Topic 5 Flashcards
NumPy
NumPy is a widely used, open source, python package used
throughout the STEM community, due to its efficiency in handling
large datasets
How to use NumPy
import numpy as np
array
An array is a structure for storing and retrieving data.
Imagine as cell grid in space where each cell can hold a single element of data
create an array from integers
array = np.array([1, 2, 3, 4, 5, 6])
create an array from strings
array = np.array([“Nanna”, “Dan”, “Julia”, “John”])
create an array from a list
a_list = [1, 2, 3, 4, 5, 6]
an_array = np.array(a_list)
create a 1D array
array = np.array([1, 2, 3])
[1 2 3]
create a 2D array
array = np.array([[1, 2, 3], [4, 5, 6]])
[[1 2 3]
[4 5 6]]
create a 3D array
array = np.array([[[1, 2, 3], [4, 5, 6]],
[[1, 2, 3], [4, 5, 6]]]
[[[1 2 3]
[4 5 6]]
[[1 2 3]
[4 5 6]]]
shape
returns the shape of an array
ndim
returns the number of dimensions of the array
size
returns the total number of elements in an array
dtype
returns the data type of elements in an array
create an empty array array:
array = np.empty(5)
create an array using the arange function.
Need start point, end point, and a step size:
array = np.arange(2, 9, 2)
[2, 4, 6, 8]
create an array filled with 0’s:
array = np.zeroes(5)
[0., 0., 0., 0., 0.]
can arrays be indexed and sliced?
yes
data = np.array([1, 2, 3])
data[0:2] = [1 2]
data[1:] = data[-2:] = [2 3]
data[2] = data[-1] = 3
Boolean index
can pass a Boolean mask as an index to an array which filters the array and creates a subset array
Boolean mask
selects only those elements in the array that have a true value for the same index position
For Boolean masks, you must use & to represent and and | to represent or
Element-wise arithmetic operations
Addition
Subtraction
Multiplication
Division
Exponentiation
Modulus
Scalar operations
Addition
Subtraction
Multiplication
Division
subarray =
array[boolean_mask]
In boolean masks, you must use the bitwise logical operators:
& instead of and keyword
| instead of or keyword
