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fleur: combining statistics with visualization

fleur logo

With fleur (pronounced "flur"), statistics and data visualization are done at the same time. It's meant as a modern tool for highly detailed statistical annotations in plots with high customization capabilities.

It only requires foundational libraries: matplotlib, scipy and narwhals. Learn more about fleur.

Warning

fleur is still in a very early stage: expect regular breaking changes.


Examples

Currently, fleur offers 3 features that you can benefit from:

  • BetweenStats: Use this when you want to compare numerical data across categories (e.g., customer satisfaction between two product versions)
  • ScatterStats: Use this to explore the correlation between numerical variables (e.g., the relationship between age and salary)
  • BarStats: Use this to examine the relationship between categorical variables (e.g., the relationship between gender and smoking status)
# mkdocs: render
from fleur import BetweenStats
from fleur import data

df = data.load_iris()

BetweenStats(df["sepal_length"], df["species"]).plot()

# mkdocs: render
from fleur import ScatterStats
from fleur import data

df = data.load_iris()

ScatterStats(df["sepal_length"], df["sepal_width"]).plot()

# mkdocs: render
from fleur import BarStats
from fleur import data

df = data.load_mtcars()

BarStats(df["cyl"], df["vs"]).plot()

See more examples

Installation

pip install fleur

pip install git+https://github.com/y-sunflower/fleur.git