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Is there an easy way to add a record/row to a numpy recarray without creating a new recarray? Let's say I have a recarray that takes 1Gb in memory, I want to be able to add a row to it without having python take up 2Gb of memory temporarily.
815
Use the numpy. append() Function to Add a Row to a Matrix in NumPy. The append() function from the numpy module can add elements to the end of the array. By specifying the axis as 0, we can use this function to add rows to a matrix.
Rows and columns of NumPy arrays can be selected or modified using the square-bracket indexing notation in Python. To select a row in a 2D array, use P[i] . For example, P[0] will return the first row of P . To select a column, use P[:, i] .
You can use numpy. append() to append a row to numpty array and reshape to a matrix later on.
You can call yourrecarray.resize with a shape which has one more row, then assign to that new row. Of course. numpy might still have to allocate completely new memory if it just doesn't have room to grow the array in-place, but at least you stand a chance!-)
Since an example was requested, here comes, modified off the canonical example list...:
>>> import numpy
>>> mydescriptor = {'names': ('gender','age','weight'), 'formats': ('S1', 'f4', 'f4')}
>>> a = numpy.array([('M',64.0,75.0),('F',25.0,60.0)], dtype=mydescriptor)
>>> print a
[('M', 64.0, 75.0) ('F', 25.0, 60.0)]
>>> a.shape
(2,)
>>> a.resize(3)
>>> a.shape
(3,)
>>> print a
[('M', 64.0, 75.0) ('F', 25.0, 60.0) ('', 0.0, 0.0)]
>>> a[2] = ('X', 17.0, 61.5)
>>> print a
[('M', 64.0, 75.0) ('F', 25.0, 60.0) ('X', 17.0, 61.5)]
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