lets-plot 1.2.0rc2

An open source library for statistical plotting

Lets-Plot for Python

Latest Release
License
OS	Linux, MacOS
Python versions	3.7, 3.8

Implementation Overview

The Lets-Plot python extension includes native backend and a Python API, which was mostly based on the ggplot2 package well-known to data scientists who use R.

R ggplot2 has extensive documentation and a multitude of examples and therefore is an excellent resource for those who want to learn the grammar of graphics.

Note that the Python API being very similar yet is different in detail from R. Although we have not implemented the entire ggplot2 API in our Python package, we have added a few new features to our Python API.

You can try the Lets-Plot library in Datalore. Lets-Plot is available in Datalore out-of-the-box and is almost identical to the one we ship as PyPI package. This is because Lets-Plot is an offshoot of the Datalore project from which it was extracted to a separate plotting library.

One important difference is that the python package in Datalore is named datalore.plot and the package you install from PyPI has name lets_plot.

The advantage of Datalore as a learning tool in comparison to Jupyter is that it is equipped with very friendly Python editor which comes with auto-completion, intentions, and other useful coding assistance features.

Installation

To install the Lets-Plot library, run the following command:

pip install lets-plot

Quickstart in Jupyter

To evaluate the plotting capabilities of Lets-Plot, add the following code to a Jupyter notebook:

import numpy as np
from lets_plot import *

np.random.seed(12)
data = dict(
    cond=np.repeat(['A','B'], 200),
    rating=np.concatenate((np.random.normal(0, 1, 200), np.random.normal(1, 1.5, 200)))
)

ggplot(data, aes(x='rating', fill='cond')) + ggsize(500, 250) \
+ geom_density(color='dark_green', alpha=.7) + scale_fill_brewer(type='seq') \
+ theme(axis_line_y='blank')

Examples

Try the following examples to study more features of the Lets-Plot library.

Quickstart in Jupyter: quickstart.ipynb

Histogram, density plot, box plot and facets: distributions.ipynb

Error-bars, points, lines, bars, dodge position: error_bars.ipynb

Points, point shapes, linear regression, jitter position: scatter_plot.ipynb

Points, density2d, polygons, density2df: density_2d.ipynb

Tiles, contours, polygons, contourf: contours.ipynb

Various presentation options: legend_and_axis.ipynb

Other demos

A set of interesting notebooks with Lets-Plot for visualization.

New to experienced users

The following features of Lets-Plot are not present or have different implementation in other Grammar of Graphics libraries.

Plotting functions

• ggsize() - sets the size of the plot. Used in many examples starting from quickstart.

• geom_density2df() - fills space between equal density lines on a 2D density plot. Similar to geom_density2d but supports the fill aesthetic.

  Example: density_2d.ipynb

• geom_contourf() - fills space between the lines of equal level of the bivariate function. Similar to geom_contour but supports the fill aesthetic.

  Example: contours.ipynb

• geom_image() - displays an image specified by a ndarray with shape (n,m) or (n,m,3) or (n,m,4).

  Example: image_101.ipynb

  Example: image_fisher_boat.ipynb

• gg_image_matrix() - a utility helping to combine several images into one graphical object.

  Example: image_matrix.ipynb

GGBanch

GGBunch allows to show a collection of plots on one figure. Each plot in the collection can have arbitrary location and size. There is no automatic layout inside the bunch.

Examples:

• ggbunch.ipynb
• scatter_matrix.ipynb

Data sampling

Sampling is a special technique of data transformation, which helps dealing with large datasets and overplotting.

Learn more about sampling in Lets-Plot.

Change Log

See Lets-Plot at Github.

License

Code and documentation released under the MIT license. Copyright 2019, JetBrains s.r.o.