What is the numpy.all() in Numpy?

The functions in NumPy help to make our code easier to read and accessible. Suppose you were asked to write a program to check whether the elements of an array are even/odd. The traditional way to do this is to loop through each element and check if they are even/odd.

With the help of the NumPy all() function in NumPy, we can test/evaluate all of the elements of the array in a single-go. NumPy all() function tests if all the array elements are True. All we need to do is put the condition as a parameter when we use the all() function.

Hence, the use of the NumPy all() function has single-handedly removed the use of a for loop and has made our code more accessible and easier to understand. Let's understand in-depth the all() function.

Syntax

The syntax for NumPy all() function is:

Parameters

The various parameters in NumPy all() are:

• arr: This mandatory parameter represents the input array to be tested.
• axis: This optional parameter represents the axes along which a logical AND reduction is performed. The default value is None.
• out: This optional parameter represents an alternative output array to place to the result. It must have the same shape as the expected output array.

Return Value

The NumPy all() function always returns a boolean value or an array of booleans.

Examples

• NumPy all() on a one-dimensional array

  In this example, we will use the NumPy all() function to evaluate an array that contains only True values ([True, True, True]).

  Output

  As we can see, since the array contained only True elements, the output was returned as True. This indicates that all of the values in the array are True.

• Test an array for a specific condition

  Output

  Since every element in the input array is greater than 2, the output comes out to be True.

• Using NumPy all() on two-dimensional arrays

  Output

  When we use the NumPy all() function on a two-dimensional array, it checks if every element of the two-dimensional array is greater than 2 (given condition).

  The conditional logic generates a NumPy array with just boolean values, which NumPy all() then uses to get the final True/False answer.

• Apply NumPy.all() along axis-0

  Output

  In this example, we use the axis parameter to enforce our condition on all the elements of the particular axis. In the 0th row, we have the elements [1, 2]. Since only 2 abide by our condition, it is returned as True.

• Apply NumPy.all() along axis-1

  Output

  When we select our axis as 1, we consider the column values only. In the input array, the top column values are 1 and 4. Hence, our condition is only applicable to 4.