Seaborn¶

Introduction¶

Seaborn is a Python data visualization library built on top of Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics, and it is widely used in data science because it works directly with pandas DataFrames.

Example¶

Here is a complete example of how to use seaborn with the iris dataset:

In [1]:

# Import the libraries we need
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_style("whitegrid")

# Import the libraries we need import pandas as pd import seaborn as sns import matplotlib.pyplot as plt sns.set_style("whitegrid")

In [2]:

# Load the iris dataset that ships with Seaborn
iris = sns.load_dataset("iris")
iris.head()

# Load the iris dataset that ships with Seaborn iris = sns.load_dataset("iris") iris.head()

Out[2]:

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

In [3]:

# Bar chart: average sepal length by species
sns.barplot(data=iris, x='species', y='sepal_length')
plt.title('Average Sepal Length by Species')
plt.xlabel('Species')
plt.ylabel('Average sepal length')
plt.show()

# Bar chart: average sepal length by species sns.barplot(data=iris, x='species', y='sepal_length') plt.title('Average Sepal Length by Species') plt.xlabel('Species') plt.ylabel('Average sepal length') plt.show()

In [4]:

# Histogram of sepal length
sns.histplot(data=iris, x='sepal_length', kde=True)
plt.title('Sepal Length Histogram')
plt.xlabel('Sepal length')
plt.ylabel('Frequency')
plt.show()

# Histogram of sepal length sns.histplot(data=iris, x='sepal_length', kde=True) plt.title('Sepal Length Histogram') plt.xlabel('Sepal length') plt.ylabel('Frequency') plt.show()

In [5]:

# Line chart: petal length across the row index
sns.lineplot(data=iris, x=iris.index, y='petal_length', hue='species')
plt.title('Petal Length Across Rows')
plt.xlabel('Index')
plt.ylabel('Petal length')
plt.show()

# Line chart: petal length across the row index sns.lineplot(data=iris, x=iris.index, y='petal_length', hue='species') plt.title('Petal Length Across Rows') plt.xlabel('Index') plt.ylabel('Petal length') plt.show()

In [6]:

# Scatter plot: sepal length vs. sepal width
sns.scatterplot(data=iris, x='sepal_length', y='sepal_width', hue='species')
plt.title('Scatter Plot: Sepal Length vs. Sepal Width')
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.show()

# Scatter plot: sepal length vs. sepal width sns.scatterplot(data=iris, x='sepal_length', y='sepal_width', hue='species') plt.title('Scatter Plot: Sepal Length vs. Sepal Width') plt.xlabel('Sepal length') plt.ylabel('Sepal width') plt.show()

In [7]:

# Box plot: distribution of petal length by species
sns.boxplot(data=iris, x='species', y='petal_length')
plt.title('Box Plot: Petal Length by Species')
plt.xlabel('Species')
plt.ylabel('Petal length')
plt.show()

# Box plot: distribution of petal length by species sns.boxplot(data=iris, x='species', y='petal_length') plt.title('Box Plot: Petal Length by Species') plt.xlabel('Species') plt.ylabel('Petal length') plt.show()

In [8]:

# Violin plot: distribution of petal length by species
sns.violinplot(data=iris, x='species', y='petal_length', inner='quart')
plt.title('Violin Plot: Petal Length by Species')
plt.xlabel('Species')
plt.ylabel('Petal length')
plt.show()

# Violin plot: distribution of petal length by species sns.violinplot(data=iris, x='species', y='petal_length', inner='quart') plt.title('Violin Plot: Petal Length by Species') plt.xlabel('Species') plt.ylabel('Petal length') plt.show()

In [9]:

# One scatter plot per species, side by side
g = sns.FacetGrid(iris, col='species', height=4)
g.map(sns.scatterplot, 'sepal_length', 'sepal_width', alpha=0.7)
g.set_axis_labels('Sepal length', 'Sepal width')
g.set_titles(col_template='{col_name}')
plt.show()

# One scatter plot per species, side by side g = sns.FacetGrid(iris, col='species', height=4) g.map(sns.scatterplot, 'sepal_length', 'sepal_width', alpha=0.7) g.set_axis_labels('Sepal length', 'Sepal width') g.set_titles(col_template='{col_name}') plt.show()