You can specify the color option as a list directly to the plot function.
from matplotlib import pyplot as plt
from itertools import cycle, islice
import pandas, numpy as np # I find np.random.randint to be better
# Make the data
x = [{i:np.random.randint(1,5)} for i in range(10)]
df = pandas.DataFrame(x)
# Make a list by cycling through the colors you care about
# to match the length of your data.
my_colors = list(islice(cycle(['b', 'r', 'g', 'y', 'k']), None, len(df)))
# Specify this list of colors as the `color` option to `plot`.
df.plot(kind='bar', stacked=True, color=my_colors)
To define your own custom list, you can do a few of the following, or just look up the Matplotlib techniques for defining a color item by its RGB values, etc. You can get as complicated as you want with this.
my_colors = ['g', 'b']*5 # <-- this concatenates the list to itself 5 times.
my_colors = [(0.5,0.4,0.5), (0.75, 0.75, 0.25)]*5 # <-- make two custom RGBs and repeat/alternate them over all the bar elements.
my_colors = [(x/10.0, x/20.0, 0.75) for x in range(len(df))] # <-- Quick gradient example along the Red/Green dimensions.
The last example yields the follow simple gradient of colors for me:
I didn’t play with it long enough to figure out how to force the legend to pick up the defined colors, but I’m sure you can do it.
In general, though, a big piece of advice is to just use the functions from Matplotlib directly. Calling them from Pandas is OK, but I find you get better options and performance calling them straight from Matplotlib.