Numpy cheat sheet
Numpy is a Python library for working with arrays and matrices. Selling point of the library is its high performance, as Numpy is based on C. Let's go over the most important features of Numpy.
But first, we have to install Numpy.
pip install numpy
Then, you can import it and use it in your Python project:
import numpy as np
Of course you can import Numpy "as" everything you want, but the standard is to import it as np. Therefore, calling Numpy functions works with np.function.
Arrays
Arrays can be created and manipulated like the default lists in Python.
arr = np.array([1, 2, 3])
arr[0] # 1
Also, lists can be transformed to Numpy arrays:
numbers = [1, 2, 3]
arr = np.asarray(numbers)
Creating filled arrays
Numpy holds different functions for creating arrays of a given shape, filled with the same value.
np.ones(5)
# array([1., 1., 1., 1., 1.])
np.ones
creates an array filled with ones, of size 5. The shape is by default one-dimensional. For non-one-dimensional arrays, you need to pass one more pair of brackets like this:np.ones((3, 3))
Which creates a 3 x 3 array, filled with ones.
instead of .ones
, one can also use .zeros
for filling the array with zeros.
Mathematical functions
Sinus, cosinus, tangens
All of these functions work element-wise, we pass an array of values and receive the function results.
np.sin([1, 2, 3])
# array([0.84147098, 0.90929743, 0.14112001])
For the cosinus and tangens, use np.cos
, np.tan
, np.arcsin
and so on.
Sums
With np.sum
one can sum up the values of an array-like structure.
numbers = [1, 2, 3]
np.sum(numbers)
# 6