29 Mar Joining Numpy Arrays
We can join two or more arrays in a single new array using the following:
- concatenate() method: Joining along with axis, unlike keys in SQL.
- stack method: Joining along a new axis, unlike keys in SQL.
Joining Numpy Arrays with concatenate()
import numpy as np
# Create two arrays
n1 = np.array([5, 10, 15, 20])
n2 = np.array([25, 30, 35, 40])
print("Iterating array1...")
for a in n1:
print(a)
print("\nIterating array2...")
for a in n2:
print(a)
# Joining arrays
resarr = np.concatenate((n1, n2))
print("\nAfter Joining = ", resarr)
Output
Iterating array1... 5 10 15 20 Iterating array2... 25 30 35 40 After Joining = [ 5 10 15 20 25 30 35 40]
Joining Numpy Arrays with Stack Methods
Join arrays in Numpy using the stack methods, such as
- stack()
- hstack()
- vstack()
- dstack()
- column_stack()
Let us see the examples one by one:
numpy.stack() in Python
Join Numpy arrays using the stack() method:
import numpy as np
# Create numpy arrays
n1 = np.array([5, 10, 15, 20])
n2 = np.array([25, 30, 35, 40])
print("Iterating array1...")
for a in n1:
print(a)
print("\nIterating array2...")
for a in n2:
print(a)
# Join arrays using numpy.stack()
resarr = np.stack((n1, n2))
print("\nAfter Joining = \n", resarr)
Output
Iterating array1... 5 10 15 20 Iterating array2... 25 30 35 40 After Joining = [[ 5 10 15 20] [25 30 35 40]]
numpy.hstack() in Python
Stack along rows using the hstack() method in Numpy:
import numpy as np
# Create two arrays
n1 = np.array([5, 10, 15, 20])
n2 = np.array([25, 30, 35, 40])
print("Iterating array1...")
for a in n1:
print(a)
print("\nIterating array2...")
for a in n2:
print(a)
# Join arrays using the hstack()
resarr = np.hstack((n1, n2))
print("\nAfter Joining along rows = \n", resarr)
Output
Iterating array1... 5 10 15 20 Iterating array2... 25 30 35 40 After Joining along rows = [ 5 10 15 20 25 30 35 40]
numpy.vstack() in Python
Stack along columns using the vstack() method in Numpy:
import numpy as np
# Create two arrays
n1 = np.array([5, 10, 15, 20])
n2 = np.array([25, 30, 35, 40])
print("Iterating array1...")
for a in n1:
print(a)
print("\nIterating array2...")
for a in n2:
print(a)
# Join arrays
resarr = np.vstack((n1, n2))
print("\nAfter Joining along columns = \n", resarr)
Output
Iterating array1... 5 10 15 20 Iterating array2... 25 30 35 40 After Joining along columns = [[ 5 10 15 20] [25 30 35 40]]
numpy.dstack() in Python
Stack along depth i.e. height using the dstack() method in Numpy:
import numpy as np
# Create two arrays
n1 = np.array([5, 10, 15, 20])
n2 = np.array([25, 30, 35, 40])
print("Iterating array1...")
for a in n1:
print(a)
print("\nIterating array2...")
for a in n2:
print(a)
# Join arrays
resarr = np.dstack((n1, n2))
print("\nAfter Joining along height = \n", resarr)
Output
Iterating array1... 5 10 15 20 Iterating array2... 25 30 35 40 After Joining along height = [[[ 5 25] [10 30] [15 35] [20 40]]]
numpy.column_stack() in Python
Stack according to columns using the column_stack() method in Numpy:
# Create two arrays
n1 = np.array([5, 10, 15, 20])
n2 = np.array([25, 30, 35, 40])
print("Iterating array1...")
for a in n1:
print(a)
print("\nIterating array2...")
for a in n2:
print(a)
# Join arrays
resarr = np.column_stack((n1, n2))
print("\nAfter Joining = \n", resarr)
Output
Iterating array1... 5 10 15 20 Iterating array2... 25 30 35 40 After Joining = [[ 5 25] [10 30] [15 35] [20 40]]
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