Pythonic way to generate seaborn heatmap subplots

I have a dataframe that contains 7 columns. The Regressor column has 3 different regressors (DT, DT-2, and DT-4).

I wanted to generate a correlation heatmap plot.

            df_dt = df[(df["Regressor"]=="DT")]
            df_dt_corr = df_dt.drop(["Regressor"], axis=1).corr()

            df_dt2 = df[(df["Regressor"]=="DT-2")]
            df_dt2_corr = df_dt2.drop(["Regressor"], axis=1).corr()

            df_dt4 = df[(df["Regressor"]=="DT-4")]
            df_dt4_corr = df_dt4.drop(["Regressor"], axis=1).corr()

            #  SUBPLOTS
            fig = plt.figure(figsize=(12,6))

            plt.subplot(221)  
            plt.title('Regressor: DT')
            sns.heatmap(df_dt_corr, annot=True, fmt='.2f', square=True, cmap = 'Reds_r')

            plt.subplot(222)  
            plt.title('Regressor: DT-2')
            sns.heatmap(df_dt2_corr, annot=True, fmt='.2f', square=True, cmap = 'Blues_r')

            plt.subplot(223)
            plt.title('Regressor: DT-4')
            sns.heatmap(df_dt4_corr, annot=True, fmt='.2f', square=True, cmap = 'BuGn_r')

            plt.show()

I am also getting the plot

Now, the problem is, if I have 10 regressors, then I have to write 10 times repeated code for each regressor. Which is not a pythonic way or good programming practice.

Is there any way to do the same job in a pythonic way (i.e, using a loop, etc.)?

Please Note: In the demo dataframe I have 3 regressors but in my main dataframe I could have more regressors. So, I need a dynamic way to generate the plot based on the regressors.

Demo Data:

{'Regressor': {0: 'DT', 1: 'DT', 2: 'DT', 3: 'DT', 4: 'DT', 19: 'DT-2', 20: 'DT-2', 21: 'DT-2', 22: 'DT-2', 23: 'DT-2', 39: 'DT-4', 40: 'DT-4', 41: 'DT-4', 42: 'DT-4', 43: 'DT-4'}, 'Method': {0: 'method_1', 1: 'method_1', 2: 'method_1', 3: 'method_1', 4: 'method_1', 19: 'method_1', 20: 'method_1', 21: 'method_1', 22: 'method_1', 23: 'method_1', 39: 'method_1', 40: 'method_1', 41: 'method_1', 42: 'method_1', 43: 'method_1'}, 'CE': {0: 0.002874032327519, 1: 0.005745640214479, 2: 0.004661679592489, 3: 0.002846754581854, 4: 0.004576990206546, 19: 0.105364819313149, 20: 0.085976562255755, 21: 0.095881176731004, 22: 0.097398912201617, 23: 0.100491941499165, 39: 0.018162548523961, 40: 0.018954401200213, 41: 0.01788125083107, 42: 0.019784900032633, 43: 0.020438103824639}, 'MAE': {0: 0.737423646017325, 1: 2.00787732271062, 2: 2.86926125864208, 3: 3.32855382663718, 4: 3.77490323897613, 19: 13.345092685398, 20: 12.8063543324171, 21: 13.1292091661974, 22: 13.1451455897874, 23: 13.6537246486947, 39: 3.2667181947348, 40: 4.29467676417246, 41: 5.34081768096088, 42: 5.50421114390641, 43: 7.46988963588581}, 'MSqE': {0: 0.847829904338757, 1: 6.68342912741117, 2: 12.5560681493523, 3: 17.2772893168584, 4: 22.02275890951, 19: 232.978432669064, 20: 237.820275013751, 21: 244.5869111788, 22: 247.73962294989, 23: 266.451945948429, 39: 15.6880657226101, 40: 28.2245308508171, 41: 44.7562607712654, 42: 46.5234139459763, 43: 87.2324237935045}, 'R2': {0: 0.999729801060669, 1: 0.998038240639634, 2: 0.996528815654117, 3: 0.995203737109921, 4: 0.993477444422499, 19: 0.926657847114707, 20: 0.93726355821839, 21: 0.932221279553296, 22: 0.91924882453144, 23: 0.925514811021512, 39: 0.995151906119729, 40: 0.991723226976753, 41: 0.986284593333255, 42: 0.982615342502863, 43: 0.97292435121805}}

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