📅 2023-08-18 — Session: Developed advanced data visualization techniques in Python
🕒 20:15–20:30
🏷️ Labels: Matplotlib, Data Visualization, Python, Scatter Plot, Box Plot
📂 Project: Dev
Session Goal
The session aimed to explore and implement various [[data visualization]] techniques using Matplotlib in Python, focusing on combining scatter and box plots for enhanced data representation.
Key Activities
- Plotting Scatter and Box Plots: Initiated with plotting scatter and box plots using Matplotlib’s subplot feature, providing detailed code modifications and explanations.
- DataFrame Combinations: Implemented a Python loop to generate scatter and weighted box plots for each combination of specified columns in the
main_listasDataFrame. - Combined Plots: Created a single figure with scatter and box plots for unique data combinations from ‘main_listas’, focusing on visual considerations.
- Subplots for [[Data Visualization]]: Generated separate figures with scatter and box plots for each parameter combination in a DataFrame.
- Stacking Plots: Explained how to stack scatter and box plots vertically within a single figure using the
gridspec_kwparameter. - Overlaying Plots: Provided a detailed explanation for overlaying box plots on a scatter plot using the same X-axis, specifically for income data, with step-by-step code modifications.
Achievements
Successfully implemented advanced [[data visualization]] techniques using Matplotlib, enhancing the ability to represent complex data relationships visually.
Pending Tasks
- Further exploration of interactive plotting features in Matplotlib to enhance user engagement and data exploration capabilities.