There's more than one way to do this.
The easiest way is to just pass in a boolean array to pcolor and then choose a colormap where green is high and red is low.
For example:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Index= ['aaa', 'bbb', 'ccc', 'ddd', 'eee']
Cols = ['A', 'B', 'C', 'D']
data= np.random.random((5, 4))
df = pd.DataFrame(data, index=Index, columns=Cols)
plt.pcolor(df > 0.5, cmap='RdYlGn')
plt.yticks(np.arange(0.5, len(df.index), 1), df.index)
plt.xticks(np.arange(0.5, len(df.columns), 1), df.columns)
plt.show()
Alternately, as @Cyber mentioned, you could make a two-color colormap based on your values and use it:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
Index= ['aaa', 'bbb', 'ccc', 'ddd', 'eee']
Cols = ['A', 'B', 'C', 'D']
data= np.random.random((5, 4))
df = pd.DataFrame(data, index=Index, columns=Cols)
# Values from 0-0.5 will be red and 0.5-1 will be green
cmap, norm = mcolors.from_levels_and_colors([0, 0.5, 1], ['red', 'green'])
plt.pcolor(df, cmap=cmap, norm=norm)
plt.yticks(np.arange(0.5, len(df.index), 1), df.index)
plt.xticks(np.arange(0.5, len(df.columns), 1), df.columns)
plt.show()
(The color difference is just because the "RdYlGn" colormap uses darker greens and reds as its endpoints.)
On a side note, it's also considerably faster to use pcolormesh for this, rather than pcolor. For small arrays, it won't make a significant difference, but for large arrays pcolor is excessively slow. imshow is even faster yet, if you don't mind raster output. Use imshow(data, interpolation='nearest', aspect='auto', origin='lower') to match the defaults of pcolor and pcolormesh.
