Skip to main content

plotnine

Release License DOI Build Status Documentation 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 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 incrementaly, 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. everyting

# 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.

Release files for plotnine 0.12.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for plotnine 0.12.3
File Size Uploaded
plotnine-0.12.3.tar.gz 5.8 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for plotnine 0.12.3
File Interpreter ABI Platform
plotnine-0.12.3-py3-none-any.whl Python 3 none any Details

Total release size: 7.0 MB

Release files / plotnine-0.12.3.tar.gz

Download URL plotnine-0.12.3.tar.gz
Size 5.8 MB
Tags Source
SHA-256 checksum
How to use checksums
a38dcb3607fc003c1e59ae0c9d535dae7817650d1cbc2e56e56e5b3de88dfe99
BLAKE2b-256 checksum
How to use checksums
04dc4f25118044f9c664677cae844da83f68b44cf917327fe6d3717fb66b4d33
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.11.4

Release files / plotnine-0.12.3-py3-none-any.whl

Download URL plotnine-0.12.3-py3-none-any.whl
Size 1.3 MB
Tags Python 3
SHA-256 checksum
How to use checksums
3868a538aecaf44505f7218e2ffedc5611a7b23c2cad4a1e28e1255b403f467b
BLAKE2b-256 checksum
How to use checksums
ebc1fcc5985eee6511aa321e68c8f813d9fdbe1b506713a95d4f612a5f963270
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.11.4

Release history Release notifications | RSS feed

0.15.8

2 release files

0.15.7

2 release files

0.15.6

2 release files

0.15.3

2 release files

0.15.2

2 release files

0.15.1

2 release files

0.15.0

2 release files

0.14.6

2 release files

0.14.4

2 release files

0.14.3

2 release files

0.14.2

2 release files

0.14.0

2 release files

0.13.5

2 release files

0.13.3

2 release files

0.13.2

2 release files

0.13.0

2 release files

This release

0.12.3 This release

2 release files

0.12.2

2 release files

0.10.1

2 release files

0.10.0

2 release files

0.9.0

2 release files

0.8.0

2 release files

0.7.1

2 release files

0.7.0

2 release files

0.6.0

2 release files

0.5.1

2 release files

0.5.0

2 release files

0.4.0

2 release files

0.3.0

2 release files

0.2.1

2 release files

0.2.0

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page