matplotlib bar plot add legend from categories dataframe column - dictionary

Question

I try to add the legend which should, according to my example, output:
a red square with the word fruit and
a green square with the word
veggie.
I tried several things (the example below is just 1 of the many trials), but I can't get it work.
Can someone tell me how to solve this problem?
import pandas as pd
from matplotlib import pyplot as plt
data = [['apple', 'fruit', 10], ['nanaba', 'fruit', 15], ['salat','veggie', 144]]
data = pd.DataFrame(data, columns = ['Object', 'Type', 'Value'])
colors = {'fruit':'red', 'veggie':'green'}
c = data['Type'].apply(lambda x: colors[x])
bars = plt.bar(data['Object'], data['Value'], color=c, label=colors)
plt.legend()

Answer 1

The usual way to create a legend for objects which are not in the axes would be to create proxy artists as shown in the legend guide
Here,
colors = {'fruit':'red', 'veggie':'green'}
labels = list(colors.keys())
handles = [plt.Rectangle((0,0),1,1, color=colors[label]) for label in labels]
plt.legend(handles, labels)

Answer 2

So this is a hacky solution and I'm sure there are probably better ways to do this. What you can do is plot individual bar plots that are invisible using width=0 with the original plot colors and specify the labels. You will have to do this in a subplot though.
import pandas as pd
from matplotlib import pyplot as plt
data = [['apple', 'fruit', 10], ['nanaba', 'fruit', 15], ['salat','veggie', 144]]
data = pd.DataFrame(data, columns = ['Object', 'Type', 'Value'])
colors = {'fruit':'red', 'veggie':'green'}
c = data['Type'].apply(lambda x: colors[x])
ax = plt.subplot(111) #specify a subplot
bars = ax.bar(data['Object'], data['Value'], color=c) #Plot data on subplot axis
for i, j in colors.items(): #Loop over color dictionary
ax.bar(data['Object'], data['Value'],width=0,color=j,label=i) #Plot invisible bar graph but have the legends specified
ax.legend()
plt.show()