Using a Custom Color Palette in Stacked Bar Chart

So am trying to create a stacked bar chart where all of the slices of the chart will remain constant throughout the program, but I cannot figure out how to get df.plot to use a custom palette.

I want to make sure that if I do 20 different reports out of this program, Freeze will always be, for example, blue and Hail will always be white.

Please take a look at the example below - first, I am creating a custom palette for all of the weather values.

When I do the plotting using the "paired" palette, it works fine and looks as follows:

And this is basically what I want, except using the custom palette

However, when I do

df.plot(kind='bar', stacked=True,colormap=pal_weather)

I get an error that ends with:

C:\Anaconda3\lib\site-packages\pandas\tools\plotting.py in _get_standard_colors(num_colors, colormap, color_type, color)
157             if colormap is None:
158                 raise ValueError("Colormap {0} is not recognized".format(cmap))
--> 159         colors = lmap(colormap, np.linspace(0, 1, num=num_colors))
    160     elif color is not None:
    161         if colormap is not None:

C:\Anaconda3\lib\site-packages\pandas\compat\__init__.py in lmap(*args, **kwargs)
    116 
    117     def lmap(*args, **kwargs):
-->  118         return list(map(*args, **kwargs))
    119 
    120     def lfilter(*args, **kwargs):

TypeError: 'dict' object is not callable

Any help would be much apopreciated

Thank you!

Sample Code Follows:

weather=('Day','Freeze', 'Wind', 'Flood', 'Quake', 'Hail')

pal_weather = dict(zip(weather, sns.color_palette("cubehelix", n_colors=len(weather))))

data1 = [[ "M", 66386,  174296,   75131,  577908,   32015],
    [  "T", 58230,  381139,   78045,   99308,  160454],
    [  "W", 89135,   80552,  152558,  497981,  603535],
    [  "T", 78415,   81858,  150656,  193263,   69638],
    [ "F", 139361,  331509,  343164,  781380,   52269]]


df = DataFrame(data=data1)
df.columns = ('Day','Freeze', 'Wind', 'Flood', 'Quake', 'Hail')
df.plot(kind='bar', stacked=True,colormap='Paired')

Try with my solution:

import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
from matplotlib.colors import LinearSegmentedColormap

matplotlib.style.use('ggplot')
from pandas import DataFrame

weather = ('Day', 'Freeze', 'Wind', 'Flood', 'Quake', 'Hail')
colors = sns.color_palette("cubehelix", n_colors=len(weather))
cmap1 = LinearSegmentedColormap.from_list("my_colormap", colors)

data1 = [["M", 66386, 174296, 75131, 577908, 32015],
         ["T", 58230, 381139, 78045, 99308, 160454],
         ["W", 89135, 80552, 152558, 497981, 603535],
         ["T", 78415, 81858, 150656, 193263, 69638],
         ["F", 139361, 331509, 343164, 781380, 52269]]


df = DataFrame(data=data1)
df.columns = weather
df = df.set_index('Day')
df.plot(kind='bar', stacked=True, colormap=cmap1)
plt.show()

Output: