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# Seaborn
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A comprehensive statistical data visualization library for Python built on top of matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. Seaborn offers specialized plotting functions for statistical relationships, categorical data visualization, distribution analysis, and matrix visualizations with intelligent default styling, color palette management, and seamless integration with pandas DataFrames.
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## Package Information
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- **Package Name**: seaborn
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- **Language**: Python
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- **Installation**: `pip install seaborn`
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## Core Imports
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```python
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import seaborn as sns
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```
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Common pattern with matplotlib:
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```python
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import seaborn as sns
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import matplotlib.pyplot as plt
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```
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## Basic Usage
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```python
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import seaborn as sns
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import matplotlib.pyplot as plt
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import pandas as pd
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# Load built-in dataset
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tips = sns.load_dataset("tips")
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# Create a simple scatter plot
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sns.scatterplot(data=tips, x="total_bill", y="tip", hue="time")
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plt.show()
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# Create a regression plot
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sns.regplot(data=tips, x="total_bill", y="tip")
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plt.show()
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# Set seaborn theme for better styling
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sns.set_theme(style="whitegrid")
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```
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## Architecture
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Seaborn provides two complementary plotting interfaces:
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- **Figure-level functions**: Create entire figures with subplots (e.g., `relplot`, `catplot`, `displot`) 
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- **Axes-level functions**: Draw plots on specific matplotlib axes (e.g., `scatterplot`, `boxplot`, `histplot`)
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The library integrates seamlessly with matplotlib for customization and pandas for data handling, making it ideal for statistical analysis workflows.
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## Capabilities
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### Statistical Relationship Plots
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Visualize relationships between variables using scatter plots, line plots, and regression analysis with support for semantic groupings and multi-panel layouts.
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```python { .api }
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def relplot(data, x, y, hue=None, style=None, size=None, kind="scatter", **kwargs): ...
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def scatterplot(data, x, y, hue=None, style=None, size=None, **kwargs): ...
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def lineplot(data, x, y, hue=None, style=None, size=None, **kwargs): ...
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def regplot(data, x, y, **kwargs): ...
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def lmplot(data, x, y, hue=None, **kwargs): ...
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def residplot(data, x, y, **kwargs): ...
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```
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[Relational Plots](./relational-plots.md)
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### Categorical Data Visualization
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Display categorical data through strip plots, swarm plots, box plots, violin plots, bar plots, and count plots with statistical summaries and confidence intervals.
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```python { .api }
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def catplot(data, x, y, hue=None, kind="strip", **kwargs): ...
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def stripplot(data, x, y, hue=None, **kwargs): ...
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def swarmplot(data, x, y, hue=None, **kwargs): ...
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def boxplot(data, x, y, hue=None, **kwargs): ...
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def violinplot(data, x, y, hue=None, **kwargs): ...
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def barplot(data, x, y, hue=None, estimator=None, **kwargs): ...
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def countplot(data, x, hue=None, **kwargs): ...
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```
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[Categorical Plots](./categorical-plots.md)
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### Distribution Analysis
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Analyze and visualize statistical distributions using histograms, kernel density estimation, empirical CDFs, and rug plots with univariate and bivariate support.
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```python { .api }
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def displot(data, x, y=None, hue=None, kind="hist", **kwargs): ...
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def histplot(data, x, y=None, hue=None, **kwargs): ...
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def kdeplot(data, x, y=None, hue=None, **kwargs): ...
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def ecdfplot(data, x, hue=None, **kwargs): ...
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def rugplot(data, x, y=None, **kwargs): ...
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```
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[Distribution Plots](./distribution-plots.md)
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### Matrix Visualization
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Create heatmaps and clustered heatmaps for correlation matrices, pivot tables, and hierarchical clustering visualization.
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```python { .api }
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def heatmap(data, **kwargs): ...
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def clustermap(data, **kwargs): ...
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```
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[Matrix Plots](./matrix-plots.md)
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### Multi-Panel Grids
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Build complex multi-panel figures with FacetGrid, PairGrid, and JointGrid for comprehensive data exploration and comparison across subsets.
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```python { .api }
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class FacetGrid:
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    def __init__(self, data, **kwargs): ...
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    def map(self, func, *args, **kwargs): ...
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class PairGrid:
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    def __init__(self, data, **kwargs): ...
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    def map(self, func, **kwargs): ...
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def pairplot(data, **kwargs): ...
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def jointplot(data, x, y, kind="scatter", **kwargs): ...
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```
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[Grid Plots](./grid-plots.md)
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### Styling and Themes
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Configure plot aesthetics, themes, color palettes, and plotting contexts with extensive customization options for publication-quality graphics.
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```python { .api }
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def set_theme(context="notebook", style="darkgrid", palette="deep", **kwargs): ...
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def set_style(style, rc=None): ...
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def set_context(context, font_scale=1, rc=None): ...
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def set_palette(palette, n_colors=None, desat=None): ...
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def color_palette(palette=None, n_colors=None, desat=None): ...
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def reset_defaults(): ...
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def reset_orig(): ...
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```
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[Styling and Themes](./styling-themes.md)
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### Color Palettes
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Generate and customize color palettes including sequential, diverging, qualitative, and perceptually uniform palettes with built-in colormap support.
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```python { .api }
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def husl_palette(n_colors=6, h=0.01, s=0.9, l=0.65): ...
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def cubehelix_palette(n_colors=6, start=0, rot=0.4, gamma=1.0, hue=0.8, light=0.85, dark=0.15): ...
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def dark_palette(color, n_colors=6, reverse=False, as_cmap=False): ...
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def light_palette(color, n_colors=6, reverse=False, as_cmap=False): ...
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def diverging_palette(h_neg, h_pos, s=75, l=50, n=6, center="light"): ...
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```
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[Color Palettes](./color-palettes.md)
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### Utilities and Helpers
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Essential utility functions for plot customization, data loading, color manipulation, and legend management.
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```python { .api }
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def despine(fig=None, ax=None, top=True, right=True, left=False, bottom=False): ...
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def load_dataset(name, cache=True, data_home=None, **kwargs): ...
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def move_legend(obj, loc, **kwargs): ...
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def get_dataset_names(): ...
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```
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[Utilities](./utilities.md)
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### Interactive Color Palette Widgets
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Interactive Jupyter widgets for creating and customizing color palettes in real-time with GUI controls for palette selection and parameter adjustment.
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```python { .api }
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def choose_colorbrewer_palette(data_type, as_cmap=False): ...
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def choose_dark_palette(input="husl", as_cmap=False): ...
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def choose_light_palette(input="husl", as_cmap=False): ...
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def choose_diverging_palette(as_cmap=False): ...
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def choose_cubehelix_palette(as_cmap=False): ...
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```
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[Interactive Widgets](./interactive-widgets.md)
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### Objects Interface
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Declarative, object-oriented interface for creating statistical graphics following Grammar of Graphics principles with composable classes for flexible visualization construction.
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```python { .api }
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class Plot:
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    def __init__(self, *args, data=None, **variables): ...
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    def add(self, mark, *transforms, **kwargs): ...
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    def facet(self, col=None, row=None, **kwargs): ...
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    def scale(self, **scales): ...
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# Mark classes  
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class Dot(Mark): ...
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class Line(Mark): ...
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class Bar(Mark): ...
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# Statistical transforms
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class Hist(Stat): ...
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class KDE(Stat): ...
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class Agg(Stat): ...
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# Positional adjustments
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class Jitter(Move): ...
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class Dodge(Move): ...
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class Stack(Move): ...
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```
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[Objects Interface](./objects-interface.md)
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## Types
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Core types used throughout the seaborn API:
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```python { .api }
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# Data input types
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DataFrame = pandas.DataFrame
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Series = pandas.Series
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Array = numpy.ndarray | list
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# Color specifications
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ColorSpec = str | tuple | list  # Color names, hex codes, RGB tuples
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PaletteSpec = str | list | dict  # Palette names or color lists
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# Plot objects
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FacetGrid = seaborn.axisgrid.FacetGrid
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PairGrid = seaborn.axisgrid.PairGrid  
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JointGrid = seaborn.axisgrid.JointGrid
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```