## Reshaping data
The **`reshape()`** function in NumPy is a powerful tool which allows us to restructure an array according to the arguments we specify.

The function takes in an **array** and a new **shape** as required arguments. The new shape must exactly contain all the elements from the input array. For example, we could reshape an array with `8` elements to `(4, 2)`, but not `(4, 3)`.

`-1` is a special argument which can only be used once as a dimension. The `-1` dimension will take on the value necessary to allow the new shape to contain all the elements of the array.

NumPy also has a variety of other shaping functions, such as `flatten` or `transpose`.



import numpy as np

arr = np.array([[5, 10], [15, 20]])
# Add 10 to element values
print("Adding 10: " + repr(arr + 10))

# Multiple elements by 5
print("Multiplying by 5: " + repr(arr * 5))

# Subtract 5 from elements
print("Subtracting 5: " + repr(arr - 5))

# Matrix multiplication
arr1 = np.array([[-8, 7], [17, 20], [8, -16], [11, 4]])
arr2 = np.array([[5, -5, 10, 20], [-8, 0, 13, 2]])
print("Multiplying two arrays: " + repr(np.matmul(arr1, arr2)))

# Exponential
arr3 = np.array([[1, 5], [2.5, 2]])
# Exponential of each element
print("Taking the exponential: " + repr(np.exp(arr3)))

# Cubing all elements
print("Making each element a power of 3: " + repr(np.power(3, arr3)))

How to use the reshape() function in NumPy

Reshaping arrays in NumPy is achieved using reshape(), which restructures arrays based on specified dimensions and includes special argument -1 for automatic sizing.