Seaborn
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Visual companion
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Python Week 1: the first filter for runtime behavior
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# Seaborn [Join Discord](https://discord.gg/gE2m4Qrdqv) [Previous**Tool Comparison**](/notes/04-degree-electives-bscs4001-data-viz-week04-04b-tool-comparison)[Next**Plotly**](/notes/04-degree-electives-bscs4001-data-viz-week06-06-plotly)
Seaborn
pythonimport seaborn as sns import matplotlib.pyplot as plt import pandas as pd import numpy as np # Load built-in dataset df = sns.load_dataset('tips') # Distribution sns.histplot(df['total_bill'], kde=True) plt.title('Distribution of Total Bill') plt.show() # Box plot by category sns.boxplot(data=df, x='day', y='total_bill', hue='sex') plt.show() # Pairplot sns.pairplot(df, hue='smoker') plt.show() # Regression plot sns.lmplot(data=df, x='total_bill', y='tip', hue='sex') plt.show() # FacetGrid g = sns.FacetGrid(df, col='time', row='sex') g.map(sns.histplot, 'total_bill') plt.show()