lets-plot 1.5.4

An open source library for statistical plotting

Lets-Plot for Python

Latest Release
License
OS	Linux, MacOS, Windows
Python versions	3.6, 3.7, 3.8

• Overview
• Installation
• Quick start with Jupyter
• Example Notebooks
• GGBunch
• Data Sampling
• Export to File
• Cloud Notebooks
• Geospatial
  • GeoPandas Support
  • Interactive Maps
  • Geocoding API
• Interesting Demos
• Offline Mode
• Scientific Mode in IntelliJ IDEA / PyCharm
• What is new in 1.5.3
• Change Log
• License

Overview

The Lets-Plot for Python library includes a 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 (i.e. you can ignore the Installation chapter below).

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.

Begin with the quickstart in Datalore notebook to learn more about Datalore notebooks.

Watch the Datalore Getting Started Tutorial video for a quick introduction to Datalore.

Installation

1. For Linux and Mac users:

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

pip install lets-plot

2. For Windows users:

Install Anaconda3 (or Miniconda3), then install MinGW toolchain to Conda:

conda install m2w64-toolchain

Install the Lets-Plot library:

pip install lets-plot

Quick start with 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 *
LetsPlot.setup_html()        

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')

        

Example Notebooks

See Example Notebooks.

GGBunch

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.

• ggbunch.ipynb
• geom_smooth.ipynb
• scatter_matrix.ipynb

Data Sampling

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

Learn more: Data Sampling.

Export to File

The ggsave() function is an easy way to export plot to a file in SVG or HTML formats.

Note: The ggsave() function currently do not save images of interactive maps to SVG.

Example notebook: export_SVG_HTML

Cloud Notebooks

Examples:

• Datalore
• Kaggle
• Colab

Geospatial

GeoPandas Support

GeoPandas GeoDataFrame is supported by the following geometry layers: geom_polygon, geom_map, geom_point, geom_text, geom_rect.

Learn more: GeoPandas Support.

Interactive Maps

Interactive maps allow zooming and panning around geospatial data that can be added to the base-map layer using regular ggplot geoms.

Learn more: Interactive Maps.

Geocoding API

Geocoding is the process of converting names of places into geographic coordinates.

Learn more: Geocoding API.

Interesting Demos

A set of interesting notebooks using Lets-Plot library for visualization.

Offline Mode

In classic Jupyter notebook the LetsPlot.setup_html() statement by default pre-loads Lets-Plot JS library from CDN. Alternatively, option offline=True will force Lets-Plot adding the full Lets-Plot JS bundle to the notebook. In this case, plots in the notebook will be working without an Internet connection.

from lets_plot import *

LetsPlot.setup_html(offline=True)

Scientific mode in IntelliJ IDEA / PyCharm

Plugin "Lets-Plot in SciView" is available at the JetBrains Plugin Repository.

The plugin adds support for interactive plots in IntelliJ-based IDEs with the enabled Scientific mode.

To learn more about the plugin check: Lets-Plot in SciView plugin homepage.

What is new in 1.5.3

• Tooltip Customization

  New API for customization of tooltip contents and its position (see Tooltip Customization).

• Attribution when Configuring 3-rd Party Map-tiles

  New arguments in the maptiles_zxy() function allowing configuring attributions when using 3-rd party map-tiles as a base-map layer.

  See The Gallery of Base-maps.

• Formatting lables in geom_text()

  New parameter, 'label_format' to define a formatting pattern.

  See demo: label_format.ipynb

• Export to File

  The ggsave() function is an easy way to export plot to a file in SVG or HTML formats.

  Note: The ggsave() function currently do not save images of interactive maps to SVG.

  Example: export_SVG_HTML

• Fixed 'HUE' Scale and Other Fixes

  See CHANGELOG.md for details.

Change Log

See CHANGELOG.md.

License

Code and documentation released under the MIT license. Copyright © 2019-2020, JetBrains s.r.o.