Drop row in Pandas Series and clean up index
pandas, python, series
Solution
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))`.
Problem
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 ```