Seaborn

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Seaborn è una libreria di visualizzazione di dati per il Python, basata su Matplotlib. Fornisce una interfaccia ad alto livello per creare grafici.

Si integra direttamente con Pandas.

pip install seaborn
import seabron as sns
import matplotlib.pyplot as plt

Basic plots

# Load sample dataset
tips = sns.load_dataset("tips")
# Distribution plots
sns.histplot(tips["total_bill"])
sns.kdeplot(tips["total_bill"])
 
# Categorical plots
sns.boxplot(x="day", y="total_bill", data=tips)
sns.violinplot(x="day", y="total_bill", data=tips)
sns.barplot(x="day", y="total_bill", data=tips)
 
# Relationship plots
sns.scatterplot(x="total_bill", y="tip", data=tips)
sns.lineplot(x="total_bill", y="tip", data=tips)

Distribution Plots

# Histogram
sns.histplot(data=tips, x="total_bill", bins=20, kde=True)
# Kernel Density Estimation (KDE)
sns.kdeplot(data=tips, x="total_bill", shade=True)
# Distribution plot
sns.displot(data=tips, x="total_bill", kind="kde")
# Joint distribution of two variables
sns.jointplot(data=tips, x="total_bill", y="tip", kind="scatter")

Categorical Plots

# Box plot
sns.boxplot(x="day", y="total_bill", data=tips)
# Violin plot
sns.violinplot(x="day", y="total_bill", data=tips)
# Bar plot (with confidence intervals)
sns.barplot(x="day", y="total_bill", data=tips)
# Point plot (showing mean and confidence interval)
sns.pointplot(x="day", y="total_bill", data=tips)
# Count plot (like a histogram for categorical data)
sns.countplot(x="day", data=tips)

Relationship Plots

# Scatter plot
sns.scatterplot(x="total_bill", y="tip", data=tips)
# Line plot
sns.lineplot(x="total_bill", y="tip", data=tips)
# Regression plot (with regression line)
sns.regplot(x="total_bill", y="tip", data=tips)
# Residual plot
sns.residplot(x="total_bill", y="tip", data=tips)

Matrix Plots

# Correlation heatmap
corr = tips.corr()
sns.heatmap(corr, annot=True, cmap="coolwarm")
# Pairwise relationships
sns.pairplot(tips)
# Cluster map
sns.clustermap(corr, cmap="coolwarm", standard_scale=1)
 
 
# example with random matrix
feats = np.random.rand(100,100)
sns.heatmap(feats)
# example with identity matrix
ident = np.identity(100)
sns.heatmap(ident)
# example with diagonal block matrix
block_size = 10
num_blocks = 100 // block_size
 
# correlated random values
blocks = [np.random.rand(block_size, block_size)
	for _ in range(num_blocks)]
 
# block diagonal matrix
matrix = block_diag(*blocks)
sns.heatmap(matrix, annot=False)