In this NumPy tutorial, I will explain the **np.max function in Python NumPy**, its syntax, and parameters. I will explain different examples of the **np.max function in Python**. I will also explain what the **np.maximum function** is.

**To find the maximum value in a Python NumPy array**, We use the np.max function is a function used . It can also be used along a specified axis in multi-dimensional arrays. whereas the np.maximum is used to compare two arrays and find the maximum value at each position when comparing element by element.

## np.max function in Python NumPy

The **np.max** is a function in the NumPy Python library that returns the maximum value from an array or along a specified axis of a multidimensional array. It is a shorthand for NumPy’s **amax function** and is widely used in data manipulation and scientific computing tasks.

### Syntax of np.max

The syntax of the **np.max function** in Python NumPy is:

`numpy.max(array, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)`

### Parameters required for the numpy.max

Here, is the list of all the parameters required in the Python **numpy.max** functions

Name | Description |
---|---|

array | Input NumPy array or object that can be converted to an array in Python. |

axis | Axis or axes along which to operate. By default, flattened input is used. |

out | Alternative output Python NumPy array in which to place the result. Must be of the same shape and buffer length as the expected output. |

keepdims | If this is set to True, the axes that are reduced are left in the result as dimensions with size one. |

initial | The minimum value of an output element. Must be present to allow computation on an empty slice. |

where | Conditions that choose which elements to compare. |

**np.max()**function in Python NumPy.

## np max function in Python NumPy use cases

There can be many different use cases of the **np.max function in Python** NumPy. Let’s see some of them in detail:

### Case 1: max NumPy function on a 1D array

We can find the maximum between the values stored in a 1D array in Python through the **numpy.max()** function.

**Example:** Here’s how to find the maximum value in a one-dimensional array

```
import numpy as np
arr = np.array([1, 3, 2, 5, 4])
max_value = np.max(arr)
print("The maximum value is:", max_value)
```

**Output:** The implementation of the code is as mentioned below:

`The maximum value is: 5`

This example finds the maximum value using the **np.max function in Python** NumPy in a 1D array.

### Case 2: NumPy max value in matrix 2D

It is the basic usage of **np.max** function in Python NumPy without any additional parameters. we can easily find the maximum value element in the entire multi-dimensional array.

**Example:** Let’s take a multi-dimensional array in Python of numbers, and now we have to find the maximum of numbers through the np.max function.

```
import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
max_value = np.max(matrix)
print("The maximum value among the matrix is:", max_value)
```

**Output:** The output is mentioned below:

`The maximum value among the matrix is: 9`

This way, we can simply find the largest element in the entire multi-dimensional array through the **np.max function in Python** NumPy.

### Case 3: np.max function in Python along an Axis

The **axis** parameter is used to specify the axis along which to look for the maximum values. For a 2D array in Python, **axis=0** refers to columns, and **axis=1** refers to rows.

**Example:** For instance, we have a 2D array and must find the max values within a row and columns through NumPy max.

```
import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
max_in_columns = np.max(matrix, axis=0)
max_in_rows = np.max(matrix, axis=1)
print("Maximum value in each column:", max_in_columns)
print("Maximum value in each row:", max_in_rows)
```

**Output:** The implementation of the code is mentioned below:

```
Maximum value in each column: [7 8 9]
Maximum value in each row: [3 6 9]
```

We can use the **axis parameter** in the **np.max** function Python NumPy.

### Case 4: NumPy max function with keepdims

The **keepdims** parameter lets us retain the dimensions of the original array in Python.

**Example:** Here, we have an array trying a create a new array with the max value of the actual array through Python.

```
import numpy as np
matrix = np.array([[1, 2], [3, 4]])
max_value = np.max(matrix, keepdims=True)
print("The maximum value with original dimensions kept:")
print(max_value)
```

**Output:** After implementation, the output we get is mentioned below with a screenshot.

```
The maximum value with original dimensions kept:
[[4]]
```

We can use the **np.max** function in Python NumPy with the **keepdims** parameter.

### Case 5: NumPy max value with Initial Parameter

The **initial** parameter is useful for specifying the minimum starting value for comparison, which is especially helpful for empty slices.

**Example:** We have an array with different values and we don’t know whether our array contains a value greater than a specific number or not. To check this we can use the **initial parameter**.

```
import numpy as np
arr = np.array([1, 2, 3, -1, -2, -3])
max_value = np.max(arr, initial=1)
print("The maximum value considering the initial value is:", max_value)
```

**Output:** The implementation of the given Python code is mentioned below:

`The maximum value considering the initial value is: 3`

We can use the **np.max** function in Python NumPy with an initial parameter.

## np.maximum in Python

The **np.maximum** is a function used to compare two arrays and find the element-wise maximum in Python. It broadcasts the arrays against each other if they do not have the same shape. It returns a NumPy array with the maximum values picked from the input arrays for each position.

**Example:** Say, we have two different arrays in Python and we have to find the maximum of each column.

```
import numpy as np
a = np.array([1, 2, 3])
b = np.array([3, 1, 2])
result = np.maximum(a, b)
print(result)
```

**Output:** The implementation of the code is given below:

`[3 2 3]`

This way we can compare the elements of two arrays and find the maximum values of all, using **np.maximum** in Python.

## Conclusion

The **np.max function in Python** is a versatile tool for finding the maximum values within arrays of any dimension. It becomes even more powerful when we leverage parameters like **axis**, **keepdims**, **where**, and **initial** to fine-tune the function’s behavior according to specific needs.

Through the examples provided, we can see how **np.max() **can be applied in different situations, making it an essential function for data manipulation and analysis in Python.

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I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etcâ€¦ for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.