Enhanced Data Visualization Techniques with Matplotlib

📅 2023-01-13 — Session: Enhanced Data Visualization Techniques with Matplotlib

🕒 14:30–15:55
🏷️ Labels: Matplotlib, Data Visualization, Python, Pandas, Feature Importance, Machine Learning
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal:

The session aimed to enhance data visualization techniques using Matplotlib and Pandas, focusing on customizing plots and improving readability through various methods.

Key Activities:

• Explored methods to modify x-tick labels in Matplotlib using xticks(), set_xticks(), and set_xticklabels() to prevent overlap and enhance clarity.
• Implemented solutions for setting and customizing legend names in plots.
• Discussed methods for extracting middle values from tick labels using list comprehensions and regular expressions.
• Addressed setting multi-indexes in Pandas DataFrames and renaming index axes.
• Investigated feature importance methods in machine learning, including Permutation Importance and RandomForestClassifier.
• Demonstrated techniques for finding maximum values in DataFrame rows and calculating correlations.
• Created scatter plots and dual bar charts using Matplotlib and Pandas, focusing on feature importance and correlations.

Achievements:

• Successfully customized x-tick labels and legend names in Matplotlib plots.
• Enhanced understanding of feature importance in machine learning models.
• Improved skills in data manipulation with Pandas, including multi-indexing and correlation analysis.

Pending Tasks:

• Further exploration of advanced data visualization techniques and their applications in machine learning.
• Continued practice on integrating Pandas and Matplotlib for comprehensive data analysis.