pandas aggregated data to a numpy array : data structure conversion
pandas, python
Solution
Try:
result = fdf.groupby(['row',col'])['percent'].sum()
result.unstack('col').values
Alternately:
fdf.pivot_table('percent', rows='row', cols='col', aggfunc='sum').values
Problem
I have aggregated data using pandas data frame. Below is some actual data shown and how I aggregated it. `fdf.groupby(['row',col'])['percent'].sum()` http://pastebin.com/R8XWpgtU What I would like to do is create a 2d numpy array of this (rows = row, columns = col). Any slick way to do this ? Another way I did something similar was create a pivot table `pivot_table(fdf,values='percent',rows='row',cols='col', aggfunc=np.sum)` In this case I want to convert this pivot table to 2d numpy array. Is there a way for me to index into each cell of this table. If so then I probably will be Ok with the table itself.