diff --git a/demo/guide-python/boost_from_prediction.py b/demo/guide-python/boost_from_prediction.py
index 4870fc49c..dfb7d2ff9 100755
--- a/demo/guide-python/boost_from_prediction.py
+++ b/demo/guide-python/boost_from_prediction.py
@@ -4,21 +4,21 @@ import xgboost as xgb
 
 dtrain = xgb.DMatrix('../data/agaricus.txt.train')
 dtest = xgb.DMatrix('../data/agaricus.txt.test')
-watchlist  = [(dtest,'eval'), (dtrain,'train')]
+watchlist = [(dtest, 'eval'), (dtrain, 'train')]
 ###
 # advanced: start from a initial base prediction
 #
 print ('start running example to start from a initial prediction')
 # specify parameters via map, definition are same as c++ version
-param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
+param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
 # train xgboost for 1 round
-bst = xgb.train( param, dtrain, 1, watchlist )
+bst = xgb.train(param, dtrain, 1, watchlist)
 # Note: we need the margin value instead of transformed prediction in set_base_margin
 # do predict with output_margin=True, will always give you margin values before logistic transformation
 ptrain = bst.predict(dtrain, output_margin=True)
-ptest  = bst.predict(dtest, output_margin=True)
+ptest = bst.predict(dtest, output_margin=True)
 dtrain.set_base_margin(ptrain)
 dtest.set_base_margin(ptest)
 
 print ('this is result of running from initial prediction')
-bst = xgb.train( param, dtrain, 1, watchlist )
+bst = xgb.train(param, dtrain, 1, watchlist)