So, while writing a Python script to generate the visuals in Power BI, there is a problem of misalignment due to image resolution, aspect ratio, margins, or settings of the size of the figure. Following is a discussion of why this occurs and a solution for each:
Common Causes and Their Fixes
1. Incompatible Figure Size and DPI Settings
Problem: While Power BI uses a specific canvas size for a Python visual (usually measured in pixels), the figures are normally produced by the Python library imagery-used matplot lib or seaborne without specific measurement settings.
Solution:
You explicitly set figure size and dpi to the expected rendering of power bi.
import matplotlib.pyplot as plt
plt.figure(figsize=(8, 4), dpi=100) # Adjust size as needed
Modify the `figsize` parameters to align with the Power BI visual container.
2. Excessive Padding and Margins
The Problem: Matplotlib automatically inserts extra padding around figures, making the visuals look disarranged.
The Solution: Get rid of these white spaces using
plt.tight_layout() # Reduces unnecessary padding
3. Mismatch of Aspect Ratio
Problem: Power BI can resize Python visuals according to the size of the container, and hence, it can break the original aspect ratio.
Solution: For that, keep a fixed aspect ratio by modifying the limit:
ax.set_aspect('auto') # Ensures flexible resizing
4. Lettering & Labels Overlap
Problem: Labels, legends, and tick marks may be drawn outside actual limits.
A possible remedy is to use the default option bbox_inches='tight':
plt.savefig('plot.png', bbox_inches='tight', dpi=100)
plt.xticks(fontsize=8)
plt.yticks(fontsize=8)
5. Limitations Regarding Power BI Rendering
Challenge: Power BI refreshes Python visuals and saves them as images, which could get distorted when stretching.
Solution:
Explore using different container sizes in Power BI to examine the attractive size.
Make use of plt.gca().set_position([0, 0, 1, 1]) so the plot fills the entire space allocated.
