Demonstrating Seaborn Modin Interoperability

All the examples in this section are taken / adapted from https://seaborn.pydata.org/tutorial/introduction.html. Each visualization is created with a Modin and then pandas dataframe for comparison.

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import seaborn as sns
import modin.pandas as pd

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# Apply the default theme
sns.set_theme()

# Load an example dataset
pandas_tips = sns.load_dataset("tips")
modin_tips = pd.DataFrame(pandas_tips)

type(modin_tips)

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modin_tips.head()

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# Create a visualization with Modin df
sns.relplot(
    data=modin_tips,
    x="total_bill", y="tip", col="time", col_order=["Lunch", "Dinner"],
    hue="smoker", style="smoker", size="size",
)

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# Create a visualization with pandas df
sns.relplot(
    data=pandas_tips,
    x="total_bill", y="tip", col="time", col_order=["Lunch", "Dinner"],
    hue="smoker", style="smoker", size="size",
)

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# Create a visualization with Modin df
sns.scatterplot(data=modin_tips, x="total_bill", y="tip")
sns.rugplot(data=modin_tips, x="total_bill", y="tip")

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# Create a visualization with pandas df
sns.scatterplot(data=pandas_tips, x="total_bill", y="tip")
sns.rugplot(data=pandas_tips, x="total_bill", y="tip")

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# Create a visualization with Modin df
sns.lineplot(data=modin_tips, x="total_bill", y="tip")

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# Create a visualization with pandas df
sns.lineplot(data=pandas_tips, x="total_bill", y="tip")

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# Create a visualization with Modin df
sns.displot(data=modin_tips, x="total_bill", kde=True)

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# Create a visualization with pandas df
sns.displot(data=pandas_tips, x="total_bill", kde=True)

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# Create a visualization with Modin df
sns.histplot(data=modin_tips, x="total_bill", stat='frequency')

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# Create a visualization with pandas df
sns.histplot(data=pandas_tips, x="total_bill", stat='frequency')

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# Create a visualization with Modin df
sns.residplot(data=modin_tips, x="total_bill", y="tip")

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# Create a visualization with pandas df
sns.residplot(data=pandas_tips, x="total_bill", y="tip")

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# Create a visualization with Modin df 
sns.jointplot(data=modin_tips, x="total_bill", y="tip", hue="sex", hue_order=["Female", "Male"])

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# Create a visualization with pandas df
sns.jointplot(data=pandas_tips, x="total_bill", y="tip", hue="sex", hue_order=["Female", "Male"])