Metadata-Version: 2.1
Name: plotnine
Version: 0.0.post1+gc71769d.d20221026
Summary: A grammar of graphics for python
Home-page: https://plotnine.readthedocs.io/en/stable
Author: Hassan Kibirige
Author-email: has2k1@gmail.com
License: MIT
Project-URL: Documentation, https://plotnine.readthedocs.io/en/stable
Project-URL: Source, https://github.com/has2k1/plotnine
Project-URL: Bug Tracker, https://github.com/has2k1/plotnine/issues
Project-URL: CI, https://github.com/has2k1/plotnine/actions
Platform: any
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Framework :: Matplotlib
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib (>=3.5.0)
Requires-Dist: mizani (>=0.8.1)
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Requires-Dist: patsy (>=0.5.1)
Requires-Dist: scipy (>=1.5.0)
Requires-Dist: statsmodels (>=0.13.2)
Provides-Extra: all
Requires-Dist: plotnine[extra] ; extra == 'all'
Requires-Dist: plotnine[test] ; extra == 'all'
Requires-Dist: plotnine[doc] ; extra == 'all'
Requires-Dist: plotnine[dev] ; extra == 'all'
Provides-Extra: dev
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Provides-Extra: doc
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Provides-Extra: test
Requires-Dist: flake8 ; extra == 'test'
Requires-Dist: pytest-cov ; extra == 'test'

# plotnine

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[![Documentation](https://readthedocs.org/projects/plotnine/badge/?version=latest)](https://plotnine.readthedocs.io/en/latest/)
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plotnine is an implementation of a *grammar of graphics* in Python,
it is based on [ggplot2](https://github.com/tidyverse/ggplot2).
The grammar allows users to compose plots by explicitly mapping data
to the visual objects that make up the plot.

<img width="33%" align="right" src="./doc/images/logo-540.png">

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](https://plotnine.readthedocs.io/en/latest/).
Since plotnine has an API similar to ggplot2, where we lack in coverage the
[ggplot2 documentation](http://ggplot2.tidyverse.org/reference/index.html)
may be of some help.


## Example

```python
from plotnine import *
from plotnine.data import mtcars
```

Building a complex plot piece by piece.

1. Scatter plot

   ```python
   (ggplot(mtcars, aes('wt', 'mpg'))
    + geom_point())
   ```

   <img width="90%" align="center" src="./doc/images/readme-image-1.png">

2. Scatter plot colored according some variable

   ```python
   (ggplot(mtcars, aes('wt', 'mpg', color='factor(gear)'))
    + geom_point())
   ```

   <img width="90%" align="center" src="./doc/images/readme-image-2.png">

3. Scatter plot colored according some variable and
   smoothed with a linear model with confidence intervals.

   ```python
   (ggplot(mtcars, aes('wt', 'mpg', color='factor(gear)'))
    + geom_point()
    + stat_smooth(method='lm'))
   ```

   <img width="90%" align="center" src="./doc/images/readme-image-3.png">

4. Scatter plot colored according some variable,
   smoothed with a linear model with confidence intervals and
   plotted on separate panels.

   ```python
   (ggplot(mtcars, aes('wt', 'mpg', color='factor(gear)'))
    + geom_point()
    + stat_smooth(method='lm')
    + facet_wrap('~gear'))
   ```

   <img width="90%" align="center" src="./doc/images/readme-image-4.png">

5. Adjust the themes

   I) Make it playful

   ```python
   (ggplot(mtcars, aes('wt', 'mpg', color='factor(gear)'))
    + geom_point()
    + stat_smooth(method='lm')
    + facet_wrap('~gear')
    + theme_xkcd())
   ```

   <img width="90%" align="center" src="./doc/images/readme-image-5.png">

   II) Or professional

   ```python
   (ggplot(mtcars, aes('wt', 'mpg', color='factor(gear)'))
    + geom_point()
    + stat_smooth(method='lm')
    + facet_wrap('~gear')
    + theme_tufte())
   ```

   <img width="90%" align="center" src="./doc/images/readme-image-5alt.png">


## Installation

Official release

```console
# 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

```console
$ 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](https://github.com/has2k1/plotnine-examples).

If you discover a bug checkout the [issues](https://github.com/has2k1/plotnine/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.

```console
$ pip install matplotlib --no-binary matplotlib
```

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