It's the inverse. While matplotlib can do pretty much everything, seaborn only provides a small subset of options. So using matplotlib, you can plot a PatchCollection of circles as shown below. Note: You could equally use a scatter plot, but since scatter dot sizes are in absolute units it would be rather hard to scale them into the grid.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection

N = 10
M = 11
ylabels = ["".join(np.random.choice(list("PQRSTUVXYZ"), size=7)) for _ in range(N)]
xlabels = ["".join(np.random.choice(list("ABCDE"), size=3)) for _ in range(M)]

x, y = np.meshgrid(np.arange(M), np.arange(N))
s = np.random.randint(0, 180, size=(N,M))
c = np.random.rand(N, M)-0.5

fig, ax = plt.subplots()

R = s/s.max()/2
circles = [plt.Circle((j,i), radius=r) for r, j, i in zip(R.flat, x.flat, y.flat)]
col = PatchCollection(circles, array=c.flatten(), cmap="RdYlGn")
ax.add_collection(col)

ax.set(xticks=np.arange(M), yticks=np.arange(N),
       xticklabels=xlabels, yticklabels=ylabels)
ax.set_xticks(np.arange(M+1)-0.5, minor=True)
ax.set_yticks(np.arange(N+1)-0.5, minor=True)
ax.grid(which='minor')

fig.colorbar(col)
plt.show()

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One option is to use matplotlib's scatter plots with legends and grid. You can specify size of those circles with specifying the scales. You can also change the color of each circle. You should somehow specify X,Y values so that the circles sit straight on lines. This is an example I got from here:

volume = np.random.rayleigh(27, size=40)
amount = np.random.poisson(10, size=40)
ranking = np.random.normal(size=40)
price = np.random.uniform(1, 10, size=40)

fig, ax = plt.subplots()

# Because the price is much too small when being provided as size for ``s``,
# we normalize it to some useful point sizes, s=0.3*(price*3)**2
scatter = ax.scatter(volume, amount, c=ranking, s=0.3*(price*3)**2,
                     vmin=-3, vmax=3, cmap="Spectral")

# Produce a legend for the ranking (colors). Even though there are 40 different
# rankings, we only want to show 5 of them in the legend.
legend1 = ax.legend(*scatter.legend_elements(num=5),
                    loc="upper left", title="Ranking")
ax.add_artist(legend1)

# Produce a legend for the price (sizes). Because we want to show the prices
# in dollars, we use the *func* argument to supply the inverse of the function
# used to calculate the sizes from above. The *fmt* ensures to show the price
# in dollars. Note how we target at 5 elements here, but obtain only 4 in the
# created legend due to the automatic round prices that are chosen for us.
kw = dict(prop="sizes", num=5, color=scatter.cmap(0.7), fmt="$ {x:.2f}",
          func=lambda s: np.sqrt(s/.3)/3)
legend2 = ax.legend(*scatter.legend_elements(**kw),
                    loc="lower right", title="Price")

plt.show()

Output:

Seaborn heatmap half, Heatmap with circles indicating size of population. seed(42) sns. 2 or higher; set the heatmap limits manually (ax. When plotting heatmaps with seaborn (and� GigaPromo is the website to compare Website Heatmap Free. Search and save now!

Here's a possible solution using Bokeh Plots:

import pandas as pd
from bokeh.palettes import RdBu
from bokeh.models import LinearColorMapper, ColumnDataSource, ColorBar
from bokeh.models.ranges import FactorRange
from bokeh.plotting import figure, show
from bokeh.io import output_notebook

import numpy as np

output_notebook()

d = dict(x = ['A','A','A', 'B','B','B','C','C','C','D','D','D'], 
         y = ['B','C','D', 'A','C','D','B','D','A','A','B','C'], 
         corr = np.random.uniform(low=-1, high=1, size=(12,)).tolist())

df = pd.DataFrame(d)

df['size'] = np.where(df['corr']<0, np.abs(df['corr']), df['corr'])*50
#added a new column to make the plot size

colors = list(reversed(RdBu[9]))
exp_cmap = LinearColorMapper(palette=colors, 
                             low = -1, 
                             high = 1)


p = figure(x_range = FactorRange(), y_range = FactorRange(), plot_width=700, 
           plot_height=450, title="Correlation",
           toolbar_location=None, tools="hover")

p.scatter("x","y",source=df, fill_alpha=1,  line_width=0, size="size", 
          fill_color={"field":"corr", "transform":exp_cmap})

p.x_range.factors = sorted(df['x'].unique().tolist())
p.y_range.factors = sorted(df['y'].unique().tolist(), reverse = True)

p.xaxis.axis_label = 'Values'
p.yaxis.axis_label = 'Values'

bar = ColorBar(color_mapper=exp_cmap, location=(0,0))
p.add_layout(bar, "right")

show(p)

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4. Visualization with Matplotlib, In [ 4 ]: plt . plot ( x , y , '-ok' ); # line (-), circle marker (o), black (k) Here is an example where we'll use the size of points to indicate populations of California� Hi I would like to produce a heatmap in Python, similar to the one shown, where the size of the circle indicates the size of the sample in that cell. I looked in seaborn's gallery and couldn't find anything, and I don't think I can do this with matplotlib.

[PDF] Heat Maps, You can use colors on the spectrum from blue to red to show population scatter x=xcen y=ycen / markerchar=label markercharattrs=(size=5); The following step creates a heat map of the same data: sides than squares (hexagons are closer geometrically to circles than squares are), which indicates USA registration. With this df, I'd like to plot a heatmap as the following: I have tried the suggestion of this post: Heatmap with circles indicating size of population. However, it is not working. Thank you for the help!