Enhanced Data Visualization Techniques with Matplotlib

📅 2023-11-02 — Session: Enhanced Data Visualization Techniques with Matplotlib

🕒 16:50–18:15
🏷️ Labels: Matplotlib, Data Visualization, Python, Plotting, Annotations
📂 Project: Dev
⭐ Priority: MEDIUM

The session aimed to explore advanced data visualization techniques using Matplotlib, focusing on enhancing readability and presentation of plots.

Developed a Python script to visualize rolling averages with conditional area fills and custom y-axis formatting.
Implemented text highlighting using the bbox argument in Matplotlib’s fig.text() method.
Unified y-axes in plots by sharing scales or aligning tick labels.
Combined multiple datasets into a single plot with shared y-axis.
Modified plots to display yearly averages as bars, resampling data by year.
Adjusted bar positions to prevent overlap and enhance clarity.
Applied alpha transparency to bar plots based on boolean values in a DataFrame.
Set background color with transparency for annotations using bbox in annotate function.
Rotated x-axis labels and adjusted text annotations for better alignment.

Successfully implemented various visualization techniques to improve data presentation and clarity.
Enhanced plots with conditional formatting, transparency, and annotation adjustments.

Further exploration of dynamic plot adjustments based on user interaction or data updates.