In this tutorial, we will be learning about the NumPy zeros_like method and also seeing a lot of examples regarding the same. So let us begin!

*Recommended Read: NumPy zeros – A Complete Guide*

## What is NumPy zeros_like?

The `zeros_like`

method in NumPy is a function that returns an array of zeros having the same shape and size as the given array.

## Syntax of zeros_like

**numpy.****zeros_like****(***a***, ***dtype=None***, ***order=’K’***, ***subok=True***, ***shape=None***)**

Parameter | Description | Required/Optional |

a (array_like) | An object which defines the shape and data type of the array to be returned. | Required |

dtype (data type) | The data type of the desired array. Overrides the data type of the result. | Optional |

order | The desired order in which the multi-dimensional data is to be stored in the memory. It can be row-major (â€˜Câ€™), column-major (â€˜Fâ€™), ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ implies matching the layout of a as much as possible. | Optional |

subok (bool) | Determines whether the newly created array will use the sub-class type of a (subok=True) or will be a base class array (subok=False).The default value is True. | Optional |

shape | The shape of the desired array. Overrides the shape of the result. | Optional |

**Returns:**

An array with the same shape and data type as the given array.

## Examples of Numpy zeros_like function

Let’s now take a look at how the numpy.zeros_like() function works and what is the expected output for different types of inputs.

### 1-dimensional array using zeros_like

```
import numpy as np
a = np.arange(10)
print("a =", a)
b = np.zeros_like(a)
print("b =", b)
```

**Output:**

```
a = [0 1 2 3 4 5 6 7 8 9]
b = [0 0 0 0 0 0 0 0 0 0]
```

### 2-dimensional array using zeros_like

**N x N array**

```
import numpy as np
a = np.arange(10).reshape(2, 5)
print("a =\n", a)
b = np.zeros_like(a)
print("b =\n", b)
```

**Output:**

```
a =
[[0 1 2 3 4]
[5 6 7 8 9]]
b =
[[0 0 0 0 0]
[0 0 0 0 0]]
```

**1 x N array**

```
import numpy as np
a = np.arange(10).reshape(1, 10)
print("a =\n", a)
b = np.zeros_like(a)
print("b =\n", b)
```

**Output:**

```
a =
[[0 1 2 3 4 5 6 7 8 9]]
b =
[[0 0 0 0 0 0 0 0 0 0]]
```

**N x 1 array**

```
import numpy as np
a = np.arange(10).reshape(10, 1)
print("a =\n", a)
b = np.zeros_like(a)
print("b =\n", b)
```

**Output:**

```
a =
[[0]
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]]
b =
[[0]
[0]
[0]
[0]
[0]
[0]
[0]
[0]
[0]
[0]]
```

### 1-dimensional float-type array

```
import numpy as np
a = np.arange(10)
print("a =", a)
b = np.zeros_like(a, dtype=float)
print("b =", b)
```

**Output:**

```
a = [0 1 2 3 4 5 6 7 8 9]
b = [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
```

### 2-dimensional float-type array

```
import numpy as np
a = np.arange(10).reshape(2, 5)
print("a =\n", a)
b = np.zeros_like(a, dtype=float)
print("b =\n", b)
```

**Output:**

```
a =
[[0 1 2 3 4]
[5 6 7 8 9]]
b =
[[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]]
```

## Difference between zeros and zeros_like

Note that, in the `zeros`

method we are creating a new array of our desired shape and data type having all the values as 0. But, here, we are directly passing an array or an array-like object to get an array of the same shape and data type. The NumPy `zeros_like`

function takes more time than the NumPy `zeros`

function to produce an array with all 0.

## Conclusion

Thatâ€™s all! In this tutorial, we learned about theÂ **Numpy zeros_like**Â method and practiced different types of examples using the same.