# Accumulate the result of applying the operator to all elements in Numpy

NumpyServer Side ProgrammingProgramming

To Accumulate the result of applying the operator to all elements, use the numpy.accumulate() method in Python Numpy. We have shown examples of add and multiple. The add.accumulate() is equivalent to np.cumsum().

The numpy.ufunc has functions that operate element by element on whole arrays. The ufuncs are written in C (for speed) and linked into Python with NumPy’s ufunc facility.

A universal function (or ufunc for short) is a function that operates on ndarrays in an element-byelement fashion, supporting array broadcasting, type casting, and several other standard features. That is, a ufunc is a “vectorized” wrapper for a function that takes a fixed number of specific inputs and produces a fixed number of specific outputs.

## Steps

At first, import the required library −

import numpy as np

Create a 1d array −

arr = np.array([2, 3, 5])


Display the array −

print("Array...\n", arr)

Get the type of the array −

print("\nOur Array type...\n", arr.dtype)


Get the dimensions of the Array −

print("\nOur Array Dimensions...\n",arr.ndim)

To Accumulate the result of applying the operator to all elements, use the numpy.accumulate() method in Python Numpy −

print("\nAdd accumulate...\n",np.add.accumulate(arr))


Multiply accumulate −

print("\nMultiply accumulate...\n",np.multiply.accumulate(arr))

## Example

import numpy as np
import numpy.ma as ma

# The numpy.ufunc has functions that operate element by element on whole arrays.

# ufuncs are written in C (for speed) and linked into Python with NumPy’s ufunc facility

# Create a 1d array
arr = np.array([2, 3, 5])

# Display the array
print("Array...\n", arr)

# Get the type of the array
print("\nOur Array type...\n", arr.dtype)

# Get the dimensions of the Array
print("\nOur Array Dimensions...\n",arr.ndim)

# To Accumulate the result of applying the operator to all elements, use the numpy.accumulate() method in Python Numpy
# The add.accumulate() is equivalent to np.cumsum().
print("\nAdd accumulate...\n",np.add.accumulate(arr))

## Output

Array...
[2 3 5]

Our Array type...
int64

Our Array Dimensions...
1

[ 2 5 10]