I have a numpy_array. Something like [ a b c ].
And then I want to concatenate it with another NumPy array (just like we create a list of lists). How do we create a NumPy array containing NumPy arrays?
I tried to do the following without any luck
>>> M = np.array([])
>>> M
array([], dtype=float64)
>>> M.append(a,axis=0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'numpy.ndarray' object has no attribute 'append'
>>> a
array([1, 2, 3])
In [1]: import numpy as np
In [2]: a = np.array([[1, 2, 3], [4, 5, 6]])
In [3]: b = np.array([[9, 8, 7], [6, 5, 4]])
In [4]: np.concatenate((a, b))
Out[4]:
array([[1, 2, 3],
[4, 5, 6],
[9, 8, 7],
[6, 5, 4]])
or this:
In [1]: a = np.array([1, 2, 3])
In [2]: b = np.array([4, 5, 6])
In [3]: np.vstack((a, b))
Out[3]:
array([[1, 2, 3],
[4, 5, 6]])
Well, the error message says it all: NumPy arrays do not have an append() method. There's a free function numpy.append() however:
numpy.append(M, a)
This will create a new array instead of mutating M in place. Note that using numpy.append() involves copying both arrays. You will get better performing code if you use fixed-sized NumPy arrays.
You may use numpy.append()...
import numpy
B = numpy.array([3])
A = numpy.array([1, 2, 2])
B = numpy.append( B , A )
print B
> [3 1 2 2]
This will not create two separate arrays but will append two arrays into a single dimensional array.
I found this link while looking for something slightly different, how to start appending array objects to an empty numpy array, but tried all the solutions on this page to no avail.
Then I found this question and answer: How to add a new row to an empty numpy array
The gist here:
The way to "start" the array that you want is:
arr = np.empty((0,3), int)
Then you can use concatenate to add rows like so:
arr = np.concatenate( ( arr, [[x, y, z]] ) , axis=0)
See also https://docs.scipy.org/doc/numpy/reference/generated/numpy.concatenate.html
Sven said it all, just be very cautious because of automatic type adjustments when append is called.
In [2]: import numpy as np
In [3]: a = np.array([1,2,3])
In [4]: b = np.array([1.,2.,3.])
In [5]: c = np.array(['a','b','c'])
In [6]: np.append(a,b)
Out[6]: array([ 1., 2., 3., 1., 2., 3.])
In [7]: a.dtype
Out[7]: dtype('int64')
In [8]: np.append(a,c)
Out[8]:
array(['1', '2', '3', 'a', 'b', 'c'],
dtype='|S1')
As you see based on the contents the dtype went from int64 to float32, and then to S1
Actually one can always create an ordinary list of numpy arrays and convert it later.
In [1]: import numpy as np
In [2]: a = np.array([[1,2],[3,4]])
In [3]: b = np.array([[1,2],[3,4]])
In [4]: l = [a]
In [5]: l.append(b)
In [6]: l = np.array(l)
In [7]: l.shape
Out[7]: (2, 2, 2)
In [8]: l
Out[8]:
array([[[1, 2],
[3, 4]],
[[1, 2],
[3, 4]]])
I had the same issue, and I couldn't comment on @Sven Marnach answer (not enough rep, gosh I remember when Stackoverflow first started...) anyway.
Adding a list of random numbers to a 10 X 10 matrix.
myNpArray = np.zeros([1, 10])
for x in range(1,11,1):
randomList = [list(np.random.randint(99, size=10))]
myNpArray = np.vstack((myNpArray, randomList))
myNpArray = myNpArray[1:]
Using np.zeros() an array is created with 1 x 10 zeros.
array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
Then a list of 10 random numbers is created using np.random and assigned to randomList. The loop stacks it 10 high. We just have to remember to remove the first empty entry.
myNpArray
array([[31., 10., 19., 78., 95., 58., 3., 47., 30., 56.],
[51., 97., 5., 80., 28., 76., 92., 50., 22., 93.],
[64., 79., 7., 12., 68., 13., 59., 96., 32., 34.],
[44., 22., 46., 56., 73., 42., 62., 4., 62., 83.],
[91., 28., 54., 69., 60., 95., 5., 13., 60., 88.],
[71., 90., 76., 53., 13., 53., 31., 3., 96., 57.],
[33., 87., 81., 7., 53., 46., 5., 8., 20., 71.],
[46., 71., 14., 66., 68., 65., 68., 32., 9., 30.],
[ 1., 35., 96., 92., 72., 52., 88., 86., 94., 88.],
[13., 36., 43., 45., 90., 17., 38., 1., 41., 33.]])
So in a function:
def array_matrix(random_range, array_size):
myNpArray = np.zeros([1, array_size])
for x in range(1, array_size + 1, 1):
randomList = [list(np.random.randint(random_range, size=array_size))]
myNpArray = np.vstack((myNpArray, randomList))
return myNpArray[1:]
a 7 x 7 array using random numbers 0 - 1000
array_matrix(1000, 7)
array([[621., 377., 931., 180., 964., 885., 723.],
[298., 382., 148., 952., 430., 333., 956.],
[398., 596., 732., 422., 656., 348., 470.],
[735., 251., 314., 182., 966., 261., 523.],
[373., 616., 389., 90., 884., 957., 826.],
[587., 963., 66., 154., 111., 529., 945.],
[950., 413., 539., 860., 634., 195., 915.]])
Try this code :
import numpy as np
a1 = np.array([])
n = int(input(""))
for i in range(0,n):
a = int(input(""))
a1 = np.append(a, a1)
a = 0
print(a1)
Also you can use array instead of "a"
If I understand your question, here's one way. Say you have:
a = [4.1, 6.21, 1.0]
so here's some code...
def array_in_array(scalarlist):
return [(x,) for x in scalarlist]
Which leads to:
In [72]: a = [4.1, 6.21, 1.0]
In [73]: a
Out[73]: [4.1, 6.21, 1.0]
In [74]: def array_in_array(scalarlist):
....: return [(x,) for x in scalarlist]
....:
In [75]: b = array_in_array(a)
In [76]: b
Out[76]: [(4.1,), (6.21,), (1.0,)]
This is for people working with numpy's ndarrays. The function numpy.concatenate() does work as well.
>>a = np.random.randint(0,9, size=(10,1,5,4))
>>a.shape
(10, 1, 5, 4)
>>b = np.random.randint(0,9, size=(15,1,5,4))
>>b.shape
(15, 1, 5, 4)
>>X = np.concatenate((a, b))
>>X.shape
(25, 1, 5, 4)
Much the same way as vstack()
>>Y = np.vstack((a,b))
>>Y.shape
(25, 1, 5, 4)
As you want to concatenate along an existing axis (row wise), np.vstack or np.concatenate will work for you.
For a detailed list of concatenation operations, refer to the official docs.
There is a handfull of method to stack arrays together, depending on the direction of the stack. You may consider np.stack() (doc), np.vstack() (doc) and np.hstack() (doc) for example.
