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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! 🚀