

Heat Map

import matplotlib.pyplot as plt
import seaborn as sns

import maidr 

# Load an example dataset from seaborn
glue = sns.load_dataset("glue").pivot(index="Model", columns="Task", values="Score")

# Plot a heatmap
plt.figure(figsize=(8, 8))
heatmap = sns.heatmap(glue, annot=True, fill_label="Score") 
plt.title("Model Scores by Task")

# Show the plot
# plt.show()
maidr.show(heatmap)

Box Plot

import matplotlib.pyplot as plt
import seaborn as sns
from seaborn import load_dataset

import maidr 

# Load the iris dataset
iris = load_dataset("iris")

# Create the horizontal boxplot
horz_box_plot = sns.boxplot(x="petal_length", y="species", data=iris, orient="h") 
plt.ylabel("Species")
plt.xlabel("Petal Length")
plt.title("Petal Length by Species from Iris Dataset")
# plt.show()

# Show the plot
maidr.show(horz_box_plot)

Scatter Plot

import matplotlib.pyplot as plt
import seaborn as sns

import maidr 

# Create a scatter plot
scatter_plot = sns.scatterplot( 
    data=iris, x="sepal_length", y="sepal_width", hue="species"
)

# Adding title and labels (optional)
plt.title("Iris Sepal Length vs Sepal Width")
plt.xlabel("Sepal Length")
plt.ylabel("Sepal Width")

# Show the plot
# plt.show()
maidr.show(scatter_plot)

Regression Plots

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

import maidr 

# Generate sample data
np.random.seed(42)
x = np.linspace(0, 10, 50)
y = 2 * x + 1 + np.random.normal(0, 2, 50)

# Create a regression plot
plt.figure(figsize=(6, 6))
reg_plot = sns.regplot(
    x=x,
    y=y,
    scatter_kws={"s": 50, "alpha": 0.7},
    line_kws={"color": "red", "lw": 2},
) 

plt.title("Regression Plot with Fitted Line")
plt.xlabel("X values")
plt.ylabel("Y values")

# plt.show()
maidr.show(reg_plot)

Multi-Layered Plots

import matplotlib.pyplot as plt
import numpy as np
import maidr 

# Generate sample data
x = np.arange(5)
bar_data = np.array([3, 5, 2, 7, 3])
line_data = np.array([10, 8, 12, 14, 9])

# Create a figure and a set of subplots
fig, ax1 = plt.subplots(figsize=(8, 5)) 

# Create the bar chart on the first y-axis
ax1.bar(x, bar_data, color="skyblue", label="Bar Data")
ax1.set_xlabel("X values")
ax1.set_ylabel("Bar values", color="blue")
ax1.tick_params(axis="y", labelcolor="blue")

# Create a second y-axis sharing the same x-axis
ax2 = ax1.twinx()

# Create the line chart on the second y-axis
ax2.plot(x, line_data, color="red", marker="o", linestyle="-", label="Line Data")
ax2.set_xlabel("X values")
ax2.set_ylabel("Line values", color="red")
ax2.tick_params(axis="y", labelcolor="red")

# Add title and legend
plt.title("Multilayer Plot Example")

# Add legends for both axes
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")

# Adjust layout
fig.tight_layout()

maidr.show(fig)

Multi-Panel Plots (Multiple Subplots)

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

import maidr 

# Set the plotting style
sns.set_theme(style="whitegrid")

# Data for line plot
x_line = np.array([1, 2, 3, 4, 5, 6, 7, 8])
y_line = np.array([2, 4, 1, 5, 3, 7, 6, 8])
line_data = {"x": x_line, "y": y_line}

# Data for first bar plot
categories = ["A", "B", "C", "D", "E"]
values = np.random.rand(5) * 10
bar_data = {"categories": categories, "values": values}

# Data for second bar plot
categories_2 = ["A", "B", "C", "D", "E"]
values_2 = np.random.randn(5) * 100
bar_data_2 = {"categories": categories_2, "values": values_2}

# Create a figure with 3 subplots arranged vertically
fig, axs = plt.subplots(3, 1, figsize=(6, 12)) 

# First panel: Line plot using seaborn
sns.lineplot(x="x", y="y", data=line_data, color="blue", linewidth=2, ax=axs[0])
axs[0].set_title("Line Plot: Random Data")
axs[0].set_xlabel("X-axis")
axs[0].set_ylabel("Values")

# Second panel: Bar plot using seaborn
sns.barplot(
    x="categories", y="values", data=bar_data, color="green", alpha=0.7, ax=axs[1]
)
axs[1].set_title("Bar Plot: Random Values")
axs[1].set_xlabel("Categories")
axs[1].set_ylabel("Values")

# Third panel: Bar plot using seaborn
sns.barplot(
    x="categories", y="values", data=bar_data_2, color="blue", alpha=0.7, ax=axs[2]
)
axs[2].set_title("Bar Plot 2: Random Values")  # Fixed the typo in the title
axs[2].set_xlabel("Categories")
axs[2].set_ylabel("Values")

# Adjust layout to prevent overlap
plt.tight_layout()

# Display the figure
maidr.show(fig)

Facet Plot

import matplotlib.pyplot as plt
import numpy as np

import maidr 

categories = ["A", "B", "C", "D", "E"]

np.random.seed(42)
data_group1 = np.random.rand(5) * 10
data_group2 = np.random.rand(5) * 100
data_group3 = np.random.rand(5) * 36
data_group4 = np.random.rand(5) * 42

data_sets = [data_group1, data_group2, data_group3, data_group4]
condition_names = ["Group 1", "Group 2", "Group 3", "Group 4"]

fig, axs = plt.subplots(2, 2, figsize=(7, 7), sharey=True, sharex=True)
axs = axs.flatten()

all_data = np.concatenate(data_sets)
y_min, y_max = np.min(all_data) * 0.9, np.max(all_data) * 1.1

# Create a bar plot in each subplot
for i, (data, condition) in enumerate(zip(data_sets, condition_names)):
    axs[i].bar(categories, data, color=f"C{i}", alpha=0.7)
    axs[i].set_title(f"{condition}")
    axs[i].set_ylim(y_min, y_max)  # Set consistent y-axis limits

    # Add value labels on top of each bar
    for j, value in enumerate(data):
        axs[i].text(
            j,
            value + (y_max - y_min) * 0.02,
            f"{value:.1f}",
            ha="center",
            va="bottom",
            fontsize=9,
        )

# Add common labels
fig.text(0.5, 0.04, "Categories", ha="center", va="center", fontsize=14)
fig.text(
    0.06, 0.5, "Values", ha="center", va="center", rotation="vertical", fontsize=14
)

# Add a common title
fig.suptitle("Facet Plot: Bar Charts by Condition", fontsize=16)

# Adjust layout
plt.tight_layout(rect=(0.08, 0.08, 0.98, 0.95))

maidr.show(fig)

Candlestick Chart

import mplfinance as mpf
import pandas as pd

import maidr 

# Load the sample data
daily = pd.read_csv("../example/candle_stick/volcandat.csv", index_col=0, parse_dates=True)

# Create the candlestick chart with moving averages and volume
fig, ax = mpf.plot(
    daily,
    type="candle",
    volume=True,
    mav = (3,6,9),
    returnfig=True,
    ylabel="Price ($)",
    ylabel_lower="Volume",
    xlabel="Date",
    title="Stock Price with Volume",
)  
fig.tight_layout()

# Display with maidr
maidr.show(fig)

Reactive Dashboard

Shiny

Check out a reactive Shiny dashboard example with maidr and its source code is available on GitHub.

Streamlit

Check out this Streamlit dashboard with maidr, and its source code is available on GitHub.

* Note: `Streamlit` framework has some "Unlabeled 0 Button" which does not have to do with our maidr package. This issue needs to be addressed by the `Streamlit` team.

Interactive Computing (Jupyter Notebooks, Jupyter Labs, Google Colab)

Check out this interactive notebook in Google Colab.

Other Examples

We provide some example code for using py-maidr with matplotlib, seaborn, Jupyter Notebook, Quarto, Shiny, and Streamlit.

Keyboard Shortcuts and Controls

To interact with the plots using maidr, follow these steps:

Press the Tab key to focus on the plot element.
Use the arrow keys to move around the plot.
Press B to toggle Braille mode.
Press T to toggle Text mode.
Press S to toggle Sonification (tones) mode.
Press R to toggle Review mode.

Below is a detailed list of keyboard shortcuts for various functions:

Table 1: Keyboard Shortcuts

Function	Windows and Linux Key	Mac Key
Toggle Braille Mode	b	b
Toggle Text Mode	t	t
Toggle Sonification Mode	s	s
Toggle Review Mode	r	r
Move around plot	Arrow keys	Arrow keys
Go to the very left right up down	Ctrl + Arrow key	CMD + Arrow key
Select the first element	Ctrl + Home	CMD + Home
Select the last element	Ctrl + End	CMD + End
Repeat current sound	Space	Space
Auto-play outward in direction of arrow	Ctrl + Shift + Arrow key	CMD + Shift + Arrow key
Stop Auto-play	Ctrl	Ctrl
Auto-play speed up	Period (.)	Period (.)
Auto-play speed down	Comma (,)	Comma (,)
Auto-play speed reset	Slash (/)	Slash (/)
Check label for the title of current plot	l t	l t
Check label for the x axis of current plot	l x	l x
Check label for the y axis of current plot	l y	l y
Check label for the fill (z) axis of current plot	l f	l f
Switch to next layer	PageUp	PageUp
Switch to previous layer	PageDown	PageDown
Move around subplot list	Arrow keys	Arrow keys
Activate selected subplot in the list	Enter	Enter
Escape from current subplot to return to the subplot list	ESC	ESC
Open settings	Ctrl + comma (,)	CMD + comma (,)
Open Chat View	Question (?)	Question (?)
Open keyboard help	Ctrl + Slash (/)	CMD + Slash (/)

Demo Video

Bug Report

If you encounter a bug, have usage questions, or want to share ideas to make this package better, please feel free to file an issue.

Code of Conduct

Please note that the maidr project is released with a contributor code of conduct.
By participating in this project you agree to abide by its terms.

📄 License

maidr is licensed under the GPL3 license.

🏛️ Governance

This project is primarily maintained by JooYoung Seo and Saairam Venkatesh. Other authors may occasionally assist with some of these duties.