Operations with Numpy Arrays II
Numpy Array Shape and Reshape
Shape
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A NumPy array's shape can be found using the shape attribute which returns a tuple with each index representing the number of elements in each dimension.
Example
Here, we are going to print the shape of a 2-D array
import numpy as np
arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
print(arr.shape) # prints (2, 4)
The above code prints a tuple (2, 4) meaning 2 elements in the first dimension and 4 elements in the second dimension.
Example
Now, we are going to create a 3-Dimensional array using ndmin attribute.
import numpy as np
arr = np.array([11, 22, 33, 44, 55, 66], ndmin=3)
print(arr) # prints [[[11 22 33 44 55 66]]]
print('shape of array :', arr.shape) # prints shape of array : (1, 1, 6)
This will print that in the first two dimensions the number of elements is one and in the third dimension the number of elements are six.
Reshape
-
The shape means the number of elements in each dimension.
-
Reshaping means changing the shape of an array
-
Which can allow us to add or remove elements in each dimension.
Example
Here, we are going to convert the 1-D array into 2-D using reshape attribute, which will change the array into 2 arrays of 4 elements each.
import numpy as np
arr = np.array([11, 22, 33, 44, 55, 66, 77, 88])
newarr = arr.reshape(2, 4)
print(newarr) # prints [[11 22 33 44]
# [55 66 77 88]]
Note
-
We can Reshape into any shape as long as the number of elements is equal in both shapes.
-
For instance, below is the code demonstration of converting a 1-D array with 8 elements into a 2-D array with 2 elements in each dimension which causes an error.
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
newarr = arr.reshape(2, 2)
print(newarr)
Output
Traceback (most recent call last):
File "./prog.py", line 5, in <module>
ValueError: cannot reshape array of size 8 into shape (2,2)
Array Iterating
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Iterating in arrays means going through each element in the array one by one in the case of a 1-D array.
-
In multi-dimensional arrays, we can do this using for loop of python.
Example
Iterating on elements of a 1-D array:
import numpy as np
arr = np.array([11, 22, 44, 33, 55, 99])
print(arr)
print('---------------------------------------')
print('---------------------------------------')
for I in arr:
print(I)
Output:
[11 22 44 33 55 99]
---------------------------------------
---------------------------------------
11
22
44
33
55
99
Iterating Multi-Dimensional Arrays
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In Multi-dimensional arrays, we can go through all the rows, and also we can go through all the rows and each of their elements.
-
If we are iterating an n-dim array, the iterator will go through the n-1 the dim one by one.
Example
Iterate through the 2-D array first by row and then go into each element of the row.
import numpy as np
arr = np.array([[11, 22, 33], [44, 55, 66]])
print("The array is ", arr)
print("------------------------------------------")
print("printing the array row wise!")
for x in arr:
print(x)
print("Now printing the array element wise!")
for m in arr:
for n in m:
print(n)
Output:
('The array is ', array([[11, 22, 33],
[44, 55, 66]]))
------------------------------------------
printing the array row wise!
[11 22 33]
[44 55 66]
Now printing the array element wise!
11
22
33
44
55
66
Example
Iterating through a 3-D array
import numpy as np
arr = np.array([[[11, 22, 33], [44, 55, 66], [77, 88, 99]]])
print("The array is ", arr)
print("------------------------------------------")
print("printing the array row wise!")
for x in arr:
print(x)
print("Now printing the array element wise!")
for m in arr:
for n in m:
for o in n:
print(o)
Output:
('The array is ', array([[[11, 22, 33],
[44, 55, 66],
[77, 88, 99]]]))
------------------------------------------
printing the array row wise!
[[11 22 33]
[44 55 66]
[77 88 99]]
Now printing the array element wise!
11
22
33
44
55
66
77
88
99
