# NumPy - stack() function

The NumPy stack() function joins a sequence of arrays along a new axis. The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.

### Syntax

```numpy.stack(arrays, axis=0, out=None)
```

### Parameters

 `arrays` `Required. `Specify arrays (array_like) to be stacked. Each array must have the same shape. `axis` `Optional. `Specify axis in the result array along which the input arrays are stacked. `out` `Optional. `Specify output array for the result. The default is None. If provided, it must have the same shape as output.

### Return Value

Returns the stacked array. It has one more dimension than the input arrays.

### Example:

In the example below, stack() function is used to stack two given arrays.

```import numpy as np

Arr1 = np.array([[10,20],[30, 40]])
Arr2 = np.array([[50,60],[70, 80]])

#stacking arrays along axis=0
Arr3 = np.stack((Arr1, Arr2), axis=0)
#stacking arrays along axis=1
Arr4 = np.stack((Arr1, Arr2), axis=1)

#displaying results
print("Arr1 is:")
print(Arr1)
print("\nArr2 is:")
print(Arr2)
print("\nArr3 is:")
print(Arr3)
print("\nArr4 is:")
print(Arr4)
```

The output of the above code will be:

```Arr1 is:
[[10 20]
[30 40]]

Arr2 is:
[[50 60]
[70 80]]

Arr3 is:
[[[10 20]
[30 40]]

[[50 60]
[70 80]]]

Arr4 is:
[[[10 20]
[50 60]]

[[30 40]
[70 80]]]
```

### Example:

Consider one more example.

```import numpy as np

Arr1 = np.array([10, 20, 30])
Arr2 = np.array([40, 50, 60])

#stacking arrays along axis=0
Arr3 = np.stack((Arr1, Arr2), axis=0)
#stacking arrays along axis=1
Arr4 = np.stack((Arr1, Arr2), axis=1)

#displaying results
print("Arr1 is:")
print(Arr1)
print("\nArr2 is:")
print(Arr2)
print("\nArr3 is:")
print(Arr3)
print("\nArr4 is:")
print(Arr4)
```

The output of the above code will be:

```Arr1 is:
[10 20 30]

Arr2 is:
[40 50 60]

Arr3 is:
[[10 20 30]
[40 50 60]]

Arr4 is:
[[10 40]
[20 50]
[30 60]]
```

❮ NumPy - Functions

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