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Dec 6, 2025
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feat(plotnine): implement box-basic #292

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212 changes: 39 additions & 173 deletions plots/plotnine/boxplot/box-basic/default.py
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Original file line number Diff line number Diff line change
@@ -1,183 +1,49 @@
"""
box-basic: Basic Box Plot
Implementation for: plotnine
Variant: default
Python: 3.10+
Library: plotnine
"""

from typing import TYPE_CHECKING, Optional

import numpy as np
import pandas as pd
from plotnine import (
aes,
element_line,
element_text,
geom_boxplot,
ggplot,
labs,
scale_fill_brewer,
theme,
theme_minimal,
from plotnine import aes, element_text, geom_boxplot, ggplot, labs, scale_fill_manual, theme, theme_minimal


# Data
np.random.seed(42)
data = pd.DataFrame(
{
"group": ["A"] * 50 + ["B"] * 50 + ["C"] * 50 + ["D"] * 50,
"value": np.concatenate(
[
np.random.normal(50, 10, 50),
np.random.normal(60, 15, 50),
np.random.normal(45, 8, 50),
np.random.normal(70, 20, 50),
]
),
}
)


if TYPE_CHECKING:
from plotnine import ggplot as GGPlot


def create_plot(
data: pd.DataFrame,
values: str,
groups: str,
title: Optional[str] = None,
xlabel: Optional[str] = None,
ylabel: Optional[str] = None,
fill_palette: str = "Set2",
width: int = 16,
height: int = 9,
show_outliers: bool = True,
**kwargs,
) -> GGPlot:
"""
Create a basic box plot showing statistical distribution of multiple groups using plotnine (ggplot2 syntax).

Args:
data: Input DataFrame with required columns
values: Column name containing numeric values
groups: Column name containing group categories
title: Plot title (optional)
xlabel: Custom x-axis label (optional, defaults to groups column name)
ylabel: Custom y-axis label (optional, defaults to values column name)
fill_palette: Color palette for boxes (default: 'Set2')
width: Figure width in inches (default: 16)
height: Figure height in inches (default: 9)
show_outliers: Whether to show outliers (default: True)
**kwargs: Additional parameters for geom_boxplot

Returns:
plotnine ggplot object

Raises:
ValueError: If data is empty
KeyError: If required columns not found

Example:
>>> data = pd.DataFrame({
... 'Group': ['A', 'A', 'B', 'B', 'C', 'C'],
... 'Value': [1, 2, 2, 3, 3, 4]
... })
>>> plot = create_plot(data, values='Value', groups='Group')
"""
# Input validation
if data.empty:
raise ValueError("Data cannot be empty")

# Check required columns
for col in [values, groups]:
if col not in data.columns:
available = ", ".join(data.columns)
raise KeyError(f"Column '{col}' not found. Available columns: {available}")

# Create the ggplot object
plot = (
ggplot(data, aes(x=groups, y=values, fill=groups))
+ geom_boxplot(
alpha=0.7,
outlier_alpha=0.5 if show_outliers else 0,
outlier_size=2,
outlier_color="red",
width=0.6,
**kwargs,
)
+ scale_fill_brewer(type="qual", palette=fill_palette, guide=None) # Hide legend
+ labs(title=title or "Box Plot Distribution", x=xlabel or groups, y=ylabel or values)
+ theme_minimal()
+ theme(
figure_size=(width, height),
plot_title=element_text(size=14, weight="bold", ha="center"),
axis_title=element_text(size=11),
axis_text=element_text(size=10),
panel_grid_major_x=element_line(alpha=0),
panel_grid_major_y=element_line(alpha=0.3, linetype="dashed"),
panel_grid_minor=element_line(alpha=0),
)
)

# Rotate x-axis labels if there are many groups
unique_groups = data[groups].nunique()
if unique_groups > 5:
plot = plot + theme(axis_text_x=element_text(angle=45, ha="right"))

# Add sample size annotations
# plotnine doesn't have easy text annotations like ggplot2's annotate,
# but we can add them as a separate layer
from plotnine import geom_text

# Calculate group statistics for annotations
group_stats = data.groupby(groups).agg(count=(values, "count"), min_val=(values, "min")).reset_index()

# Adjust y position for annotations
y_range = data[values].max() - data[values].min()
y_position = data[values].min() - y_range * 0.05

group_stats["y_pos"] = y_position
group_stats["label"] = "n=" + group_stats["count"].astype(str)

# Add annotations as a separate layer
plot = plot + geom_text(
aes(x=groups, y="y_pos", label="label"), data=group_stats, size=9, alpha=0.7, va="top", ha="center"
)

return plot


if __name__ == "__main__":
# Sample data for testing with different distributions per group
np.random.seed(42) # For reproducibility

# Generate sample data with 4 groups
data_dict = {"Group": [], "Value": []}

# Group A: Normal distribution, mean=50, std=10
group_a_data = np.random.normal(50, 10, 40)
# Add some outliers
group_a_data = np.append(group_a_data, [80, 85, 15])

# Group B: Normal distribution, mean=60, std=15
group_b_data = np.random.normal(60, 15, 35)
# Add outliers
group_b_data = np.append(group_b_data, [100, 10])

# Group C: Normal distribution, mean=45, std=8
group_c_data = np.random.normal(45, 8, 45)

# Group D: Skewed distribution
group_d_data = np.random.gamma(2, 2, 30) + 40
# Add outliers
group_d_data = np.append(group_d_data, [75, 78, 20])

# Combine all data
for group, values in zip(
["Group A", "Group B", "Group C", "Group D"],
[group_a_data, group_b_data, group_c_data, group_d_data],
strict=False,
):
data_dict["Group"].extend([group] * len(values))
data_dict["Value"].extend(values)

data = pd.DataFrame(data_dict)

# Create plot
plot = create_plot(
data,
values="Value",
groups="Group",
title="Statistical Distribution Comparison Across Groups",
ylabel="Measurement Value",
xlabel="Categories",
# Colors from style guide
colors = ["#306998", "#FFD43B", "#DC2626", "#059669"]

# Create plot
plot = (
ggplot(data, aes(x="group", y="value", fill="group"))
+ geom_boxplot(alpha=0.8, outlier_alpha=0.6)
+ scale_fill_manual(values=colors)
+ labs(x="Group", y="Value", title="Basic Box Plot")
+ theme_minimal()
+ theme(
figure_size=(16, 9),
plot_title=element_text(size=20),
axis_title=element_text(size=20),
axis_text=element_text(size=16),
legend_title=element_text(size=16),
legend_text=element_text(size=16),
legend_position="none",
)
)

# Save for inspection
plot.save("plot.png", dpi=300, verbose=False)
print("Plot saved to plot.png")
# Save
plot.save("plot.png", dpi=300)
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