[Python] Add a new row as the average of columns

Give the following dataframe:

_BETTER _SAME _WORSE ___dataset Metric
0.373802 0.816794 0.568783 Train precision
0.391304 0.865229 0.519324 Train recall
0.382353 0.840314 0.542929 Train f1-score
0.500000 1.000000 0.583333 Val precision
0.333333 1.000000 0.736842 Val recall
0.400000 1.000000 0.651163 Val f1-score
0.000000 0.000000 0.666667 Test precision
0.000000 0.000000 0.500000 Test recall
0.000000 0.000000 0.571429 Test f1-score


would like to add the followings:

_BETTER _SAME _WORSE ___dataset Metric
0.373802 0.816794 0.568783 Train precision
0.391304 0.865229 0.519324 Train recall
0.382353 0.840314 0.542929 Train f1-score
0.500000 1.000000 0.583333 Val precision
0.333333 1.000000 0.736842 Val recall
0.400000 1.000000 0.651163 Val f1-score
0.000000 0.000000 0.666667 Test precision
0.000000 0.000000 0.500000 Test recall
0.000000 0.000000 0.571429 Test f1-score
mean_p_b mean_p_s mean_p_w All precision_avg
mean_r_b mean_r_s mean_r_w All recall_avg
mean_f1_b mean_f1_s mean_f1_w All f1_score_avg


where mean_p_b mean_p_s mean_p_w is obtained by the average of the precision row, w.r.t the three colums, respectively. Likewise the mean_r_b mean_r_s mean_r_w and mean_f1_b mean_f1_s mean_f1_w.

Applying each separately:

df_avg_precision["BETTER"] = (df_train_precision['_BETTER'].values + df_val_precision['_BETTER'].values +
df_test_precision['_BETTER'].values)/3
df_avg_precision["Metric"] = "precision_avg"
df_avg_recall["BETTER"] = (df_train_recall['_BETTER'].values +
df_val_recall['_BETTER'].values + df_test_recall['_BETTER'].values)/3
df_avg_recall["Metric"] = "recall_avg"
df_avg_f1["BETTER"] = (df_train_f1['_BETTER'].values +
df_val_f1['_BETTER'].values + df_test_f1['_BETTER'].values)/3
df_avg_f1["Metric"] = "f1_avg"`

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