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Python’s ** numpy.fmax()** computes the element-wise maximum of an array. It compares two arrays and returns a new array containing the maximum values.

The difference between the

`fmax()`

and`maximum()`

functions in numpy is that the`fmax()`

function ignores`NaN`

values whereas the`maximum()`

function propagates`NaN`

values.

`numpy.fmax()`

is declared as follows:

```
numpy.fmax(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'fmax'>
```

In the syntax given above, `x1`

and `x2`

are non-optional parameters, and the rest are optional parameters.

A universal function (ufunc) is a function that operates on ndarrays in an element-by-element fashion. The

`fmax()`

method is a universal function.

The `numpy.fmax()`

method takes the following compulsory parameters:

and`x1`

[`x2`

*array-like*] - arrays holding the values that need to be compared. If the ofshape the shape of an array is the number of elements in each dimension `x1`

and`x2`

is different, they must be broadcastable to a common shape for representing the output.

The `numpy.fmax()`

method takes the following optional parameters:

Parameter | Description |

out | represents the location into which the output of the method is stored |

where | True value indicates that a universal function should be calculated at this position |

casting | controls the type of datacasting that should occur. The |

order | controls the memory layout order of the output function. The option |

dtype | represents the desired data type of the array |

subok | decides if subclasses should be made or not. If True, subclasses will be passed through. |

`numpy.fmax()`

returns the maximum of `x1`

and `x2`

element-wise. The return type is ndarray or scalar, depending on the input. Only if both `x1`

and `x2`

are scalar will the value returned be scalar.

If one of the elements being compared is

`NaN`

(Not a Number), then thenon-nanelement is returned.

If both elements being compared are

`NaN`

(Not a Number), then`NaN`

is returned.

The examples below show different ways in which `numpy.fmax()`

is used in Python.

The following code example outputs the maximum of two numbers `a`

and `b`

:

import numpy as np a = 10 b = np.nan print(np.fmax(a,b))

The example below outputs the result after comparing the arrays `arr1`

and `arr2`

:

import numpy as np arr1 = np.array([1.5,3.5,4,np.nan]) arr2 = np.array([np.nan,6.5,2,np.nan]) print(np.fmax(arr1,arr2))

The example below shows the result of comparing the arrays `arr3`

and `arr4`

:

import numpy as np arr3 = np.array([[1,2,3], [4,5,6]]) arr4 = np.array([[3,np.nan,1], [6,-5,4]]) print(np.fmax(arr3,arr4))

RELATED TAGS

numpy

python

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Grokking Modern System Design Interview for Engineers & Managers

Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.

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