diff --git a/content/numpy/concepts/ndarray/terms/size/size.md b/content/numpy/concepts/ndarray/terms/size/size.md new file mode 100644 index 00000000000..4b540fe545f --- /dev/null +++ b/content/numpy/concepts/ndarray/terms/size/size.md @@ -0,0 +1,85 @@ +--- +Title: 'size' +Description: 'Returns the number of elements in a NumPy array.' +Subjects: + - 'Computer Science' + - 'Data Science' +Tags: + - 'Arrays' + - 'Attributes' + - 'NumPy' + - 'Properties' +CatalogContent: + - 'learn-python-3' + - 'paths/computer-science' +--- + +NumPy’s **`size`** attribute is used to find the total number of elements in an array. + +## Syntax + +```pseudo +ndarray.size +``` + +**Parameters:** + +`size` doesn’t take any parameters because it’s an attribute, not a method. + +**Return value:** + +Returns an integer representing the total number of elements in the array. + +## Example 1: Getting the Size of an Array Using `size` + +In this example, the code prints the total number of elements in the array: + +```py +import numpy as np + +np_array = np.array([[1, 2, 3], [4, 5, 6]]) +print(np_array.size) +``` + +The output of this code is: + +```shell +6 +``` + +The array has 2 rows × 3 columns = 6 elements in total. + +## Example 2: Comparing `.shape` and `.size` + +In this example, `shape` displays the array’s dimensions, while `size` shows the total number of elements in the array: + +```py +import numpy as np + +arr = np.array([[10, 20, 30], [40, 50, 60]]) +print("Shape:", arr.shape) +print("Size:", arr.size) +``` + +The output of this code is: + +```shell +Shape: (2, 3) +Size: 6 +``` + +`shape` returns the dimensions of the array, while `size` gives the total number of elements. + +## Codebyte Example: Using `.size` in a NumPy Operation + +In this example, `size` returns the total number of elements (12) in a reshaped 3×4 NumPy array: + +```codebyte/python +import numpy as np + +array = np.arange(12).reshape(3, 4) +print("Array:") +print(array) + +print("Total elements:", array.size) +```