Python Numpy empty() - Create Empty Array

Introduction

The numpy.empty() function in Python is a part of the NumPy library, commonly used for generating arrays with uninitialized entries. This method proves useful primarily when you need to allocate an array quickly without immediately populating it with specific initial values. The principal advantage here is the potential increase in performance when instantiating large arrays, as it avoids the overhead of initializing the array elements.

In this article, you will learn how to effectively use the numpy.empty() function to create arrays of various shapes and data types. Discover the benefits of initializing arrays without default values and see how using empty() compares with other NumPy array creation functions like zeros() and ones().

Utilizing numpy.empty() Function

Create a Basic Array

1. Import the NumPy library.

2. Use numpy.empty() to create an array.

   python Copy

   import numpy as np
   
   arr = np.empty(3)
   print(arr)
   

   This code initializes an array of length 3 with indeterminate values. The contents of the array will be whatever values happen to already be at those memory locations.

Specify Array Shape

1. Define the desired shape in a tuple.

2. Create an array using the shape tuple.

   python Copy

   shape = (2, 3)  # Shape means 2 rows and 3 columns
   arr = np.empty(shape)
   print(arr)
   

   Here, the empty() function creates a 2x3 array. The values in the array are uninitialized.

Using Specific Data Types

1. Set the dtype parameter to define the type of array elements.

2. Create the array specifying the type.

   python Copy

   arr = np.empty(5, dtype=int)
   print(arr)
   

   By setting dtype to int, the array is configured to hold integers. Values remain uninitialized, but any operations on the array assume integer data type.

Comparisons with Other Array Creation Functions

Compare empty() with zeros()

1. Be aware that empty() allocates space for the array but does not set values.

2. Recognize that zeros() creates an array filled with zeroes.

   For performance-sensitive applications, empty() may perform faster since it does not bother to initialize the array elements:

   python Copy

   zero_array = np.zeros(5)
   print("Array with zeros:", zero_array)
   

   Using zeros() guarantees that all elements are initialized to zero.

Compare empty() with ones()

1. Note that ones() creates an array filled with ones, which involves initialization.

   Similarly to zeros(), the operation is slower compared to empty() for the same reason:

   python Copy

   one_array = np.ones(5)
   print("Array with ones:", one_array)
   

   This initialization guarantees specific default values, unlike empty().

Conclusion

The numpy.empty() function is a valuable tool for creating arrays very quickly when initial values are not necessary. However, always ensure that the array is populated with appropriate values after creation before using it to avoid unpredictable results. This flexibility in initialization can significantly affect performance, particularly with large arrays. By using numpy.empty(), you optimize your NumPy array-based computations, especially when you're confident about subsequent array fill operations.