Kaggle competition: Santander Customer Transaction Prediction
question2.quest_pct = round(question2.quest_pct,5 )<drop_column>
predict_list = []
predict_list.append(pd.read_csv('.. /input/90-lines-solution-0-901-fast/lgb_submission.csv')[LABELS].values)
 predict_list.append(pd.read_csv('.. /input/eda-pca-lgbm-santander-transactions/submission34.csv')[LABELS].values)
 predict_list.append(pd.read_csv('.. /input/eda-pca-simple-lgbm-on-kfold-technique/submission26.csv')[LABELS].values)
 predict_list.append(pd.read_csv('.. /input/lgb-2-leaves-augment/lgb_submission.csv')[LABELS].values)
 predict_list.append(pd.read_csv('.. /input/lightgbm-with-data-augmentation/2019-04-01_07_56_sub.csv')[LABELS].values)
 predict_list.append(pd.read_csv('.. /input/santander-augment-to-the-rescue/submission.csv')[LABELS].values)
 predict_list.append(pd.read_csv('.. /input/others/output_v3.csv')[LABELS].values)
 predict_list.append(pd.read_csv('.. /input/others/output_v2.csv')[LABELS].values)
 predict_list.append(pd.read_csv('.. /input/others/output_v1.csv')[LABELS].values )
train.drop(['timestamp', 'content_type_id', 'question_id', 'part'], axis=1, inplace=True )<filter>
print("Rank averaging on ", len(predict_list), " files")
 predictions = np.zeros_like(predict_list[0])
 for predict in predict_list:
for i in range(1):
predictions[:, i] = np.add(predictions[:, i], rankdata(predict[:, i])/predictions.shape[0])
 predictions /= len(predict_list)
 submission = pd.read_csv('.. /input/santander-customer-transaction-prediction/sample_submission.csv')
 submission[LABELS] = predictions
submission.to_csv('super_blend.csv', index=False )