In NumPy, the primary data structure is the ndarray (N-dimensional array). An array can have one or more dimensions, and it organizes your data efficiently.

Code to create a 2D array

``` python

import numpy as np

numbers = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])

print(numbers)

The `reshape()` function allows you to rearrange the data within a NumPy array.

It take 2 arguements, row and columns. The `reshape()` can add or remove the dimensions. For instance, array can convert a 1D array into a 2D array or vice versa.

``` python

arr_1d = np.array([1, 2, 3, 4, 5, 6]) # 1D array

arr_2d = arr_1d.reshape(2, 3) # Reshaping with 2rows and 3cols

print(arr_2d)

The `resize()` function allows you to modify the shape of a NumPy array directly.

It take 2 arguements, row and columns.

``` python

import numpy as np

arr_1d = np.array([1, 2, 3, 4, 5, 6])

arr_1d.resize((2, 3)) # 2rows and 3cols

print(arr_1d)

| Reshape | Resize |

| ----------- | ----------- |

| Does not modify the original array | Modifies the original array in-place |

| Creates a new array | Changes the shape of the array |

| Returns a reshaped array | Doesn't return anything |

| Compatibility between dimensions | Does not compatibility between dimensions |

| Syntax: reshape(row,col) | Syntax: resize((row,col)) |