[Term Entry] Numpy methods: .squeeze()

content/numpy/concepts/ndarray/terms/squeeze/squeeze.md

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+---
+Title: '.squeeze()'
+Description: 'Removes dimensions of size 1 from an ndarray.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Arrays'
+  - 'Math'
+  - 'Methods'
+  - 'NumPy'
+  - 'Python'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+[Numpy's](https://www.codecademy.com/resources/docs/numpy) **`.squeeze()`** is used to remove dimensions of size 1 from an array, returning a reshaped array without those singleton dimensions.
+
+## Syntax
+
+```pseudo
+ndarray.squeeze(axis=None)
+```
+
+**Parameters:**
+
+- `axis`: Specifies which axis or axes to squeeze. If set to `None` (default), all dimensions of size 1 are removed. If any specified axis is not of size 1, it raises a `ValueError`.
+
+**Return value:**
+
+Returns a view of the input array with the specified singleton dimensions removed.
+
+## Example 1: Removing All Singleton Dimensions Using `.squeeze()`
+
+In this example, `.squeeze()` is used without the `axis` parameter to remove all dimensions of size 1 from a 3D array:
+
+```py
+import numpy as np
+
+np_array = np.array([[[1, 2, 3], [1, 2, 3]]])
+print(np_array.shape)  # (1, 2, 3)
+
+squeezed_array = np.squeeze(np_array)
+print(squeezed_array.shape)
+```
+
+The output of this code is:
+
+```shell
+(2, 3)
+```
+
+## Example 2: Removing a Specific Dimension Using `.squeeze()`
+
+In this example, `.squeeze(axis=0)` removes the first dimension explicitly from an array with shape `(1, 2, 3)`:
+
+```py
+import numpy as np
+
+np_array = np.array([[[1, 2, 3], [1, 2, 3]]])
+squeezed_array = np.squeeze(np_array, axis=0)
+
+# Output shape after squeezing
+print(squeezed_array.shape)
+print(squeezed_array)
+```
+
+The output of this code is:
+
+```shell
+(2, 3)
+[[1 2 3]
+ [1 2 3]]
+```
+
+Only axis 0 is removed since it has size 1, resulting in a 2D array.
+
+## Codebyte Example: Removing Multiple Dimensions With a Tuple Using `.squeeze()`
+
+In this example, `.squeeze(axis=(0, 2))` removes both the first and third dimensions from a shape `(1, 3, 1)`:
+
+```codebyte/python
+import numpy as np
+
+array_of_zeros = np.zeros((1, 3, 1))
+print(array_of_zeros.shape)
+
+squeezed = np.squeeze(array_of_zeros, axis=(0, 2))
+print(squeezed.shape)
+print(squeezed)
+```
+
+Axes 0 and 2, both of size 1, will be removed, leaving a flat array of shape `(3,)`.