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 Plot
import matplotlib.pyplot as pltimport seaborn as sns# Just import maidr package import maidr # Load the penguins datasetpenguins = sns.load_dataset("penguins")# Create a bar plot showing the average body mass of penguins by speciesplt.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)
Histogram
import matplotlib.pyplot as pltimport seaborn as snsimport maidr # Load the Iris datasetiris = sns.load_dataset("iris")# Select the petal lengthspetal_lengths = iris["petal_length"]# Plot a histogram of the petal lengthsplt.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)
Line Plot
import matplotlib.pyplot as pltimport seaborn as snsimport maidr # Load the 'tips' dataset from seaborntips = 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 plotplt.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)
Heat Map
import matplotlib.pyplot as pltimport seaborn as snsimport maidr # Load an example dataset from seabornglue = sns.load_dataset("glue").pivot(index="Model", columns="Task", values="Score")# Plot a heatmapplt.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
Note: Visual highlight feature has not been implemented in the box plot yet.
import matplotlib.pyplot as pltimport seaborn as snsfrom seaborn import load_datasetimport maidr # Load the iris datasetiris = load_dataset("iris")# Create the horizontal boxplothorz_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 plotmaidr.show(horz_box_plot)
Scatter Plot
import matplotlib.pyplot as pltimport seaborn as snsimport maidr # Create a scatter plotscatter_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)
* 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)
