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Dec 6, 2025
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feat(pygal): implement box-basic #311

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199 changes: 42 additions & 157 deletions plots/pygal/box/box-basic/default.py
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Original file line number Diff line number Diff line change
@@ -1,166 +1,51 @@
"""
box-basic: Basic Box Plot
Implementation for: pygal
Variant: default
Python: 3.10+
Library: pygal
"""

from typing import TYPE_CHECKING, Optional

import numpy as np
import pandas as pd
import pygal
from pygal.style import Style


if TYPE_CHECKING:
from pygal import Box


def create_plot(
data: pd.DataFrame,
values: str,
groups: str,
title: Optional[str] = None,
xlabel: Optional[str] = None,
ylabel: Optional[str] = None,
width: int = 1600,
height: int = 900,
show_legend: bool = True,
**kwargs,
) -> Box:
"""
Create a basic box plot showing statistical distribution of multiple groups using pygal.

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)
width: Figure width in pixels (default: 1600)
height: Figure height in pixels (default: 900)
show_legend: Whether to show legend (default: True)
**kwargs: Additional parameters for pygal configuration

Returns:
pygal Box chart 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]
... })
>>> chart = 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 custom style
custom_style = Style(
background="white",
plot_background="white",
foreground="#333",
foreground_strong="#333",
foreground_subtle="#555",
opacity=0.7,
opacity_hover=0.9,
colors=("#66c2a5", "#fc8d62", "#8da0cb", "#e78ac3", "#a6d854", "#ffd92f", "#e5c494", "#b3b3b3"),
font_family="Arial, sans-serif",
major_guide_stroke_dasharray="3,3",
guide_stroke_dasharray="1,1",
)

# Create box plot
box_chart = pygal.Box(
title=title or "Box Plot Distribution",
x_title=xlabel or groups,
y_title=ylabel or values,
width=width,
height=height,
show_legend=show_legend,
style=custom_style,
box_mode="tukey", # Use Tukey method (1.5 * IQR for whiskers)
print_values=False,
print_zeroes=False,
**kwargs,
)

# Calculate box plot data for each group
group_names = sorted(data[groups].unique())

for group in group_names:
group_data = data[data[groups] == group][values].dropna()

# Pygal's Box chart expects data in a specific format:
# [min, Q1, median, Q3, max] or the raw values (pygal will calculate)
# We'll provide the raw values and let pygal handle the calculations
values_list = group_data.tolist()

# Add the series with label
box_chart.add(f"{group} (n={len(values_list)})", values_list)

return box_chart


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
chart = create_plot(
data,
values="Value",
groups="Group",
title="Statistical Distribution Comparison Across Groups",
ylabel="Measurement Value",
xlabel="Categories",
)

# Save as PNG
chart.render_to_png("plot.png")
print("Plot saved to plot.png")
# Data
np.random.seed(42)
group_a = np.random.normal(50, 10, 50).tolist()
group_b = np.random.normal(60, 15, 50).tolist()
group_c = np.random.normal(45, 8, 50).tolist()
group_d = np.random.normal(70, 20, 50).tolist()

# Custom style using project palette
custom_style = Style(
background="white",
plot_background="white",
foreground="#333333",
foreground_strong="#333333",
foreground_subtle="#666666",
colors=("#306998", "#FFD43B", "#DC2626", "#059669", "#8B5CF6", "#F97316"),
title_font_size=48,
label_font_size=40,
legend_font_size=36,
value_font_size=32,
)

# Create box plot
chart = pygal.Box(
width=4800,
height=2700,
title="Basic Box Plot",
x_title="Group",
y_title="Value",
style=custom_style,
show_legend=True,
legend_at_bottom=True,
)

# Add data series
chart.add("A", group_a)
chart.add("B", group_b)
chart.add("C", group_c)
chart.add("D", group_d)

# Save
chart.render_to_png("plot.png")
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