Applicability of Design thinking to the Education Sector
Description
This code visualizes statistical evidence supporting various design thinking principles using a bar chart. It highlights the percentage improvements or impacts associated with principles such as User-Centered Design, Empathy, and Accessibility. The chart includes color-coded bars for easy distinction and displays data labels above each bar to indicate exact percentages. A title and axis labels provide context, while the x-axis labels are rotated for readability. Additionally, a horizontal grid improves data interpretation. The tight_layout() function ensures a clean presentation, optimizing spacing for a visually appealing and informative plot.
Files
Steps to reproduce
import matplotlib.pyplot as plt # Data for design thinking principles and their statistical evidence principles = [ "User-Centered Design", "Empathy", "Clarity", "Consistency", "Feedback", "Flexibility", "Aesthetics", "Accessibility" ] # Corresponding statistics (percentage improvements or impacts) statistics = [ 50, # User-Centered Design 30, # Empathy 25, # Clarity 23, # Consistency 20, # Feedback 35, # Flexibility 15, # Aesthetics 20 # Accessibility ] # Create a bar chart plt.figure(figsize=(10, 6)) bars = plt.bar(principles, statistics, color=['#66b3ff', '#ff9999', '#99ff99', '#ffcc99', '#c2c2f0', '#ffb3e6', '#c2f0c2', '#b3b3cc']) # Add title and labels plt.title('Statistical Evidence Supporting Design Thinking Principles', fontsize=14) plt.xlabel('Design Thinking Principles', fontsize=12) plt.ylabel('Percentage Improvement (%)', fontsize=12) # Add data labels on top of bars for bar in bars: yval = bar.get_height() plt.text(bar.get_x() + bar.get_width()/2, yval + 1, f"{yval}%", ha='center', va='bottom') # Rotate x-axis labels for better readability plt.xticks(rotation=15) plt.grid(axis='y') # Show the plot plt.tight_layout() plt.show()