## NumPy empty() function in Python

Have you ever needed an empty array in Python? It’s a common requirement for developers, particularly when dealing with mathematical operations and data structures.

Fortunately, NumPy provides a solution in the form of the empty() function. In this article, we’ll take a closer look at how this function works and how you can use it to manipulate arrays and perform high-level mathematical functions.

### Introduction to NumPy and its Purpose

NumPy is a popular Python package that is used for numerical computations and mathematical operations.

It provides a wide range of functions that are aimed at making mathematical operations more efficient. The package is particularly useful when dealing with large datasets, where manual computation is impractical.

NumPy can also manipulate arrays and matrices and perform various mathematical operations such as Fourier transforms and linear algebra.

### Explanation of the empty() Function, Its Parameters, and Returns

The empty() function in NumPy is used to create an empty array without initializing the data.

This is useful when you want to create an array whose elements will be replaced later during program execution. The function takes several parameters, such as shape, dtype, order, and object arrays.

The shape parameter specifies the dimensions of the array, while dtype specifies the data type of the array. The order parameter specifies the memory layout of the array, and object arrays specify whether the array is an array of objects.

## Implementation of NumPy empty() Function

Let’s now take a look at how to implement the NumPy empty() function.

### Importing NumPy and Passing Integer as the Shape Parameter

The first step is to import the NumPy package. This is done by using the import statement.

After that, we can pass the shape parameter, which is an integer that specifies the number of elements in the array. Here, we are creating an array x with five elements.

```
import numpy as np
x = np.empty(5)
```

### Passing Tuple as the Shape Parameter and Assigning Other Parameters

The second step is to pass a tuple as the shape parameter. This will create a multi-dimensional array with the specified shape.

For example, let’s say we want to create a 2-dimensional array with shape (3, 2), dtype of np.float16, and order of ‘C’. We can do this by passing a tuple with these parameters, as shown below:

```
y = np.empty((3, 2), dtype=np.float16, order='C')
```

This will create an empty array y with shape (3,2), dtype np.float16, and order C.

## Conclusion

In conclusion, the NumPy empty() function is a useful tool for creating empty arrays without initializing the data. This is particularly useful when dealing with large datasets and mathematical operations.

By understanding how to use this function, you can take advantage of the data manipulation capabilities that NumPy offers.

## NumPy empty_like() function in Python

When working with arrays in Python, it’s common to need an empty array with the same shape as an existing array. This is where NumPy’s empty_like() function comes in handy.

The function allows you to create a new array with the same shape and data type as an existing array, without initializing the data. In this article, we’ll take a closer look at how the empty_like() function works and how you can use it in your Python code.

### Explanation of the empty_like() Function, Its Parameters, and Returns

The empty_like() function is similar to the empty() function in that it creates an array without initializing its data. However, it differs in that it creates an array with the same shape and data type as a prototype array.

The prototype array serves as a template for the shape and data type of the new array. The function also takes several parameters, including the prototype array, dtype, order, and subok.

The prototype array is the array used as a template for the shape and data type of the new array. The dtype parameter specifies the data type of the new array, while the order parameter determines the memory layout of the array.

The subok parameter specifies whether to return an instance of a subclass of ndarray or an ndarray object. The empty_like() function can also be used in conjunction with other functions such as zeros_like() and ones_like().

These functions create arrays with the same shape and data type as the prototype array, but with all elements initialized to zero or one, respectively.

### Implementing empty_like() Function with Prototype and Assigning Other Parameters

To use the empty_like() function, you first need to define a prototype array, which will be used as the template for the shape and data type of the new array. Let’s say we have an existing array named a with shape (2, 3) and data type np.float32.

To create an empty array with the same shape and data type as a using the empty_like() function, the following code can be used:

```
import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
b = np.empty_like(a)
print(b)
```

Here, an empty array b is created that has the same shape and data type as the prototype array a. If you want to specify the data type and order of the new array, you can pass those parameters as well.

### For example:

```
c = np.empty_like(a, dtype=np.int16, order='C')
print(c)
```

This will create a new array with the same shape as a, but with data type np.int16 and memory layout C.

## Summary

In summary, the NumPy empty_like() function is a useful tool for creating an array without initialization, but with the same shape and data type as a prototype array. This function saves time by allowing developers to create a new array that matches the shape and data type of an existing array.

By understanding how to use this function, you can take advantage of the data manipulation capabilities that NumPy offers. Together with the empty() function, the empty_like() function is an essential tool for manipulating arrays in Python.

In this article, we explored the NumPy empty() and empty_like() functions in Python. We learned that the empty() function creates an empty array without initializing the data, while the empty_like() function creates an array with the same shape and data type as a prototype array.

Both functions take parameters such as shape, dtype, and order. By understanding how to use these functions, developers can efficiently manipulate arrays and perform high-level mathematical functions in Python.

Takeaways from this article are that NumPy provides essential tools for manipulating arrays. By making use of the empty and empty_like functions, developers can make data manipulation more efficient.