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Python’s ** numpy.add()** method adds two arrays element-wise.

`numpy.add()`

is declared as shown below:

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

A

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

method is a universal function.

The `numpy.add()`

method takes the following compulsory parameters:

and`x1`

[`x2`

*array-like*] - arrays that need to be added. 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.add()`

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.add()`

returns the sum of the two arrays. The return type is either `ndarray`

or `scalar`

, depending on the input type.

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

is used in Python.

The code below outputs the sum of two numbers, 12 and 21. The result is shown below:

import numpy as npa = 12b = 21result = np.add(a,b)print (result)

The example below shows the result of adding two arrays, `arr1`

and `arr2`

:

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

The example below shows the result of adding two arrays, `arr3`

and `arr4`

:

import numpy as nparr3 = np.array([[20,30,40], [-2,-3,-4]])arr4 = np.array([[-2,-3,-4], [30,40,50]])result = np.add(arr3,arr4)print (result)

RELATED TAGS

numpy

python

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