# Apply accumulate for a multi-dimensional array along axis 0 in Numpy

To Accumulate the result of applying the operator to all elements, use the numpy.accumulate() method in Python Numpy. For a multi-dimensional array, accumulate is applied along only one axis. We will apply along axis 0.

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-by-element 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 2d array. The numpy.eye() returns a 2-D array with 1's as the diagonal and 0's elsewhere −

arr = np.eye(3)


Display the array −

print("Array...", arr)

Get the type of the masked array −

print("Our Array type...", arr.dtype)


Get the dimensions of the Masked Array −

print("Our Array Dimensions...",arr.ndim)

To Accumulate the result of applying the operator to all elements, use the numpy.accumulate() method in Python Numpy. For a multi-dimensional array, accumulate is applied along only one axis −

Add accumulate: Accumulate along axis 0 (rows) −

print("Add accumulate...",np.add.accumulate(arr, 0))


Multiply accumulate −

print("Multiply accumulate...",np.multiply.accumulate(arr, 0))

## Example

import numpy as np

# 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 2d array
# The numpy.eye() returns a 2-D array with 1's as the diagonal and 0's elsewhere.
arr = np.eye(3)

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

# Get the type of the masked array
print("Our Array type...", arr.dtype)

# Get the dimensions of the Masked Array
print("Our Array Dimensions...",arr.ndim)

# To Accumulate the result of applying the operator to all elements, use the numpy.accumulate() method in Python Numpy
# For a multi-dimensional array, accumulate is applied along only one axis

# Accumulate along axis 0 (rows)

# Multiply accumulate
print("Multiply accumulate...",np.multiply.accumulate(arr, 0))

## Output

Array...
[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 1.]]

Our Array type...
float64

Our Array Dimensions...
2

[0. 0. 0.]]