The following works for me, so either the elements of datas are not flat arrays like your question suggests, the potential rows have different lengths (this turned out to be the reason, see comments), or perhaps you are using an older version that has a problem with a 1-dimensional object in vstack? (although I think that is unlikely)
In[14]: datas = [np.asarray([0, 0, 0, 0, 0, 0]), np.asarray([0, 0, 0, 0, 0, 1])]
In[15]: datas
Out[15]: [array([0, 0, 0, 0, 0, 0]), array([0, 0, 0, 0, 0, 1])]
In[16]: datas[0].shape
Out[16]: (6, )
In[17]: np.vstack(datas)
Out[17]:
array([
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1]
])This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by vsplit.,Stack arrays in sequence vertically (row wise).,The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length.,Stack arrays in sequence depth wise (along third axis).
>>> a = np.array([1, 2, 3]) >>> b = np.array([4, 5, 6]) >>> np.vstack((a, b)) array([ [1, 2, 3], [4, 5, 6] ])
>>> a = np.array([
[1],
[2],
[3]
]) >>>
b = np.array([
[4],
[5],
[6]
]) >>>
np.vstack((a, b))
array([
[1],
[2],
[3],
[4],
[5],
[6]
])Last Updated : 06 Jan, 2019
1 st Input array: [1 2 3]
2n d Input array: [4 5 6]
Output vertically stacked array: [
[1 2 3]
[4 5 6]
]1 st Input array: [
[1 2 3]
[-1 - 2 - 3]
]
2n d Input array: [
[4 5 6]
[-4 - 5 - 6]
]
Output stacked array: [
[1 2 3]
[-1 - 2 - 3]
[4 5 6]
[-4 - 5 - 6]
]The vstack() function is used to stack arrays in sequence vertically (row wise). This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). T,stacked : ndarray The array formed by stacking the given arrays.,SQL Exercises, Practice, Solution - exercises on Soccer Database,SQL Exercises, Practice, Solution - exercises on Employee Database
Syntax:
numpy.vstack(tup)
Example-1: numpy.vstack() function
>>>
import numpy as np
>>>
x = np.array([3, 5, 7]) >>>
y = np.array([5, 7, 9]) >>>
np.vstack((x, y))
array([
[3, 5, 7],
[5, 7, 9]
])Example-2: numpy.vstack() function
>>>
import numpy as np
>>>
x = np.array([
[3],
[5],
[7]
]) >>>
y = np.array([
[5],
[7],
[9]
]) >>>
np.vstack((x, y))
array([
[3],
[5],
[7],
[5],
[7],
[9]
])You can use the numpy vstack() function to stack numpy arrays vertically. It concatenates the arrays in sequence vertically (row-wise). The following is the syntax.,You can also stack more than two arrays at once with the numpy vstack() function. Just pass the arrays to be stacked as a tuple. For example, let’s stack three 1D arrays vertically at once.,In this tutorial, we will look at how to use the numpy vstack method to vertically stack (or concat) numpy arrays with the help of some examples.,Here we concatenated three arrays vertically. The resulting array is of shape (3, 4). Similarly, you can stack multiple arrays, just pass them in the order you want as a sequence.
You can use the numpy vstack() function to stack numpy arrays vertically. It concatenates the arrays in sequence vertically (row-wise). The following is the syntax.
import numpy as np # tup is a tuple of arrays to be concatenated, e.g.(ar1, ar2, ..) ar_v = np.vstack(tup)
Let’s stack two one-dimensional arrays together vertically.
import numpy as np # create two 1 d arrays ar1 = np.array([1, 2, 3, 4]) ar2 = np.array([5, 6, 7, 8]) # vstack the arrays ar_v = np.vstack((ar1, ar2)) # display the concatenated array print(ar_v)
Output:
[ [1 2 3 4] [5 6 7 8] ]
Now let’s stack a 1D array with a 2D array vertically.
# create a 1 d array ar1 = np.array([1, 2, 3, 4]) # create a 2 d array ar2 = np.array([ [0, 0, 0, 0], [1, 1, 1, 1] ]) # vstack the arrays ar_v = np.vstack((ar1, ar2)) # display the concatenated array print(ar_v)
You can also vertically stack two 2D arrays together in a similar way.
# create two 2 d arrays ar1 = np.array([ [1, 2, 3, 4], [5, 6, 7, 8] ]) ar2 = np.array([ [0, 0, 0, 0], [1, 1, 1, 1] ]) # vstack the arrays ar_v = np.vstack((ar1, ar2)) # display the concatenated array print(ar_v)
Python Numpy List To Array And Vstack, 6 days ago Apr 01, 2021 · Arrays are stackable. I’ll show how to stack array vertically and horizontally in Python Numpy. Using vstack. To stack arrays vertically use Numpy vstack function. , › See also: Python Arrays List Numpy , › See also: Python Arrays Function Numpy
from scipy.io.wavfile
import read filepath = glob.glob('*.wav') rates = [] datas = []
for fp in filepath: rate, data = read(fp) rates.append(rate) datas.append(data)In[14]: datas = [np.asarray([0, 0, 0, 0, 0, 0]), np.asarray([0, 0, 0, 0, 0, 1])] In[15]: datas Out[15]: [array([0, 0, 0, 0, 0, 0]), array([0, 0, 0, 0, 0, 1])] In[16]: datas[0].shape Out[16]: (6, ) In[17]: np.vstack(datas) Out[17]: array([
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1]
])from scipy.io.wavfile
import read filepath = glob.glob('*.wav') rates = [] datas = []
for fp in filepath: rate, data = read(fp) rates.append(rate) datas.append(data)[array([0, 0, 0, ..., 0, 0, 0], dtype = int16), array([0, 0, 0, ..., 0, 0, 1], dtype = int16), array([0, 0, 0, ..., 0, 0, 0], dtype = int16), ..., array([0, 0, 0, ..., 0, 0, 0], dtype = int16)]
new_array = numpy.vstack([datas])
[ [array([0, 0, 0, ..., 0, 0, 0], dtype = int16) array([0, 0, 0, ..., 0, 0, 1], dtype = int16) array([0, 0, 0, ..., 0, 0, 0], dtype = int16)...array([0, 0, 0, ..., 0, 0, 0], dtype = int16)] ]
vstack() takes tuple of arrays as argument, and returns a single ndarray that is a vertical stack of the arrays in the tuple.,To vertically stack two or more numpy arrays, you can use vstack() function.,In this tutorial of Python Examples, we learned how to stack numpy arrays vertically using vstack() function, with the help of well detailed example programs.,In this example, we shall take two 2D arrays of size 2×2 and shall vertically stack them using vstack() method.
Python Program
import numpy as np
#initialize arrays
A = np.array([
[2, 1],
[5, 4]
])
B = np.array([
[3, 4],
[7, 8]
])
#vertically stack arrays
output = np.vstack((A, B))
print(output)Output
[ [2 1] [5 4] [3 4] [7 8] ]
Indexing of arrays: Getting and setting the value of individual array elements,In a multi-dimensional array, items can be accessed using a comma-separated tuple of indices:,To index from the end of the array, you can use negative indices:,Reshaping of arrays: Changing the shape of a given array
import numpy as np np.random.seed(0) # seed for reproducibility x1 = np.random.randint(10, size = 6) # One - dimensional array x2 = np.random.randint(10, size = (3, 4)) # Two - dimensional array x3 = np.random.randint(10, size = (3, 4, 5)) # Three - dimensional array
print("x3 ndim: ", x3.ndim)
print("x3 shape:", x3.shape)
print("x3 size: ", x3.size)x3 ndim: 3 x3 shape: (3, 4, 5) x3 size: 60
print("dtype:", x3.dtype)dtype: int64
print("itemsize:", x3.itemsize, "bytes")
print("nbytes:", x3.nbytes, "bytes")