seaborn

Name: seaborn
Author: ai4protein

244stars35forks

How to install seaborn?

ai4protein/venusfactory2/seaborn

$npx -y skills add ai4protein/VenusFactory2 --skill seaborn

Installs into the current project.

Summary

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for p

Files · 1

View on GitHub

SKILL.md

1	# Seaborn Statistical Visualization
2
3	## Overview
4
5	Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.
6
7	## Design Philosophy
8
9	Seaborn follows these core principles:
10
11	1. Dataset-oriented: Work directly with DataFrames and named variables rather than abstract coordinates
12	2. Semantic mapping: Automatically translate data values into visual properties (colors, sizes, styles)
13	3. Statistical awareness: Built-in aggregation, error estimation, and confidence intervals
14	4. Aesthetic defaults: Publication-ready themes and color palettes out of the box
15	5. Matplotlib integration: Full compatibility with matplotlib customization when needed
16
17	## Quick Start
18
19	```python
20	import seaborn as sns
21	import matplotlib.pyplot as plt
22	import pandas as pd
23
24	# Load example dataset
25	df = sns.load_dataset('tips')
26
27	# Create a simple visualization
28	sns.scatterplot(data=df, x='total_bill', y='tip', hue='day')
29	plt.show()
30	```
31
32	## Core Plotting Interfaces
33
34	### Function Interface (Traditional)
35
36	The function interface provides specialized plotting functions organized by visualization type. Each category has axes-level functions (plot to single axes) and figure-level functions (manage entire figure with faceting).
37
38	When to use:
39	- Quick exploratory analysis
40	- Single-purpose visualizations
41	- When you need a specific plot type
42
43	### Objects Interface (Modern)
44
45	The `seaborn.objects` interface provides a declarative, composable API similar to ggplot2. Build visualizations by chaining methods to specify data mappings, marks, transformations, and scales.
46
47	When to use:
48	- Complex layered visualizations
49	- When you need fine-grained control over transformations
50	- Building custom plot types
51	- Programmatic plot generation
52
53	```python
54	from seaborn import objects as so
55
56	# Declarative syntax
57	(
58	so.Plot(data=df, x='total_bill', y='tip')
59	.add(so.Dot(), color='day')
60	.add(so.Line(), so.PolyFit())
61	)
62	```
63
64	## Plotting Functions by Category
65
66	### Relational Plots (Relationships Between Variables)
67
68	Use for: Exploring how two or more variables relate to each other
69
70	- `scatterplot()` - Display individual observations as points
71	- `lineplot()` - Show trends and changes (automatically aggregates and computes CI)
72	- `relplot()` - Figure-level interface with automatic faceting
73
74	Key parameters:
75	- `x`, `y` - Primary variables
76	- `hue` - Color encoding for additional categorical/continuous variable
77	- `size` - Point/line size encoding
78	- `style` - Marker/line style encoding
79	- `col`, `row` - Facet into multiple subplots (figure-level only)
80
81	```python
82	# Scatter with multiple semantic mappings
83	sns.scatterplot(data=df, x='total_bill', y='tip',
84	hue='time', size='size', style='sex')
85
86	# Line plot with confidence intervals
87	sns.lineplot(data=timeseries, x='date', y='value', hue='category')
88
89	# Faceted relational plot
90	sns.relplot(data=df, x='total_bill', y='tip',
91	col='time', row='sex', hue='smoker', kind='scatter')
92	```
93
94	### Distribution Plots (Single and Bivariate Distributions)
95
96	Use for: Understanding data spread, shape, and probability density
97
98	- `histplot()` - Bar-based frequency distributions with flexible binning
99	- `kdeplot()` - Smooth density estimates using Gaussian kernels
100	- `ecdfplot()` - Empirical cumulative distribution (no parameters to tune)
101	- `rugplot()` - Individual observation tick marks
102	- `displot()` - Figure-level interface for univariate and bivariate distributions
103	- `jointplot()` - Bivariate plot with marginal distributions
104	- `pairplot()` - Matrix of pairwise relationships across dataset
105
106	Key parameters:
107	- `x`, `y` - Variables (y optional for univariate)
108	- `hue` - Separate distributions by category
109	- `stat` - Normalization: "count", "frequency", "probability", "density"
110	- `bins` / `binwidth` - Histogram binning control
111	- `bw_adjust` - KDE bandwidth multiplier (higher = smoother)
112	- `fill` - Fill area under curve
113	- `multiple` - How to handle hue: "layer", "stack", "dodge", "fill"
114
115	```python
116	# Histogram with density normalization
117	sns.histplot(data=df, x='total_bill', hue='time',
118	stat='density', multiple='stack')
119
120	# Bivariate KDE with contours
121	sns.kdeplot(data=df, x='total_bill', y='tip',
122	fill=True, levels=5, thresh=0.1)
123
124	# Joint plot with marginals
125	sns.jointplot(data=df, x='total_bill', y='tip',
126	kind='scatter', hue='time')
127
128	# Pairwise

People who liked this also viewed

6 picks

Type

seaborn

244stars35forks

View on GitHub

How to install seaborn?

ai4protein/venusfactory2/seaborn

$npx -y skills add ai4protein/VenusFactory2 --skill seaborn

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Seaborn Statistical Visualization
2
3	## Overview
4
5	Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.
6
7	## Design Philosophy
8
9	Seaborn follows these core principles:
10
11	1. Dataset-oriented: Work directly with DataFrames and named variables rather than abstract coordinates
12	2. Semantic mapping: Automatically translate data values into visual properties (colors, sizes, styles)
13	3. Statistical awareness: Built-in aggregation, error estimation, and confidence intervals
14	4. Aesthetic defaults: Publication-ready themes and color palettes out of the box
15	5. Matplotlib integration: Full compatibility with matplotlib customization when needed
16
17	## Quick Start
18
19	```python
20	import seaborn as sns
21	import matplotlib.pyplot as plt
22	import pandas as pd
23
24	# Load example dataset
25	df = sns.load_dataset('tips')
26
27	# Create a simple visualization
28	sns.scatterplot(data=df, x='total_bill', y='tip', hue='day')
29	plt.show()
30	```
31
32	## Core Plotting Interfaces
33
34	### Function Interface (Traditional)
35
36	The function interface provides specialized plotting functions organized by visualization type. Each category has axes-level functions (plot to single axes) and figure-level functions (manage entire figure with faceting).
37
38	When to use:
39	- Quick exploratory analysis
40	- Single-purpose visualizations
41	- When you need a specific plot type
42
43	### Objects Interface (Modern)
44
45	The `seaborn.objects` interface provides a declarative, composable API similar to ggplot2. Build visualizations by chaining methods to specify data mappings, marks, transformations, and scales.
46
47	When to use:
48	- Complex layered visualizations
49	- When you need fine-grained control over transformations
50	- Building custom plot types
51	- Programmatic plot generation
52
53	```python
54	from seaborn import objects as so
55
56	# Declarative syntax
57	(
58	so.Plot(data=df, x='total_bill', y='tip')
59	.add(so.Dot(), color='day')
60	.add(so.Line(), so.PolyFit())
61	)
62	```
63
64	## Plotting Functions by Category
65
66	### Relational Plots (Relationships Between Variables)
67
68	Use for: Exploring how two or more variables relate to each other
69
70	- `scatterplot()` - Display individual observations as points
71	- `lineplot()` - Show trends and changes (automatically aggregates and computes CI)
72	- `relplot()` - Figure-level interface with automatic faceting
73
74	Key parameters:
75	- `x`, `y` - Primary variables
76	- `hue` - Color encoding for additional categorical/continuous variable
77	- `size` - Point/line size encoding
78	- `style` - Marker/line style encoding
79	- `col`, `row` - Facet into multiple subplots (figure-level only)
80
81	```python
82	# Scatter with multiple semantic mappings
83	sns.scatterplot(data=df, x='total_bill', y='tip',
84	hue='time', size='size', style='sex')
85
86	# Line plot with confidence intervals
87	sns.lineplot(data=timeseries, x='date', y='value', hue='category')
88
89	# Faceted relational plot
90	sns.relplot(data=df, x='total_bill', y='tip',
91	col='time', row='sex', hue='smoker', kind='scatter')
92	```
93
94	### Distribution Plots (Single and Bivariate Distributions)
95
96	Use for: Understanding data spread, shape, and probability density
97
98	- `histplot()` - Bar-based frequency distributions with flexible binning
99	- `kdeplot()` - Smooth density estimates using Gaussian kernels
100	- `ecdfplot()` - Empirical cumulative distribution (no parameters to tune)
101	- `rugplot()` - Individual observation tick marks
102	- `displot()` - Figure-level interface for univariate and bivariate distributions
103	- `jointplot()` - Bivariate plot with marginal distributions
104	- `pairplot()` - Matrix of pairwise relationships across dataset
105
106	Key parameters:
107	- `x`, `y` - Variables (y optional for univariate)
108	- `hue` - Separate distributions by category
109	- `stat` - Normalization: "count", "frequency", "probability", "density"
110	- `bins` / `binwidth` - Histogram binning control
111	- `bw_adjust` - KDE bandwidth multiplier (higher = smoother)
112	- `fill` - Fill area under curve
113	- `multiple` - How to handle hue: "layer", "stack", "dodge", "fill"
114
115	```python
116	# Histogram with density normalization
117	sns.histplot(data=df, x='total_bill', hue='time',
118	stat='density', multiple='stack')
119
120	# Bivariate KDE with contours
121	sns.kdeplot(data=df, x='total_bill', y='tip',
122	fill=True, levels=5, thresh=0.1)
123
124	# Joint plot with marginals
125	sns.jointplot(data=df, x='total_bill', y='tip',
126	kind='scatter', hue='time')
127
128	# Pairwise

People who liked this also viewed

6 picks

Type