Array Constraints
Lesson 3 of 18 in Coddy's Numpy Fundamentals course.
Arrays are fixed-sized. After creating an array with n elements, the size of the array will be n and it cannot be changed later. To have a bigger or smaller array we need to create a new one and populate it with data. Arrays don't support append nor delete like Python lists do.
For example:
ary = np.array([34, 59, 5])The ary variable holds a 1-dimensional array that contains 3 elements.
To access each element we use [] brackets just like a regular Python list:
ary[1] # holds the value 59To delete an element use np.delete. it won't affect the original ary, but rather it will return a new array without the element it is deleting.
To replace the new smaller array with the original ary assign the result to the ary variable:
ary = np.delete(ary, 1)To add an element use np.append
ary = np.append(ary, 5)Now the ary will hold [34, 59, 5, 5]. np.append also returns a new array and does not modify the original array.
To access the last element we can use multiple options:
ary[-1]
ary[len(ary)-1]
ary[2]Just like with a normal Python list
This lesson includes a short quiz. Start the lesson to answer it and track your progress.
This lesson includes a short quiz. Start the lesson to answer it and track your progress.
This lesson includes a short quiz. Start the lesson to answer it and track your progress.
Challenge
EasyCreate a function named array_modifier that receives 3 values: a list, a value, and an index.
Convert the list to a numpy array, and insert the new value in the specified index. You are not allowed to use list functionalities.
This problem can easily be solved with the builtin .insert, but I want you to practice other functionalities.
Try it yourself
import numpy as np
def array_modifier(lst, value, index):
passAll lessons in Numpy Fundamentals
2N-Dimensional Array Creation
Higher DimensionUnderstanding ShapesPopulate with Fixed ValuesNumpy TypesRangeReshape