Data Coverage Plot using matplotlib and Pandas DataFrame

Question

I have created data Coverage of time series which is in pandas Data Frame and would like to plot the data coverage in Matplotlib or PyQtGraph.

DATA FRAME

DateTime    WD98    WS120   WS125B  WD123   WS125A
31-07-2013  100 99.9    99.9    NaN NaN
31-08-2013  100 100 100 NaN NaN
30-09-2013  100 100 100 NaN NaN
31-10-2013  100 100 100 NaN NaN
30-11-2013  100 100 100 100 100
31-12-2013  100 100 100 100 100
31-01-2014  100 100 100 100 100
28-02-2014  100 100 100 100 100
31-03-2014  100 100 100 100 100
30-04-2014  100 100 100 100 100
31-05-2014  67.1    100 100 67.1    7.7
30-06-2014  NaN NaN 100 0   69.2
31-07-2014  NaN NaN 100 0   100
31-08-2014  NaN NaN 100 0   96.2

I would like to plot in below fashion (Broken bar Chart)

The above plot was done using Excel Conditional Formatting. Please help me.

DataCoverage >= 90 (Green)
DataCoverage >= 75 and DataCoverage < 90 (Yellow)
DataCoverage < 75 (red)

Answer 1

you can use seaborn.heatmap:

import seaborn as sns

df = df.set_index(df.pop('DateTime').dt.strftime('%d-%m-%Y'))
g = sns.heatmap(df, cmap=['r','y','g'], annot=True, fmt='.0f')
g.set_yticklabels(g.get_yticklabels(), rotation=0, fontsize=8)

Result:

UPDATE: corrected version:

x = df.set_index(df['DateTime'].dt.strftime('%d-%m-%Y')).drop('DateTime', 1)    
z = pd.cut(x.stack(), bins=[-np.inf, 75, 90, np.inf], labels=[1.,2.,3.]).unstack().apply(pd.to_numeric)    
g = sns.heatmap(z, cmap=['r','y','g'], fmt='.0f', cbar=False)    
g.set_yticklabels(g.get_yticklabels(), rotation = 0, fontsize = 8)

Result: