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+---
+Title: '.square()'
+Description: 'Calculates the square of each element in an array.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+  - 'Discrete Math'
+Tags:
+  - 'Arrays'
+  - 'Functions'
+  - 'NumPy'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+In NumPy, the **`.square()`** method computes the square of a number or the square of the elements in an array. It is commonly used in mathematical calculations, machine learning, data analysis, engineering, and graphics.
+
+## Syntax
+
+```pseudo
+numpy.square(x, out = None, where = True, dtype = None)
+```
+
+- `x`: The input data, which can be a number, an array, or a multidimensional array.
+- `out` (Optional): A location where the result is stored. If provided, it must have the same shape as the expected output.
+- `where` (Optional): A boolean array specifying which elements to compute. The result is only computed for elements where `where` is `True`.
+- `dtype` (Optional): The desired data type for the output array. If not specified, it defaults to the data type of x.
+
+## Examples
+
+### Modifying the output array
+
+The output array for NumPy operations cannot be a Python [list](https://www.codecademy.com/resources/docs/python/built-in-functions/list) because lists are not optimized for numerical computations. NumPy arrays are composed of contiguous blocks of memory, which enhances performance. Therefore, the array passed for the out parameter must be a NumPy array initialized with the `numpy.array` function:
+
+```py
+import numpy as np
+
+output_array = np.array([0, 0, 0, 0, 0])
+```
+
+This array can then be used as the `out` parameter in the `numpy.square()` function:
+
+```py
+import numpy as np
+
+output_array = np.array([0, 0, 0, 0, 0])
+
+array = [1, 2, 3, 4, 5]
+np.square(array, out = output_array)
+print(output_array)
+```
+
+This generates the output as follows:
+
+```shell
+[1, 4, 9, 16, 25]
+```
+
+### Operating conditionally
+
+Using the `where` parameter, the function will execute conditionally. The `where` parameter specifies where to apply the operation, based on a condition. If the condition is `True` at a particular index, the corresponding element in the array will be squared. If the condition is `False`, the element will remain unchanged. For instance:
+
+```py
+import numpy as np
+
+array = np.array([1, 2, 3, 4, 5])
+conditions = np.array([False, True, True, False, True])
+
+result = np.square(array, where=conditions)
+print(result)
+```
+
+Output:
+
+```shell
+array([1, 4, 9, 4, 25])
+```
+
+The `where` parameter takes a boolean array or condition. It determines where the squaring operation will take place:
+
+- True at an index: The element at that index will be squared.
+- False at an index: The element at that index will remain unchanged.
+
+If the `where` parameter is set to a single boolean value (either `True` or `False`), the entire array is either squared (if `True`) or left unchanged (if `False`).
+
+### Changing types
+
+Sometimes, it is important to increase or decrease the size of the datatype of the output array. This can be done by setting the `dtype` parameter to an np datatype, like:
+
+```py
+import numpy as np
+array = np.array([1, 2, 3, 4, 5])  # Ensuring it's a numpy array
+result = np.square(array, dtype=np.float32)
+
+# Print the result
+print(result)
+```
+
+Output generated will be as follows:
+
+```shell
+array([ 1.,  4.,  9., 16., 25.], dtype=float32)
+```
+
+## Codebyte Example
+
+Run the following example to understand how the `.square()` method works:
+
+```codebyte/python
+import numpy as np
+
+# Create a NumPy array
+array = np.array([1, 2, 3, 4, 5])
+
+# Create an output array initialized with zeros
+output_array = np.zeros_like(array)
+
+# Set the condition for the 'where' parameter (square values where condition is True)
+conditions = np.array([False, True, True, False, True])
+
+# Use numpy.square() with all parameters
+result = np.square(array, out=output_array, where=conditions)
+
+# Print the result
+print("Squared values with conditions:", result)
+```