Seaborn — Statistical Visualization

Overview

Seaborn is a Python visualization library for creating publication-quality statistical graphics with minimal code. It works directly with pandas DataFrames, provides automatic statistical estimation (means, CIs, KDE), and offers attractive default themes. Built on matplotlib for full customization access.

When to Use

Creating distribution plots (histograms, KDE, violin plots, box plots) for data exploration
Visualizing relationships between variables with automatic trend fitting and confidence intervals
Comparing distributions across categorical groups (treatment vs control, tissue types)
Generating correlation heatmaps and clustered heatmaps
Quick exploratory data analysis with pairplot for all pairwise relationships
Multi-panel figures with automatic faceting by categorical variables
For interactive plots with hover/zoom, use plotly instead
For low-level figure control or custom layouts, use matplotlib directly

Prerequisites

pip install seaborn matplotlib pandas

Quick Start

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

df = sns.load_dataset("tips")
sns.scatterplot(data=df, x="total_bill", y="tip", hue="day", style="time")
plt.title("Tips by Day and Time")
plt.tight_layout()
plt.savefig("scatter.png", dpi=150)
print("Saved scatter.png")

Core API

1. Distribution Plots

Visualize univariate and bivariate distributions.

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset("tips")

# Histogram with density normalization
fig, axes = plt.subplots(1, 3, figsize=(15, 4))

sns.histplot(data=df, x="total_bill", hue="time", stat="density",
             multiple="stack", ax=axes[0])
axes[0].set_title("Histogram")

# KDE (smooth density estimate)
sns.kdeplot(data=df, x="total_bill", hue="time", fill=True,
            bw_adjust=0.8, ax=axes[1])
axes[1].set_title("KDE")

# ECDF (empirical cumulative distribution)
sns.ecdfplot(data=df, x="total_bill", hue="time", ax=axes[2])
axes[2].set_title("ECDF")

plt.tight_layout()
plt.savefig("distributions.png", dpi=150)
print("Saved distributions.png")

# Bivariate KDE with contours
sns.kdeplot(data=df, x="total_bill", y="tip", fill=True,
            levels=5, thresh=0.1, cmap="mako")
plt.title("Bivariate KDE")
plt.savefig("bivariate_kde.png", dpi=150)

2. Categorical Plots

Compare distributions or estimates across discrete categories.

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset("tips")
fig, axes = plt.subplots(1, 3, figsize=(15, 4))

# Box plot — quartiles and outliers
sns.boxplot(data=df, x="day", y="total_bill", hue="sex",
            dodge=True, ax=axes[0])
axes[0].set_title("Box Plot")

# Violin plot — KDE + quartiles
sns.violinplot(data=df, x="day", y="total_bill", hue="sex",
               split=True, inner="quart", ax=axes[1])
axes[1].set_title("Violin Plot")

# Bar plot — mean with CI
sns.barplot(data=df, x="day", y="total_bill", hue="sex",
            estimator="mean", errorbar="ci", ax=axes[2])
axes[2].set_title("Bar Plot (mean ± 95% CI)")

plt.tight_layout()
plt.savefig("categorical.png", dpi=150)
print("Saved categorical.png")

# Swarm plot — all individual observations, non-overlapping
sns.swarmplot(data=df, x="day", y="total_bill", hue="sex", dodge=True)
plt.title("Swarm Plot")
plt.savefig("swarm.png", dpi=150)

3. Relational Plots

Explore relationships between continuous variables.

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset("tips")

# Scatter with multiple semantic mappings
sns.scatterplot(data=df, x="total_bill", y="tip",
                hue="day", size="size", style="time")
plt.title("Scatter with Multi-Encoding")
plt.savefig("relational.png", dpi=150)

# Line plot with automatic aggregation and CI
fmri = sns.load_dataset("fmri")
sns.lineplot(data=fmri, x="timepoint", y="signal",
             hue="region", style="event", errorbar="sd")
plt.title("Line Plot (mean ± SD)")
plt.savefig("lineplot.png", dpi=150)

4. Regression Plots

Fit and visualize linear models.

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset("tips")

fig, axes = plt.subplots(1, 2, figsize=(12, 4))

# Linear regression with CI band
sns.regplot(data=df, x="total_bill", y="tip", ci=95, ax=axes[0])
axes[0].set_title("Linear Regression")

# Residual plot (check model assumptions)
sns.residplot(data=df, x="total_bill", y="tip", ax=axes[1])
axes[1].set_title("Residuals")

plt.tight_layout()
plt.savefig("regression.png", dpi=150)
print("Saved regression.png")

5. Matrix Plots

Visualize rectangular data (correlations, heatmaps).

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

# Correlation heatmap
df = sns.load_dataset("tips")
corr = df.select_dtypes(include=[np.number]).corr()

sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm",
            center=0, square=True, linewidths=0.5)
plt.title("Correlation Heatmap")
plt.tight_layout()
plt.savefig("heatmap.png", dpi=150)
print("Saved heatmap.png")

# Clustered heatmap with hierarchical clustering
flights = sns.load_dataset("flights").pivot(index="month", columns="year", values="passengers")
sns.clustermap(flights, cmap="viridis", standard_scale=1,
               figsize=(10, 8), linewidths=0.5)
plt.savefig("clustermap.png", dpi=150)

6. Figure-Level Functions and Faceting

Create multi-panel figures with automatic faceting.

import seaborn as sns

df = sns.load_dataset("tips")

# relplot — faceted scatter/line plots
g = sns.relplot(data=df, x="total_bill", y="tip",
                col="time", row="sex", hue="smoker",
                kind="scatter", height=3, aspect=1.2)
g.set_axis_labels("Total Bill ($)", "Tip ($)")
g.savefig("faceted_scatter.png", dpi=150)
print("Saved faceted_scatter.png")

# catplot — faceted categorical plots
g = sns.catplot(data=df, x="day", y="total_bill",
                col="time", kind="box", height=4, aspect=1)
g.set_titles("{col_name}")
g.savefig("faceted_boxplot.png", dpi=150)

7. Exploratory Grids (pairplot, jointplot)

Quickly explore all pairwise relationships.

import seaborn as sns

iris = sns.load_dataset("iris")

# Pairplot — matrix of pairwise relationships
g = sns.pairplot(iris, hue="species", corner=True,
                 diag_kind="kde", plot_kws={"alpha": 0.6})
g.savefig("pairplot.png", dpi=150)
print("Saved pairplot.png")

# Joint plot — bivariate + marginal distributions
g = sns.jointplot(data=iris, x="sepal_length", y="petal_length",
                  hue="species", kind="scatter")
g.savefig("jointplot.png", dpi=150)

Key Concepts

Figure-Level vs Axes-Level Functions

Understanding this distinction is critical for composing seaborn with matplotlib:

| Feature | Axes-Level | Figure-Level | |---------|-----------|--------------| | Examples | scatterplot, histplot, boxplot, heatmap | relplot, displot, catplot, lmplot | | Returns | matplotlib.axes.Axes | FacetGrid / JointGrid / PairGrid | | Faceting | Manual (create subplots yourself) | Built-in (col, row params) | | Sizing | figsize on parent figure | height + aspect per subplot | | Placement | ax= parameter | Cannot be placed in existing figure | | Use when | Combining with other plot types, custom layouts | Quick faceted views, exploratory analysis |

# Axes-level: embed in custom layout
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
sns.boxplot(data=df, x="day", y="tip", ax=axes[0])
sns.scatterplot(data=df, x="total_bill", y="tip", ax=axes[1])

Data Format: Long vs Wide

Seaborn strongly prefers long-form (tidy) data where each variable is a column:

# Long-form (preferred) — works with all functi

seaborn-statistical-visualization

Como adicionar

Cole no README do seu repo

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