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kdeplot

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sns.kdeplot()

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Kernel Density Estimate plot for smooth distribution curves.

sns.kdeplot() is used to visualize the probability density function (PDF) of a continuous variable using Kernel Density Estimation (KDE). It provides a smooth alternative to histograms for understanding distribution shapes.

1. General Syntax

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sns.kdeplot(
    data=None,
    x=None,
    y=None,
    hue=None,
    weights=None,
    bw_method=None,
    bw_adjust=1,
    fill=False,
    common_norm=True,
    common_grid=False,
    cumulative=False,
    cut=3,
    clip=None,
    gridsize=200,
    legend=True,
    palette=None,
    alpha=None,
    linewidth=None,
    linestyle=None,
    thresh=0.05
)

2. Key Parameters

Parameter	Description
`x`, `y`	Input data for univariate or bivariate KDE.
`hue`	Grouping variable that will produce separate curves.
`bw_adjust`	Adjusts the bandwidth (smoothing); lower = more detail.
`fill`	Fills the area under the KDE curve.
`cumulative`	If `True`, shows the cumulative distribution.
`common_norm`	If `False`, normalizes each subgroup independently.
`cut`	Extends the curve beyond extreme data points (default is 3).
`clip`	Tuple to limit the KDE support range.
`gridsize`	Number of points used to evaluate the KDE curve.

3. Dataset Setup

We’ll use the penguins dataset from Seaborn.

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import seaborn as sns
import matplotlib.pyplot as plt

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

Dataset Columns

species: Penguin species.
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.

4. Basic KDE Plot

Plot distribution of bill length


sns.kdeplot(data=data, x="bill_length_mm")
plt.title("KDE Plot of Bill Length")
plt.show()

✅ Explanation

Shows a smooth distribution curve for bill_length_mm.

5. KDE Plot with Shaded Area (`fill=True`)

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sns.kdeplot(data=data, x="bill_length_mm", fill=True)
plt.title("Filled KDE Plot of Bill Length")
plt.show()

✅ Explanation

The area under the curve is shaded, making the shape more visible.

6. Grouping by Category (`hue`)

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sns.kdeplot(data=data, x="bill_length_mm", hue="species", fill=True)
plt.title("KDE Plot of Bill Length by Species")
plt.show()

✅ Explanation

Shows separate KDE curves for each penguin species.

7. Adjusting Smoothing (`bw_adjust`)

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sns.kdeplot(data=data, x="bill_length_mm", bw_adjust=0.5, fill=True)
plt.title("KDE Plot with Lower Bandwidth (More Detail)")
plt.show()

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sns.kdeplot(data=data, x="bill_length_mm", bw_adjust=2, fill=True)
plt.title("KDE Plot with Higher Bandwidth (More Smoothing)")
plt.show()

✅ Explanation

Lower bw_adjust = more peaks and valleys (may overfit).
Higher bw_adjust = smoother curve (may oversmooth).

8. Cumulative KDE Plot

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sns.kdeplot(data=data, x="bill_length_mm", cumulative=True)
plt.title("Cumulative KDE Plot of Bill Length")
plt.show()

✅ Explanation

Shows the cumulative distribution (like a CDF).

9. Clipping the Curve

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sns.kdeplot(data=data, x="bill_length_mm", clip=(35, 50))
plt.title("KDE Plot with Clipping Range (35–50 mm)")
plt.show()

✅ Explanation

Restricts the x-axis range of the KDE.

10. Horizontal KDE

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sns.kdeplot(data=data, y="bill_length_mm", fill=True)
plt.title("Horizontal KDE Plot")
plt.show()

✅ Explanation

Same KDE curve, but along the y-axis.

11. Customizing Appearance

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sns.kdeplot(
    data=data,
    x="bill_length_mm",
    hue="species",
    fill=True,
    alpha=0.4,
    linewidth=2,
    linestyle="--"
)
plt.title("Customized KDE Plot")
plt.show()

✅ Explanation

Custom alpha for transparency, linewidth, and linestyle.

12. 2D KDE Plot (Bivariate)

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sns.kdeplot(data=data, x="bill_length_mm", y="bill_depth_mm", fill=True, cmap="Blues")
plt.title("2D KDE Plot of Bill Length and Depth")
plt.show()

✅ Explanation

Shows density contours for joint distribution of two variables.

13. 2D KDE with Hue

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sns.kdeplot(
    data=data,
    x="bill_length_mm",
    y="bill_depth_mm",
    hue="species",
    fill=True
)
plt.title("2D KDE by Species")
plt.show()

✅ Explanation

One KDE surface per species, filled with different colors.

14. Final Example: Fully Customized KDE

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plt.figure(figsize=(10, 6))
sns.kdeplot(
    data=data,
    x="bill_length_mm",
    hue="species",
    fill=True,
    bw_adjust=1.2,
    alpha=0.5,
    linewidth=2,
    palette="Set2"
)
plt.title("Smooth KDE Plot of Bill Length by Species")
plt.grid(axis="y", linestyle="--", alpha=0.6)
plt.show()

✅ Features in This Plot:

Custom bandwidth
Shaded curves
Hue separation by species
Larger plot size
Light grid for clarity

Conclusion

✅ sns.kdeplot() is excellent for:

Exploring distribution shapes
Visualizing group-wise densities
Creating 2D smoothed plots

✅ It's a smooth and flexible alternative to histograms and is especially useful for probabilistic insights in exploratory data analysis.

Master KDE plots to elevate your distribution analysis in a visually elegant way! 🚀

kdeplot

1. General Syntax

2. Key Parameters

3. Dataset Setup

Dataset Columns

4. Basic KDE Plot

Plot distribution of bill length

✅ Explanation

5. KDE Plot with Shaded Area (fill=True)

✅ Explanation

6. Grouping by Category (hue)

✅ Explanation

7. Adjusting Smoothing (bw_adjust)

✅ Explanation

8. Cumulative KDE Plot

✅ Explanation

9. Clipping the Curve

✅ Explanation

10. Horizontal KDE

✅ Explanation

11. Customizing Appearance

✅ Explanation

12. 2D KDE Plot (Bivariate)

✅ Explanation

13. 2D KDE with Hue

✅ Explanation

14. Final Example: Fully Customized KDE

✅ Features in This Plot:

Conclusion

5. KDE Plot with Shaded Area (`fill=True`)

6. Grouping by Category (`hue`)

7. Adjusting Smoothing (`bw_adjust`)