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I have just started using NumPy. What is the difference between resize and reshape for arrays?
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The shape tool gives a tuple of array dimensions and can be used to change the dimensions of an array. The reshape tool gives a new shape to an array without changing its data. It creates a new array and does not modify the original array itself.
Reshaping numpy array simply means changing the shape of the given array, shape basically tells the number of elements and dimension of array, by reshaping an array we can add or remove dimensions or change number of elements in each dimension.
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. Array to be resized.
Gives a new shape to an array without changing its data. Array to be reshaped.
Reshape doesn't change the data as mentioned here. Resize changes the data as can be seen here.
Here are some examples:
>>> numpy.random.rand(2,3) array([[ 0.6832785 , 0.23452056, 0.25131171], [ 0.81549186, 0.64789272, 0.48778127]]) >>> ar = numpy.random.rand(2,3) >>> ar.reshape(1,6) array([[ 0.43968751, 0.95057451, 0.54744355, 0.33887095, 0.95809916, 0.88722904]]) >>> ar array([[ 0.43968751, 0.95057451, 0.54744355], [ 0.33887095, 0.95809916, 0.88722904]]) After reshape the array didn't change, but only output a temporary array reshape.
>>> ar.resize(1,6) >>> ar array([[ 0.43968751, 0.95057451, 0.54744355, 0.33887095, 0.95809916, 0.88722904]]) After resize the array changed its shape.
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