diff --git a/plots/altair/bar/histogram-basic/default.py b/plots/altair/bar/histogram-basic/default.py
new file mode 100644
index 0000000000..a98f8eb813
--- /dev/null
+++ b/plots/altair/bar/histogram-basic/default.py
@@ -0,0 +1,26 @@
+"""
+histogram-basic: Basic Histogram
+Library: altair
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data
+np.random.seed(42)
+data = pd.DataFrame({"value": np.random.normal(100, 15, 500)})
+
+# Create histogram chart
+chart = (
+ alt.Chart(data)
+ .mark_bar(color="#306998", opacity=0.8)
+ .encode(alt.X("value:Q", bin=alt.Bin(maxbins=30), title="Value"), alt.Y("count()", title="Frequency"))
+ .properties(width=1600, height=900, title="Basic Histogram")
+ .configure_axis(labelFontSize=16, titleFontSize=20)
+ .configure_title(fontSize=20)
+)
+
+# Save as PNG (1600 × 900 at scale 3 = 4800 × 2700)
+chart.save("plot.png", scale_factor=3.0)
diff --git a/plots/bokeh/quad/histogram-basic/default.py b/plots/bokeh/quad/histogram-basic/default.py
new file mode 100644
index 0000000000..07bb421191
--- /dev/null
+++ b/plots/bokeh/quad/histogram-basic/default.py
@@ -0,0 +1,51 @@
+"""
+histogram-basic: Basic Histogram
+Library: bokeh
+"""
+
+import numpy as np
+from bokeh.io import export_png
+from bokeh.plotting import figure
+
+
+# Data - 500 normally distributed values (mean=100, std=15)
+np.random.seed(42)
+values = np.random.normal(100, 15, 500)
+
+# Compute histogram bins
+hist, edges = np.histogram(values, bins=30)
+
+# Create figure (4800 x 2700 px for high resolution)
+p = figure(width=4800, height=2700, title="Basic Histogram", x_axis_label="Value", y_axis_label="Frequency")
+
+# Draw histogram using quad glyph
+p.quad(
+ top=hist,
+ bottom=0,
+ left=edges[:-1],
+ right=edges[1:],
+ fill_color="#306998",
+ fill_alpha=0.7,
+ line_color="white",
+ line_width=1,
+)
+
+# Style title
+p.title.text_font_size = "20pt"
+p.title.align = "center"
+
+# Style axis labels
+p.xaxis.axis_label_text_font_size = "20pt"
+p.yaxis.axis_label_text_font_size = "20pt"
+p.xaxis.major_label_text_font_size = "16pt"
+p.yaxis.major_label_text_font_size = "16pt"
+
+# Style grid - subtle
+p.xgrid.grid_line_alpha = 0.3
+p.ygrid.grid_line_alpha = 0.3
+
+# Ensure y-axis starts at zero
+p.y_range.start = 0
+
+# Save output
+export_png(p, filename="plot.png")
diff --git a/plots/highcharts/histogram/histogram-basic/default.py b/plots/highcharts/histogram/histogram-basic/default.py
new file mode 100644
index 0000000000..1d39589cd2
--- /dev/null
+++ b/plots/highcharts/histogram/histogram-basic/default.py
@@ -0,0 +1,124 @@
+"""
+histogram-basic: Basic Histogram
+Library: highcharts
+"""
+
+import tempfile
+import time
+import urllib.request
+from pathlib import Path
+
+import numpy as np
+from highcharts_core.chart import Chart
+from highcharts_core.options import HighchartsOptions
+from highcharts_core.options.series.bar import ColumnSeries
+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+
+
+# Data
+np.random.seed(42)
+values = np.random.normal(100, 15, 500) # 500 values, mean=100, std=15
+
+# Calculate histogram bins
+bins = 30
+counts, bin_edges = np.histogram(values, bins=bins)
+
+# Create bin labels (center of each bin)
+bin_centers = [(bin_edges[i] + bin_edges[i + 1]) / 2 for i in range(len(counts))]
+bin_labels = [f"{bin_edges[i]:.1f}-{bin_edges[i + 1]:.1f}" for i in range(len(counts))]
+
+# Create chart with container ID
+chart = Chart(container="container")
+chart.options = HighchartsOptions()
+
+# Chart configuration
+chart.options.chart = {
+ "type": "column",
+ "width": 4800,
+ "height": 2700,
+ "backgroundColor": "#ffffff",
+ "spacingBottom": 120, # Add space for x-axis title
+}
+
+# Title
+chart.options.title = {"text": "Basic Histogram", "style": {"fontSize": "48px", "fontWeight": "bold"}}
+
+# X-axis configuration
+chart.options.x_axis = {
+ "categories": bin_labels,
+ "title": {"text": "Value", "style": {"fontSize": "40px"}},
+ "labels": {
+ "rotation": 315, # 315 degrees = -45 degrees
+ "style": {"fontSize": "28px"},
+ "step": 3, # Show every 3rd label to avoid overlap
+ },
+}
+
+# Y-axis configuration
+chart.options.y_axis = {
+ "title": {"text": "Frequency", "style": {"fontSize": "40px"}},
+ "min": 0,
+ "gridLineWidth": 1,
+ "gridLineDashStyle": "Dot",
+ "gridLineColor": "rgba(0, 0, 0, 0.15)",
+ "labels": {"style": {"fontSize": "32px"}},
+}
+
+# Create series with histogram data
+series = ColumnSeries()
+series.data = counts.tolist()
+series.name = "Frequency"
+series.color = "#306998" # Python Blue
+series.border_color = "white"
+series.border_width = 1
+
+# Plot options for histogram appearance
+chart.options.plot_options = {"column": {"pointPadding": 0, "groupPadding": 0, "borderWidth": 1, "opacity": 0.8}}
+
+chart.add_series(series)
+
+# Legend (single series, hide)
+chart.options.legend = {"enabled": False}
+
+# Credits
+chart.options.credits = {"enabled": False}
+
+# Download Highcharts JS (required for headless Chrome)
+highcharts_url = "https://code.highcharts.com/highcharts.js"
+with urllib.request.urlopen(highcharts_url, timeout=30) as response:
+ highcharts_js = response.read().decode("utf-8")
+
+# Generate HTML with inline scripts
+html_str = chart.to_js_literal()
+html_content = f"""
+
+
+
+
+
+
+
+
+
+"""
+
+# Write temp HTML and take screenshot
+with tempfile.NamedTemporaryFile(mode="w", suffix=".html", delete=False, encoding="utf-8") as f:
+ f.write(html_content)
+ temp_path = f.name
+
+chrome_options = Options()
+chrome_options.add_argument("--headless")
+chrome_options.add_argument("--no-sandbox")
+chrome_options.add_argument("--disable-dev-shm-usage")
+chrome_options.add_argument("--disable-gpu")
+chrome_options.add_argument("--window-size=4800,2800") # Slightly larger to capture full chart
+
+driver = webdriver.Chrome(options=chrome_options)
+driver.get(f"file://{temp_path}")
+time.sleep(5) # Wait for chart to render
+driver.save_screenshot("plot.png")
+driver.quit()
+
+Path(temp_path).unlink() # Clean up temp file
diff --git a/plots/letsplot/histogram/histogram-basic/default.py b/plots/letsplot/histogram/histogram-basic/default.py
new file mode 100644
index 0000000000..ca5a3afb99
--- /dev/null
+++ b/plots/letsplot/histogram/histogram-basic/default.py
@@ -0,0 +1,28 @@
+"""
+histogram-basic: Basic Histogram
+Library: letsplot
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import LetsPlot, aes, element_text, geom_histogram, ggplot, ggsave, ggsize, labs, theme, theme_minimal
+
+
+LetsPlot.setup_html()
+
+# Data
+np.random.seed(42)
+data = pd.DataFrame({"value": np.random.normal(100, 15, 500)})
+
+# Plot
+plot = (
+ ggplot(data, aes(x="value"))
+ + geom_histogram(bins=30, fill="#306998", color="white", alpha=0.8)
+ + labs(x="Value", y="Frequency", title="Basic Histogram")
+ + theme_minimal()
+ + theme(plot_title=element_text(size=20), axis_title=element_text(size=20), axis_text=element_text(size=16))
+ + ggsize(1600, 900)
+)
+
+# Save - scale 3x to get 4800 x 2700 px
+ggsave(plot, "plot.png", path=".", scale=3)
diff --git a/plots/matplotlib/hist/histogram-basic/default.py b/plots/matplotlib/hist/histogram-basic/default.py
new file mode 100644
index 0000000000..ac2191c2ab
--- /dev/null
+++ b/plots/matplotlib/hist/histogram-basic/default.py
@@ -0,0 +1,27 @@
+"""
+histogram-basic: Basic Histogram
+Library: matplotlib
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+
+# Data
+np.random.seed(42)
+data = pd.DataFrame({"value": np.random.normal(100, 15, 500)})
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+ax.hist(data["value"], bins=30, alpha=0.8, color="#306998", edgecolor="white", linewidth=0.5)
+
+# Labels and styling
+ax.set_xlabel("Value", fontsize=20)
+ax.set_ylabel("Frequency", fontsize=20)
+ax.set_title("Basic Histogram", fontsize=20)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, axis="y")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")
diff --git a/plots/plotly/histogram/histogram-basic/default.py b/plots/plotly/histogram/histogram-basic/default.py
new file mode 100644
index 0000000000..c64ca0d2f4
--- /dev/null
+++ b/plots/plotly/histogram/histogram-basic/default.py
@@ -0,0 +1,31 @@
+"""
+histogram-basic: Basic Histogram
+Library: plotly
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+
+
+# Data
+np.random.seed(42)
+values = np.random.normal(100, 15, 500) # 500 values, mean=100, std=15
+
+# Create figure
+fig = go.Figure()
+fig.add_trace(go.Histogram(x=values, marker={"color": "#306998", "line": {"color": "white", "width": 1}}, opacity=0.85))
+
+# Layout
+fig.update_layout(
+ title={"text": "Basic Histogram", "font": {"size": 40}, "x": 0.5, "xanchor": "center"},
+ xaxis_title="Value",
+ yaxis_title="Frequency",
+ template="plotly_white",
+ font={"size": 32},
+ xaxis={"title_font": {"size": 40}, "tickfont": {"size": 32}, "showgrid": True, "gridcolor": "rgba(0,0,0,0.1)"},
+ yaxis={"title_font": {"size": 40}, "tickfont": {"size": 32}, "showgrid": True, "gridcolor": "rgba(0,0,0,0.1)"},
+ bargap=0.05,
+)
+
+# Save
+fig.write_image("plot.png", width=1600, height=900, scale=3)
diff --git a/plots/plotnine/histogram/histogram-basic/default.py b/plots/plotnine/histogram/histogram-basic/default.py
new file mode 100644
index 0000000000..657ace1c28
--- /dev/null
+++ b/plots/plotnine/histogram/histogram-basic/default.py
@@ -0,0 +1,30 @@
+"""
+histogram-basic: Basic Histogram
+Library: plotnine
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import aes, element_text, geom_histogram, ggplot, labs, theme, theme_minimal
+
+
+# Data
+np.random.seed(42)
+data = pd.DataFrame({"value": np.random.normal(100, 15, 500)})
+
+# Plot
+plot = (
+ ggplot(data, aes(x="value"))
+ + geom_histogram(bins=30, fill="#306998", color="white", alpha=0.8)
+ + labs(x="Value", y="Frequency", title="Basic Histogram")
+ + 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),
+ )
+)
+
+# Save
+plot.save("plot.png", dpi=300)
diff --git a/plots/pygal/histogram/histogram-basic/default.py b/plots/pygal/histogram/histogram-basic/default.py
new file mode 100644
index 0000000000..debf7edcdf
--- /dev/null
+++ b/plots/pygal/histogram/histogram-basic/default.py
@@ -0,0 +1,55 @@
+"""
+histogram-basic: Basic Histogram
+Library: pygal
+"""
+
+import numpy as np
+import pygal
+from pygal.style import Style
+
+
+# Data
+np.random.seed(42)
+values = np.random.normal(100, 15, 500) # 500 values, mean=100, std=15
+
+# Calculate histogram bins
+counts, bin_edges = np.histogram(values, bins=20)
+
+# Convert to pygal histogram format: (height, start, end)
+histogram_data = [(int(count), float(bin_edges[i]), float(bin_edges[i + 1])) for i, count in enumerate(counts)]
+
+# Custom style matching default style guide colors
+custom_style = Style(
+ background="white",
+ plot_background="white",
+ foreground="#333333",
+ foreground_strong="#333333",
+ foreground_subtle="#666666",
+ opacity="0.8",
+ opacity_hover="0.9",
+ colors=("#306998",), # Python Blue
+ guide_stroke_color="#cccccc",
+ major_guide_stroke_color="#cccccc",
+ title_font_size=48,
+ label_font_size=36,
+ major_label_font_size=36,
+)
+
+# Create histogram chart
+chart = pygal.Histogram(
+ width=4800,
+ height=2700,
+ title="Basic Histogram",
+ x_title="Value",
+ y_title="Frequency",
+ style=custom_style,
+ show_legend=False,
+ show_y_guides=True,
+ show_x_guides=False,
+)
+
+# Add histogram data
+chart.add("Distribution", histogram_data)
+
+# Save to PNG
+chart.render_to_png("plot.png")
diff --git a/plots/seaborn/histplot/histogram-basic/default.py b/plots/seaborn/histplot/histogram-basic/default.py
index 09bd3f3583..37918c4162 100644
--- a/plots/seaborn/histplot/histogram-basic/default.py
+++ b/plots/seaborn/histplot/histogram-basic/default.py
@@ -1,113 +1,28 @@
"""
histogram-basic: Basic Histogram
-Implementation for: seaborn
-Variant: default
-Python: 3.10+
+Library: seaborn
"""
-from typing import TYPE_CHECKING, Optional
-
import matplotlib.pyplot as plt
+import numpy as np
import pandas as pd
import seaborn as sns
-if TYPE_CHECKING:
- from matplotlib.figure import Figure
-
-
-def create_plot(
- data: pd.DataFrame,
- column: str,
- bins: int = 30,
- color: str = "steelblue",
- alpha: float = 0.8,
- edgecolor: str = "black",
- title: Optional[str] = None,
- xlabel: Optional[str] = None,
- ylabel: Optional[str] = None,
- figsize: tuple[float, float] = (16, 9),
- **kwargs,
-) -> "Figure":
- """
- Create a basic histogram showing the distribution of numeric data using seaborn.
-
- Args:
- data: Input DataFrame with required column
- column: Column name for numeric values to visualize
- bins: Number of bins for histogram (default: 30)
- color: Bar color (default: "steelblue")
- alpha: Transparency level 0.0-1.0 (default: 0.8)
- edgecolor: Border color for bins (default: "black")
- title: Plot title (default: None)
- xlabel: Custom x-axis label (default: column name)
- ylabel: Custom y-axis label (default: "Frequency")
- figsize: Figure size as (width, height) (default: (16, 9))
- **kwargs: Additional parameters passed to sns.histplot()
-
- Returns:
- Matplotlib Figure object
-
- Raises:
- ValueError: If data is empty
- KeyError: If required column not found
- ValueError: If column contains no numeric values
-
- Example:
- >>> data = pd.DataFrame({'Values': [1.5, 2.1, 2.3, 3.2, 3.5, 4.1, 4.5]})
- >>> fig = create_plot(data, column='Values', bins=20)
- """
- # Input validation
- if data.empty:
- raise ValueError("Data cannot be empty")
-
- # Check required column
- if column not in data.columns:
- available = ", ".join(data.columns)
- raise KeyError(f"Column '{column}' not found. Available: {available}")
-
- # Check if column has numeric data
- if not pd.api.types.is_numeric_dtype(data[column]):
- raise ValueError(f"Column '{column}' must contain numeric values")
-
- # Remove NaN values for histogram
- values = data[column].dropna()
-
- if len(values) == 0:
- raise ValueError(f"Column '{column}' contains no valid numeric values")
-
- # Create figure
- fig, ax = plt.subplots(figsize=figsize)
-
- # Create histogram using seaborn
- sns.histplot(data=data, x=column, bins=bins, color=color, alpha=alpha, edgecolor=edgecolor, ax=ax, **kwargs)
-
- # Apply styling
- ax.set_xlabel(xlabel or column)
- ax.set_ylabel(ylabel or "Frequency")
- ax.grid(True, axis="y", alpha=0.3, linestyle="--")
-
- # Title
- if title:
- ax.set_title(title, fontsize=14, fontweight="bold")
-
- # Tight layout to avoid label clipping
- plt.tight_layout()
-
- return fig
-
-
-if __name__ == "__main__":
- # Sample data for testing
- import numpy as np
+# Data
+np.random.seed(42)
+data = pd.DataFrame({"value": np.random.normal(100, 15, 500)})
- np.random.seed(42)
- data = pd.DataFrame({"Values": np.random.normal(loc=100, scale=15, size=1000)})
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+sns.histplot(data=data, x="value", bins=30, color="#306998", alpha=0.8, edgecolor="white", ax=ax)
- # Create plot
- fig = create_plot(data, column="Values", bins=40, title="Distribution of Values")
+# Labels and styling
+ax.set_xlabel("Value", fontsize=20)
+ax.set_ylabel("Frequency", fontsize=20)
+ax.set_title("Basic Histogram", fontsize=20)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="-", linewidth=0.5)
- # Save for inspection
- plt.savefig("plot.png", dpi=300, bbox_inches="tight")
- print("Plot saved to plot.png")
- plt.show()
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")