mpl-chart-styles 1.0.1

Professional presentation-ready chart styles and color palettes for matplotlib

mpl-presentation-styles

Presentation-quality matplotlib charts in one line.

Stop formatting charts for board meetings. Start presenting.

Install

pip install mpl-presentation-styles

Three ways to use it

1. Quick charts (zero config)

import mpl_presentation_styles as mps

mps.bar({"Q1": 2.5, "Q2": 3.1, "Q3": 2.8, "Q4": 3.6}, title="Revenue ($M)")

2. With your existing matplotlib code (recommended)

import mpl_presentation_styles as mps
import matplotlib.pyplot as plt

mps.apply_palette("boardroom")

# All your existing code just looks better
plt.bar(["Q1", "Q2", "Q3", "Q4"], [2.5, 3.1, 2.8, 3.6])
plt.title("Revenue ($M)")
plt.show()

If you specifically want matplotlib style names, plt.style.use("mps-boardroom") is also supported in many environments, but apply_palette() is the most reliable API across notebook runtimes.

3. Scoped theming

from mpl_presentation_styles import theme, bar

with theme("startup"):
    bar(df, x="Region", y="Revenue", title="Q4 Revenue by Region")
# Previous style is automatically restored

Works with DataFrames

import pandas as pd
import mpl_presentation_styles as mps

df = pd.read_csv("sales.csv")
mps.bar(df, x="Region", y="Revenue", title="Revenue by Region")
mps.line(df, x="Month", y="Growth", title="Monthly Growth Trend")
mps.pie(df, x="Category", y="Share", title="Market Share")

Palettes

Name	Style	Best For
boardroom	Navy/red professional	Board decks, executive summaries
startup	Purple/green modern	Pitch decks, investor updates
classic	Muted traditional	Academic papers, journal submissions
dashboard	High-contrast	KPI dashboards, monitoring
print	Grayscale	Print reports, black & white
warm	Amber/orange	Marketing, inviting presentations

Smart defaults

• Auto-register themes: mpl-presentation-styles registers custom themes automatically via a .pth startup hook, so plt.style.use("mps-boardroom") is available immediately after install — no explicit import required.
• Auto-format values: $1.2M, $450K, $3,750
• DataFrame-native: Pass column names directly
• Auto-detect environment: Saves to /mnt/data/ in notebooks, ./ locally
• Spines removed: Clean, modern look by default
• 150 DPI: Presentation-ready resolution

Chart types

Function	Type	DataFrame	Dict
mps.bar()	Vertical/horizontal bars	✅	✅
mps.line()	Trend lines with markers	✅	✅
mps.pie()	Pie charts with % labels	✅	✅
mps.scatter()	Scatter with annotations	✅	✅

License

MIT