My original question was posted here. I have a dataframe as follows:

ID  START   END  SEQ
1   11      12   1
1   14      15   3 
1   13      14   2 
2   10      14   1
3   11      15   1
3   16      17   2

I wanted to transform it into this DataFrame:

ID  START_1  END_1  SEQ_1  START_2  END_2  SEQ_2 START_3  END_3  SEQ_3
1   11       12     1      13       14     2     14       15     3 
2   10       14     1      NA       NA     NA    NA       NA     NA   
3   11       15     1      16       17     2     NA       NA     NA 

After pivot_table transformations I received a DataFrame that has an additional blank row after the header:

test_2['SEQ1'] = test_2.SEQ
test_2 = test_2.pivot_table(index= ['ID','SEQ1']).unstack()
test_2 = test_2.sort_index(axis=1, level=1)
test_2.columns = ['_'.join((col[0], str(col[1]))) for col in test_2]
test_2

test_2

    START_1  END_1  SEQ_1  START_2  END_2  SEQ_2 START_3  END_3  SEQ_3
ID
1   11       12     1      13       14     2     14       15     3 
2   10       14     1      NA       NA     NA    NA       NA     NA   
3   11       15     1      16       17     2     NA       NA     NA 

How can I delete these row and align all the headers? I tried to delte the row in a conventional way using test2[:2], but it didn't delete the blank row.

EDIT:

This is the more realistic dataset's extract:

ID  INDEX           START                   END                 SEQ     NUM_PREV     NUM_ACTUAL   NUM_NEXT             TIME   PRE_TIME      LOC_IND
079C    333334.0    2016-06-23 12:45:32 2016-06-23 12:51:05 1   1      23456           25456           29456           30      2               YES
079C    333334.0    2016-06-23 12:47:05 2016-06-23 12:51:05 2   2     29456           39458           39945           20      0               NO

Consider resetting index after the pivot/unstack operation:

from io import StringIO
import pandas as pddata='''
ID  START   END  SEQ
1   11      12   1
1   14      15   3 
1   13      14   2 
2   10      14   1
3   11      15   1
3   16      17   2
'''test_2 = pd.read_table(StringIO(data), sep="\\s+")
seq = set(test_2['SEQ'].tolist())test_2['SEQ1'] = test_2.SEQ
test_2 = test_2.pivot_table(index= ['ID','SEQ1']).unstack()
test_2 = test_2.sort_index(axis=1, level=1)
test_2.columns = ['_'.join((col[0], str(col[1]))) for col in test_2]test_2 = test_2.reset_index()
#    ID  END_1  SEQ_1  START_1  END_2  SEQ_2  START_2  END_3  SEQ_3  START_3
# 0   1   12.0    1.0     11.0   14.0    2.0     13.0   15.0    3.0     14.0
# 1   2   14.0    1.0     10.0    NaN    NaN      NaN    NaN    NaN      NaN
# 2   3   15.0    1.0     11.0   17.0    2.0     16.0    NaN    NaN      NaN

However, as you can see it changes column ordering, so consider a nested list comprehension with sum() to flatten it, all for a suitable order:

seqmax = max(seq)+1
colorder = ['ID'] +  sum([['START_'+str(i),'END_'+str(i),'SEQ_'+str(i)]for i in range(1, seqmax) if i in seq],[])test_2 = test_2[colorder]#    ID  START_1  END_1  SEQ_1  START_2  END_2  SEQ_2  START_3  END_3  SEQ_3
# 0   1     11.0   12.0    1.0     13.0   14.0    2.0     14.0   15.0    3.0
# 1   2     10.0   14.0    1.0      NaN    NaN    NaN      NaN    NaN    NaN
# 2   3     11.0   15.0    1.0     16.0   17.0    2.0      NaN    NaN    NaN