📅 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
⭐ Priority: MEDIUM
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.