First of all, let me explain a bit what NumPy is and why you might need it. NumPy is a Python library that is used for working with arrays. It stands short for Numeric Python
This, of course, is still a bit vague. In general, it makes working with arrays (lists) about 50x faster than traditional python lists.
Installing and using NumPy
To install NumPy, we must run a pip install command for it.
pip install numpy
Then we have to import it into our Python file.
import numpy
Now we can convert a list into a numpy array:
arr = numpy.array([1, 2, 3, 4, 5])
print(arr)
However, it’s quite often used to have the numpy imported as the np alias.
We can do so like this:
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr)
This now does the exact same thing, but it’s easier to write.
If you ever wonder what version of numpy you have installed, you can simply print that out.
print(np.__version__)
# 1.20.3
Types of arrays
The cool part about the NumPy arrays is that they can be built from all array-like data types of Python.
Which include the list, tuple, dictionary.
tuple = np.array((1, 2, 3, 4, 5))
print(tuple)
list = np.array(["dog", "cat", "penguin"])
print(list)
set = np.array({"dog", "cat", "penguin"})
print(set)
It’s super easy to convert this stuff to NumPy arrays since we can eventually do more stuff and faster!
In a follow-up article, I’ll go more in-depth about the options for the NumPy arrays.
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