Journal Reproduction | Python Drawing of Combination of Dual Y-axis Line Chart and Bar Chart

2026-03-06 · Source: Data Science on Medium · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, quick

Summary

This content provides a Python implementation for reproducing a dual Y-axis line and bar chart, specifically imitating a figure from the paper "Amplified local cooling effect of forestation in warming Europe." The code utilizes `matplotlib.pyplot` for plotting, `scipy.stats` for statistical functions, and `pandas` for data handling. Key steps include importing necessary libraries, configuring font settings to 'Times New Roman' serif, and defining a color library with specific hex codes for 'forest_light', 'forest_dark', 'openland_light', 'openland_dark', 'delta_light', and 'delta_dark'. The provided snippet focuses on the initial setup, including font and color scheme selection, before the actual plotting logic is detailed.

Key takeaway

For data scientists and researchers aiming to reproduce scientific figures with high fidelity, prioritize meticulous setup of plotting environments. Ensure your `matplotlib` configurations, especially font families like 'Times New Roman' and custom color palettes, are established early to match publication standards and maintain visual consistency across your visualizations.

Key insights

Reproducing complex scientific charts in Python requires careful font and color scheme configuration.

Principles

• Use serif fonts for scientific figures.
• Define color palettes for consistent visualization.

Method

Import `matplotlib`, `scipy`, `pandas`; set `rcParams` for font family and unicode minus; define a color scheme dictionary with hex codes; select a scheme.

In practice

• Configure `plt.rcParams` for publication-ready fonts.
• Create a color dictionary for reusable palettes.

Topics

• Matplotlib
• Data Visualization
• Python Programming
• Scientific Plotting
• Journal Figure Reproduction

Best for: Research Scientist, Data Scientist, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Science on Medium.