As we know, joining means bringing elements of two or more arrays whose dimension is the same in a single array.
In Numpy there are various functions available in Numpy Packages that can be used to join two or more arrays.
numpy.concatenate:
numpy.concatenate is used to join two or more arrays of the same dimension and same shape along the given axis.
Syntax:
numpy.concatenate((array1, array2, ...), axis=0)Example:
For 1-D Array:
import numpy as np
a=np.arange(1,4)
b=np.arange(5)
c=np.concatenate((a,b))
print(c)Output:
[1 2 3 0 1 2 3 4]
For 2-D Array:
When axis=0
import numpy as np
a=np.array([[1,2],[3,4]])#(2,2)
b=np.array([[5,6]])#(1,2)
c=np.concatenate((a,b))#axis=0
print(c)Output:
[[1 2] [3 4] [5 6]]
When axis=1
import numpy as np
a=np.array([[1,2],[3,4]])#axis=(2,2)
b=np.array([[5,6]])#axis(1,2)
c=np.concatenate((a,b),axis=1)#concatenating along axis=1
print(c)Output:
ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 2 and the array at index 1 has size 1.
To avoid Error:
import numpy as np
a=np.array([[1,2],[3,4]])#axis=(2,2)
b=np.array([[5,6]])#axis(1,2)
c=np.concatenate((a,b.T),axis=1)#b axis will change from(1,2) to (2,1)
print(c)Output:
[[1 2 5] [3 4 6]]