Categorical Plots
Existing Python plotting libraries such as seaborn
nad plotly
have excellent support for high-level categorical plotting methods that use
DataFrame objects as input.
In whitecanvas
, we provide similar functionality, but these methods
does not depend on any external plotting libraries or DataFrames.
The cat
namespace
The cat
namespace converts a tabular data into a categorical plotter.
Currently, following objects are allowed as input:
dict
of array-like objectspandas.DataFrame
polars.DataFrame
from whitecanvas import new_canvas
canvas = new_canvas("matplotlib")
# sample data
df = {
"label": ["A"] * 60 + ["B"] * 30 + ["C"] * 40,
"value": np.random.normal(size=130),
}
canvas.cat(df, by="label").add_stripplot("value")
canvas.show()
You can directly pass a categorized dict
object. In this case, you should
not specify the column name parameters.
from whitecanvas import new_canvas
canvas = new_canvas("matplotlib")
# sample data
df = {
"A": np.random.normal(size=60),
"B": np.random.normal(size=30),
"C": np.random.normal(size=40),
}
canvas.cat(df).add_stripplot()
canvas.show()
Several plotting methods are available in the cat
namespace:
add_stripplot()
add_boxplot()
add_violinplot()
add_swarmplot()
add_countplot()