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# np.empty: How to Create An Empty Array in Numpy

Numpy empty() function creates a new array of given shapes and types without initializing entries. On the other side, it requires the user to set all the values in the array manually and should be used with caution. A Numpy array is a very diverse data structure from a list and is designed to be used differently.

## Understanding Numpy array

Numpy array is the central data structure of the Numpy library. On a structural level, an array is nothing but pointers. It’s a combination of the memory address, data type, shape, and strides. To make a numpy array, you can use the np.array() function. All you need to do is pass a list to it, and optionally, you can also specify the data type of the data.

```import numpy as np

list = ['Python', 'Golang', 'PHP', 'Javascript']
arr = np.array(list)
print(arr)
```

#### Output

`['Python' 'Golang' 'PHP' 'Javascript']`

As you can see in the output, we have created a list of strings and then passed the list to the np.array() function, and as a result, it will create a numpy array.

## How to Create Empty Array in Python

To create a numpy empty array, we can pass the empty list to the np.array() function, making the empty array. Numpy empty, unlike the zeros() method, does not set array values to zero and may, hence, be marginally faster.

```import numpy as np

list = []
arr = np.array(list)
print(arr)```

#### Output

`[]`

You can see that we have created an empty array using the np.array().

We can also check its data type.

```import numpy as np

list = []
arr = np.array(list)
print(arr.dtype)
```

#### Output

`float64`

## np.empty

The np.empty(shape, dtype=float, order=’C’) is a numpy array function that returns a new array of given shape and type, without initializing entries. To create an empty array in Numpy (e.g., a 2D array m*n to store), in case you don’t know m how many rows you will add and don’t care about the computational cost, then you can squeeze to 0 the dimension to which you want to append to arr = np.empty(shape=[0, n]).

```import numpy as np

arr = np.empty([0, 2])
print(arr)
```

`[]`

## How to initialize an Efficiently numpy array

NumPy arrays are stored in the contiguous blocks of memory. Therefore, if you need to append rows or columns to an existing array, the entire array must be copied to the new memory block, creating gaps for the new items to be stored. This is very inefficient if done repeatedly to create an array.

In adding rows, this is the best case if you have to create the array as big as your dataset will eventually be and then insert the data row-by-row.

```import numpy as np

arr = np.zeros([4, 3])
print(arr)
```

#### Output

```[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]]```

And then, you can add the data of row by row, and that is how you initialize the array and then append the value to the numpy array.

## Conclusion

To create an empty numpy array, you can use np.empty() or np.zeros() function.