numpy5.py

"""
Reshaping means changing the shape of an array.
The shape of an array is the number of elements in each dimension.
By reshaping we can add or remove dimensions or change number of elements in each dimension.
"""
#Reshape From 1-D to 2-D
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(4, 3)
print(newarr)

#Reshape From 1-D to 3-D
arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(2, 3, 2)
print(newarr)


arr = np.array([1, 2, 3, 4, 5, 6, 7, 8])
newarr = arr.reshape(2, 2, -1)
print(newarr)
"""
Given the original array has 8 elements, the reshaping is done as follows:
The first dimension will have 2 elements.
The second dimension will have 2 elements.
The third dimension will be inferred. Since 
2×2×x=8, 
x=2. Thus, the third dimension will have 2 elements.
The new shape of newarr is (2, 2, 2)
"""
#We can not pass -1 to more than one dimension.


#Flattening array means converting a multidimensional array into a 1D array.
#We can use reshape(-1) to do this.
arr = np.array([[1, 2, 3], [4, 5, 6]])
newarr = arr.reshape(-1)
print(newarr)



arr = np.array([[1, 2, 3], [4, 5, 6]])
for x in arr:
  for y in x:
    print(y)


arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
for x in arr:
  print("x represents the 2-D array:")
  print(x)

arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
for x in arr:
  for y in x:
    for z in y:
      print(z)


arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
for x in np.nditer(arr):
  print(x)

#Iterating Array With Different Data Types
arr = np.array([1, 2, 3])
for x in np.nditer(arr, flags=['buffered'], op_dtypes=['S']):
  print(x)

arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
for x in np.nditer(arr[:, ::2]):
  print(x)

arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
for idx, x in np.ndenumerate(arr):
  print(idx, x)