Array creation in Python

Arrays are the essential elements of many programming languages. Arrays are sequential objects that behave very similarly to lists, except that the types of elements contained in them are constrained. The type is specified when the object is created using a single character called type code.

Getting ready

In this recipe, we will cover an array creation procedure. We will first create an array using the NumPy library, and then display its structure.

How to do it...

Let's see how to create an array in Python:

To start off, import the NumPy library as follows:

>> import numpy as np

We just imported a necessary package, numpy. This is the fundamental package for scientific computing with Python. It contains, among other things, the following:

A powerful N-dimensional array object
Sophisticated broadcasting functions
Tools for integrating C, C++, and FORTRAN code
Useful linear algebra, Fourier transform, and random number capabilities

Besides its obvious uses, NumPy is also used as an efficient multidimensional container of generic data. Arbitrary data types can be found. This enables NumPy to integrate with different types of databases.

Remember, to import a library that is not present in the initial distribution of Python, you must use the pip install command followed by the name of the library. This command should be used only once and not every time you run the code.

Let's create some sample data. Add the following line to the Python Terminal:

>> data = np.array([[3, -1.5, 2, -5.4], [0, 4, -0.3, 2.1], [1, 3.3, -1.9, -4.3]])

The np.array function creates a NumPy array. A NumPy array is a grid of values, all of the same type, indexed by a tuple of non-negative integers. rank and shape are essential features of a NumPy array. The rank variable is the number of dimensions of the array. The shape variable is a tuple of integers that returns the size of the array along each dimension.

We display the newly created array with this snippet:

>> print(data)

The following result is returned:

[[ 3. -1.5  2.  -5.4]
 [ 0.  4.  -0.3  2.1]
 [ 1.  3.3 -1.9 -4.3]]

We are now ready to operate on this data.

How it works...

NumPy is an extension package in the Python environment that is fundamental for scientific calculation. This is because it adds to the tools that are already available, the typical features of N-dimensional arrays, element-by-element operations, a massive number of mathematical operations in linear algebra, and the ability to integrate and recall source code written in C, C++, and FORTRAN. In this recipe, we learned how to create an array using the NumPy library.

There's more...

NumPy provides us with various tools for creating an array. For example, to create a one-dimensional array of equidistant values with numbers from 0 to 10, we would use the arange() function, as follows:

>> NpArray1 = np.arange(10)
>> print(NpArray1)

The following result is returned:

[0 1 2 3 4 5 6 7 8 9]

To create a numeric array from 0 to 50, with a step of 5 (using a predetermined step between successive values), we will write the following code:

>> NpArray2 = np.arange(10, 100, 5)
>> print(NpArray2)

The following array is printed:

[10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95]

Also, to create a one-dimensional array of 50 numbers between two limit values and that are equidistant in this range, we will use the linspace() function:

>> NpArray3 = np.linspace(0, 10, 50)
>> print(NpArray3)

The following result is returned:

[ 0. 0.20408163 0.40816327 0.6122449 0.81632653 1.02040816
 1.2244898 1.42857143 1.63265306 1.83673469 2.04081633 2.24489796
 2.44897959 2.65306122 2.85714286 3.06122449 3.26530612 3.46938776
 3.67346939 3.87755102 4.08163265 4.28571429 4.48979592 4.69387755
 4.89795918 5.10204082 5.30612245 5.51020408 5.71428571 5.91836735
 6.12244898 6.32653061 6.53061224 6.73469388 6.93877551 7.14285714
 7.34693878 7.55102041 7.75510204 7.95918367 8.16326531 8.36734694
 8.57142857 8.7755102 8.97959184 9.18367347 9.3877551 9.59183673
 9.79591837 10. ]

These are just some simple samples of NumPy. In the following sections, we will delve deeper into the topic.