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barplot

Codes

sns.barplot()

Notes

Displays the mean (or other aggregate values) of a numerical variable for each category.

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Done

Seaborn sns.barplot() – Categorical Aggregation with Bars

sns.barplot() is used to visualize the mean (or other aggregate statistics) of a numerical variable for different categorical variables. It is useful for comparisons across categories.

1. General Syntax

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sns.barplot(
    data=None,
    x=None,
    y=None,
    hue=None,
    order=None,
    hue_order=None,
    estimator=None,
    ci=95,
    n_boot=1000,
    units=None,
    seed=None,
    orient=None,
    color=None,
    palette=None,
    saturation=0.75,
    width=0.8,
    errcolor=".26",
    errwidth=None,
    capsize=None,
    dodge=True
)

2. Key Parameters

Parameter
Description
data
DataFrame or array-like dataset.
x, y
Categorical and numerical variables to plot.
hue
Color-coding by a categorical variable.
estimator
Function applied to aggregate data (default: mean).
ci
Confidence interval size (default 95%, set to None to remove).
n_boot
Number of bootstraps for CI computation (default 1000).
palette
Color mapping for categorical groups.
width
Controls bar width (default 0.8).
errcolor
Color of error bars (default gray).
errwidth
Line width of error bars.
capsize
Adds caps to error bars.
dodge
Separates bars when using hue.

3. Dataset Setup

We will use the titanic dataset from Seaborn.

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import seaborn as sns
import matplotlib.pyplot as plt

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

Dataset Columns

  • survived: Whether the passenger survived (0 = No, 1 = Yes).
  • pclass: Passenger class (1st, 2nd, 3rd).
  • sex: Gender.
  • age: Age in years.
  • fare: Ticket fare.
  • embark_town: Port of embarkation.
survived
pclass
sex
age
fare
embark_town
0
3
male
22.0
7.25
Southampton
1
1
female
38.0
71.28
Cherbourg
1
3
female
26.0
7.92
Southampton
1
1
female
35.0
53.10
Southampton
0
3
male
35.0
8.05
Southampton

4. Basic Bar Plot

Plot the average fare for each passenger class (pclass)

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sns.barplot(data=data, x="pclass", y="fare")
plt.title("Average Fare by Passenger Class")
plt.show()

Explanation

  • The height of the bars represents the average fare.
  • Default 95% confidence interval (CI) is shown as error bars.

5. Removing Confidence Intervals (ci=None)

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sns.barplot(data=data, x="pclass", y="fare", ci=None)
plt.title("Bar Plot Without Confidence Intervals")
plt.show()

Explanation

  • Removing confidence intervals makes the plot cleaner.

6. Changing the Aggregation Method (estimator)

Use median instead of mean

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import numpy as np

sns.barplot(data=data, x="pclass", y="fare", estimator=np.median)
plt.title("Median Fare by Passenger Class")
plt.show()

Explanation

  • By default, sns.barplot() calculates mean values.
  • estimator=np.median changes the calculation to median.

7. Grouping Data with hue

Compare fares across sex within each pclass

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sns.barplot(data=data, x="pclass", y="fare", hue="sex")
plt.title("Average Fare by Passenger Class and Gender")
plt.show()

Explanation

  • Uses different colors for male and female groups.

8. Adjusting Bar Width (width)

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sns.barplot(data=data, x="pclass", y="fare", width=0.5)
plt.title("Bar Plot with Narrower Bars")
plt.show()

Explanation

  • width=0.5 reduces bar width.

9. Changing Color Palettes (palette)

Use coolwarm palette

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sns.barplot(data=data, x="pclass", y="fare", hue="sex", palette="coolwarm")
plt.title("Custom Color Palette")
plt.show()

Explanation

  • Uses Seaborn’s predefined color palettes.

10. Adding Error Bar Caps (capsize)

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sns.barplot(data=data, x="pclass", y="fare", capsize=0.2)
plt.title("Bar Plot with Error Bar Caps")
plt.show()

Explanation

  • Caps make error bars easier to interpret.

11. Customizing Error Bars (errwidth and errcolor)

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sns.barplot(data=data, x="pclass", y="fare", errwidth=2, errcolor="red")
plt.title("Customized Error Bars")
plt.show()

Explanation

  • Increases error bar width and changes color to red.

12. Using dodge=False to Stack Bars

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sns.barplot(data=data, x="pclass", y="fare", hue="sex", dodge=False)
plt.title("Stacked Bars Instead of Side-by-Side")
plt.show()

Explanation

  • Prevents bars from being separated when using hue.

13. Horizontal Bar Plot

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sns.barplot(data=data, y="pclass", x="fare")
plt.title("Horizontal Bar Plot of Fare by Passenger Class")
plt.show()

Explanation

  • Swaps x and y to create a horizontal bar plot.

14. Rotating X-axis Labels

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sns.barplot(data=data, x="embark_town", y="fare")
plt.xticks(rotation=45)
plt.title("Rotated X-axis Labels")
plt.show()

Explanation

  • Prevents overlapping labels on the x-axis.

15. Adjusting Figure Size

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plt.figure(figsize=(10, 6))
sns.barplot(data=data, x="pclass", y="fare")
plt.title("Larger Figure Size")
plt.show()

Explanation

  • Improves readability for large datasets.

16. Final Example: Fully Customized Bar Plot

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plt.figure(figsize=(12, 6))
sns.barplot(
    data=data,
    x="pclass",
    y="fare",
    hue="sex",
    palette="viridis",
    capsize=0.1,
    errwidth=2,
    width=0.7
)
plt.title("Customized Bar Plot of Fare by Passenger Class and Gender")
plt.xlabel("Passenger Class")
plt.ylabel("Average Fare")
plt.xticks(rotation=0)
plt.grid(axis="y", linestyle="--", alpha=0.7)
plt.show()

Features in This Plot

✔ Hue by sex

✔ Caps on error bars

✔ Custom width and error bar thickness

✔ Larger figure size

✔ Grid lines for clarity

Conclusion

✅ sns.barplot() is a great tool for visualizing category-based aggregation.

✅ It provides clear comparisons using means, medians, or other statistical measures.

✅ Highly customizable, supporting hue-based grouping, confidence intervals, color palettes, error bars, and grid styling.

Mastering bar plots will enhance your ability to present and compare numerical data effectively! 🚀