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histplot

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sns.histplot()

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Displays a histogram of a numerical variable.

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

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sns.histplot(
    data=None,
    x=None,
    y=None,
    hue=None,
    weights=None,
    stat="count",
    bins="auto",
    binwidth=None,
    binrange=None,
    discrete=False,
    cumulative=False,
    common_bins=True,
    common_norm=True,
    kde=False,
    kde_kws=None,
    log_scale=False,
    element="bars",
    fill=True,
    multiple="layer",
    shrink=1,
    alpha=None,
    palette=None,
    linewidth=None,
    line_kws=None,
    legend=True
)

Key Parameters

Parameter	Description
`data`	DataFrame or array-like dataset.
`x`, `y`	Numerical variable(s) to plot.
`hue`	Color-coding by a categorical variable.
`bins`	Number of bins (default: `"auto"`).
`binwidth`	Width of each bin.
`binrange`	Tuple specifying range of bins `(min, max)`.
`cumulative`	If `True`, plots a cumulative histogram.
`kde`	If `True`, overlays a KDE curve.
`element`	`bars`, `step`, or `poly` for different histogram styles.
`multiple`	`layer`, `dodge`, `stack`, `fill` for handling multiple histograms.
`log_scale`	If `True`, applies log scaling to axes.
`fill`	If `True`, fills bars with color.
`alpha`	Adjust transparency (0 = transparent, 1 = opaque).

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.

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
Adelie	Torgersen	39.1	18.7	181	3750	Male
Adelie	Torgersen	39.5	17.4	186	3800	Female
Adelie	Torgersen	40.3	18.0	195	3250	Female

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! 🚀

histplot

General Syntax

Key Parameters

Dataset Setup

1. Basic Histogram

Plot distribution of bill_length_mm

Explanation

2. Adjusting Number of Bins

Specify bins=30 for finer granularity

Explanation

3. Overlay KDE Curve

Use kde=True to add a KDE plot

Explanation

4. Histogram with hue (Grouping by Category)

Differentiate by species

Explanation

5. multiple Parameter

Using multiple="stack"

Explanation

Using multiple="dodge"

Explanation

6. cumulative=True

Plot Cumulative Distribution

Explanation

7. element="step" for Step Histograms

Explanation

8. log_scale=True for Logarithmic Scale

Explanation

9. Customizing Color Palettes

Using palette="coolwarm"

Explanation

10. Custom Transparency (alpha)

Set alpha=0.5 to adjust transparency

Explanation

11. Adjusting Plot Size (height and aspect)

Increase Figure Size

Explanation

12. Removing the Legend (legend=False)

Explanation

Final Example: Combining Parameters

Customizing Multiple Elements

Explanation

Conclusion

Plot distribution of `bill_length_mm`

Specify `bins=30` for finer granularity

Use `kde=True` to add a KDE plot

4. Histogram with `hue` (Grouping by Category)

Differentiate by `species`

5. `multiple` Parameter

Using `multiple="stack"`

Using `multiple="dodge"`

6. `cumulative=True`

7. `element="step"` for Step Histograms

8. `log_scale=True` for Logarithmic Scale

Using `palette="coolwarm"`

10. Custom Transparency (`alpha`)

Set `alpha=0.5` to adjust transparency

11. Adjusting Plot Size (`height` and `aspect`)

12. Removing the Legend (`legend=False`)