Due to the lack of a true 3D graphical rendering backend (such as OpenGL) and proper algorithm for detecting 3D objects' intersections, the 3D plotting capabilities of Matplotlib are not great but just adequate for typical applications. In the official Matplotlib FAQ (https://matplotlib.org/mpl_toolkits/mplot3d/faq.html), the author noted that 3D plots may not look right at certain angles. Besides, we also reported that mplot3d would fail to clip bar charts if zlim is set (https://github.com/matplotlib/matplotlib/issues/8902; see also https://github.com/matplotlib/matplotlib/issues/209). Without improvements in the 3D rendering backend, these issues are hard to fix.

To better illustrate the latter issue, let's try to add ax.set_zlim3d(bottom=110, top=150) right above plt.tight_layout() in the previous 3D bar chart:

Clearly, something is going wrong, as the bars overshoot the lower boundary of the axes. We will try to address the latter issue through the following workaround:

# FuncFormatter to add 110 to the tick labels
def major_formatter(x, pos):
    return "{}".format(x+110)
 
fig = plt.figure(figsize=(9,7))
ax = fig.add_subplot(111, projection='3d')

# Create one color-coded bar chart for Open, High, Low and Close prices.
for color, col, z in zip(['r', 'g', 'b', 'y'], ["Open", "High", "Low", 
                          "Close"], [30, 20, 10, 0]):
    xs = np.arange(ohlc_5d.shape[0])
    ys = ohlc_5d[col]

    # Assign color to the bars 
    colors = [color] * len(xs)
 
    # Truncate the y-values by 110
    ax.bar(xs, ys-110, zs=z, zdir='y', color=colors, alpha=0.8)

# Manually assign the ticks and tick labels
ax.set_xticks(np.arange(ohlc_5d.shape[0]))
ax.set_xticklabels(ohlc_5d["Date"], rotation=20,
                   verticalalignment='baseline',
                   horizontalalignment='right',
                   fontsize='8')

# Set the z-axis label
ax.set_yticks([30, 20, 10, 0])
ax.set_yticklabels(["Open", "High", "Low", "Close"])
ax.zaxis.set_major_formatter(FuncFormatter(major_formatter))
ax.set_zlabel('Price (US $)')

# Rotate the viewport
ax.view_init(azim=-42, elev=31)

plt.tight_layout()
plt.show()

Basically, we truncated the y values by 110, and then we used a tick formatter (major_formatter) to shift the tick value back to the original. For 3D scatter plots, we can simply remove the data points that exceed the boundary of set_zlim3d() in order to generate a proper figure. However, these workarounds may not work for every 3D plot type.