numpy.resize() function
The resize() function is used to create a new array with the specified shape.
If the new array is larger than the original array, then the new array is filled with repeated copies of a.
Syntax:
numpy.resize(a, new_shape)Version: 1.15.0
Parameter:
a | Array to be resized. | Required |
new_shape | Shape of resized array. | Required |
Return value:
reshaped_array : ndarray - The new array is formed from the data in the old array, repeated if necessary to fill out the required number of elements. The data are repeated in the order that they are stored in memory.
Example-1: numpy.resize() function
>>> import numpy as np >>> a = np.array([[1,2], [3,4]]) >>> np.resize(a, (3,2)) array([[1, 2], [3, 4], [1, 2]])Pictorial Presentation:
Example-2: numpy.resize() ffunction
>>> import numpy as np >>> a = np.array([[1,2], [3,4]]) >>> np.resize(a, (2,3)) array([[1, 2, 3], [4, 1, 2]])Pictorial Presentation:
Example-3: numpy.resize() ffunction
Pictorial Presentation:
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my_list = ['A', 'B', 'C', 'D', 'E'] print(my_list) # [A,B,C,D,E] print(len(my_list)) # 5 # change list size by removing last 2 items my_list = my_list[:-2] print(my_list) # [A,B,C] print(len(my_list)) # 3View Discussion
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With the help of Numpy numpy.resize(), we can resize the size of an array. Array can be of any shape but to resize it we just need the size i.e (2, 2), (2, 3) and many more. During resizing numpy append zeros if values at a particular place is missing.
Parameters:
new_shape : [tuple of ints, or n ints] Shape of resized array
refcheck : [bool, optional] This parameter is used to check the reference counter. By Default it is True.Returns: None
Most of you are now thinking that what is the difference between reshape and resize. When we talk about reshape then an array changes it’s shape as temporary but when we talk about resize then the changes made permanently.
Example #1:
In this example we can see that with the help of .resize() method, we have changed the shape of an array from 1×6 to 2×3.
import numpy as np
gfg = np.array([1, 2, 3, 4, 5, 6])
gfg.resize(2, 3)
print(gfg)
Output:
[[1 2 3] [4 5 6]]Example #2:
In this example we can see that, we are trying to resize the array of that shape which is type of out of bound values. But numpy handles this situation to append the zeros when values are not existed in the array.
import numpy as np
gfg = np.array([1, 2, 3, 4, 5, 6])
gfg.resize(3, 4)
print(gfg)
Output:
[[1 2 3 4] [5 6 0 0] [0 0 0 0]]