spg_style 0.1.1

S&P Global charting style according to EDP style guideline

S&P Global EDP Chart Style for Python

Disclaimer: This is NOT an official package provided by S&P Global. It is a community-driven project created by analysts to style visualizations in Python. Please reach out to [e-mail] for official support, and more information on the use of S&P Global data.

A Python implementation of the S&P Global Editorial, Design & Publishing (EDP) standards for data visualization using Matplotlib. This package ensures your charts adhere to strict brand guidelines regarding colors, typography, layout, and formatting.

🚀 Key Features

• Strict Brand Colors: Full access to the S&P Global color palette (Levels 1-12) for all brand colors.
• Theming: Native support for Standard Light and Standard Dark modes.
• Auto-Styling: Specialized methods to style bar charts, line charts, and dual-axis charts according to EDP rules.
• Intelligent Layout:
  • EDP Figure Sizing: get_edp_figsize() returns exact dimensions for digital reports (Word/InDesign) or PowerPoint (16x9).
  • Header/Footer Management: Automatic footnote generation including dynamic "As of" dates, source attribution, and copyright.
  • Legend Positioning: Automatically aligns legends to the top with smart column calculation.
• Advanced Formatting:
  • Forecast Tints: Automatically apply lighter tints for forecast data.
  • Monochromatic Palettes: Compliant palettes for 2 to 5 classes.
  • Date Alignment: Standardized 3-letter month abbreviations (Jan, Feb, Sep, etc.) without periods.

📦 Installation

This project is managed by Poetry.

git clone https://github.com/kovzhu/spg_style.git
cd spg_style
poetry install

🛠 Usage

1. Basic Setup in Jupyter Notebook

To see changes reflected immediately when editing spg_style.py, use %autoreload:

%load_ext autoreload
%autoreload 2

import spg_style as spg
import matplotlib.pyplot as plt

# Initialize the EDP style (sets global rcParams)
style = spg.SpglobalStyle(theme='light')

2. Creating an EDP Compliant Chart

import pandas as pd

# 1. Get the correct dimensions for a half-page report graphic
figsize = spg.get_edp_figsize(width="full", height="1/2", target="report")
fig, ax = plt.subplots(figsize=figsize)

# 2. Plot your data
data = pd.Series([10, 15, 7, 12], index=['Q1', 'Q2', 'Q3', 'Q4 (F)'])
bars = ax.bar(data.index, data.values)

# 3. Apply EDP styling rules
# - tints Q4 if marked as forecast
# - adds value labels
# - sets standard bar widths
style.style_bar_single(ax, bars, is_forecast_mask=[False, False, False, True])

# 4. Final Polish
style.set_y_unit_label(ax, "USD Millions")
style.add_footnotes(fig, source="S&P Global Commodity Insights")

plt.show()

🎨 Color Palette

The package provides the full SPG_FULL dictionary containing hex codes transcribed from the EDP guidelines.

from spg_style import SPG_FULL

# Access Maroon Level 7
primary_color = SPG_FULL["Maroon"][7]

# Access Sentiment colors
from spg_style import SENTIMENT
positive_color = SENTIMENT["positive"]

🏗 Project Structure

• spg_style/spg_style.py: The core engine containing the SpglobalStyle class and color definitions.
• notebook/notebook.ipynb: Examples and scratchpad for testing new chart styles.
• pyproject.toml: Dependency management and project metadata.

📄 Reference

This implementation is based on the S&P Global EDP Visual Style Guide (Jan 2026).