RDP v1.2 Remove duplicate experimental data

My seniors ask for help to remove redundant duplicate data points. Here is a python program to solve this problem.

Release 1.1

• Greatly improved computational efficiency

Release 1.2

• Add the ability to export similar data separately as a table

try :
    import  pandas  as pd
    times = 0
    print('****************** RDP v1.2 **********************')
    print('            s20180318@xs.ustb.edu.cn')
    print('     Unvi. of science & technology Beijing')
    print(' Copyright © 2019, lixin.fun. All rights reserved.')
    print('**************************************************')
    print('Please input file (.xls or .xlsx) that you want to Remove duplicate points：')
    fileName = input()
    print('Please set the output file name, and set .csv as a suffix (e.g. out.csv):')
    outName = input()
    print('Please set the deleted output file name, and set .csv as a suffix (e.g. out.csv):')
    outName2 = input()
    print('Please set the filter record file name, and set .txt as a suffix (e.g. out.txt):')
    outName3 = input()
    dictr = dict()
    numb = []
    numb2 = []
    df=pd.read_excel(fileName)
    rows = df.shape[0]
    #columns = df.shape[1] # columns
    i = 0
    while i < rows - 1:
        data=df.iloc[i].values # It doesn't include a header, and starts from zero
        j = i + 1
        k = 0
        while j > i and j <= rows - 1:
            data2=df.iloc[j].values
            comp = abs((data[3] - data2[3])/ data[3]) # Comparison range
            comp2 = abs((data[4] - data2[4])/ data[4])
            comp3 = abs((data[9] - data2[9])/ data[9])
            comp4 = abs((data[10] - data2[10])/ data[10])
            comp5 = abs((data[11] - data2[11])/ data[11])
            comp6 = abs((data[12] - data2[12])/ data[12])
            comp7 = abs((data[13] - data2[13])/ data[13])
            comp8 = abs((data[16] - data2[16])/ data[16])
            comp9 = abs((data[44] - data2[44])/ data[44])
            if comp >= 0 and comp <= 0.06 :
                if comp2 >= 0 and comp2 <= 0.06 :
                    if comp3 >= 0 and comp3 <= 0.06 :
                        if comp4 >= 0 and comp4 <= 0.06 :
                            if comp5 >= 0 and comp5 <= 0.06 :
                                if comp6 >= 0 and comp6 <= 0.06 : 
                                    if comp7 >= 0 and comp7 <= 0.06 :
                                        if comp8 >= 0 and comp8 <= 0.06 :
                                            if comp9 >= 0 and comp9 <= 0.06 :
                                                times = times + 1
                                                print(times)
                                                dictr[data2[0]] = data[0]
                                                print(str(data[0]) +","+ str(data2[0])+"\n")
                                                numb.append(j)
                                                numb2.append(i)
                                                numb2.append(j)
            j = j + 1
        i = i + 1
    dfl = df.iloc[numb2]
    dfl.to_csv(outName2,index=False)    
    df.drop(numb,inplace=True) # Delete a row
    df.to_csv(outName,index=False) # Output
    print('There are ' + str(times) +' data deleted.')
    print(dictr)
    f = open(outName3,'w')
    f.write(str(dictr))
    f.close()
    print('************* Mission Completed!******************')
except :
    print('*****************Error Message********************')
    print('Plase check your input and run this program again!')
    print('**************************************************')

How to use

Linux (root) : python3 RDPv1.py