Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

plotnine

Release License DOI Build Status Coverage

plotnine is an implementation of a grammar of graphics in Python based on ggplot2. The grammar allows you to compose plots by explicitly mapping variables in a dataframe to the visual characteristics (position, color, size etc.) of objects that make up the plot.

Plotting with a grammar of graphics is powerful. Custom (and otherwise complex) plots are easy to think about and build incrementally, while the simple plots remain simple to create.

To learn more about how to use plotnine, check out the documentation. Since plotnine has an API similar to ggplot2, where it lacks in coverage the ggplot2 documentation may be helpful.

Example

from plotnine import *
from plotnine.data import mtcars

Building a complex plot piece by piece.

  1. Scatter plot

    (
        ggplot(mtcars, aes("wt", "mpg"))
        + geom_point()
    )
    
  2. Scatter plot colored according some variable

    (
        ggplot(mtcars, aes("wt", "mpg", color="factor(gear)"))
        + geom_point()
    )
    
  3. Scatter plot colored according some variable and smoothed with a linear model with confidence intervals.

    (
        ggplot(mtcars, aes("wt", "mpg", color="factor(gear)"))
        + geom_point()
        + stat_smooth(method="lm")
    )
    
  4. Scatter plot colored according some variable, smoothed with a linear model with confidence intervals and plotted on separate panels.

    (
        ggplot(mtcars, aes("wt", "mpg", color="factor(gear)"))
        + geom_point()
        + stat_smooth(method="lm")
        + facet_wrap("gear")
    )
    
  5. Adjust the themes

    I) Make it playful

    (
        ggplot(mtcars, aes("wt", "mpg", color="factor(gear)"))
        + geom_point()
        + stat_smooth(method="lm")
        + facet_wrap("gear")
        + theme_xkcd()
    )
    

    II) Or professional

    (
        ggplot(mtcars, aes("wt", "mpg", color="factor(gear)"))
        + geom_point()
        + stat_smooth(method="lm")
        + facet_wrap("gear")
        + theme_tufte()
    )
    

Installation

Official release

# Using pip
$ pip install plotnine             # 1. should be sufficient for most
$ pip install 'plotnine[extra]'    # 2. includes extra/optional packages
$ pip install 'plotnine[test]'     # 3. testing
$ pip install 'plotnine[doc]'      # 4. generating docs
$ pip install 'plotnine[dev]'      # 5. development (making releases)
$ pip install 'plotnine[all]'      # 6. everything

# Or using conda
$ conda install -c conda-forge plotnine

Development version

$ pip install git+https://github.com/has2k1/plotnine.git

Contributing

Our documentation could use some examples, but we are looking for something a little bit special. We have two criteria:

  1. Simple looking plots that otherwise require a trick or two.
  2. Plots that are part of a data analytic narrative. That is, they provide some form of clarity showing off the geom, stat, ... at their differential best.

If you come up with something that meets those criteria, we would love to see it. See plotnine-examples.

If you discover a bug checkout the issues if it has not been reported, yet please file an issue.

And if you can fix a bug, your contribution is welcome.

Testing

Plotnine has tests that generate images which are compared to baseline images known to be correct. To generate images that are consistent across all systems you have to install matplotlib from source. You can do that with pip using the command.

$ pip install matplotlib --no-binary matplotlib

Otherwise there may be small differences in the text rendering that throw off the image comparisons.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

plotnine-0.15.0a8.tar.gz (6.8 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

plotnine-0.15.0a8-py3-none-any.whl (1.3 MB view details)

Uploaded Python 3

File details

Details for the file plotnine-0.15.0a8.tar.gz.

File metadata

  • Download URL: plotnine-0.15.0a8.tar.gz
  • Upload date:
  • Size: 6.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for plotnine-0.15.0a8.tar.gz
Algorithm Hash digest
SHA256 5dd4b4a645d8501d2b4078f3c19f7e69c49b66d4fdf018a2142e392610d4e64f
MD5 62bcdccbfa556716581ab1bc1fd06926
BLAKE2b-256 0d6a889a9a3a3e9b7fd71a8738a0da2c79b22c64a87b572855c0c05a673ab847

See more details on using hashes here.

Provenance

The following attestation bundles were made for plotnine-0.15.0a8.tar.gz:

Publisher: release.yml on has2k1/plotnine

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file plotnine-0.15.0a8-py3-none-any.whl.

File metadata

  • Download URL: plotnine-0.15.0a8-py3-none-any.whl
  • Upload date:
  • Size: 1.3 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for plotnine-0.15.0a8-py3-none-any.whl
Algorithm Hash digest
SHA256 93fec03c23c698b8b1fe104207155bdc922ed0de90b447424e614839d8936a96
MD5 cd879c15fe01bb42b52446a991898846
BLAKE2b-256 fe6ca41f29d3affe06e7699f72fdaaf4ce3bac0ea76ee5b475e9d1f982a6b7e0

See more details on using hashes here.

Provenance

The following attestation bundles were made for plotnine-0.15.0a8-py3-none-any.whl:

Publisher: release.yml on has2k1/plotnine

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page