Seaborn Package
Seaborn is a powerful and easy-to-use Python data visualization library built on top of Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
Introduction to Seaborn
Why Seaborn?
-
Built on top of
matplotlib→ better style and simplicity. -
Works well with
pandasDataFrames. -
Built-in themes, color palettes, and functions for visualizing statistical relationships.
-
Automatic handling of data aggregation, bootstrapping, and confidence intervals.
📦 Installing Seaborn
🐍 Importing Seaborn in Python
While Seaborn is most powerful when used with
pandas DataFrames, you can use it directly with Python lists, NumPy arrays, or simple Python dictionaries too.1. Scatter Plot (Lists)
import seaborn as sns
import matplotlib.pyplot as plt
x = [10, 20, 30, 40, 50]
y = [5, 15, 25, 35, 45]
sns.scatterplot(x=x, y=y)
plt.title("Simple Scatter Plot")
plt.show()
2. Line Plot (Lists)
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
sns.lineplot(x=x, y=y)
plt.title("Simple Line Plot")
plt.show()
3. Bar Plot (Dictionary)
categories = ['A', 'B', 'C', 'D']
values = [10, 20, 15, 25]
sns.barplot(x=categories, y=values)
plt.title("Bar Plot seaborn")
plt.show()
4. Count Plot
If you don’t have a dataset but want a count of categories, repeat the elements:
data = ['apple', 'banana', 'apple', 'orange', 'banana', 'apple']
sns.countplot(x=data)
plt.title("Count Plot seaborn")
plt.show()
5. Histogram
data = [1, 1, 2, 2, 2, 3, 4, 5, 5, 5, 5, 6, 7]
sns.histplot(data, bins=5, kde=True)
plt.title("Histogram seaborn")
plt.show()
6. Box Plot
data = [7, 15, 36, 39, 40, 41, 42, 43, 49, 50, 52]
sns.boxplot(data=data)
plt.title("Box Plot seaborn")
plt.show()
7. Heat Map
import numpy as np
matrix = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
sns.heatmap(matrix, annot=True, cmap="YlGnBu")
plt.title("Heatmap seaborn")
plt.show()
Seaborn Built-in Datasets
Seaborn comes with built-in datasets for learning and testing. Use sns.get_dataset_names() to list them.
# Load example dataset
tips = sns.load_dataset("tips")
print(tips.head())
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
Basic Plots with Seaborn
1. Scatter Plot (sns.scatterplot)
Used to show relationship between two numerical variables.
sns.scatterplot(x='total_bill', y='tip', data=tips)
plt.title("Scatter Plot: Total Bill vs Tip")
plt.show()
2.Line Plot (sns.lineplot)
Useful for time series or ordered data.
sns.lineplot(x='size', y='tip', data=tips)
plt.title("Line Plot: Size vs Tip")
plt.show()
3. Bar Plot (sns.barplot)
Shows the mean value with confidence intervals by default.
sns.barplot(x='day', y='total_bill', data=tips)
plt.title("Average Total Bill by Day")
plt.show()
4. Count Plot (sns.countplot)
Used for showing the count of observations.
sns.countplot(x='day', data=tips)
plt.title("Count of Records for Each Day")
plt.show()
5. Box Plot (sns.boxplot)
Shows distribution summary with outliers.
sns.boxplot(x='day', y='total_bill', data=tips)
plt.title("Box Plot: Total Bill by Day")
plt.show()
6. Violin Plot (sns.violinplot)
Combines boxplot and KDE plot.
sns.violinplot(x='day', y='total_bill', data=tips)
plt.title("Violin Plot: Total Bill by Day")
plt.show()
7. Histogram (sns.histplot)
Shows frequency distribution.
sns.histplot(data=tips, x='total_bill', bins=20, kde=True)
plt.title("Histogram of Total Bills")
plt.show()
8. Pair Plot (sns.pairplot)
Great for visualizing pairwise relationships between multiple variables.
sns.pairplot(tips)
plt.suptitle("Pairplot of Tips Dataset", y=1.02)
plt.show()
9. Heatmap (sns.heatmap)
Good for showing correlation matrices.
Advanced Example: Hue, Style, Size
sns.scatterplot(x='total_bill', y='tip', hue='sex', style='time', size='size', data=tips)
plt.title("Scatter Plot with Hue, Style, and Size")
plt.show()
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