feat: add area-basic implementation (9 libraries)

[MarkusNeusinger]

Summary

Adds area-basic plot implementation.

Libraries

• Merged: 9 (all libraries)
• Not Feasible: 0

Links

• Spec: specs/area-basic.md
• Parent Issue: [area-basic] [update] Regenerate with KISS style #201

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🤖 Auto-generated by pyplots CI

[github-actions]

✅ Plot Tests Passed

Python 3.13 (required): passed

Compatibility:

Note: Only Python 3.13 is required to pass. Python 3.12 is tested for compatibility.

[Copilot]

Pull request overview

This PR attempts to add area-basic plot implementations across all 9 supported libraries. However, all implementations have a critical architectural issue: they use hardcoded data in the main module body instead of following the project's established pattern of providing a reusable create_plot() function with proper type hints, docstrings, and validation.

Critical Issues:

• All 10 implementation files lack the required create_plot(data, x, y, **params) function
• Implementations have hardcoded data in the main body, preventing programmatic use via the API
• Missing input validation, type hints, and comprehensive docstrings
• No if __name__ == "__main__": guard for example code

Impact:

• These implementations cannot be used programmatically through the pyplots API
• They deviate from 100% of existing implementations in the repository (all other plots follow the function-based pattern)
• The spec file correctly shows create_plot() usage in examples (lines 50, 55), but implementations don't provide this function

Reviewed changes

Copilot reviewed 11 out of 12 changed files in this pull request and generated 10 comments.

Show a summary per file

File	Description
specs/area-basic.md	Updated spec with comprehensive requirements, parameters, and examples - looks good and follows expected format
plots/seaborn/fill_between/area-basic/default.py	Missing create_plot() function; has hardcoded data instead
plots/pygal/line/area-basic/default.py	Missing create_plot() function; has hardcoded data instead
plots/pygal/area/area-basic/default.py	Missing create_plot() function; has hardcoded data instead
plots/plotnine/area/area-basic/default.py	Missing create_plot() function; has hardcoded data instead
plots/plotly/scatter/area-basic/default.py	Missing create_plot() function; has hardcoded data instead
plots/matplotlib/fill_between/area-basic/default.py	Missing create_plot() function; has hardcoded data instead
plots/letsplot/area/area-basic/default.py	Missing create_plot() function; has hardcoded data instead
plots/highcharts/area/area-basic/default.py	Missing create_plot() function; has hardcoded data instead
plots/bokeh/varea/area-basic/default.py	Missing create_plot() function; has hardcoded data instead
plots/altair/area/area-basic/default.py	Missing create_plot() function; has hardcoded data instead

[Copilot]

This implementation is missing the required create_plot() function. All plot implementations in this project must follow the standard pattern:

1. Define a create_plot(data: pd.DataFrame, x: str, y: str, **params) function with:

   • Proper type hints (use TYPE_CHECKING for forward references)
   • Comprehensive docstring with Args, Returns, Raises, and Example sections
   • Input validation (empty data, missing columns, data type checks)
   • Parameter handling with sensible defaults
   • Return the appropriate plot object (matplotlib Figure in this case)

2. Use if __name__ == "__main__": block for example/test code

The current implementation has hardcoded data in the main body, which prevents the plot from being used programmatically via the API. Please restructure this to match the pattern used in existing implementations like plots/matplotlib/plot/line-basic/default.py.

[Copilot]

This implementation is missing the required create_plot() function. All plot implementations in this project must follow the standard pattern:

1. Define a create_plot(data: pd.DataFrame, x: str, y: str, **params) function with:

   • Proper type hints (use TYPE_CHECKING for forward references)
   • Comprehensive docstring with Args, Returns, Raises, and Example sections
   • Input validation (empty data, missing columns, data type checks)
   • Parameter handling with sensible defaults
   • Return the appropriate plot object (lets-plot ggplot in this case)

2. Use if __name__ == "__main__": block for example/test code

The current implementation has hardcoded data in the main body, which prevents the plot from being used programmatically via the API. Please restructure this to match the pattern used in existing implementations like plots/letsplot/line/line-basic/default.py.

[Copilot]

This implementation is missing the required create_plot() function. All plot implementations in this project must follow the standard pattern:

1. Define a create_plot(data: pd.DataFrame, x: str, y: str, **params) function with:

   • Proper type hints (use TYPE_CHECKING for forward references)
   • Comprehensive docstring with Args, Returns, Raises, and Example sections
   • Input validation (empty data, missing columns, data type checks)
   • Parameter handling with sensible defaults
   • Return the appropriate plot object (altair Chart in this case)

2. Use if __name__ == "__main__": block for example/test code

The current implementation has hardcoded data in the main body, which prevents the plot from being used programmatically via the API. Please restructure this to match the pattern used in existing implementations like plots/altair/line/line-basic/default.py.

Suggested change

	# Data
	data = pd.DataFrame(
	{
	"month": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
	"sales": [100, 150, 130, 180, 200, 220, 195, 240, 260, 230, 280, 310],
	}
	)
	# Create chart with area and line
	chart = (
	alt.Chart(data)
	.mark_area(opacity=0.5, color="#306998", line={"color": "#306998", "strokeWidth": 2})
	.encode(
	x=alt.X("month:Q", title="Month", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
	y=alt.Y("sales:Q", title="Sales", axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
	)
	.properties(width=1600, height=900, title=alt.TitleParams(text="Basic Area Chart", fontSize=20))
	.configure_view(strokeWidth=0)
	.configure_axis(grid=True, gridOpacity=0.3)
	)
	# Save (1600 × 900 × 3 = 4800 × 2700)
	chart.save("plot.png", scale_factor=3.0)
	from typing import TYPE_CHECKING, Any
	if TYPE_CHECKING:
	from altair import Chart
	def create_plot(
	data: pd.DataFrame,
	x: str,
	y: str,
	*,
	width: int = 1600,
	height: int = 900,
	area_color: str = "#306998",
	opacity: float = 0.5,
	line_color: str = "#306998",
	line_width: int = 2,
	x_title: str | None = None,
	y_title: str | None = None,
	title: str = "Basic Area Chart",
	grid: bool = True,
	grid_opacity: float = 0.3,
	**params: Any,
	) -> "alt.Chart":
	"""
	Create a basic area chart using Altair.
	Args:
	data (pd.DataFrame): Input data.
	x (str): Column name for x-axis (must be numeric or datetime).
	y (str): Column name for y-axis (must be numeric).
	width (int, optional): Chart width in pixels. Defaults to 1600.
	height (int, optional): Chart height in pixels. Defaults to 900.
	area_color (str, optional): Area fill color. Defaults to "#306998".
	opacity (float, optional): Area opacity. Defaults to 0.5.
	line_color (str, optional): Line color. Defaults to "#306998".
	line_width (int, optional): Line width. Defaults to 2.
	x_title (str, optional): X-axis title. Defaults to column name.
	y_title (str, optional): Y-axis title. Defaults to column name.
	title (str, optional): Chart title. Defaults to "Basic Area Chart".
	grid (bool, optional): Show grid. Defaults to True.
	grid_opacity (float, optional): Grid opacity. Defaults to 0.3.
	**params: Additional keyword arguments for extensibility.
	Returns:
	alt.Chart: Altair area chart object.
	Raises:
	ValueError: If data is empty, or x/y columns are missing or of wrong type.
	Example:
	>>> import pandas as pd
	>>> from plots.altair.area.area-basic.default import create_plot
	>>> df = pd.DataFrame({'month': [1,2,3], 'sales': [100, 150, 130]})
	>>> chart = create_plot(df, x='month', y='sales')
	>>> chart.show()
	"""
	if data.empty:
	raise ValueError("Input data is empty.")
	if x not in data.columns:
	raise ValueError(f"Column '{x}' not found in data.")
	if y not in data.columns:
	raise ValueError(f"Column '{y}' not found in data.")
	# Check types
	if not pd.api.types.is_numeric_dtype(data[x]) and not pd.api.types.is_datetime64_any_dtype(data[x]):
	raise ValueError(f"Column '{x}' must be numeric or datetime.")
	if not pd.api.types.is_numeric_dtype(data[y]):
	raise ValueError(f"Column '{y}' must be numeric.")
	x_axis_title = x_title if x_title is not None else x.capitalize()
	y_axis_title = y_title if y_title is not None else y.capitalize()
	chart = (
	alt.Chart(data)
	.mark_area(opacity=opacity, color=area_color, line={"color": line_color, "strokeWidth": line_width})
	.encode(
	x=alt.X(f"{x}:Q", title=x_axis_title, axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
	y=alt.Y(f"{y}:Q", title=y_axis_title, axis=alt.Axis(labelFontSize=16, titleFontSize=20)),
	)
	.properties(width=width, height=height, title=alt.TitleParams(text=title, fontSize=20))
	.configure_view(strokeWidth=0)
	.configure_axis(grid=grid, gridOpacity=grid_opacity)
	)
	return chart
	if __name__ == "__main__":
	# Example usage
	data = pd.DataFrame(
	{
	"month": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
	"sales": [100, 150, 130, 180, 200, 220, 195, 240, 260, 230, 280, 310],
	}
	)
	chart = create_plot(data, x="month", y="sales")
	# Save (1600 × 900 × 3 = 4800 × 2700)
	chart.save("plot.png", scale_factor=3.0)

[Copilot]

This implementation is missing the required create_plot() function. All plot implementations in this project must follow the standard pattern:

1. Define a create_plot(data: pd.DataFrame, x: str, y: str, **params) function with:

   • Proper type hints (use TYPE_CHECKING for forward references)
   • Comprehensive docstring with Args, Returns, Raises, and Example sections
   • Input validation (empty data, missing columns, data type checks)
   • Parameter handling with sensible defaults
   • Return the appropriate plot object (pygal Line chart in this case)

2. Use if __name__ == "__main__": block for example/test code

The current implementation has hardcoded data in the main body, which prevents the plot from being used programmatically via the API. Please restructure this to match the pattern used in existing implementations like plots/pygal/line/line-basic/default.py.

[Copilot]

This implementation is missing the required create_plot() function. All plot implementations in this project must follow the standard pattern:

1. Define a create_plot(data: pd.DataFrame, x: str, y: str, **params) function with:

   • Proper type hints (use TYPE_CHECKING for forward references)
   • Comprehensive docstring with Args, Returns, Raises, and Example sections
   • Input validation (empty data, missing columns, data type checks)
   • Parameter handling with sensible defaults
   • Return the appropriate plot object (plotnine ggplot in this case)

2. Use if __name__ == "__main__": block for example/test code

The current implementation has hardcoded data in the main body, which prevents the plot from being used programmatically via the API. Please restructure this to match the pattern used in existing implementations like plots/plotnine/line/line-basic/default.py.

[Copilot]

This implementation is missing the required create_plot() function. All plot implementations in this project must follow the standard pattern:

1. Define a create_plot(data: pd.DataFrame, x: str, y: str, **params) function with:

   • Proper type hints (use TYPE_CHECKING for forward references)
   • Comprehensive docstring with Args, Returns, Raises, and Example sections
   • Input validation (empty data, missing columns, data type checks)
   • Parameter handling with sensible defaults
   • Return the appropriate plot object (plotly Figure in this case)

2. Use if __name__ == "__main__": block for example/test code

The current implementation has hardcoded data in the main body, which prevents the plot from being used programmatically via the API. Please restructure this to match the pattern used in existing implementations like plots/plotly/line/line-basic/default.py.

[Copilot]

This implementation is missing the required create_plot() function. All plot implementations in this project must follow the standard pattern:

1. Define a create_plot(data: pd.DataFrame, x: str, y: str, **params) function with:

   • Proper type hints (use TYPE_CHECKING for forward references)
   • Comprehensive docstring with Args, Returns, Raises, and Example sections
   • Input validation (empty data, missing columns, data type checks)
   • Parameter handling with sensible defaults
   • Return the appropriate plot object (highcharts Chart in this case)

2. Use if __name__ == "__main__": block for example/test code

The current implementation has hardcoded data in the main body, which prevents the plot from being used programmatically via the API. Please restructure this to match the pattern used in existing implementations like plots/highcharts/line/line-basic/default.py.

[Copilot]

This implementation is missing the required create_plot() function. All plot implementations in this project must follow the standard pattern:

1. Define a create_plot(data: pd.DataFrame, x: str, y: str, **params) function with:

   • Proper type hints (use TYPE_CHECKING for forward references)
   • Comprehensive docstring with Args, Returns, Raises, and Example sections
   • Input validation (empty data, missing columns, data type checks)
   • Parameter handling with sensible defaults
   • Return the appropriate plot object (bokeh Figure in this case)

2. Use if __name__ == "__main__": block for example/test code

The current implementation has hardcoded data in the main body, which prevents the plot from being used programmatically via the API. Please restructure this to match the pattern used in existing implementations like plots/bokeh/line/line-basic/default.py.

[Copilot]

This implementation is missing the required create_plot() function. All plot implementations in this project must follow the standard pattern:

1. Define a create_plot(data: pd.DataFrame, x: str, y: str, **params) function with:

   • Proper type hints (use TYPE_CHECKING for forward references)
   • Comprehensive docstring with Args, Returns, Raises, and Example sections
   • Input validation (empty data, missing columns, data type checks)
   • Parameter handling with sensible defaults
   • Return the appropriate plot object

2. Use if __name__ == "__main__": block for example/test code

The current implementation has hardcoded data in the main body, which prevents the plot from being used programmatically via the API. Please restructure this to match the pattern used in existing implementations like plots/matplotlib/plot/line-basic/default.py or plots/seaborn/lineplot/line-basic/default.py.

[Copilot]

This implementation is missing the required create_plot() function. All plot implementations in this project must follow the standard pattern:

1. Define a create_plot(data: pd.DataFrame, x: str, y: str, **params) function with:

   • Proper type hints (use TYPE_CHECKING for forward references)
   • Comprehensive docstring with Args, Returns, Raises, and Example sections
   • Input validation (empty data, missing columns, data type checks)
   • Parameter handling with sensible defaults
   • Return the appropriate plot object (pygal Line chart in this case)

2. Use if __name__ == "__main__": block for example/test code

The current implementation has hardcoded data in the main body, which prevents the plot from being used programmatically via the API. Please restructure this to match the pattern used in existing implementations like plots/pygal/line/line-basic/default.py.

Suggested change

	# Data - monthly sales example from spec
	months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
	sales = [100, 150, 130, 180, 200, 220]
	# Custom style with project palette
	custom_style = Style(
	background="white",
	plot_background="white",
	foreground="#333333",
	foreground_strong="#333333",
	foreground_subtle="#666666",
	colors=("#306998",),
	font_family="sans-serif",
	title_font_size=60,
	label_font_size=48,
	major_label_font_size=48,
	legend_font_size=48,
	value_font_size=36,
	opacity=0.5,
	opacity_hover=0.7,
	)
	# Create area chart (Line chart with fill=True)
	chart = pygal.Line(
	width=4800,
	height=2700,
	title="Monthly Sales",
	x_title="Month",
	y_title="Sales ($)",
	style=custom_style,
	show_legend=False,
	fill=True,
	show_x_guides=False,
	show_y_guides=True,
	dots_size=8,
	stroke_style={"width": 4},
	)
	# Add data
	chart.x_labels = months
	chart.add("Sales", sales)
	# Save
	chart.render_to_png("plot.png")
	import pandas as pd
	from typing import TYPE_CHECKING, Any
	if TYPE_CHECKING:
	from pygal import Line
	def create_plot(
	data: pd.DataFrame,
	x: str,
	y: str,
	*,
	title: str = "Monthly Sales",
	x_title: str = "Month",
	y_title: str = "Sales ($)",
	color: str = "#306998",
	width: int = 4800,
	height: int = 2700,
	fill: bool = True,
	show_legend: bool = False,
	show_x_guides: bool = False,
	show_y_guides: bool = True,
	dots_size: int = 8,
	stroke_width: int = 4,
	opacity: float = 0.5,
	opacity_hover: float = 0.7,
	style: Style = None,
	**params: Any,
	) -> "Line":
	"""
	Create a basic area chart using pygal.
	Args:
	data (pd.DataFrame): Input data.
	x (str): Column name for x-axis (categorical).
	y (str): Column name for y-axis (numeric).
	title (str, optional): Chart title. Defaults to "Monthly Sales".
	x_title (str, optional): X-axis label. Defaults to "Month".
	y_title (str, optional): Y-axis label. Defaults to "Sales ($)".
	color (str, optional): Area color. Defaults to "#306998".
	width (int, optional): Chart width in px. Defaults to 4800.
	height (int, optional): Chart height in px. Defaults to 2700.
	fill (bool, optional): Fill area under line. Defaults to True.
	show_legend (bool, optional): Show legend. Defaults to False.
	show_x_guides (bool, optional): Show x-axis grid lines. Defaults to False.
	show_y_guides (bool, optional): Show y-axis grid lines. Defaults to True.
	dots_size (int, optional): Size of dots. Defaults to 8.
	stroke_width (int, optional): Line width. Defaults to 4.
	opacity (float, optional): Area opacity. Defaults to 0.5.
	opacity_hover (float, optional): Opacity on hover. Defaults to 0.7.
	style (Style, optional): Custom pygal style. If None, uses default project style.
	**params: Additional keyword arguments for pygal.Line.
	Returns:
	pygal.Line: Configured pygal Line chart object (with fill=True for area).
	Raises:
	ValueError: If data is empty, or columns are missing/invalid.
	TypeError: If y column is not numeric.
	Example:
	>>> import pandas as pd
	>>> df = pd.DataFrame({'Month': ['Jan', 'Feb'], 'Sales': [100, 150]})
	>>> chart = create_plot(df, x='Month', y='Sales')
	>>> chart.render_to_png('plot.png')
	"""
	if data is None or data.empty:
	raise ValueError("Input data is empty.")
	if x not in data.columns:
	raise ValueError(f"Column '{x}' not found in data.")
	if y not in data.columns:
	raise ValueError(f"Column '{y}' not found in data.")
	if not pd.api.types.is_numeric_dtype(data[y]):
	raise TypeError(f"Column '{y}' must be numeric.")
	x_labels = data[x].astype(str).tolist()
	y_values = data[y].tolist()
	if style is None:
	style = Style(
	background="white",
	plot_background="white",
	foreground="#333333",
	foreground_strong="#333333",
	foreground_subtle="#666666",
	colors=(color,),
	font_family="sans-serif",
	title_font_size=60,
	label_font_size=48,
	major_label_font_size=48,
	legend_font_size=48,
	value_font_size=36,
	opacity=opacity,
	opacity_hover=opacity_hover,
	)
	chart = pygal.Line(
	width=width,
	height=height,
	title=title,
	x_title=x_title,
	y_title=y_title,
	style=style,
	show_legend=show_legend,
	fill=fill,
	show_x_guides=show_x_guides,
	show_y_guides=show_y_guides,
	dots_size=dots_size,
	stroke_style={"width": stroke_width},
	**params,
	)
	chart.x_labels = x_labels
	chart.add(y_title if not show_legend else y, y_values)
	return chart
	if __name__ == "__main__":
	# Example usage
	import pandas as pd
	months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]
	sales = [100, 150, 130, 180, 200, 220]
	df = pd.DataFrame({"Month": months, "Sales": sales})
	chart = create_plot(df, x="Month", y="Sales")
	chart.render_to_png("plot.png")