A small exploratory-data-analysis tool that shows how a target variable behaves across grouped values of a feature. It is designed with categorical / binary targets in mind (it plots the observed class rate), but it also works for continuous targets (it plots the mean).
This tool was originally called a Partial Dependence Plot (PDP), but that name is technically inaccurate. A true PDP requires a fitted model and marginalizes its predictions over the other features.
The EDP uses no model and does no marginalization — it plots the observed conditional mean of the target given a single feature, straight from the data. That makes it closely related to the M-plot (marginal plot) from the ALE literature (Apley & Zhu, 2020). Calling it Empirical Dependence Plot reflects exactly what it computes.
The old pdp function still works as a deprecated alias (see below).
pip install edp-toolIn a notebook / Colab cell:
!pip install -q edp-tool
from edp_tool import edpThe distribution is published on PyPI as
edp-tool; the import name is edp_tool
(pip install edp_tool also works — pip normalizes the two spellings).
Alternatively, just copy edp_tool.py next to your notebook:
import os
url = 'https://raw.githubusercontent.com/attilalr/edp-tool/main/edp_tool.py'
if not os.path.isfile('edp_tool.py'):
!wget -q {url}
from edp_tool import edpRequirements: numpy, pandas, matplotlib (installed automatically by pip;
see also requirements.txt).
from edp_tool import edp
# Binary target -> positive-class rate per bin, with a Wilson CI band
edp(df, ['age', 'income'], 'converted')
# Multiclass target -> one line per class
edp(df, ['petal length (cm)'], 'species')
# Continuous target -> mean per bin, with a standard-error band
edp(df, ['age'], 'price')
# Save figures instead of showing them
edp(df, features, 'target', writefolder='figs')edp() returns a list of dicts (feature, fig, ax, and path when saved),
so you can post-process or embed the figures.
| Parameter | Default | Meaning |
|---|---|---|
n |
4 |
Number of bins for continuous features (upper bound) |
kind |
"auto" |
"auto" / "continuous" / "categorical" treatment per feature |
binning |
"quantile" |
"quantile" or "uniform" bin edges |
ci |
"auto" |
"wilson", "sem", "auto", or None (Wilson for classes, SEM for regression) |
max_categories |
10 |
Numeric columns with ≤ this many distinct values are treated as categorical |
show_bincount |
True |
Draw per-bin sample count on a secondary axis |
show_baseline |
True |
Draw the global target mean/rate as a reference line |
ylim_origin |
True |
Start the y-axis at 0 |
even_spaced_ticks |
False |
Place continuous bins at real midpoints |
writefolder |
None |
Save PNGs to this folder instead of showing |
Multiclass targets are handled natively — no manual one-hot encoding needed.
- Renamed to EDP (
edp_tool.edp);pdp_tool.pdpkept as a deprecated alias. - Fixed the dead categorical branch (feature type is now detected correctly).
- Fixed
nleaking across features and the maximum value being dropped from the last bin. - Native multiclass support, Wilson confidence intervals for rates, optional baseline line and uniform binning.
- Figures are returned and properly closed; validation raises real exceptions.
git clone https://github.com/attilalr/edp-tool
cd edp-tool
pip install -e ".[dev]"
pytestMIT — see LICENSE.