Array
Arrays are a Numpy data type similar to lists, but optimized with additional support for mathematical functions in Numpy.
import numpy as np
array = np.array([3, 4, 5])
temperature_list = [68, 73, 70, 74, 76, 72, 74]
temperature_array = np.array([68, 73, 70, 74, 76, 72, 74])
another_temperature_array = np.array(temperature_list) # no brackets because temperature_list is already a list We can access elements in an array or list by their position.
In Python, elements are 0-indexed(meaning the position of the first element in an array is 0, not 1).
One interpretation of this is that an element’s position represents the number of elements before it.
temperature_array # array([68, 73, 70, 74, 76, 72, 74])
temperature_array[0] # 68
temperature_array[1] # 73 Accessing an element at the end of an array can be done in multiple ways:
temperature_array[6] # 74, only works for this array
temperature_array[len(temperature_array) - 1] # 74, we subtract 1 because the array is 0-indexed
temperature_array[-1] # 74, counts backwards
temperature_array[-2] # 72, two from the back of the array One difference between lists and arrays is that arrays can only store one data type.
nums_and_strings_lst = ['uc', 'sd', 1961, 3.14]
np.array(nums_and_strings_lst) # array(['uc', 'sd', '1961', '3.14'], dtype='<U32'), numpy converted the numerical types into strings Operations with arrays are handled with a behavior called broadcasting.