You are right. I was doubting whether to make a more complicated example -- but formatting is poor in text boxes. Let me give you a more complex one.

Let's say we want a 3-column plot: colormesh, polar, and geo plot.

UltraPlot:

  import ultraplot as uplt, numpy as np
  fig, ax = uplt.subplots(
      ncols=3, share=0, proj="cart polar merc".split(), journal="nat2"
  )
  ax[0].pcolormesh(
      np.random.rand(10, 10), cmap="viko", colorbar="r",
      colorbar_kw=dict(title="some interesting colors")
  )
  angles, radii = np.random.rand(100) * 360, np.random.rand(100)
  ax[1].scatter(angles, radii, c=radii, cmap="spectral_r")
  x, y = np.meshgrid(np.linspace(-30, 30, 100), np.linspace(-60, 60, 100))
  z = np.exp(-(x*2 + y*2) / 100)
  ax[2].pcolormesh(x, y, z, cmap="Fire")
  ax[2].format(landcolor="green", land=True, grid=True, lonlabels=True, latlabels=True)
  ax.format(abc="[A]")
  fig.show()

Matplotlib equivalent:

  import matplotlib.pyplot as plt, numpy as np, cartopy.crs as ccrs
  fig = plt.figure(figsize=(15, 5))
  ax0 = fig.add_subplot(1, 3, 1)
  pcm = ax0.pcolormesh(np.random.rand(10, 10), cmap="viridis")
  cbar = plt.colorbar(pcm, ax=ax0)
  cbar.set_label("some interesting colors")
  cbar.ax.yaxis.label.set_color("r")
  
  ax1 = fig.add_subplot(1, 3, 2, projection="polar")
  angles = np.random.rand(100) * 2 * np.pi
  radii = np.random.rand(100)
  sc = ax1.scatter(angles, radii, c=radii, cmap="Spectral_r")
  
  ax2 = fig.add_subplot(1, 3, 3, projection=ccrs.Mercator())
  x, y = np.meshgrid(np.linspace(-30, 30, 100), np.linspace(-60, 60, 100))
  z = np.exp(-(x*2 + y*2) / 100)
  pcm2 = ax2.pcolormesh(x, y, z, cmap="magma", transform=ccrs.PlateCarree())
  ax2.coastlines()
  ax2.gridlines(draw_labels=True)
  ax2.set_extent([-30, 30, -60, 60], crs=ccrs.PlateCarree())
  import cartopy.feature as cfeature
  ax2.add_feature(cfeature.LAND, facecolor="green")
  for i, ax in enumerate([ax0, ax1, ax2]):
      ax.set_title(f"[{chr(65+i)}]")
  plt.tight_layout()
  plt.show()

The aim isn't to replace matplotlib but make publication-ready plots with fewer keystrokes and better defaults. We also bundle plot types not available in matplotlib like graph plotting, lollipop charts, heatmaps etc.