There are two ways an empty NumPy array can be created: **`numpy.zeros`** and **`numpy.empty`**. Both of these methods differ slightly, as shown below:

# Syntax

The syntax for using `numpy.zeros` and `numpy.empty` is shown below:
```python
numpy.zeros(shape, dtype=float, order='C')

numpy.empty(shape, dtype=float, order='C')

# Shape -> Shape of the new array, e.g., (2, 3) or 2.

# dtype -> The desired data-type for the array,e.g., numpy.int8. Default is numpy.float64. This parameter is optional.

# order -> Indicates whether multi-dimensional data should be stored in row-major (C-style) or column-major (Fortran-style) order in memory. This parameter is optional.
```

# Code
Consider the code snippet below that demonstrates how to use `numpy.zeros` to create an empty NumPy array:

import numpy as np
myArr = np.zeros((2,3))
print(myArr)

The code snippet below demonstrates how to use `numpy.empty` to create an empty NumPy array:

import numpy as np
myArr = np.empty((2,3))
print(myArr)

> `numpy.empty`, unlike `numpy.zeros`, does not set the array values to zero and, ​therefore, may be marginally faster. On the other hand, it requires the user to manually set all the values in the array ​and should be used with caution.

 

How to create an empty NumPy array

numpy.zeros creates arrays filled with zeros, while numpy.empty provides uninitialized arrays.