循环后将颜色条添加到散点图

Question

I am trying to add a continuous colorbar to a seaborn scatterplot (similar to the answers here and in here ). For my purposes, I am building the scatterplot with a loop, and then trying to add the continuous colorbar, but I dont know what object to include as argument of fig.colorbar() . How would you do this?

import pandas as pd
import seaborn as sb
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(42)

df=pd.DataFrame(np.random.rand(2, 100), index=['S1','S2']).T
tars=np.random.choice([0,0.3,0.5,0.8,1], 100)
df=pd.concat([df,pd.Series(tars, name='group')],1)
colors = matplotlib.cm.viridis(np.linspace(0,1,len(pd.unique(tars))))

fig = plt.figure(figsize = (10,8), dpi=300)
ax = fig.add_subplot(1,1,1) 
targets=pd.unique(tars)
for target, color in zip(targets,colors):
    ...
    g=ax.scatter(
        df.loc[df.group==target, 'S1'], 
        df.loc[df.group==target, 'S2'],
        color = [color]
    )
fig.colorbar(g)
plt.show()

If I add ax.legend(targets) instead of fig.colorbar(g) , the legend displays correctly but is categorical.

df=pd.DataFrame(np.random.rand(2, 100), index=['S1','S2']).T
tars=np.random.choice([0,0.3,0.5,0.8,1], 100)
df=pd.concat([df,pd.Series(tars, name='group')],1)

cmap=matplotlib.cm.gnuplot2
colors = cmap(np.linspace(0,1,len(pd.unique(tars))))

fig = plt.figure(figsize = (10,8), dpi=300)
ax = fig.add_subplot(1,1,1) 
targets=pd.unique(tars)
for target, color in zip(targets,colors):
    ...
    g=ax.scatter(
        df.loc[df.group==target, 'S1'], 
        df.loc[df.group==target, 'S2'],
        color = [color]
    )
ax.legend(targets)
plt.show()

Answer 1

I am not entirely sure to understand what you are trying to achieve with your for-loop.

Is this the output that you are looking for?

fig = plt.figure()
ax = fig.add_subplot(1,1,1) 
g = ax.scatter(df['S1'],df['S2'],c=df['group'],cmap='viridis')
cbar = fig.colorbar(g)
plt.show()

Answer 2

Thanks to this answer I could edit my code to show the continuous colorbar.

df=pd.DataFrame(np.random.rand(2, 100), index=['S1','S2']).T
tars=np.random.choice([0,0.3,0.5,0.8,1], 100)
df=pd.concat([df,pd.Series(tars, name='group')],1)

cmap=matplotlib.cm.viridis
colors = cmap(np.linspace(0,1,len(pd.unique(tars))))

fig = plt.figure(figsize = (10,8), dpi=300)
ax = fig.add_subplot(1,1,1) 
targets=pd.unique(tars)
for target, color in zip(targets,colors):
    g=ax.scatter(
        df.loc[df.group==target, 'S1'], 
        df.loc[df.group==target, 'S2'],
        color=[color]
    )

norm = plt.Normalize(np.min(tars), np.max(tars))
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
ax.figure.colorbar(sm)
plt.show()