Note
Click here to download the full example code
Categorical Plot¶
What it's for:
The categorical_plot function is a versatile tool for visualizing data that is
grouped into discrete categories (e.g., different models, sites, or seasons). It
supports various underlying plot types like bars, boxes, or violins.
When to use: Use this to compare aggregate statistics across different groups. It is ideal for showing how Mean Bias or RMSE varies between different model versions or different geographic regions.
How to read: * X-axis: Represents discrete categories. * Y-axis: Represents the numerical variable being compared. * Interpretation: In a bar plot (as shown here), the height of the bar usually represents the mean or median of the group. Error bars (if present) show the variability or uncertainty within that category.
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from monet_plots.plots.categorical import categorical_plot
# 1. Prepare sample data
# Create a sample xarray DataArray with a categorical dimension
np.random.seed(42) # for reproducibility
data = xr.DataArray(
np.random.normal(loc=10, scale=2, size=(100, 3)),
coords={"sample": np.arange(100), "category": ["Group A", "Group B", "Group C"]},
dims=["sample", "category"],
name="measurement",
)
# 2. Create a basic bar plot
# Note: categorical_plot handles conversion to dataframe internally if needed
fig, ax = categorical_plot(
data,
x="category",
y="measurement",
kind="bar",
title="Mean Measurement per Category",
xlabel="Category",
ylabel="Measurement Value",
)
plt.tight_layout()
plt.show()
Total running time of the script: ( 0 minutes 0.556 seconds)
Download Python source code: plot_categorical.py