Introduction to NumPy Arrays:
Have you ever wondered what an array is? An array is a data structure that stores a collection of elements of the same data type.
NumPy is a Python library utilized for scientific computing that introduces the NumPy array. NumPy arrays are essential in scientific python programming.
It is a fundamental tool for researchers, data scientists, and engineers. The NumPy library is the foundation of several highlevel Python libraries like SciPy, Pandas, Matplotlib, and Scikitlearn.
In this article, we will discuss what NumPy arrays are, their features, and how to create a NumPy array.
Definition of a NumPy Array:
A NumPy array is a data structure that stores a collection of elements of the same data type in contiguous memory locations.
NumPy arrays are homogeneous, which means, the elements of the array must be of the same data type. NumPy arrays can either be onedimensional or multidimensional.
Because of the way NumPy arrays are stored, NumPy code can effortlessly integrate with C and FORTRAN code. NumPy provides various data types like int, float, bool, string, etc.
NumPy arrays have equal sizes, and indexing and slicing of the array are easier and faster.
Features of a NumPy Array:

Homogeneous: NumPy arrays are homogeneous arrays; hence, all elements of an array must be of the same data type.

OneDimensional: A NumPy array can be a onedimensional array. An example of a onedimensional array is [1, 2, 3, 4, 5], which can be stored as [1, 2, 3, 4, 5].

Multidimensional: NumPy arrays can be multidimensional.
Multidimensional arrays allow storage of complex data structures. For example, an array that stores data about people can be a 2dimensional array having the dimensions [people, attributes].

Code Integration: NumPy arrays are stored in contiguous memory locations, which helps in integrating python code with FORTRAN and C programs.

Data Types: NumPy provides various data types like int, float, bool, and string.

Equal Sizes: NumPy arrays have equal sizes, which means that arrays of the same size can efficiently perform arithmetic operations.
Creating a NumPy Array:
Creating a NumPy array is quick and straightforward. All you need to do is import the NumPy library, use the np.array() method, and enter the elements of the array inside the bracket as shown below:
import numpy as np
x = np.array([1, 2, 3])
print(x)
print(type(x))
Output:
[1 2 3]
numpy.ndarray
In our code, the first line imports the NumPy library, while the second line creates a onedimensional NumPy array, x, holding three elements – 1, 2, and 3 – using the np.array() method. The third line prints the NumPy array x.
The fourth line prints the type of the NumPy array x, which is “numpy.ndarray,” indicating that it is indeed a NumPy array.
Copying a NumPy Array:
NumPy arrays are often required to be manipulated and changed in various ways throughout data manipulation. When working with NumPy arrays, it is essential to know how to copy a NumPy array properly.
In this section, we will go into the various methods of copying a NumPy array.
Using the NumPy.copy() Method:
One of the most popular ways of copying a NumPy array is to use the NumPy.copy() method.
This method returns a new array, thus creating a different object in memory from the original array. Syntax for using .copy() method with NumPy arrays is:
new_array = numpy.copy(original_array, order='K')
The `order` attribute is optional and specifies the memory layout of the copy.
There are three order options: C (for Cstyle), F (for Fortranstyle), and A (results in an array that has the same memory layout as the original array, so avoids a copy if possible).
Copying MultiDimensional Numpy Arrays:
NumPy arrays can be multidimensional, as previously mentioned, and creating copies of multidimensional arrays requires being mindful of the axis of the array.
A shallow copy of a multidimensional array only copies the references to its nested objects in memory, while a deep copy creates new objects in memory for all of the nested objects.
To perform a deep copy, the .copy() method can be used.
In comparison, to create a shallow copy of the array, .view() or .copy() can be used.
Using the Assignment Operator:
Another way of copying a NumPy array is to use the assignment operator.
This method creates a new object in the memory, which is a copy of the original array. When using the assignment operator to copy an array, all the modifications done to one array do not reflect on the other.
The syntax for assigning a new variable to hold the same value as your original array is:
new_array = original_array
While this is the simplest method, it should not be used when trying to avoid modifying the original array.
Using NumPy.empty_like() Method:
NumPy provides another method that uses the numpy.empty_like() method to copy a NumPy array.
This method creates an arbitrary datafilled NumPy array having the same shape and type as the original NumPy array. The syntax for using the .empty_like() method to create a copy of the original array is:
new_array = numpy.empty_like(original_array)
This method is useful when you want to initialize and assign a new array to hold data like the original array.
Summary:
In summary, NumPy arrays are essential for data manipulation in scientific computing and other programming disciplines. It is vital to know how to copy a NumPy array for situations where you want to manipulate the data without modifying the original array.
In this article, we discussed various methods of copying a NumPy array, including the NumPy.copy() method, assignment operator, the .empty_like() method, and copying multidimensional arrays. It’s crucial to understand the differences between these methods to avoid errors during data processing.
NumPy arrays are a complex data structure that can be transformed in multiple ways to extract useful results, and understanding the various methods of copying arrays is a fundamental component of it. NumPy provides numerous methods for array manipulation and selection, making it a powerful tool for data scientists, researchers, and engineers.
Understanding NumPy arrays and their manipulation techniques is vital in today’s datadriven world, where data operations have become more complex than ever.