NumPy - Arrays - Reshaping an Array

reshape()

reshape() function is used to create a new array of the same size (as the original array) but of different desired dimensions.

reshape() function will create an array with the same number of elements as the original array, i.e. of the same size as that of the original array. If you want to convert the original array to a bigger array, reshape() can’t add more elements(than available in the original array) to give you a bigger array.

Below is the syntax for reshape() function

ndarray.reshape(new_shape)

There may be two cases, as mentioned below when we are reshaping the array:

(1) We already know the desired number dimensions (say rows and columns) of the reshaped array.

(2) We know the desired number of columns, but don't know the desired number of rows of the reshaped array.

Lets us take the first case - Reshaping a numpy array when the number of rows and number of columns of the reshaped array is known.

• This technique is used when you already are aware - how many numbers of rows and columns your reshaped array will have.

  my_arr = np.arange(6)
  
  print(my_arr)
  

• output will be

  [0,1,2,3,4,5]
  

• Now, let us reshape this array

  my_new_arr = my_arr.reshape(2,3)
  
  print(my_new_arr)
  

• output will be

  [ [0,1,2],
    [3,4,5] ]
  

Now, let us take the second case - Reshaping a numpy array when the desired number of columns is known, but the desired number of rows is unknown.

• This specific technique is used when your reshaped array is going to be of bigger size than the original array, and you are sure about how many columns you want in the reshaped array, but you don't have an idea how many rows will be there in the reshaped (output) array.

• To achieve this, you use a value of '-1' in place of 'rows' field in the reshape() function, and use the desired value of the number of columns you want in the columns field in the reshape() function.

• Example - Say you want to reshape an existing array to an array with 3 columns and an unknown number of rows, as shown in the code below:

  my_arr = np.arange(9)
  
  print(my_arr)
  

• output will be

  [0,1,2,3,4,5,6,7,8]
  

• Now, let us reshape this array

  my_new_arr = my_arr.reshape(-1,3)
  
  print(my_new_arr)
  

• output will be

  [ [0,1,2],
    [3,4,5],
    [6,7,8] ]
  

When you can't use reshape() function?

You can't use reshape() function, when the size of the original array is different from your desired reshaped array. If you try to reshape(), it will throw an error.

• Example

  my_arr = np.arange(8)
  print(my_arr)
  

• output will be

  [0,1,2,3,4,5,6,7]
  my_arr.reshape(2,3)
  

  the output will be an error as shown below

  ValueError: cannot reshape array of size 8 into shape (2,3)
  

• Now, again try with some other shape as shown below:

  my_arr.reshape(3,3)
  

• the output will be an error as shown below

  ValueError: cannot reshape array of size 8 into shape (3,3)
  

Difference between resize() and reshape() :

reshape() will create an array with the same number of elements as the original array, i.e. of the same ‘size’ as that of the original array. If you want to convert the original array to a bigger array, reshape() can’t add more elements(than available in the original array) to give you a bigger array. Hence, reshape() is used to create a new array of the same size (as the original array) but of different desired dimensions.

resize() can create an array of larger size than the original array. To convert the original array into a bigger array, resize() will add more elements (than available in the original array) by copying the existing elements (repeated as many times as required) to fill the desired larger size. resize() is used to create a new array of different sizes and dimensions.