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Umme Ammara

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Python’s ** numpy.positive()** method computes the positive of a number or array element-wise.

`numpy.positive()`

is declared as shown below:

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

A

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

method is a universal function.

The `numpy.positive()`

method takes the following compulsory parameters:

[`x`

*array-like or scalar*] - this is the input array.

The `numpy.positive()`

method takes the following optional parameters:

Parameter | Description |

out | Represents the location into which the output of the method is stored. If not provided or None, a freshly-allocated array is returned. |

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.positive()`

returns the positive of the input, i.e. y = +x.
The return type is an array or scaler depending on the input type.

The examples below show the different ways `numpy.positive()`

is used in Python.

The code below outputs the numerical positive of -17.5 and 12. The result is shown below:

import numpy as npa = -17.5b = -20print (np.positive(a))print (np.positive(b))

The example below outputs the element-wise numerical positive of arrays `arr1`

and `arr2`

:

import numpy as nparr1 = np.array([20,-30,40])arr2 = np.array([2,-3,4])print(np.positive(arr1))print(np.positive(arr2))

The example below outputs the element-wise numerical positive of arrays `arr3`

and `arr4`

:

import numpy as nparr3 = np.array([[2.5,100,-10], [-2.9,90,89]])arr4 = np.array([[-2,-3,-4], [30,40,50]])print(np.positive(arr3))print(np.positive(arr4))

RELATED TAGS

numpy

python

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Umme Ammara

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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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