limit range of colorbar on bar graph in matplotlib

Question

I've been attempting to limit the range on the colorbar function in matplotlib. For whatever reason, I cannot use the clim function. Ideally I would like 80 and 20 to be the max values of the colorbar, and all values above or below those values to be a single dark blue/red, and the entire colorbar to be fit within the range of 20 and 80.

import requests
from bs4 import BeautifulSoup
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.cm import ScalarMappable
import matplotlib as mpl
import numpy as np
Gpercent=40
xGpercent = 60
SCFpercent = 55
CFpercent = 45
Analytics = ['GF%','xGF%','SCF%','CF%']
AnalyticsValues = [Gpercent,xGpercent,SCFpercent,CFpercent]
AnalyticsValues = [float(val) for val in AnalyticsValues]
data_height_normalized = [x / 100 for x in AnalyticsValues]

fig, ax = plt.subplots(figsize=(15, 4))
#my_cmap = plt.cm.get_cmap('RdBu')
my_cmap = plt.cm.get_cmap('coolwarm_r')
colors = my_cmap(data_height_normalized)

rects = ax.bar(Analytics, AnalyticsValues, color=colors)

sm = ScalarMappable(cmap=my_cmap, norm=plt.Normalize(0,100))

plt.ylim(0, 100)
cbar = plt.colorbar(sm)

plt.yticks(np.arange(0, 100.8, 10))
plt.title('bob' + (" On Ice 5v5 Impact"))
plt.xlabel('Analytical Metric')
plt.ylabel('%')
fig.patch.set_facecolor('xkcd:white')
plt.show()

The plot comes out as follows. I'd like the colorbar to be more defined in a shorter range, while still showing the % from 0-100

Answer 1

The intent of your question is to add an upper and lower limit to the color bar only. I would like to set the lower limit to 20 and the upper limit to 80. I will answer with the understanding that The gist of the code is to create a new colormap from the defined colormap using LinearSegmentedColormap with the upper and lower color range. My answer was modified from this excellent answer to fit your assignment.

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.cm import ScalarMappable
from matplotlib.colors import LinearSegmentedColormap # add
import matplotlib as mpl
import numpy as np

Gpercent=40
xGpercent = 60
SCFpercent = 55
CFpercent = 45
Analytics = ['GF%','xGF%','SCF%','CF%']
AnalyticsValues = [Gpercent,xGpercent,SCFpercent,CFpercent]
AnalyticsValues = [float(val) for val in AnalyticsValues]
data_height_normalized = [x / 100 for x in AnalyticsValues]

fig, ax = plt.subplots(figsize=(15, 4))
#my_cmap = plt.cm.get_cmap('RdBu')
my_cmap = plt.cm.get_cmap('coolwarm_r')
colors = my_cmap(data_height_normalized)

rects = ax.bar(Analytics, AnalyticsValues, color=colors)

# update
vmin,vmax = 20,80
colors2 = my_cmap(np.linspace(1.-(vmax-vmin)/float(vmax), 1, my_cmap.N))
color_map = LinearSegmentedColormap.from_list('cut_coolwarm', colors2)

sm = ScalarMappable(cmap=color_map, norm=plt.Normalize(vmin, vmax))

plt.ylim(0, 100)
cbar = plt.colorbar(sm)

plt.yticks(np.arange(0, 100.8, 10))
plt.title('bob' + (" On Ice 5v5 Impact"))
plt.xlabel('Analytical Metric')
plt.ylabel('%')
fig.patch.set_facecolor('xkcd:white')
plt.show()