import numpy as np
def my_grader(GS=[],S=[-2,-1,0,1]):
    import pandas as pd
    from pandas import Series, DataFrame
    import numpy as np
    from string import ascii_uppercase
    import itertools
    GR=[]
    for i in  range(0,len(GS)):
        GR=GR+list(GS[i])
    nGR=[]
    for i in range(0,len(GR)):
        nGR.append((GR[i]-mean(GR))/std(GR))
    SecN=list(ascii_uppercase)
    Sec=SecN[0:len(GR)/50]*50
    Sec=Sec.sort()
    K={'Raw Grade':GR, 'Section':Sec, 'Normalized Grade':nGR}
    df=DataFrame(K,columns=['Raw Grade','Section','Normalized Grade','Letter Grade'])
    df['Letter Grade'][df['Normalized Grade']>S[3]]='A'
    return df
A=80+10*np.random.randn(50)
B=80+10*np.random.randn(50)
C=80+10*np.random.randn(50)
GS=[A,B,C]
my_grader(GS)# your code goes here

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