https://github.com/Kitware/seaborn_altair

Seaborn-compatible API for interactive Vega-Lite plots via Altair
https://github.com/Kitware/seaborn_altair

Last synced: 5 days ago
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Seaborn-compatible API for interactive Vega-Lite plots via Altair

• Host: GitHub
• URL: https://github.com/Kitware/seaborn_altair
• Owner: Kitware
• License: apache-2.0
• Created: 2018-06-02T23:52:02.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-09-01T02:11:23.000Z (about 6 years ago)
• Last Synced: 2024-04-24T09:20:02.863Z (7 months ago)
• Language: Jupyter Notebook
• Homepage: https://kitware.github.io/seaborn_altair/
• Size: 17.5 MB
• Stars: 20
• Watchers: 5
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# seaborn_altair

Seaborn-compatible API for interactive Vega-Lite plots via Altair.

## Installation

    pip install seaborn_altair

Works in Jupyter lab or Jupyter notebooks.

## Usage

    import seaborn_altair as salt

    import seaborn as sns

    tips = sns.load_dataset("tips")

    # Use salt as you would sns

    salt.barplot(x="day", y="total_bill", data=tips)

![barplot](https://github.com/kitware/seaborn_altair/raw/master/img/visualization.png)

## API

This is only a proof of concept at this time. Only a subset of Seaborn plots (the ones with example links) are currently available with limited support.

### Axis grids

* [FacetGrid](http://kitware.github.io/seaborn_altair/facetgrid.html) Subplot grid for plotting conditional relationships.

* [catplot](http://kitware.github.io/seaborn_altair/catplot.html) Draw a categorical plot onto a FacetGrid.

* [lmplot](http://kitware.github.io/seaborn_altair/lmplot.html) Plot data and regression model fits across a FacetGrid.

* PairGrid Subplot grid for plotting pairwise relationships in a dataset.

* pairplot Plot pairwise relationships in a dataset.

* JointGrid Grid for drawing a bivariate plot with marginal univariate plots.

* jointplot Draw a plot of two variables with bivariate and univariate graphs.

### Categorical plots

* [stripplot](http://kitware.github.io/seaborn_altair/stripplot.html) Draw a scatterplot where one variable is categorical.

* swarmplot Draw a categorical scatterplot with non-overlapping points.

* [boxplot](http://kitware.github.io/seaborn_altair/boxplot.html) Draw a box plot to show distributions with respect to categories.

* violinplot Draw a combination of boxplot and kernel density estimate.

* lvplot Draw a letter value plot to show distributions of large datasets.

* [pointplot](http://kitware.github.io/seaborn_altair/pointplot.html) Show point estimates and confidence intervals using scatter plot glyphs.

* [barplot](http://kitware.github.io/seaborn_altair/barplot.html) Show point estimates and confidence intervals as rectangular bars.

* [countplot](http://kitware.github.io/seaborn_altair/countplot.html) Show the counts of observations in each categorical bin using bars.

### Distribution plots

* distplot Flexibly plot a univariate distribution of observations.

* kdeplot Fit and plot a univariate or bivariate kernel density estimate.

* rugplot Plot datapoints in an array as sticks on an axis.

### Regression plots

* [regplot](http://kitware.github.io/seaborn_altair/regplot.html) Plot data and a linear regression model fit.

* residplot Plot the residuals of a linear regression.

### Matrix plots

* heatmap Plot rectangular data as a color-encoded matrix.

* clustermap Plot a matrix dataset as a hierarchically-clustered heatmap.

### Timeseries plots

* tsplot Plot one or more timeseries with flexible representation of uncertainty.

### Miscellaneous plots

* palplot Plot the values in a color palette as a horizontal array.

### matplotlib.pyplot utilities

* [pyplot.hist](http://kitware.github.io/seaborn_altair/pyplot-hist.html)

* [pyplot.plot](http://kitware.github.io/seaborn_altair/pyplot-plot.html)

* [pyplot.scatter](http://kitware.github.io/seaborn_altair/pyplot-scatter.html)

## Credit

Idea from [Jake VanderPlas](https://twitter.com/jakevdp/status/996041414596214784). [I](https://twitter.com/jeffbaumes) know Python and Vega-Lite reasonably well, so decided to give it a shot.

Contributions and suggestions welcome!