📅 2023-03-06 — Session: Developed Data Visualization Functions for Treatment Analysis
🕒 14:35–15:15
🏷️ Labels: Python, Data Visualization, Seaborn, Matplotlib, Percentiles
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal:
The aim of this session was to develop Python functions for visualizing data related to treatment and control groups, with a focus on computing percentiles and plotting balance information using Seaborn and Matplotlib.
Key Activities:
- Computed the 25%, 50%, and 75% percentiles for covariates in treatment and control groups using a Pandas dataframe.
- Resolved column mismatch errors in DataFrame percentile calculations by transposing results.
- Developed a function using Seaborn and Matplotlib to plot balance information, incorporating error bars for statistical accuracy.
- Modified Seaborn’s
catplot()to adjust bar width and ensure error bars align with data points. - Enhanced plot functions to visualize treatment group balance with error bars, using Seaborn’s pointplot.
- Fixed Seaborn plot styles and x-labels to improve visual clarity.
- Updated the
plot_balancefunction to include legends and customize plot aesthetics for treated and control groups.
Achievements:
- Successfully created and refined multiple Python functions for data visualization that handle error bars and legends effectively.
- Improved the accuracy and aesthetics of balance plots for treatment analysis.
Pending Tasks:
- Further testing and validation of the developed functions with diverse datasets to ensure robustness and adaptability.