NumPy Array cheatsheet

NumPy Python library provides custom array data type that supports standard array functionality like indexing and slicing but works differently in arithmetic operations: performs them on each array element

Element-wise arithmetic operations

You can perform the same operation on each element: add the same number or multiply by the same number:

>>> import numpy as np
>>> arr = np.array([1, 2, 3])
>>> arr
array([1, 2, 3])
>>> arr + 10
array([11, 12, 13])
>>> arr * 2
array([2, 4, 6])

or sum two arrays of same dimensionality:

>>> arr + np.array([10, 20, 30])
array([11, 22, 33])

or calculate the value from the all array elements:

>>> np.mean(arr)
2.0
>>> np.std(arr)  # standard deviation
0.816496580927726

You have to use comprehension or for loop with standard array for such calculations:

>>> arr = [1, 2, 3]
>>> [e * 2 for e in arr]
[2, 4, 6]

or even more complex to sum two arrays:

>>> a = [1, 2, 3]
>>> b = [10, 20, 30]
>>> [x+y for x, y in zip(a, b)]
[11, 22, 33]

Indexing in different way

Getting element for many-dimensional array:

>>> a = [[1, 2, 3], [11, 12, 13]]
>>> arr = np.array([[1, 2, 3], [11, 12, 13]])  # 2 rows, 3 columns
>>> arr.shape  # array dimensionality
(2, 3)

>>> a[1][2]
13
>>> arr[1][2]
13
>>> arr[1,2]  # tuple indexing
13

also NumPy array provides column slicing as well, with tuple indexing with slicing on row index:

>>> arr[:,1]
array([ 2, 12])