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https://github.com/tpvasconcelos/ridgeplot
Beautiful ridgeline plots in Python
https://github.com/tpvasconcelos/ridgeplot
data-analysis data-science data-visualization distplot ggridges graphing joyplot plot plotly plotting python ridgeline visualization
Last synced: 1 day ago
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Beautiful ridgeline plots in Python
- Host: GitHub
- URL: https://github.com/tpvasconcelos/ridgeplot
- Owner: tpvasconcelos
- License: mit
- Created: 2021-01-21T18:21:30.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-10-17T01:12:58.000Z (3 months ago)
- Last Synced: 2024-10-17T06:24:24.438Z (3 months ago)
- Topics: data-analysis, data-science, data-visualization, distplot, ggridges, graphing, joyplot, plot, plotly, plotting, python, ridgeline, visualization
- Language: Python
- Homepage: https://ridgeplot.readthedocs.io/
- Size: 7.53 MB
- Stars: 65
- Watchers: 4
- Forks: 3
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
- awesome - tpvasconcelos/ridgeplot - Beautiful ridgeline plots in Python (Python)
README
ridgeplot: beautiful ridgeline plots in Python
-----------------
`ridgeplot` is a Python package that provides a simple interface for plotting beautiful and interactive [ridgeline plots](https://ridgeplot.readthedocs.io/en/stable/getting_started/getting_started.html) within the extensive [Plotly](https://plotly.com/python/) ecosystem.
## Installation
`ridgeplot` can be installed and updated from [PyPi](https://pypi.org/project/ridgeplot/) using [pip](https://pip.pypa.io/en/stable/quickstart/):
```shell
pip install -U ridgeplot
```
For more information, see the [installation guide](https://ridgeplot.readthedocs.io/en/stable/getting_started/installation.html).
## Getting started
Take a look at the [getting started guide](https://ridgeplot.readthedocs.io/en/stable/getting_started/getting_started.html), which provides a quick introduction to the `ridgeplot` library.
The full official documentation can be found at: https://ridgeplot.readthedocs.io/en/stable/
### Basic example
For those in a hurry, here's a very basic example on how to quickly get started with [ridgeplot()](https://ridgeplot.readthedocs.io/en/stable/api/public/ridgeplot.ridgeplot.html) function.
```python
import numpy as np
from ridgeplot import ridgeplot
my_samples = [np.random.normal(n / 1.2, size=600) for n in range(7, 0, -1)]
fig = ridgeplot(samples=my_samples)
fig.show()
```
### Flexible configuration
In this example, we will try to replicate the first ridgeline plot in this [_from Data to Viz_ post](https://www.data-to-viz.com/graph/ridgeline.html). The example in the post was created using the [_"Perception of Probability Words"_ dataset](https://ridgeplot.readthedocs.io/en/stable/api/public/ridgeplot.datasets.load_probly.html) and the popular [ggridges](https://wilkelab.org/ggridges/) R package. In the end, we will see how the `ridgeplot` Python library can be used to create a (nearly) identical plot, thanks to its extensive configuration options.
```python
import numpy as np
from ridgeplot import ridgeplot
from ridgeplot.datasets import load_probly
# Load the probly dataset
df = load_probly()
# Let's grab the subset of columns used in the example
column_names = [
"Almost Certainly",
"Very Good Chance",
"We Believe",
"Likely",
"About Even",
"Little Chance",
"Chances Are Slight",
"Almost No Chance",
]
df = df[column_names]
# Not only does 'ridgeplot(...)' come configured with sensible defaults
# but is also fully configurable to your own style and preference!
fig = ridgeplot(
samples=df.to_numpy().T,
bandwidth=4,
kde_points=np.linspace(-12.5, 112.5, 500),
colorscale="viridis",
colormode="row-index",
opacity=0.6,
labels=column_names,
spacing=5 / 9,
)
# And you can still update and extend the final
# Plotly Figure using standard Plotly methods
fig.update_layout(
height=560,
width=800,
font_size=16,
plot_bgcolor="white",
xaxis_tickvals=[-12.5, 0, 12.5, 25, 37.5, 50, 62.5, 75, 87.5, 100, 112.5],
xaxis_ticktext=["", "0", "", "25", "", "50", "", "75", "", "100", ""],
xaxis_gridcolor="rgba(0, 0, 0, 0.1)",
yaxis_gridcolor="rgba(0, 0, 0, 0.1)",
yaxis_title=dict(text="Assigned Probability (%)", font_size=13),
showlegend=False,
)
# Show us the work!
fig.show()
```
### More examples
For more examples, take a look at the [getting started guide](https://ridgeplot.readthedocs.io/en/stable/getting_started/getting_started.html). For instance, this example demonstrates how you can also draw [multiple traces](https://ridgeplot.readthedocs.io/en/stable/getting_started/getting_started.html#more-traces) per row in your ridgeline plot:
```python
import numpy as np
from ridgeplot import ridgeplot
from ridgeplot.datasets import load_lincoln_weather
# Load test data
df = load_lincoln_weather()
# Transform the data into a 3D (ragged) array format of
# daily min and max temperature samples per month
months = df.index.month_name().unique()
samples = [
[
df[df.index.month_name() == month]["Min Temperature [F]"],
df[df.index.month_name() == month]["Max Temperature [F]"],
]
for month in months
]
# And finish by styling it up to your liking!
fig = ridgeplot(
samples=samples,
labels=months,
colorscale="Inferno",
bandwidth=4,
kde_points=np.linspace(-40, 110, 400),
spacing=0.3,
)
fig.update_layout(
title="Minimum and maximum daily temperatures in Lincoln, NE (2016)",
height=600,
width=800,
font_size=14,
plot_bgcolor="rgb(245, 245, 245)",
xaxis_gridcolor="white",
yaxis_gridcolor="white",
xaxis_gridwidth=2,
yaxis_title="Month",
xaxis_title="Temperature [F]",
showlegend=False,
)
fig.show()
```
