concat_dataframe.py

# Concatenate results from all models
all_models_dataframe = pd.concat([tf_pred_dataframe, 
                                  tflite_pred_dataframe, 
                                  tflite_q_pred_dataframe], 
                                 keys=['TF Model', 'TFLite', 'TFLite quantized'],
                                 axis='columns')

# Swap columns to hava side by side comparison
all_models_dataframe = all_models_dataframe.swaplevel(axis='columns')[tflite_pred_dataframe.columns]

# Highlight TFLite models predictions that are different from original model
def highlight_diff(data, color='yellow'):
    attr = 'background-color: {}'.format(color)
    other = data.xs('TF Model', axis='columns', level=-1)
    return pd.DataFrame(np.where(data.ne(other, level=0), attr, ''),
                        index=data.index, columns=data.columns)
  
all_models_dataframe.style.apply(highlight_diff, axis=None)