# Convert Matrix to Array in Numpy

Numpy has many functions and classes available for performing different operations on matrices.

In this tutorial, we will learn how to convert a matrix to an array in Numpy.

## Use the `numpy.flatten()` Function to Convert a Matrix to an Array in Numpy

The `flatten()` takes an N-Dimensional array and converts it to a single dimension array.

It works only with ndarray objects.

It can convert a matrix to an array as shown below.

``````import numpy as np
arr = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(arr.flatten())
``````

Output:

``````[1 2 3 4 5 6 7 8 9]
``````

Note that if we work with a matrix type object, we have to use the `asarray()` function to convert it to an array and then use the `flatten()` function. It can be done for all the methods.

For example,

``````import numpy as np
arr = np.matrix([[1,2,3],[4,5,6],[7,8,9]])
arr_d = (np.asarray(arr)).flatten()
print(arr_d)
``````

Output:

``````[1 2 3 4 5 6 7 8 9]
``````

## Use the `numpy.ravel()` Function to Convert a Matrix to an Array in Numpy

The `ravel()` function works exactly like the `flatten()` function with a few notable differences. Both are used to transform N-Dimensional arrays to single dimension arrays.

However, the `ravel()` function is a library function and can also work on objects like a list of arrays. The `flatten()` returns a copy of the original, whereas `ravel()` always returns a view of the original whenever possible.

In the following code, we will use this function to convert a matrix.

``````import numpy as np
arr = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(arr.ravel())
``````

Output:

``````[1 2 3 4 5 6 7 8 9]
``````

## Use the `numpy.reshape()` Function to Convert a Matrix to an Array in Numpy

The `reshape()` modified the overall shape of the array without altering its contents. If we assign the new shape of a matrix as `-1`, we get a one-dimensional array.

For example,

``````import numpy as np
arr = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(arr.reshape(-1))
``````

Output:

``````[1 2 3 4 5 6 7 8 9]
``````
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