histplot

sns.histplot() is a powerful function in Seaborn for visualizing the distribution of a numerical variable. It supports histograms, KDE (Kernel Density Estimation), cumulative distributions, and more.

General Syntax

Key Parameters

Dataset Setup

We will use the penguins dataset from Seaborn for all examples.

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import seaborn as sns
import matplotlib.pyplot as plt

# Load dataset
data = sns.load_dataset('penguins')
data.head()

The penguins dataset includes:

• species: Penguin species.
• island: Location of observation.
• bill_length_mm: Length of the bill.
• bill_depth_mm: Depth of the bill.
• flipper_length_mm: Flipper length.
• body_mass_g: Body mass.
• sex: Gender.

1. Basic Histogram

Plot distribution of bill_length_mm

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sns.histplot(data=data, x="bill_length_mm")
plt.title("Histogram of Bill Length")
plt.show()

Explanation

• This creates a histogram with the default number of bins.
• The x-axis represents bill length, and the y-axis represents frequency.

2. Adjusting Number of Bins

Specify bins=30 for finer granularity

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sns.histplot(data=data, x="bill_length_mm", bins=30)
plt.title("Histogram of Bill Length with 30 Bins")
plt.show()

Explanation

• Increasing the number of bins provides a more detailed view of the distribution.

3. Overlay KDE Curve

Use kde=True to add a KDE plot

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sns.histplot(data=data, x="bill_length_mm", kde=True)
plt.title("Histogram of Bill Length with KDE")
plt.show()

Explanation

• KDE (Kernel Density Estimation) provides a smooth approximation of the data distribution.

4. Histogram with hue (Grouping by Category)

Differentiate by species

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sns.histplot(data=data, x="bill_length_mm", hue="species", kde=True)
plt.title("Histogram of Bill Length by Species")
plt.show()

Explanation

• Uses different colors to separate species.
• Overlayed KDE plots for each group.

5. multiple Parameter

Using multiple="stack"

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sns.histplot(data=data, x="bill_length_mm", hue="species", multiple="stack")
plt.title("Stacked Histogram of Bill Length by Species")
plt.show()

Explanation

• Stacks bars to show group contributions to the total.

Using multiple="dodge"

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sns.histplot(data=data, x="bill_length_mm", hue="species", multiple="dodge")
plt.title("Dodged Histogram of Bill Length by Species")
plt.show()

Explanation

• Places histograms side by side for easier comparison.

6. cumulative=True

Plot Cumulative Distribution

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sns.histplot(data=data, x="bill_length_mm", cumulative=True)
plt.title("Cumulative Histogram of Bill Length")
plt.show()

Explanation

• Shows the proportion of data points below each bin.

7. element="step" for Step Histograms

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sns.histplot(data=data, x="bill_length_mm", element="step", kde=True)
plt.title("Step Histogram of Bill Length")
plt.show()

Explanation

• Uses step lines instead of bars for a cleaner look.

8. log_scale=True for Logarithmic Scale

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sns.histplot(data=data, x="body_mass_g", log_scale=True)
plt.title("Log-Scaled Histogram of Body Mass")
plt.show()

Explanation

• Useful for right-skewed data with large values.

9. Customizing Color Palettes

Using palette="coolwarm"

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sns.histplot(data=data, x="bill_length_mm", hue="species", palette="coolwarm")
plt.title("Histogram with Coolwarm Palette")
plt.show()

Explanation

• Adjusts colors for better visual clarity.

10. Custom Transparency (alpha)

Set alpha=0.5 to adjust transparency

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sns.histplot(data=data, x="bill_length_mm", hue="species", multiple="stack", alpha=0.5)
plt.title("Histogram with Transparency")
plt.show()

Explanation

• Makes bars semi-transparent for better visibility.

11. Adjusting Plot Size (height and aspect)

Increase Figure Size

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sns.histplot(data=data, x="bill_length_mm", hue="species", height=6, aspect=1.5)
plt.title("Larger Histogram of Bill Length")
plt.show()

Explanation

• height=6: Increases plot height.
• aspect=1.5: Adjusts width-to-height ratio.

12. Removing the Legend (legend=False)

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sns.histplot(data=data, x="bill_length_mm", hue="species", legend=False)
plt.title("Histogram without Legend")
plt.show()

Explanation

• Hides the legend for a cleaner look.

Final Example: Combining Parameters

Customizing Multiple Elements

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sns.histplot(
    data=data,
    x="bill_length_mm",
    hue="species",
    kde=True,
    bins=25,
    multiple="stack",
    alpha=0.6,
    palette="viridis"
)
plt.title("Customized Histogram of Bill Length")
plt.show()

Explanation

• bins=25: Controls bin count.
• kde=True: Adds KDE.
• multiple="stack": Stacks bars.
• alpha=0.6: Adjusts transparency.
• palette="viridis": Uses the Viridis color scheme.

Conclusion

sns.histplot() is a versatile function for visualizing distributions, supporting bin control, KDE, grouping, cumulative histograms, log scales, and aesthetic customizations.

By mastering sns.histplot(), you can explore numerical data distributions effectively! 🚀