How to fill matplotlib bars with a gradient?

I would be very interested in filling matplotlib/seaborn bars of a barplot with different gradients exactly like done here (not with matplotlib as far as I understood):

I have also checked this related topic Pyplot: vertical gradient fill under curve?.

Is this only possible via gr-framework: or are there alternative strategies?

Just as depicted in Pyplot: vertical gradient fill under curve? one may use an image to create a gradient plot.

Since bars are rectangular the extent of the image can be directly set to the bar's position and size. One can loop over all bars and create an image at the respective position. The result is a gradient bar plot.

import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots()

bar = ax.bar([1,2,3,4,5,6],[4,5,6,3,7,5])

def gradientbars(bars):
    grad = np.atleast_2d(np.linspace(0,1,256)).T
    ax = bars[0].axes
    lim = ax.get_xlim()+ax.get_ylim()
    for bar in bars:
        bar.set_zorder(1)
        bar.set_facecolor("none")
        x,y = bar.get_xy()
        w, h = bar.get_width(), bar.get_height()
        ax.imshow(grad, extent=[x,x+w,y,y+h], aspect="auto", zorder=0)
    ax.axis(lim)

gradientbars(bar)

plt.show() 

I adapted @ImportanceOfBeingErnest's answer here using Seaborn instead of Matplotlib.

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

def gradientbars(bars):
    grad = np.atleast_2d(np.linspace(0,1,256)).T # Gradient of your choice

    rectangles = bars.containers[0]
    # ax = bars[0].axes
    fig, ax = plt.subplots()

    xList = []
    yList = []
    for rectangle in rectangles:
        x0 = rectangle._x0
        x1 = rectangle._x1
        y0 = rectangle._y0
        y1 = rectangle._y1

        xList.extend([x0,x1])
        yList.extend([y0,y1])

        ax.imshow(grad, extent=[x0,x1,y0,y1], aspect="auto", zorder=0)

    ax.axis([min(xList), max(xList), min(yList), max(yList)*1.1]) # *1.1 to add some buffer to top of plot

    return fig,ax


sns.set(style="whitegrid", color_codes=True)
np.random.seed(sum(map(ord, "categorical")))

# Load dataset
titanic = sns.load_dataset("titanic")

# Make Seaborn countplot
seabornAxHandle = sns.countplot(x="deck", data=titanic, palette="Greens_d")
plt.show() # Vertical bars with horizontal gradient

# Call gradientbars to make vertical gradient barplot using Seaborn ax
figVerticalGradient, axVerticalGradient = gradientbars(seabornAxHandle)

# Styling using the returned ax
axVerticalGradient.xaxis.grid(False)
axVerticalGradient.yaxis.grid(True)

# Labeling plot to match Seaborn
labels=titanic['deck'].dropna().unique().to_list() # Chaining to get tick labels as a list
labels.sort()
plt.ylabel('count')
plt.xlabel('deck')
plt.xticks(range(0,len(labels)), labels)  # Set locations and labels

plt.show() # Vertical bars with vertical gradient

Output from Seaborn countplot:

Output with vertical gradient bars: