plotnine
Latest Release
License
DOI
Build Status
Coverage
Documentation
plotnine is an implementation of a grammar of graphics in Python,
it is based on ggplot2. The grammar allows users to compose plots
by explicitly mapping data to the visual objects that make up the
plot.
Plotting with a grammar is powerful, it makes custom (and otherwise
complex) plots easy to think about and then create, while the
simple plots remain simple.
To find out about all building blocks that you can use to create a
plot, check out the documentation. Since plotnine has an API
similar to ggplot2, where we lack in coverage the
ggplot2 documentation may be of some help.
Example
from plotnine import *
from plotnine.data import mtcars
Building a complex plot piece by piece.
Scatter plot
(ggplot(mtcars, aes(‘wt’, ‘mpg’))
+ geom_point())
Scatter plot colored according some variable
(ggplot(mtcars, aes(‘wt’, ‘mpg’, color=’factor(gear)’))
+ geom_point())
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’))
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’))
Make it playful
(ggplot(mtcars, aes(‘wt’, ‘mpg’, color=’factor(gear)’))
+ geom_point()
+ stat_smooth(method=’lm’)
+ facet_wrap(‘~gear’)
+ theme_xkcd())
Installation
Official release
# Using pip
$ pip install plotnine # 1. should be sufficient for most
$ pip install ‘plotnine[all]’ # 2. includes extra/optional packages
# 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:
Simple looking plots that otherwise require a trick or two.
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.
今天的文章python主要应用于哪些方面_Python的起源分享到此就结束了,感谢您的阅读。
版权声明:本文内容由互联网用户自发贡献,该文观点仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容, 请发送邮件至 举报,一经查实,本站将立刻删除。
如需转载请保留出处:https://bianchenghao.cn/62507.html