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search.json

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     "title": "Welcome",
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-    "text": "Getting Started\nMaking 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.\nSimply 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).\nCheck more examples in the galleries.\n\nSample Bar Plot\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Just import maidr package \nimport maidr \n\n\n# Load the penguins dataset\npenguins = sns.load_dataset(\"penguins\")\n\n# Create a bar plot showing the average body mass of penguins by species\nplt.figure(figsize=(10, 6))\n\n# Assign the plot to a variable \nbar_plot = sns.barplot( \n    x=\"species\", y=\"body_mass_g\", data=penguins, errorbar=\"sd\", palette=\"Blues_d\"\n)\nplt.title(\"Average Body Mass of Penguins by Species\")\nplt.xlabel(\"Species\")\nplt.ylabel(\"Body Mass (g)\")\n\n# plt.show()\n\n# Use maidr.show() to display your plot \nmaidr.show(bar_plot)"
+    "text": "Getting Started\nMaking 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.\nSimply 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).\nCheck more examples in the galleries.\n\nSample Bar Plot\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Just import maidr package \nimport maidr \n\n\n# Load the penguins dataset\npenguins = sns.load_dataset(\"penguins\")\n\n# Create a bar plot showing the average body mass of penguins by species\nplt.figure(figsize=(6, 6))\n\n# Assign the plot to a variable \nbar_plot = sns.barplot( \n    x=\"species\", y=\"body_mass_g\", data=penguins, errorbar=\"sd\", palette=\"Blues_d\"\n)\nplt.title(\"Average Body Mass of Penguins by Species\")\nplt.xlabel(\"Species\")\nplt.ylabel(\"Body Mass (g)\")\n\n# plt.show()\n\n# Use maidr.show() to display your plot \nmaidr.show(bar_plot)"
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-    "text": "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.\nSimply 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).\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Just import maidr package \nimport maidr \n\n\n# Load the penguins dataset\npenguins = sns.load_dataset(\"penguins\")\n\n# Create a bar plot showing the average body mass of penguins by species\nplt.figure(figsize=(10, 6))\n\n# Assign the plot to a variable \nbar_plot = sns.barplot( \n    x=\"species\", y=\"body_mass_g\", data=penguins, errorbar=\"sd\", palette=\"Blues_d\"\n)\nplt.title(\"Average Body Mass of Penguins by Species\")\nplt.xlabel(\"Species\")\nplt.ylabel(\"Body Mass (g)\")\n\n# plt.show()\n\n# Use maidr.show() to display your plot \nmaidr.show(bar_plot) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport maidr \n\n\n# Load the Iris dataset\niris = sns.load_dataset(\"iris\")\n\n# Select the petal lengths\npetal_lengths = iris[\"petal_length\"]\n\n# Plot a histogram of the petal lengths\nplt.figure(figsize=(10, 6))\n\nhist_plot = sns.histplot(petal_lengths, kde=True, color=\"blue\", binwidth=0.5) \n\nplt.title(\"Petal Lengths in Iris Dataset\")\nplt.xlabel(\"Petal Length (cm)\")\nplt.ylabel(\"Frequency\")\n\n# plt.show()\nmaidr.show(hist_plot) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport maidr \n\n\n# Load the 'tips' dataset from seaborn\ntips = sns.load_dataset(\"tips\") \n\n# Choose a specific subset of the dataset (e.g., data for 'Thursday')\nsubset_data = tips[tips[\"day\"] == \"Thur\"]\n\n# Create a line plot\nplt.figure(figsize=(10, 6))\nline_plot = sns.lineplot(\n    data=subset_data,\n    x=\"total_bill\",\n    y=\"tip\",\n    markers=True,\n    style=\"day\",\n    legend=False,\n)\nplt.title(\"Tips vs Total Bill (Thursday)\")\nplt.xlabel(\"Total Bill\")\nplt.ylabel(\"Tip\")\n\n# plt.show()\nmaidr.show(line_plot) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# Load an example dataset from seaborn\nglue = sns.load_dataset(\"glue\").pivot(index=\"Model\", columns=\"Task\", values=\"Score\")\n\n# Plot a heatmap\nplt.figure(figsize=(10, 8))\nheatmap = sns.heatmap(glue, annot=True, fill_label=\"Score\")\nplt.title(\"Model Scores by Task\")\n\n# Show the plot\n# plt.show()\nmaidr.show(heatmap)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# Create a scatter plot\nscatter_plot = sns.scatterplot(\n    data=iris, x=\"sepal_length\", y=\"sepal_width\", hue=\"species\"\n)\n\n# Adding title and labels (optional)\nplt.title(\"Iris Sepal Length vs Sepal Width\")\nplt.xlabel(\"Sepal Length\")\nplt.ylabel(\"Sepal Width\")\n\n# Show the plot\n# plt.show()\nmaidr.show(scatter_plot)"
+    "text": "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.\nSimply 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).\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Just import maidr package \nimport maidr  \n\n\n# Load the penguins dataset\npenguins = sns.load_dataset(\"penguins\")\n\n# Create a bar plot showing the average body mass of penguins by species\nplt.figure(figsize=(6, 6))\n\n# Assign the plot to a variable \nbar_plot = sns.barplot(  \n    x=\"species\", y=\"body_mass_g\", data=penguins, errorbar=\"sd\", palette=\"Blues_d\"\n)\nplt.title(\"Average Body Mass of Penguins by Species\")\nplt.xlabel(\"Species\")\nplt.ylabel(\"Body Mass (g)\")\n\n# plt.show()\n\n# Use maidr.show() to display your plot \nmaidr.show(bar_plot)  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport maidr \n\n\n# Load the Iris dataset\niris = sns.load_dataset(\"iris\")\n\n# Select the petal lengths\npetal_lengths = iris[\"petal_length\"]\n\n# Plot a histogram of the petal lengths\nplt.figure(figsize=(6, 6))\n\nhist_plot = sns.histplot(petal_lengths, kde=True, color=\"blue\", binwidth=0.5) \n\nplt.title(\"Petal Lengths in Iris Dataset\")\nplt.xlabel(\"Petal Length (cm)\")\nplt.ylabel(\"Frequency\")\n\n# plt.show()\nmaidr.show(hist_plot) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport maidr \n\n\n# Load the 'tips' dataset from seaborn\ntips = sns.load_dataset(\"tips\") \n\n# Choose a specific subset of the dataset (e.g., data for 'Thursday')\nsubset_data = tips[tips[\"day\"] == \"Thur\"]\n\n# Create a line plot\nplt.figure(figsize=(6, 6))\nline_plot = sns.lineplot( \n    data=subset_data,\n    x=\"total_bill\",\n    y=\"tip\",\n    markers=True,\n    style=\"day\",\n    legend=False,\n)\nplt.title(\"Tips vs Total Bill (Thursday)\")\nplt.xlabel(\"Total Bill\")\nplt.ylabel(\"Tip\")\n\n# plt.show()\nmaidr.show(line_plot) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport maidr \n\n# Load an example dataset from seaborn\nglue = sns.load_dataset(\"glue\").pivot(index=\"Model\", columns=\"Task\", values=\"Score\")\n\n# Plot a heatmap\nplt.figure(figsize=(8, 8))\nheatmap = sns.heatmap(glue, annot=True, fill_label=\"Score\") \nplt.title(\"Model Scores by Task\")\n\n# Show the plot\n# plt.show()\nmaidr.show(heatmap) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport maidr \n\n# Create a scatter plot\nscatter_plot = sns.scatterplot( \n    data=iris, x=\"sepal_length\", y=\"sepal_width\", hue=\"species\"\n)\n\n# Adding title and labels (optional)\nplt.title(\"Iris Sepal Length vs Sepal Width\")\nplt.xlabel(\"Sepal Length\")\nplt.ylabel(\"Sepal Width\")\n\n# Show the plot\n# plt.show()\nmaidr.show(scatter_plot)"
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-    "text": "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.\nSimply 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).\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Just import maidr package \nimport maidr \n\n\n# Load the penguins dataset\npenguins = sns.load_dataset(\"penguins\")\n\n# Create a bar plot showing the average body mass of penguins by species\nplt.figure(figsize=(10, 6))\n\n# Assign the plot to a variable \nbar_plot = sns.barplot( \n    x=\"species\", y=\"body_mass_g\", data=penguins, errorbar=\"sd\", palette=\"Blues_d\"\n)\nplt.title(\"Average Body Mass of Penguins by Species\")\nplt.xlabel(\"Species\")\nplt.ylabel(\"Body Mass (g)\")\n\n# plt.show()\n\n# Use maidr.show() to display your plot \nmaidr.show(bar_plot) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport maidr \n\n\n# Load the Iris dataset\niris = sns.load_dataset(\"iris\")\n\n# Select the petal lengths\npetal_lengths = iris[\"petal_length\"]\n\n# Plot a histogram of the petal lengths\nplt.figure(figsize=(10, 6))\n\nhist_plot = sns.histplot(petal_lengths, kde=True, color=\"blue\", binwidth=0.5) \n\nplt.title(\"Petal Lengths in Iris Dataset\")\nplt.xlabel(\"Petal Length (cm)\")\nplt.ylabel(\"Frequency\")\n\n# plt.show()\nmaidr.show(hist_plot) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport maidr \n\n\n# Load the 'tips' dataset from seaborn\ntips = sns.load_dataset(\"tips\") \n\n# Choose a specific subset of the dataset (e.g., data for 'Thursday')\nsubset_data = tips[tips[\"day\"] == \"Thur\"]\n\n# Create a line plot\nplt.figure(figsize=(10, 6))\nline_plot = sns.lineplot(\n    data=subset_data,\n    x=\"total_bill\",\n    y=\"tip\",\n    markers=True,\n    style=\"day\",\n    legend=False,\n)\nplt.title(\"Tips vs Total Bill (Thursday)\")\nplt.xlabel(\"Total Bill\")\nplt.ylabel(\"Tip\")\n\n# plt.show()\nmaidr.show(line_plot) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# Load an example dataset from seaborn\nglue = sns.load_dataset(\"glue\").pivot(index=\"Model\", columns=\"Task\", values=\"Score\")\n\n# Plot a heatmap\nplt.figure(figsize=(10, 8))\nheatmap = sns.heatmap(glue, annot=True, fill_label=\"Score\")\nplt.title(\"Model Scores by Task\")\n\n# Show the plot\n# plt.show()\nmaidr.show(heatmap)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# Create a scatter plot\nscatter_plot = sns.scatterplot(\n    data=iris, x=\"sepal_length\", y=\"sepal_width\", hue=\"species\"\n)\n\n# Adding title and labels (optional)\nplt.title(\"Iris Sepal Length vs Sepal Width\")\nplt.xlabel(\"Sepal Length\")\nplt.ylabel(\"Sepal Width\")\n\n# Show the plot\n# plt.show()\nmaidr.show(scatter_plot)"
+    "text": "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.\nSimply 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).\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Just import maidr package \nimport maidr  \n\n\n# Load the penguins dataset\npenguins = sns.load_dataset(\"penguins\")\n\n# Create a bar plot showing the average body mass of penguins by species\nplt.figure(figsize=(6, 6))\n\n# Assign the plot to a variable \nbar_plot = sns.barplot(  \n    x=\"species\", y=\"body_mass_g\", data=penguins, errorbar=\"sd\", palette=\"Blues_d\"\n)\nplt.title(\"Average Body Mass of Penguins by Species\")\nplt.xlabel(\"Species\")\nplt.ylabel(\"Body Mass (g)\")\n\n# plt.show()\n\n# Use maidr.show() to display your plot \nmaidr.show(bar_plot)  \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport maidr \n\n\n# Load the Iris dataset\niris = sns.load_dataset(\"iris\")\n\n# Select the petal lengths\npetal_lengths = iris[\"petal_length\"]\n\n# Plot a histogram of the petal lengths\nplt.figure(figsize=(6, 6))\n\nhist_plot = sns.histplot(petal_lengths, kde=True, color=\"blue\", binwidth=0.5) \n\nplt.title(\"Petal Lengths in Iris Dataset\")\nplt.xlabel(\"Petal Length (cm)\")\nplt.ylabel(\"Frequency\")\n\n# plt.show()\nmaidr.show(hist_plot) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport maidr \n\n\n# Load the 'tips' dataset from seaborn\ntips = sns.load_dataset(\"tips\") \n\n# Choose a specific subset of the dataset (e.g., data for 'Thursday')\nsubset_data = tips[tips[\"day\"] == \"Thur\"]\n\n# Create a line plot\nplt.figure(figsize=(6, 6))\nline_plot = sns.lineplot( \n    data=subset_data,\n    x=\"total_bill\",\n    y=\"tip\",\n    markers=True,\n    style=\"day\",\n    legend=False,\n)\nplt.title(\"Tips vs Total Bill (Thursday)\")\nplt.xlabel(\"Total Bill\")\nplt.ylabel(\"Tip\")\n\n# plt.show()\nmaidr.show(line_plot) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport maidr \n\n# Load an example dataset from seaborn\nglue = sns.load_dataset(\"glue\").pivot(index=\"Model\", columns=\"Task\", values=\"Score\")\n\n# Plot a heatmap\nplt.figure(figsize=(8, 8))\nheatmap = sns.heatmap(glue, annot=True, fill_label=\"Score\") \nplt.title(\"Model Scores by Task\")\n\n# Show the plot\n# plt.show()\nmaidr.show(heatmap) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport maidr \n\n# Create a scatter plot\nscatter_plot = sns.scatterplot( \n    data=iris, x=\"sepal_length\", y=\"sepal_width\", hue=\"species\"\n)\n\n# Adding title and labels (optional)\nplt.title(\"Iris Sepal Length vs Sepal Width\")\nplt.xlabel(\"Sepal Length\")\nplt.ylabel(\"Sepal Width\")\n\n# Show the plot\n# plt.show()\nmaidr.show(scatter_plot)"
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