Visualization

Data in a Polars DataFrame can be visualized using common visualization libraries.

We illustrate plotting capabilities using the Iris dataset. We read a CSV and then plot one column against another, colored by a yet another column.

Python

import polars as pl

path = "docs/assets/data/iris.csv"

df = pl.read_csv(path)
print(df)

shape: (150, 5)
┌──────────────┬─────────────┬──────────────┬─────────────┬───────────┐
│ sepal_length ┆ sepal_width ┆ petal_length ┆ petal_width ┆ species   │
│ ---          ┆ ---         ┆ ---          ┆ ---         ┆ ---       │
│ f64          ┆ f64         ┆ f64          ┆ f64         ┆ str       │
╞══════════════╪═════════════╪══════════════╪═════════════╪═══════════╡
│ 5.1          ┆ 3.5         ┆ 1.4          ┆ 0.2         ┆ Setosa    │
│ 4.9          ┆ 3.0         ┆ 1.4          ┆ 0.2         ┆ Setosa    │
│ 4.7          ┆ 3.2         ┆ 1.3          ┆ 0.2         ┆ Setosa    │
│ 4.6          ┆ 3.1         ┆ 1.5          ┆ 0.2         ┆ Setosa    │
│ 5.0          ┆ 3.6         ┆ 1.4          ┆ 0.2         ┆ Setosa    │
│ …            ┆ …           ┆ …            ┆ …           ┆ …         │
│ 6.7          ┆ 3.0         ┆ 5.2          ┆ 2.3         ┆ Virginica │
│ 6.3          ┆ 2.5         ┆ 5.0          ┆ 1.9         ┆ Virginica │
│ 6.5          ┆ 3.0         ┆ 5.2          ┆ 2.0         ┆ Virginica │
│ 6.2          ┆ 3.4         ┆ 5.4          ┆ 2.3         ┆ Virginica │
│ 5.9          ┆ 3.0         ┆ 5.1          ┆ 1.8         ┆ Virginica │
└──────────────┴─────────────┴──────────────┴─────────────┴───────────┘

Built-in plotting with Altair

Polars has a plot method to create plots using Altair:

Python

(
    df.plot.point(
        x="sepal_length",
        y="sepal_width",
        color="species",
    )
    .properties(width=500)
    .configure_scale(zero=False)
)

This is shorthand for:

import altair as alt

(
    alt.Chart(df).mark_point(tooltip=True).encode(
        x="sepal_length",
        y="sepal_width",
        color="species",
    )
    .properties(width=500)
    .configure_scale(zero=False)
)

and is only provided for convenience, and to signal that Altair is known to work well with Polars.

hvPlot

If you import hvplot.polars, then it registers a hvplot method which you can use to create interactive plots using hvPlot.

Python

import hvplot.polars
df.hvplot.scatter(
    x="sepal_width",
    y="sepal_length",
    by="species",
    width=650,
)

hvplot_scatter

Matplotlib

To create a scatter plot we can pass columns of a DataFrame directly to Matplotlib as a Series for each column. Matplotlib does not have explicit support for Polars objects but can accept a Polars Series by converting it to a NumPy array (which is zero-copy for numeric data without null values).

Note that because the column 'species' isn't numeric, we need to first convert it to numeric values so that it can be passed as an argument to c.

Python

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.scatter(
    x=df["sepal_width"],
    y=df["sepal_length"],
    c=df["species"].cast(pl.Categorical).to_physical(),
)

Seaborn and Plotly

Seaborn and Plotly can accept a Polars DataFrame by leveraging the dataframe interchange protocol, which offers zero-copy conversion where possible. Note that the protocol does not support all Polars data types (e.g. List) so your mileage may vary here.

Seaborn

Python

import seaborn as sns
sns.scatterplot(
    df,
    x="sepal_width",
    y="sepal_length",
    hue="species",
)

Plotly

Python

import plotly.express as px

px.scatter(
    df,
    x="sepal_width",
    y="sepal_length",
    color="species",
    width=650,
)