Welcome

Welcome to the Plotynium framework documentation.

What is Plotynium ?

Plotynium is a Data Visualization framework for Python, inspired by Observable Plot.

• Easy to use

  ---

  Implements concise code to explore your data

• Low dependencies

  ---

  plotynium requires only detroit (<3MB) and lxml (<12MB) dependencies.

• Open Source

  ---

  plotynium is licensed under MIT.

Example

import polars as pl
from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

import plotynium as ply

mnist = load_digits()
scaler = StandardScaler()
X_scaled = scaler.fit_transform(mnist.data)
pca = PCA(n_components=2)
components = pca.fit_transform(X_scaled)

# Prepare your data with Polars, Pandas or manually
df = pl.DataFrame(components, schema=["Component 1", "Component 2"])
df = df.insert_column(2, pl.Series("digit", mnist.target))

plot = ply.plot(
    width=960,
    height=657,
    marks=[
        ply.dot(
            df.to_dicts(),
            x="Component 1",
            y="Component 2",
            stroke="digit", # (1)!
            symbol="digit", # (2)!
        )
    ],
    color={"scheme": ply.Interpolation.RAINBOW}, # (3)!
    symbol={"legend": True}, # (4)!
    style={"color": "#e6edf3"}, # (5)!
)

with open("pca.svg", "w") as file:
    file.write(str(plot))

1. Colors of points are chosen given its digit value
2. Symbols of points are chosen given its digit value
3. Check out the colorscheme section to see all available colorschemes.
4. It adds a legend of symbols (color included).
5. Style your plot as you want. Checkout StyleOptions for more option details.