Python Arrays

Basics of an Array

NumPy is the fundamental package for numerical computing in Python, offering a powerful array object and a suite of functions for working efficiently with these arrays. The advantages of Numpy are:

• Multidimensional: Supports more than just one-dimensional arrays.

• Powerful operations: Includes a vast library of mathematical and statistical functions.

• Vectorization: Allows for operations on entire arrays without the need for explicit loops, enhancing performance.

• Integration: Works seamlessly with other scientific libraries like SciPy, Pandas, and Matplotlib.

Let's start creating an array using Numpy. You first import NumPy and then use the array() function to create an array. The array() function takes a list as an input.

import numpy
my_array = numpy.array([0, 1, 2, 3, 4])
print(my_array)

[0, 1, 2, 3, 4]

The type of my_array is a numpy.ndarray.

print(type(my_array))

<class 'numpy.ndarray'>

Array Examples

Example of creating an Array

In the below example, you will convert a list to an array using the array() function from NumPy. You will create a list a_list comprising of integers. Then, using the array() function, convert it an array.

import numpy as np

a_list = [1, 2, 3, 4]
a_list

[1, 2, 3, 4]

an_array = np.array(a_list)
an_array

array([1, 2, 3, 4])

Example of an Array operation

In the below example, you add two numpy arrays. The result is an element-wise sum of both the arrays.

import numpy as np

array_A = np.array([1, 2, 3])
array_B = np.array([4, 5, 6])

print(array_A + array_B)

[5 7 9]

Example of Array indexing

You can select a specific index element of an array using indexing notation.

import numpy as np

months_array = np.array(['Jan', 'Feb', 'March', 'Apr', 'May'])
print(months_array[3])

Apr

You can also slice a range of elements using the slicing notation specifying a range of indices.

print(months_array[2:5])

['March', 'Apr', 'May']

Interactive Example of a List to an Array

In the below example, you will import numpy using the alias np. Create prices_array and earnings_array arrays from the lists prices and earnings, respectively. Finally, print both the arrays.

# IMPORT numpy as np
import numpy as np

# Lists
prices = [170.12, 93.29, 55.28, 145.30, 171.81, 59.50, 100.50]
earnings = [9.2, 5.31, 2.41, 5.91, 15.42, 2.51, 6.79]

# NumPy arrays
prices_array = np.array(prices)
earnings_array = np.array(earnings)

# Print the arrays
print(prices_array)
print(earnings_array)

When you run the above code, it produces the following result:

[170.12  93.29  55.28 145.3  171.81  59.5  100.5 ]
[ 9.2   5.31  2.41  5.91 15.42  2.51  6.79]

Try it for yourself.

To learn more about NumPy arrays in Python, please see this video from our course Introduction to Python for Finance.

This content is taken from DataCamp’s Introduction to Python for Finance course by Adina Howe.

Check out our Python Numpy Array Tutorial.