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# NumPy | zeros_like method

schedule Aug 12, 2023
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Numpy's `zeros_like(~)` method creates a Numpy array from an existing array, and fills it with all zeroes. This is similar to the other Numpy `_like` methods such as `full_like(~)` and `full_empty(~)`.

# Parameters

1. `a` | `array_like`

Source array that will be used to construct the Numpy array. By default, the Numpy array will adopt the data type of the values as well as the size of the source array.

2. `dtype` | `string` or `type` | `optional`

The desired data type for the Numpy array. This overrides the default behavior of using the same data-type as the source array.

# Return value

A Numpy array filled with zeros, with the same shape and type as the source array.

# Examples

## Using Numpy arrays

``` x = np.arange(3) # array([0,1,2])np.zeros_like(x) array([0, 0, 0]) ```

## Using Python arrays

``` x = [1,2,3]np.zeros_like(x) array([0, 0, 0]) ```

## Specifying type

``` x = [1,2,3]np.zeros_like(x, dtype="float") array([0., 0., 0.]) ```

Notice how the values in the output Numpy array are `4.` instead of just `0` - this means that the values are floats.

## Two-dimensional arrays

``` x = [[1,2], [3,4]]np.zeros_like(x) array([[0, 0], [0, 0]]) ```
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