📅 2023-10-29 — Session: Enhanced Data Visualization with Seaborn and Matplotlib
🕒 17:30–18:30
🏷️ Labels: Data Visualization, Python, Seaborn, Matplotlib, Statistics
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance data visualization techniques using Python libraries such as Seaborn and Matplotlib, focusing on income statistics by province.
Key Activities
- Box Plot Creation: Initiated with a guide on creating a box plot using Seaborn, covering quartile calculations and plot customization.
- Data Series Request: Addressed a reset in the code execution environment by requesting the data series again for plotting.
- Statistical Data Request: Sought data on medians and percentiles for plotting.
- Scatter Plot Development: Provided a code snippet for a scatter plot with IQR bars using Matplotlib and Seaborn.
- Customizable Plotting: Developed a customizable plot for income statistics, including data preparation and visualization adjustments.
- Engineering Notation Formatting: Created a plot with the y-axis in engineering notation for better readability.
- DataFrame Filtering: Implemented a method to filter a Pandas DataFrame by date and conditions.
- Marker Size Adjustment: Adjusted scatter plot marker sizes based on population size.
Achievements
- Successfully created and customized various plots for visualizing income statistics by province.
- Implemented engineering notation for axis formatting, enhancing readability.
- Developed methods to dynamically adjust plot features based on data characteristics.
Pending Tasks
- Further exploration of advanced customization options in Seaborn and Matplotlib.
- Integration of additional statistical measures for comprehensive data analysis.