Circle Plot with Color Bar

Question

I'm trying to do a Circle Plot with a Color Bar, almost like this:

However, the minimum value of the colour bar is currently 1; I would like to be able to set it to 0.

import pandas            as pd
import matplotlib.pyplot as plt
import matplotlib.cm     as cm
from sklearn import preprocessing

df = pd.DataFrame({'A':[1,2,1,2,3,4,2,1,4], 
                   'B':[3,1,5,1,2,4,5,2,3], 
                   'C':[4,2,4,1,3,3,4,2,1]})

# set the Colour
x              = df.values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled       = min_max_scaler.fit_transform(x)
df_S           = pd.DataFrame(x_scaled)
c1             = df['C']
c2             = df_S[2]
colors         = [cm.jet(color) for color in c2]

# Graph
plt.figure()
ax = plt.gca()
for a, b, color in zip(df['A'], df['B'], colors):
    circle = plt.Circle((a, 
                         b), 
                         1, # Size
                         color=color, 
                         lw=5, 
                         fill=False)
    ax.add_artist(circle)

plt.xlim([0,5])
plt.ylim([0,5])
plt.xlabel('A')
plt.ylabel('B')
ax.set_aspect(1.0)

sc = plt.scatter(df['A'], 
                 df['B'], 
                 s=0, 
                 c=c1, 
                 cmap='jet', 
                 facecolors='none')
plt.grid()

cbar = plt.colorbar(sc)
cbar.set_label('C', rotation=270, labelpad=10)

plt.show()

Credit to this original question: Plotting circles with no fill, colour & size depending on variables using scatter

Answer 1

Just add the vmin and vmax params in plt.scatter().

sc = plt.scatter(df['A'], 
                 df['B'], 
                 s=0, 
                 c=c1, 
                 cmap='jet',
                 vmin = 0,
                 vmax = 4,
                 facecolors='none')

If you want to ajust the color of the circles based on the color-map, then you need to use `Normalize(vmin, vmax) and pass the color-map to the circle plot with the normalized value.

Here is the code:

import pandas            as pd
import matplotlib.pyplot as plt
import matplotlib.cm     as cm
from sklearn import preprocessing
from matplotlib.colors import Normalize

df = pd.DataFrame({'A':[1,2,1,2,3,4,2,1,4], 
                   'B':[3,1,5,1,2,4,5,2,3], 
                   'C':[4,2,4,1,3,3,4,2,1]})

# set the Colour
x              = df.values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled       = min_max_scaler.fit_transform(x)
df_S           = pd.DataFrame(x_scaled)
c1             = df['C']
c2             = df_S[2]
cmap = cm.jet
vmin = 0
vmax = 5 #your max Y is 5, not 4
norm = Normalize(vmin, vmax)

# Graph
plt.figure()
ax = plt.gca()
for a, b in zip(df['A'], df['B']):
    circle = plt.Circle((a, 
                         b), 
                         1, # Size
                         color=cmap(norm(b)), 
                         lw=5, 
                         fill=False)
    ax.add_artist(circle)

plt.xlim([0,5])
plt.ylim([0,5])
plt.xlabel('A')
plt.ylabel('B')
ax.set_aspect(1.0)

sc = plt.scatter(df['A'], 
                 df['B'], 
                 s=0, 
                 c=c1, 
                 cmap='jet',
                 vmin = vmin,
                 vmax = vmax,
                 facecolors='none')
plt.grid()

cbar = plt.colorbar(sc)
cbar.set_label('C', rotation=270, labelpad=10)

plt.show()