import pandas as pd
grades = [48,99,75,80,42,80,72,68,36,78]
df = pd.DataFrame( {'ID': ["x%d" % r for r in range(10)],
'Gender' : ['F', 'M', 'F', 'M', 'F', 'M', 'F', 'M', 'M', 'M'],
 'ExamYear': ['2007','2007','2007','2008','2008','2008','2008','2009','2009','2009'],
 'Class': ['algebra', 'stats', 'bio', 'algebra', 'algebra', 'stats', 'stats', 'algebra', 'bio', 'bio'],
 'Participated': ['yes','yes','yes','yes','no','yes','yes','yes','yes','yes'],
 'Passed': ['yes' if x > 50 else 'no' for x in grades],
'Employed': [True,True,True,False,False,False,False,True,True,False],
 'Grade': grades})
print(df)

  Class Employed ExamYear Gender Grade ID Participated Passed
0 algebra  True  2007  F  48 x0   yes  no
1 stats  True  2007  M  99 x1   yes yes
2  bio  True  2007  F  75 x2   yes yes
3 algebra False  2008  M  80 x3   yes yes
4 algebra False  2008  F  42 x4   no  no
5 stats False  2008  M  80 x5   yes yes
6 stats False  2008  F  72 x6   yes yes
7 algebra  True  2009  M  68 x7   yes yes
8  bio  True  2009  M  36 x8   yes  no
9  bio False  2009  M  78 x9   yes yes

cols=['ID','Gender','ExamYear','Class','Participated','Passed','Employed','Grade']
df=df.ix[:,cols]

 ID Gender ExamYear Class Participated Passed Employed Grade
0 x0  F  2007 algebra   yes  no  True  48
1 x1  M  2007 stats   yes yes  True  99
2 x2  F  2007  bio   yes yes  True  75
3 x3  M  2008 algebra   yes yes False  80
4 x4  F  2008 algebra   no  no False  42
5 x5  M  2008 stats   yes yes False  80
6 x6  F  2008 stats   yes yes False  72
7 x7  M  2009 algebra   yes yes  True  68
8 x8  M  2009  bio   yes  no  True  36
9 x9  M  2009  bio   yes yes False  78