diff --git a/python/xgboost.py b/python/xgboost.py
index badeebed9..509fe654a 100644
--- a/python/xgboost.py
+++ b/python/xgboost.py
@@ -1,11 +1,11 @@
 # Author: Tianqi Chen, Bing Xu
 # module for xgboost
-import ctypes 
+import ctypes
 import os
 # optinally have scipy sparse, though not necessary
 import numpy
 import sys
-import numpy.ctypeslib 
+import numpy.ctypeslib
 import scipy.sparse as scp
 
 # set this line correctly
@@ -46,7 +46,7 @@ class DMatrix:
             self.handle = ctypes.c_void_p(
                 xglib.XGDMatrixCreateFromFile(ctypes.c_char_p(data.encode('utf-8')), 1))
         elif isinstance(data, scp.csr_matrix):
-            self.__init_from_csr(data)            
+            self.__init_from_csr(data)
         elif isinstance(data, numpy.ndarray) and len(data.shape) == 2:
             self.__init_from_npy2d(data, missing)
         else:
@@ -82,7 +82,7 @@ class DMatrix:
                                           ctypes.byref(length))
         return ctypes2numpy(ret, length.value)
     def __set_float_info(self, field, data):
-        xglib.XGDMatrixSetFloatInfo(self.handle,ctypes.c_char_p(field.encode('utf-8')), 
+        xglib.XGDMatrixSetFloatInfo(self.handle,ctypes.c_char_p(field.encode('utf-8')),
                                     (ctypes.c_float*len(data))(*data), len(data))
     # load data from file
     def save_binary(self, fname, silent=True):
@@ -92,7 +92,7 @@ class DMatrix:
         self.__set_float_info('label', label)
     # set weight of each instances
     def set_weight(self, weight):
-        self.__set_float_info('weight', label)
+        self.__set_float_info('weight', weight)
     # set initialized margin prediction
     def set_base_margin(self, margin):
         """
@@ -128,7 +128,7 @@ class DMatrix:
 class Booster:
     """learner class """
     def __init__(self, params={}, cache=[]):
-        """ constructor, param: """    
+        """ constructor, param: """
         for d in cache:
             assert isinstance(d, DMatrix)
         dmats = (ctypes.c_void_p  * len(cache))(*[ d.handle for d in cache])
@@ -136,13 +136,13 @@ class Booster:
         self.set_param({'seed':0})
         self.set_param(params)
     def __del__(self):
-        xglib.XGBoosterFree(self.handle) 
+        xglib.XGBoosterFree(self.handle)
     def set_param(self, params, pv=None):
         if isinstance(params, dict):
             for k, v in params.items():
                 xglib.XGBoosterSetParam(
-                    self.handle, ctypes.c_char_p(k.encode('utf-8')), 
-                    ctypes.c_char_p(str(v).encode('utf-8')))        
+                    self.handle, ctypes.c_char_p(k.encode('utf-8')),
+                    ctypes.c_char_p(str(v).encode('utf-8')))
         elif isinstance(params,str) and pv != None:
             xglib.XGBoosterSetParam(
                 self.handle, ctypes.c_char_p(params.encode('utf-8')),
@@ -153,11 +153,11 @@ class Booster:
                     self.handle, ctypes.c_char_p(k.encode('utf-8')),
                     ctypes.c_char_p(str(v).encode('utf-8')))
     def update(self, dtrain, it):
-        """ 
-        update 
+        """
+        update
           dtrain: the training DMatrix
           it: current iteration number
-        """        
+        """
         assert isinstance(dtrain, DMatrix)
         xglib.XGBoosterUpdateOneIter(self.handle, it, dtrain.handle)
     def boost(self, dtrain, grad, hess):
@@ -175,7 +175,7 @@ class Booster:
         dmats = (ctypes.c_void_p * len(evals) )(*[ d[0].handle for d in evals])
         evnames = (ctypes.c_char_p * len(evals))(
             * [ctypes.c_char_p(d[1].encode('utf-8')) for d in evals])
-        return xglib.XGBoosterEvalOneIter(self.handle, it, dmats, evnames, len(evals))        
+        return xglib.XGBoosterEvalOneIter(self.handle, it, dmats, evnames, len(evals))
     def eval(self, mat, name = 'eval', it = 0):
         return self.eval_set( [(mat,name)], it)
     def predict(self, data, output_margin=False):
@@ -196,7 +196,7 @@ class Booster:
         xglib.XGBoosterLoadModel( self.handle, ctypes.c_char_p(fname.encode('utf-8')) )
     def dump_model(self, fo, fmap=''):
         """dump model into text file"""
-        if isinstance(fo,str):            
+        if isinstance(fo,str):
             fo = open(fo,'w')
             need_close = True
         else: