How to add a new row to an empty numpy array

问题:

Using standard Python arrays, I can do the following:

arr = [] arr.append([1,2,3]) arr.append([4,5,6]) # arr is now [[1,2,3],[4,5,6]]

However, I cannot do the same thing in numpy. For example:

arr = np.array([]) arr = np.append(arr, np.array([1,2,3])) arr = np.append(arr, np.array([4,5,6])) # arr is now [1,2,3,4,5,6]

I also looked into vstack, but when I use vstack on an empty array, I get:

ValueError: all the input array dimensions except for the concatenation axis must match exactly

So how do I do append a new row to an empty array in numpy?

回答1:

The way to "start" the array that you want is:

arr = np.empty((0,3), int)

Which is an empty array but it has the proper dimensionality.

>>> arr array([], shape=(0, 3), dtype=int64)

Then be sure to append along axis 0:

arr = np.append(arr, np.array([[1,2,3]]), axis=0) arr = np.append(arr, np.array([[4,5,6]]), axis=0)

But, @jonrsharpe is right. In fact, if you're going to be appending in a loop, it would be much faster to append to a list as in your first example, then convert to a numpy array at the end, since you're really not using numpy as intended during the loop:

In [210]: %%timeit    .....: l = []    .....: for i in xrange(1000):    .....:     l.append([3*i+1,3*i+2,3*i+3])    .....: l = np.asarray(l)    .....:  1000 loops, best of 3: 1.18 ms per loop  In [211]: %%timeit    .....: a = np.empty((0,3), int)    .....: for i in xrange(1000):    .....:     a = np.append(a, 3*i+np.array([[1,2,3]]), 0)    .....:  100 loops, best of 3: 18.5 ms per loop  In [214]: np.allclose(a, l) Out[214]: True

The numpythonic way to do it depends on your application, but it would be more like:

回答2:

In this case you might want to use the functions np.hstack and np.vstack

arr = np.array([]) arr = np.hstack((arr, np.array([1,2,3]))) # arr is now [1,2,3]  arr = np.vstack((arr, np.array([4,5,6]))) # arr is now [[1,2,3],[4,5,6]]

You also can use the np.concatenate function.

Cheers

回答3:

Here is my solution:

arr = [] arr.append([1,2,3]) arr.append([4,5,6]) np_arr = np.array(arr)

回答4:

using an custom dtype definition, what worked for me was:

import numpy  # define custom dtype type1 = numpy.dtype([('freq', numpy.float64, 1), ('amplitude', numpy.float64, 1)]) # declare empty array, zero rows but one column arr = numpy.empty([0,1],dtype=type1) # store row data, maybe inside a loop row = numpy.array([(0.0001, 0.002)], dtype=type1) # append row to the main array arr = numpy.row_stack((arr, row)) # print values stored in the row 0 print float(arr[0]['freq']) print float(arr[0]['amplitude'])

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