# Python NumPy max with examples

In this Python NumPy tutorial, we will discuss Python NumPy max with a few examples like below:

• Python numpy max value
• Python numpy max index
• Python numpy max function
• Python numpy max value in column
• Python numpy max min
• Python numpy max ignore nan
• Python numpy max value and index
• Python numpy max float
• Python numpy max 2d array
• Python numpy max of two arrays

## Python numpy max

• In this section, we will discuss Python numpy max. Here we will use the function numpy.max().
• This function generates the maximum value of the numeric values stored in a NumPy array. It can also compute the maximum value of the rows and columns.
• The numpy module provides a max() function to get the maximum value from a Numpy array.
• It compares two numpy arrays and returns a new array contains the element-wise maxima. If one of the elements being compared is not a number, then that element is returned.

Syntax:

Here is the Syntax of numpy.max()

``````numpy.maximum
(
x1,
x2,
out=None,
where=True,
casting='same_kind',
dtype=None
)``````
• It consists of few parameters
• X1,X2: input array (The arrays will hold the elements to be equal).
• Out:A location into which the result is contained. If provided, it must have a shape that the inputs provide to. If not provided or None, an allocated array is returned. The out parameter allows you to specify an output array where we can store the output of np. max.
• Axis: It’s an optional parameter. The axis parameter is to specify the axis on which you will calculate the maximum values. Axes are like directions along with the array. In a 2-d array, axis 0 is the axis that will present the rows, and axis 1 is the axis that points across the columns.

Example:

Let’s take an example to check how to implement numpy.max() function.

``````import numpy as np

arr = np.array([2,4,5,6,7])
arr2 = np.array([4,5,6,7,8])
res = np.maximum(arr,arr2)
print(res)``````

In the above code, we will first import a NumPy library and then create a NumPy array using the function np.array(). Create a variable and assign a NumPy function. maximum and print the result.

It will compare the element and return a maximum value of arr1 and arr2, element-wise. If it is a scalar value then both arr1 and arr2 are scalars.

Here is the Screenshot of the following given code

## Another example to find maximum value in one dimensional

• In this example, we will discuss how to find maximum value in one-dimensional.
• In this method we can easily use the function numpy.max().
• This function is used to get a max value along a specified axis.

Syntax:

``np.max(arr,axis=None)``
• It consists of few parameters.
• arr: The parameter refers to the numpy array on which you want to apply the np.max() function.
• axis: axis parameter is optional and that helps the user to specify the axis in which we want to find the maximum values.

Example:

Let’s take an example to check how to find a maximum value in 1d array.

``````import numpy as np

arr = np.array([2,4,5,6,7])
res = np.max(arr)
print("Maximum Value",res)``````

Here is the Screenshot of the following given code

## Python numpy max value

• In this section, we will discuss Python numpy max value.
• In this method we can easily use the function numpy.amax().
• The numpy.amax() is a function that returns the maximum value of an array or maximum along the axis (if mentioned). This function takes few arguments which are array, axis, out, and keep dimension, and returns the maximum value of an array.
• It will return the maximum value of an array.

Syntax:

Here is the Syntax of numpy.amax() function

``````numpy.amax
(
arr,
axis=None,
out=None
)``````
• It consists of few parameters
• arr: input array
• axis: Axis along which to operate. By default, axis=0 input is used.
• Out: Its an optional parameter.

Example:

``````import numpy as np

arr = np.array([6,2,8,9,1])
res = np.amax(arr) #amax function
print("Maximum Value",res)``````

In the above code, we will import a NumPy library and then create a NumPy array using the numpy.array() function. Now create a variable and assign the NumPy function that is np.amax(). It provides the user to get a maximum value along a specified axis.

Here is the Screenshot of the following given code

## Python numpy max index

• In this section, we will discuss Python numpy max value.
• In this method we can easily use the function numpy.argmax().
• The numpy module provides a function called numpy. argmax(). This function will return indices of the maximum values and returned them along with the specified axis.
• It will return the indices of the max values along an axis.

Syntax:

Here is the Syntax of numpy.argmax()

``````numpy.argmax
(
arr,
axis,
out
)``````
• It consists of a few parameters
• arr: input array
• axis: It’s an optional parameter. By default, the indices are into the array, otherwise along the specified axis.
• Out: If provided, the output will be stored in this array. It should be of the proper shape and type.

Example:

Let’s take an example to check how to find numpy max index

``````import numpy as np

arr = np.random.randint(16, size=(4,4))
print("Maximum element",np.max(arr))
print("Indices of Max element",np.argmax(arr,axis=0))
print("Indices of Max element",np.argmax(arr,axis=1))``````

In the above code

• We have imported numpy with the alias name np.
• We have created an array ‘arr’ using the np.random() function with the shape of rows and columns.
• We have added 16 in each element of the array also.
• We have declared the variable and assigned the returned value of the np. argmax() function.

Here is the Screenshot of the following given code

## Python numpy max function

• In this section, we will discuss Python numpy max function.
• The NumPy max generates the maximum values in a NumPy array. This function computes the maximum value of the numeric values stored in a NumPy array.
• It can also provide the maximum value of the rows, columns. We should know that the numpy. max function is the same thing as the np. amax function.

Syntax:

Here, we’ll talk about the structure of the function, and I’ll also discuss the important parameters.

``np.max(arr, axis, keepdims=)``
• It consists of few parameters
• arr: The arr parameter provides you to specify the data that is numpy .max function. Necessary, it specifies the input array to the function.
• axis: The axis parameter provides the user to specify the axis on which you will easily calculate the maximum values.

Example:

Let’s take an example to check how to use the NumPy max function.

• Here, we’re going to provide the maximum value of a 1-dimensional NumPy array.
• To do this, we’ll first create a 1-d array that stores some random integer
• To create this array, we will use the numpy. array function.

CODE:

``````import numpy as np

arr = np.array([16,19,4,5,20])
print("Maximum element",np.max(arr))``````

Here is the Screenshot of the following given code

Read Python NumPy nan

## Python numpy max value in column

• In this section, we will discuss Python numpy max value in column
• In this method we can easily use the function np.amax to get the maximum value in column.
• In this method we are using axis=0 and axis=1 to find the maximum value in column.
• Axis are always along with the NumPy array. In a 2-d array, axis 0 is the axis that represents the rows, and axis 1 is the axis that represents the columns.
• When we use the axis parameter in the np.max function, we will be specifying the axis along which to calculate the maxima.
• when we adjust axis =0 we will be specifying that we want to compute the column maxima.

Syntax:

``````numpy.amax
(
arr,
axis=None,
out=None
)``````

Example:

Let’s take an example to find the maximum value in the column.

``````import numpy as np

a = np.array([[10, 20], [4, 40], [5, 6]])
max = np.amax(a,axis=1)
print(max)``````

Here is the Screenshot of the following given code

## Python numpy max min

• In this section, we will discuss Python numpy max-min. We will use the functions np.max() and np.min() function.
• Np.min() function is used to find the minimum value element-wise
• The min() and max() functions of numpy.ndarray returns the minimum and maximum values of a ndarray object.
• The return value of np.min() and np.max() functions is depend on the axis specified.
• If no axis is verified the value returned is based on all the elements of the given array.

Example:

• Now, let’s create a two-dimensional NumPy array.
• Now using the numpy.max() and numpy.min() functions we can find the maximum and minimum element.

Code:

``````import numpy as np

arr = np.array([[2,4,5,6],[7,8,9,2]])
max = print("Maximum value",np.max(arr))
min = print("Minimum value",np.min(arr))``````

Here is the Screenshot of the following given code

## Python numpy max ignore nan

• In this section, we will discuss Python numpy max ignore nan.
• In this example, we can easily use the function numpy. nanmax(). This function returns the max value of an array or maximum along an axis, ignoring any NaNs.

Syntax:

Here is the Syntax of numpy.nanmax() function.

``````numpy.nanmax
(
arr,
axis=None,
out=None
)``````
• It consists of few parameters
• arr: An array stores numbers whose maximum is desired. If a is not given in an array, a conversion is attempted.
• axis: Axis along which the maximum value is provided. The default value is to compute the maximum of the input array.
• Out: Its an optional parameter.
• Returns: An array with the same shape as arr, with the specified axis, is deleted.

Example:

Let’s take an example to check how to use NumPy max ignore nan

``````import numpy as np

arr = np.array([[2,4,np.nan,6],[3,4,np.nan,8]])
res = np.nanmax(arr)
print(res)``````

Here is the Screenshot of the following given code

## Python numpy max value and index

• In this section, we will discuss Python numpy max value and index.
• In this example, we can easily use the function np. argmax() to get the maximum value and index.
• Numpy. argmax() function returns the index of the maximum values along an axis. By default, the index is into the input array, otherwise along the verified axis.
• Numpy. argmax is a function that gives the index of the biggest number in the given row or column and the row or column can be provided using the axis attribute of np. argmax.

Syntax:

Here is the Syntax of numpy.argmax()

``````numpy.argmax
(
arr,
axis,
out
)``````

Example:

Let’s take an example to check how to implement numpy.argmax() function to find maximum value and index.

``````import numpy as np

arr = np.array([[2,4,5,16],[4,5,6,7]])
print("Maximum element",np.max(arr))
print("Indices of Max element",np.argmax(arr))``````

In the above code, we will import a NumPy library and create a NumPy array by using the np.array() function.

Here is the Screenshot of the following given code

## Python numpy max float

• In this section, we will discuss Python numpy max float.
• In this example, we can easily use the function numpy.max() and also assign the datatype in parameter. The float() method converts a number contains in a string or integer into a floating-point number.

Example:

Let’s take an example to check how to implement NumPy max float

``````import numpy as np

b = np.array([5,6,7.2,8.5], dtype=float)
c= np.max(b)
print(c)``````

Here is the Screenshot of the following given code

## Python numpy max 2d array

• In this section, we will discuss Python numpy max 2d array.
• In this method we can easily use the function np.max().
• Let’s create a 2Dimensional numpy array. Now let’s use numpy.amax() to find the maximum value from this numpy array by passing just array as an argument.
• It returns the maximum value from the passed numpy array.

Example:

Let’s take an example to check the numpy max 2d array

``````import numpy as np

b = np.array([[5,6,7,2,8],
[2,6,1,3,9]])
print("Maximum value 2d array",np.max(b))``````

Here is the Screenshot of the following given code

## Python numpy max of two arrays

• In this section, we will discuss Python numpy max of two arrays.
• In this method we can easily use the function numpy.maximum().
• The maximum() function is used to find the element-wise maximum of array elements. It equates to 2 arrays and returns a new array containing the element-wise maximum value.
• If one of the values being compared is a Not a number, then that value is returned. If both the elements are Not in number then the first is returned.

Syntax:

Here is the Syntax of numpy.maximum() function

``````numpy.maximum
(
x1,
x2,
out=None,
where=True,
casting='same_kind',
dtype=None
)``````

Example:

Let’s take an example to check how to find the maximum values of two arrays.

``````import numpy as np

arr1 = [4, 5, 234]
arr2 = [1, 2, 215]

print ("Input array1 : ", arr1)
print ("Input array2 : ", arr2)

res_arr = np.maximum(arr1, arr2)
print ("Output array after selecting maximum: ", res_arr)``````

Here is the Screenshot of the following given code

You may like the following Python tutorials:

In this Python tutorial, we will discuss Python NumPy max with a few examples like below:

• Python numpy max value
• Python numpy max index
• Python numpy max function
• Python numpy max value in column
• Python numpy max min
• Python numpy max ignore nan
• Python numpy max value and index
• Python numpy max float
• Python numpy max 2d array
• Python numpy max of two arrays