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+# Numpy Array Shape and Reshape
+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)
+
+# Output:
+# array([[1, 2, 3, 4],[5, 6, 7, 8]])
+```
+
+## Changing Array Shape using Reshape()
+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)
+
+# Output:
+# array([[1, 2, 3],[4, 5, 6]])
+
+```
+
+## Changing Array Shape using Resize()
+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)
+
+# Output:
+# array([[1, 2, 3],[4, 5, 6]])
+
+```
+
+## Reshape() VS Resize()
+
+| 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)) |