[Term Entry] Python NumPy - ndarray: max()

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

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+---
+Title: 'max()'
+Description: 'Returns the maximum value in a NumPy array, optionally along a specified axis.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Array'
+  - 'Data'
+  - 'NumPy'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/data-science'
+---
+
+The **`ndarray.max()`** method returns the largest element in a NumPy array. The operation can run on the entire flattened array or along a chosen axis, and supports optional parameters for masking, output arrays, and dimension preservation.
+
+## Syntax
+
+```pseudo
+ndarray.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)
+```
+
+**Parameters:**
+
+- `axis` (optional): Axis along which the maximum is computed.
+  - `None`: searches the entire array
+  - `0`: column-wise
+  - `1`: row-wise
+- `out` (optional): Output array that receives the result.
+- `keepdims` (optional): Preserves reduced axes with length 1, keeping the array's dimensionality.
+- `initial` (optional): A default value to compare against when the array is empty.
+- `where` (optional): Boolean mask indicating which elements to include in the comparison.
+
+**Return value:**
+
+The maximum value as a scalar or array, depending on the axis and output parameters.
+
+## Example 1: Maximum Value in a 1D Array
+
+In this example, the array is scanned for its largest value, and the maximum element is returned:
+
+```py
+import numpy as np
+
+arr = np.array([4, 12, 7, 19, 3])
+result = arr.max()
+print(result)
+```
+
+The output of this code is:
+
+```shell
+19
+```
+
+## Example 2: Maximum Values Along Columns
+
+In this example, `max()` is applied along axis 0, producing one maximum value per column in the 2D array:
+
+```py
+import numpy as np
+
+arr = np.array([[2, 8, 5],
+                [9, 3, 7]])
+
+result = arr.max(axis=0)
+print(result)
+```
+
+The output of this code is:
+
+```shell
+[9 8 7]
+```
+
+## Codebyte Example
+
+In this example, the `max()` method is used to compute the global maximum, row-wise maximums, and a masked maximum using both where and initial to avoid reduction errors:
+
+```codebyte/python
+import numpy as np
+
+arr = np.array([[10, 4, 6],
+                [3, 15, 2],
+                [8, 1, 9]])
+
+global_max = arr.max()
+row_max = arr.max(axis=1)
+
+masked_max = arr.max(where=arr > 5, initial=-np.inf)
+
+print("Maximum in entire array:", global_max)
+print("Maximum in each row:", row_max)
+print("Maximum among values greater than 5:", masked_max)
+```
+
+## Frequently Asked Questions
+
+### 1. What is Max NumPy in Python?
+
+In NumPy, `max()` is a method that returns the largest value within an array or along a chosen axis. The operation relies on efficient vectorized comparisons, making it significantly faster than manual iteration over Python lists.
+
+### 2. What is Max in NumPy select?
+
+In the context of `numpy.select`, `max()` often appears as a reduction step to extract the highest value from conditional outputs. Although `select()` focuses on choosing values based on conditions, `max()` can be applied afterward to determine the largest result from the selected set.
+
+### 3. Can you use `max()` on a list?
+
+Python’s built-in `max()` function works on lists and returns their largest element. This behavior is independent of NumPy; however, NumPy arrays also integrate their own optimized `ndarray.max()` method for numerical operations.