2D array indexing (row, col) in NumPy - Step-by-Step Execution

Concept Flow - 2D array indexing (row, col)

Create 2D array

↓

Choose row index

↓

Choose column index

↓

Access element at (row, col)

↓

Return element value

We start with a 2D array, pick a row and column index, then access the element at that position.

Execution Sample

NumPy

import numpy as np
arr = np.array([[10,20,30],[40,50,60],[70,80,90]])
element = arr[1,2]
print(element)

This code creates a 3x3 array and accesses the element in the 2nd row, 3rd column.

Execution Table

Step	Action	Row Index	Column Index	Element Accessed	Output
1	Create 2D array	-	-	-	[[10 20 30] [40 50 60] [70 80 90]]
2	Select row index	1	-	-	-
3	Select column index	1	2	-	-
4	Access element at (1,2)	1	2	arr[1,2]	60
5	Print element	-	-	-	60

💡 Element at row 1, column 2 is accessed and printed; execution ends.

Variable Tracker

Variable	Start	After Step 1	After Step 4	Final
arr	undefined	[[10 20 30] [40 50 60] [70 80 90]]	[[10 20 30] [40 50 60] [70 80 90]]	[[10 20 30] [40 50 60] [70 80 90]]
element	undefined	undefined	60	60
row index	undefined	undefined	1	1
column index	undefined	undefined	2	2

Key Moments - 3 Insights

Why do we use arr[1,2] instead of arr[1][2]?

In numpy, arr[1,2] accesses the element at row 1, column 2 directly in one step, which is more efficient. arr[1][2] works but accesses row 1 first as a sub-array, then column 2, which is two steps.

What happens if the row or column index is out of range?

An IndexError occurs because numpy arrays have fixed sizes. For example, arr[3,0] is invalid since row 3 does not exist (rows are 0 to 2). See execution_table step 4 for valid indexing.

Are row and column indices zero-based or one-based?

Indices start at 0 in numpy. So row 1 means the second row, column 2 means the third column. This is shown in the execution_table where arr[1,2] accesses the second row, third column.

Visual Quiz - 3 Questions

Look at the execution table, what is the value of 'element' after step 4?

A 50

B 60

C 30

D 40

💡 Hint

Check the 'Element Accessed' and 'Output' columns in step 4 of the execution_table.

At which step is the 2D array 'arr' created?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look at the 'Action' column in the execution_table for when the array is first defined.

If we change the row index to 0 and column index to 1, what element will be accessed?

A 40

B 50

C 20

D 10

💡 Hint

Refer to the variable_tracker for arr values and remember indexing starts at 0.

Concept Snapshot

2D array indexing syntax: arr[row, col]
Indices start at 0 (zero-based).
Access element by specifying row then column.
Out-of-range indices cause errors.
arr[1,2] accesses 2nd row, 3rd column element.
Use arr[row, col] for efficient access.

Full Transcript

This visual trace shows how to access elements in a 2D numpy array using row and column indices. We create a 3x3 array, then pick row 1 and column 2 to get the element 60. Indices start at zero, so row 1 is the second row. The execution table tracks each step: array creation, index selection, element access, and printing. The variable tracker shows how 'arr' and 'element' change. Key moments clarify why numpy uses arr[row, col] syntax, zero-based indexing, and what happens if indices are out of range. The quiz tests understanding of element values and indexing steps.