clean-charts 0.4.0

A Python library to generate beautiful, clean charts in premium visual styles

Clean Charts

clean-charts is a lightweight, premium Python library designed to generate beautiful, publication-quality data visualizations inspired by modern editorial styles like The Economist.

Featuring right-aligned axes, custom date boundaries, distinct line colors, smooth spline curves, automatic overlap-avoiding label placement, and dynamic scaling for varying canvas resolutions, clean-charts makes it effortless to create presentation-ready visualizations — both saved to disk and displayed inline in Jupyter notebooks.

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Key Features

• Premium Aesthetics: Light cream-gray backgrounds, minimal gridlines, elegant typography, and consistent padding.
• Jupyter Inline Display: When output_path=None, charts render directly in Jupyter notebooks via IPython.display.
• Time-Series Line Charts (plot_time_series):
  • Right-aligned Y-axes.
  • Automatically identifies date/time columns and handles multiple value series.
  • Smooth PCHIP spline curves through data points (requires scipy).
  • Optional data-point markers with configurable style.
  • Generates custom color gradients or uses a refined default palette.
  • Intelligently spaces dates and years on the X-axis to prevent label overlap.
  • Smart inline label placement at line endpoints (name, value, both, or none).
• Horizontal Bar Charts (plot_barh_chart):
  • Left-aligned categories, top-aligned numeric ticks with custom suffixes.
  • Values labeled inside/outside the end of each bar.
• Grouped Horizontal Bar Charts (plot_grouped_barh_chart):
  • Multi-series bars per category in stacked vertical lanes.
  • Gradient color palette across series, compact legend row above the chart.
  • Optional bar labels (value, name, both, none).
• Responsive Scaling: All text, line-weights, and paddings scale proportionally based on target width and height.

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Installation

pip install clean-charts

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Quick Start

1. Grouped Horizontal Bar Chart

Display multiple numeric series side-by-side for each category, with an automatic color gradient and legend.

import pandas as pd
from clean_charts import plot_grouped_barh_chart

df = pd.DataFrame({
    'Fruit': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries', 'Figs', 'Grapes'],
    '2024': [380, 410, 150, 420, 85,  280, 490],
    '2025': [510, 180, 830, 450, 190, 240, 560],
    '2026': [415, 450, 590, 310, 60,  310, 400]
})

plot_grouped_barh_chart(
    data = df,
    title="Regional Supermarket Inventory and Fruit Category Performance Analysis",
    subtitle="Comparative overview of total stock volume across key produce items to optimize supply chain distribution",
    bar_padding=0,
    group_padding=0.3,
    value_suffix = ' Kg'
)

Output Example:

---

2. Horizontal Bar Chart

Draw a single-series horizontal bar chart with category labels and value annotations. If no data is provided, a built-in survey dataset is used.

import pandas as pd
from clean_charts import plot_barh_chart

df = pd.DataFrame({
    'Category': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries', 'Figs', 'Grapes', 'Honeydew'],
    'Sales':    [400, 350, 300, 450, 120, 210, 520, 180],
})

plot_barh_chart(
    data=df,
    title="Regional Supermarket Inventory and Fruit Category Performance Analysis",
    subtitle="Comparative overview of total stock volume across key produce items to optimize supply chain distribution",
    value_suffix=' Kg',
)

Output Example:

---

3. Time-Series Line Chart

Call plot_time_series to generate a time-series line chart from a DataFrame. If no data argument is provided, the function uses a built-in sample dataset of monthly automobile sales by type from 2020 to 2025.

import pandas as pd
from clean_charts import plot_time_series

df = pd.DataFrame({
    'Dates':        pd.date_range("2026-01-01", periods=12, freq="MS"),
    'Apples':       [500, 596, 590, 523, 582, 515, 501, 551, 494, 467, 548, 490],
    'Bananas':      [350, 339, 349, 382, 328, 359, 403, 390, 459, 390, 373, 437],
    'Elderberries': [160, 118, 118, 124, 179, 126, 117, 115, 157, 114, 120, 127],
})

plot_time_series(
    data=df,
    aspect_ratio='1:1',
    title="Regional Supermarket Inventory and Fruit Category Performance Analysis",
    subtitle="Comparative overview of total stock volume across key produce items to optimize supply chain distribution",
    label_frequency="month",
    line_labels='name',
    value_suffix=' Kg',
)

Output Example:

---

Customizing Data & Advanced Usage

Custom Time-Series Data

Supply your own pandas.DataFrame containing a date/time column and one or more value columns. The library automatically parses the datetime column and maps all other numeric columns as lines.

import pandas as pd
from clean_charts import plot_time_series

data = pd.DataFrame({
    "Day": pd.date_range("2026-05-01", periods=10, freq="D"),
    "Active Users": [120, 150, 190, 240, 220, 250, 270, 310, 340, 320],
    "Signups":      [15,  22,  35,  40,  28,  30,  32,  45,  52,  48],
})

plot_time_series(
    data=data,
    output_path="daily_stats.png",
    title="Daily Server Growth",
    subtitle="Active users and registrations in May 2026",
    label_frequency="day",   # "year" | "quarter" | "month" | "week" | "day" | "hour" | "minute" | "second"
    start_color="#006400",   # Dark green gradient start
    end_color="#ffd700",     # Gold gradient end
    smooth=True,             # Smooth PCHIP spline curves (default True)
    markers=True,            # Show circle markers on data points
    line_labels="both",      # Show "Series: value" inline labels
    value_suffix="%",        # Append "%" to Y-axis ticks and inline labels
)

Custom Horizontal Bar Chart Data

Pass a pandas.DataFrame where the first column contains string labels and the second column contains numeric values.

import pandas as pd
from clean_charts import plot_barh_chart

df = pd.DataFrame({
    "Category":    ["Apples", "Bananas", "Cherries", "Dates", "Elderberries", "Figs", "Grapes", "Honeydew"],
    "Sales (tons)":[400,      350,       300,        450,     120,            210,    520,      180],
})

plot_barh_chart(
    data=df,
    output_path="fruit_sales.png",
    title="Fruit Performance Analysis",
    subtitle="Total sales volume by item",
    value_suffix=" t",
    color="#1f77b4",
)

Custom Grouped Bar Chart Data

Pass a DataFrame whose first column contains category labels and each subsequent column represents one series.

import pandas as pd
from clean_charts import plot_grouped_barh_chart

df = pd.DataFrame({
    "Country": ["Germany", "France", "Italy", "Spain", "Poland"],
    "BEV":     [18, 14, 8, 5, 3],
    "PHEV":    [9,  7,  5, 4, 2],
    "Hybrid":  [22, 19, 12, 9, 6],
})

plot_grouped_barh_chart(
    data=df,
    output_path="ev_by_country.png",
    title="EV Adoption by Country",
    subtitle="Share of new car sales by powertrain, %",
    value_suffix="%",
    bar_labels="value",       # "none" | "value" | "name" | "both"
    start_color="#005f73",
    end_color="#94d2bd",
)

---

API Reference

plot_time_series

Parameter	Type	Default	Description
data	pd.DataFrame	None	DataFrame with a datetime column and value series. If None, uses built-in default data.
output_path	str	None	File path to save the image. If None, displays inline in Jupyter.
width	int	1000	Target image width in pixels.
height	int	562	Target image height in pixels.
aspect_ratio	str	None	Predefined ratio: "square"/"1:1", "landscape"/"2:1", "vertical"/"1:2". Overrides width/height.
title	str	None	Bold title text, left-aligned. Auto-wraps to 2 lines.
subtitle	str	None	Subtitle below the title. Auto-wraps to 2 lines.
start_color	str	None	Hex color for the first series in a gradient.
end_color	str	None	Hex color for the last series in a gradient.
label_frequency	str	"year"	X-axis tick frequency: "year", "quarter", "month", "week", "day", "hour", "minute", "second".
markers	bool or str	None	Data-point markers. False/None = none, True = circles, or any matplotlib marker string (e.g. "s", "D").
line_labels	str	"name"	Inline endpoint labels: "name", "value", "both", or "none".
value_suffix	str	""	String appended to Y-axis ticks and inline value labels (e.g. "%").
smooth	bool	True	Draw smooth PCHIP spline curves. Falls back to straight lines if scipy is not installed.
scale_text	bool	False	Scale fonts and line weights proportionally to image size.

---

plot_barh_chart

Parameter	Type	Default	Description
data	pd.DataFrame	None	DataFrame: column 0 = category strings, column 1 = numeric values. If None, uses built-in survey data.
output_path	str	None	File path to save the image. If None, displays inline in Jupyter.
width	int	600	Target image width in pixels.
height	int	None	Target image height in pixels. Auto-sized by number of categories if None.
aspect_ratio	str	None	Predefined ratio: "square"/"1:1", "landscape"/"2:1", "vertical"/"1:2".
title	str	None	Bold title text.
subtitle	str	None	Subtitle below the title.
color	str	"#000000"	Hex color for the bars.
bar_padding	float	0.35	Fraction of bar slot left as gap between bars (0.0–1.0).
value_suffix	str	""	String appended to value labels and axis ticks (e.g. "%").
scale_text	bool	True	Scale fonts proportionally to image size.

---

plot_grouped_barh_chart

Parameter	Type	Default	Description
data	pd.DataFrame	None	DataFrame: column 0 = category labels, remaining columns = numeric series. If None, uses built-in example data.
output_path	str	None	File path to save the image. If None, displays inline in Jupyter.
width	int	600	Target image width in pixels.
height	int	None	Target image height in pixels. Auto-sized when None.
aspect_ratio	str	None	Predefined ratio: "square"/"1:1", "landscape"/"2:1", "vertical"/"1:2".
title	str	None	Bold title text. Auto-wraps to 2 lines.
subtitle	str	None	Subtitle below the title. Auto-wraps to 3 lines.
start_color	str	"#000000"	Hex color for the first series in the gradient.
end_color	str	"#2323FF"	Hex color for the last series in the gradient.
bar_padding	float	0	Fraction of a single bar slot left as whitespace (0–1).
group_padding	float	0.45	Fraction of the group height used as spacing between groups (0–1).
value_suffix	str	""	String appended to axis tick labels (e.g. "%").
bar_labels	str	"none"	Labels drawn on each bar: "none", "value", "name", or "both".
scale_text	bool	True	Scale fonts proportionally to image size.

---

Global Customization

Import and modify the global configuration variables to apply a consistent theme across all charts:

import clean_charts.config as config
from clean_charts import plot_time_series

# Override styling tokens before plotting
config.BACKGROUND_COLOR = "#ffffff"  # Pure white background
config.GRID_COLOR        = "#eaeaea"  # Light gridlines
config.AXIS_COLOR        = "#333333"  # Dark charcoal axes

plot_time_series(
    title="Custom White Theme",
    output_path="white_theme_chart.png"
)

Available Config Variables (clean_charts.config)

Variable	Default	Description
BACKGROUND_COLOR	"#f4f3f0"	Chart background (cream-gray).
GRID_COLOR	"#dcdbd7"	Horizontal/vertical gridline color.
AXIS_COLOR	"#000000"	Axis spine and tick color.
TITLE_COLOR	"#111111"	Title text color.
SUBTITLE_COLOR	"#444444"	Subtitle text color.
LINE_COLOR	"#000000"	Default line/bar color.
DEFAULT_COLOR	"#000000"	Fallback single-bar color.
DEFAULT_START_COLOR	"#000000"	Gradient start for grouped bars.
DEFAULT_END_COLOR	"#2323FF"	Gradient end for grouped bars.
DEFAULT_COLORS_LIST	['#000000', '#2323FF', ...]	Default multi-line color palette.

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Examples & Development

Generate Sample Charts

Run the provided script to generate a set of sample charts showcasing aspect ratios, gradient themes, custom label frequencies, and more:

python generate_sample.py

This produces:

• sales_chart_2_1.png — Landscape 2:1 time-series
• sales_chart_1_1.png — Square 1:1 time-series
• sales_chart_1_2.png — Vertical 1:2 time-series
• sales_chart_gradient.png — Gradient color demonstration
• sales_chart_daily.png — Day-level label frequency
• sales_chart_long_title.png — Long title wrapping demo

Running Tests

python -m unittest tests/test_plot.py