How to make a multidimension numpy array with a varying row size?

I would like to create a two dimensional numpy array of arrays that has a different number of elements on each row.

Trying

cells = numpy.array([[0,1,2,3], [2,3,4]])

gives an error

ValueError: setting an array element with a sequence.

We are now almost 7 years after the question was asked, and your code

cells = numpy.array([[0,1,2,3], [2,3,4]])

executed in numpy 1.12.0, python 3.5, doesn't produce any error and cells contains:

array([[0, 1, 2, 3], [2, 3, 4]], dtype=object)

You access your cells elements as cells[0][2] # (=2) .

An alternative to tom10's solution if you want to build your list of numpy arrays on the fly as new elements (i.e. arrays) become available is to use append:

d = []                 # initialize an empty list
a = np.arange(3)       # array([0, 1, 2])
d.append(a)            # [array([0, 1, 2])]
b = np.arange(3,-1,-1) #array([3, 2, 1, 0])
d.append(b)            #[array([0, 1, 2]), array([3, 2, 1, 0])]

While Numpy knows about arrays of arbitrary objects, it's optimized for homogeneous arrays of numbers with fixed dimensions. If you really need arrays of arrays, better use a nested list. But depending on the intended use of your data, different data structures might be even better, e.g. a masked array if you have some invalid data points.

If you really want flexible Numpy arrays, use something like this:

numpy.array([[0,1,2,3], [2,3,4]], dtype=object)

However this will create a one-dimensional array that stores references to lists, which means that you will lose most of the benefits of Numpy (vector processing, locality, slicing, etc.).

This isn't well supported in Numpy (by definition, almost everywhere, a "two dimensional array" has all rows of equal length). A Python list of Numpy arrays may be a good solution for you, as this way you'll get the advantages of Numpy where you can use them:

cells = [numpy.array(a) for a in [[0,1,2,3], [2,3,4]]]