Enhanced Data Visualization Techniques with Python

2 min read

📅 2023-02-20 — Session: Enhanced Data Visualization Techniques with Python

🕒 08:35–10:35
🏷️ Labels: Python, Data Visualization, Seaborn, Matplotlib, Regression Analysis
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance data visualization techniques using Python libraries, specifically focusing on Seaborn and Matplotlib.

Key Activities

  • Implemented Python code for concatenating data into a single DataFrame using pandas, followed by visualization with Seaborn.
  • Customized box plot aesthetics using Seaborn and Matplotlib, including style, color palette, and dimensions.
  • Utilized the hue parameter in Seaborn’s catplot for grouping data by multiple columns.
  • Addressed floating-point precision issues in Python, particularly in rounding numbers for plotting.
  • Customized x-axis tick labels in Seaborn catplots and rounded x-tick labels in box plots.
  • Developed functions for running experiments with matching and regression analysis, including parameter sweeps and saving results to CSV.
  • Modified regression analysis functions to use pd.concat instead of the deprecated append method.
  • Created scatter plots with error bars and added diagonal lines to Matplotlib plots.

Achievements

  • Successfully implemented and tested various data visualization techniques, improving the clarity and aesthetics of plots.
  • Enhanced the functionality of existing Python functions for data analysis and visualization.

Pending Tasks

  • Further exploration of advanced data visualization techniques and integration with larger datasets.

Graph View

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