Drop row in Pandas Series and clean up index

Question

I have a Pandas Series and based on a random number I want to pick a row (5 in the code example below) and drop that row. When the row is dropped I want to create a new index for the remaining rows (0 to 8). The code below:

print 'Original series: ', sample_mean_series
print 'Length of original series', len(sample_mean_series)
sample_mean_series = sample_mean_series.drop([5],axis=0)
print 'Series with item 5 dropped: ', sample_mean_series
print 'Length of modified series:', len(sample_mean_series)
print sample_mean_series.reindex(range(len(sample_mean_series)))

And this is the output:

Original series:  
0    0.000074
1   -0.000067
2    0.000076
3   -0.000017
4   -0.000038
5   -0.000051
6    0.000125
7   -0.000108
8   -0.000009
9   -0.000052
Length of original series 10
Series with item 5 dropped:  
0    0.000074
1   -0.000067
2    0.000076
3   -0.000017
4   -0.000038
6    0.000125
7   -0.000108
8   -0.000009
9   -0.000052
Length of modified series: 9
0    0.000074
1   -0.000067
2    0.000076
3   -0.000017
4   -0.000038
5         NaN
6    0.000125
7   -0.000108
8   -0.000009

My problem is that the row number 8 is dropped. I want to drop row "5 NaN" and keep -0.000052 with an index 0 to 8. This is what I want it to look like:

0    0.000074
1   -0.000067
2    0.000076
3   -0.000017
4   -0.000038
5    0.000125
6   -0.000108
7   -0.000009
8   -0.000052

Answer 1

Somewhat confusingly, reindex does not mean "create a new index". To create a new index, just assign to the index attribute. So at your last step just do sample_mean_series.index = range(len(sample_mean_series)).