NumPy Array Indexing
Learn all about NumPy Array Indexing in this comprehensive tutorial.
- •Array indexing is the same as accessing an array element.
- •To access elements from 2-D arrays we can use comma separated integers representing the dimension and the index of the element.
- •To access elements from 3-D arrays we can use comma separated integers representing the dimensions and the index of the element.
- •Use negative indexing to access an array from the end.
Access Array Elements
Array indexing is the same as accessing an array element.
You can access an array element by referring to its index number.
The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc.
Access 2-D Arrays
To access elements from 2-D arrays we can use comma separated integers representing the dimension and the index of the element.
Think of 2-D arrays like a table with rows and columns, where the dimension represents the row and the index represents the column.
Access 3-D Arrays
To access elements from 3-D arrays we can use comma separated integers representing the dimensions and the index of the element.
arr[0, 1, 2] prints the value 6.
And this is why:
The first number represents the first dimension, which contains two arrays: [[1, 2, 3], [4, 5, 6]] and: [[7, 8, 9], [10, 11, 12]] Since we selected 0, we are left with the first array: [[1, 2, 3], [4, 5, 6]]
The second number represents the second dimension, which also contains two arrays: [1, 2, 3] and: [4, 5, 6] Since we selected 1, we are left with the second array: [4, 5, 6]
The third number represents the third dimension, which contains three values: 4 5 6 Since we selected 2, we end up with the third value: 6
Negative Indexing
Use negative indexing to access an array from the end.
Module quiz
2 questionsWhich of the following is true about NumPy Array Indexing?
What is the most common pitfall when working with NumPy Array Indexing?
Answer all questions to submit.