How to control plot size whith different legend size matplotlib

Mastering Matplotlib: Controlling Plot Size and Legend Dimensions

Matplotlib, a powerful Python visualization library, offers a wide range of customization options for creating informative and visually appealing plots. One aspect often requiring attention is the balance between plot size and legend dimensions. This article will guide you through effective strategies for controlling both elements to achieve optimal results.

1. Adjusting Plot Size:

• figsize Parameter:
  • The most straightforward approach involves specifying the desired width and height of your plot using the figsize parameter within the plotting function. For instance:

  import matplotlib.pyplot as plt
  
  plt.figure(figsize=(10, 6))  # Width: 10 inches, Height: 6 inches
  plt.plot(x, y)
  plt.show()
  

• set_size_inches Method:
  • Alternatively, you can modify the figure size after its creation using the set_size_inches method.
    python
fig, ax = plt.subplots()
fig.set_size_inches(8, 4)
ax.plot(x, y)
plt.show()

2. Controlling Legend Size:

• fontsize Parameter:
  • The legend’s text size can be customized using the fontsize parameter within the legend() function.
    python
plt.plot(x, y, label='Data 1')
plt.plot(x, z, label='Data 2')
plt.legend(fontsize=12)
plt.show()
• prop Parameter:
  • For fine-grained control over legend properties, use the prop parameter and a matplotlib.font_manager.FontProperties object.

  from matplotlib.font_manager import FontProperties
  
  font = FontProperties(size=10, weight='bold')
  plt.legend(prop=font)
  plt.show()
  

• bbox_to_anchor and loc Parameters:
  • Adjust legend placement with bbox_to_anchor and fine-tune its position with the loc parameter.
    python
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
plt.show()

3. Managing Legend Overflow:

• ncol Parameter:
  • If your legend contains numerous entries, use the ncol parameter to arrange them into multiple columns.
    python
plt.legend(ncol=2)
plt.show()
• handlelength and handletextpad Parameters:
  • Control the space between the legend handles and text labels using handlelength and handletextpad.
    python
plt.legend(handlelength=1.5, handletextpad=0.5)
plt.show()

4. Optimizing Layout:

• tight_layout() Function:
  • Use the tight_layout() function to automatically adjust subplot parameters for optimal spacing and legend visibility.
    python
plt.tight_layout()
plt.show()

Conclusion:

By combining these techniques, you gain the flexibility to create visualizations where plot size and legend dimensions work harmoniously. Experiment with different approaches to find the perfect balance for your specific needs. Remember, effective visualization is about conveying information clearly and concisely, and mastering these techniques empowers you to create plots that are both informative and aesthetically pleasing.