Column-major order for 2D arrays refers to a traversal path which moves vertically down each column starting at the first column and ending with the last.

This ordering system also conceptualizes the 2D array into a rectangular matrix and starts the traversal at the top left element and ends at the bottom right element. Column-major order has the same starting and finishing point as row-major order, but it’s traversal is completely different

Here is a diagram which shows the path through the 2D array:

Showing column-major ordering - walking down one column at a time

In order to perform column-major traversal, we need to set up our nested loops in a different way. We need to change the outer loop from depending on the number of rows, to depending on the number of columns. Likewise we need the inner loop to depend on the number of rows in its termination condition.

Let’s look at our example 2D array from the last exercise and see what needs to be changed.

Given this 2D array of strings describing the element positions:

String[][] matrix = {{"[0][0]", "[0][1]", "[0][2]"}, {"[1][0]", "[1][1]", "[1][2]"}, {"[2][0]", "[2][1]", "[2][2]"}, {"[3][0]", "[3][1]", "[3][2]"}};

Let’s keep track of the total number of iterations as we traverse the 2D array. We also need to change the termination condition (middle section) within the outer and inner for loop.

int stepCount = 0; for(int a = 0; a < matrix[0].length; a++) { for(int b = 0; b < matrix.length; b++) { System.out.print("Step: " + stepCount); System.out.print(", Element: " + matrix[b][a]); System.out.println(); stepCount++; } }

Here is the output of the above code:

Step: 0, Element: [0][0] Step: 1, Element: [1][0] Step: 2, Element: [2][0] Step: 3, Element: [3][0] Step: 4, Element: [0][1] Step: 5, Element: [1][1] Step: 6, Element: [2][1] Step: 7, Element: [3][1] Step: 8, Element: [0][2] Step: 9, Element: [1][2] Step: 10, Element: [2][2] Step: 11, Element: [3][2]

As you can see in the code above, the way we accessed the elements from our 2D array of strings called matrix is different from the way we accessed them when using row-major order. Let’s remember that the way we get the number of columns is by using matrix[0].length and the way we get the number of rows is by using matrix.length. Because of these changes to our for loops, our iterator a now iterates through every column while our iterator b iterates through every row. Since our iterators now represent the opposite values, whenever we access an element from our 2D array, we need to keep in mind what indices we are passing to our accessor. Remember the format we use for accessing the elements matrix[row][column]? Since a now iterates through our column indices, we place it in the right set of brackets, and the b is now placed in the left set of brackets.

Here is a diagram showing which loop accesses which part of the 2D array for column-major order:

The outer loop controls the column while the inner loop controls the row in that column.

Why Use Column-Major Order?

Column major order is important because there are a lot of cases when you need to process data vertically. Let’s say that we have a chart of information which includes temperature data about each day. The top of each column is labeled with a day, and each row represents an hour. In order to find the average temperature per day, we would need to traverse the data vertically since each column represents a day. As mentioned in the last exercise, data can be provided in many different formats and shapes and you will need to know how to traverse it accordingly.

Let’s look at our sum example from the last exercise, but now using column-major order.

Given a 6X3 2D array of doubles:

double[][] data = {{0.51,0.99,0.12}, {0.28,0.99,0.89}, {0.05,0.94,0.05}, {0.32,0.22,0.61}, {1.00,0.95,0.09}, {0.67,0.22,0.17}};

Calculate the sum of each column using column-major order:

double colSum = 0.0; for(int o = 0; o < data[0].length; o++) { colSum = 0.0; for(int i = 0; i < data.length; i++) { colSum += data[i][o]; } System.out.println("Column: " + o +", Sum: " + colSum); }

The output of the above code is:

Column: 0, Sum: 2.83 Column: 1, Sum: 4.31 Column: 2, Sum: 1.93

Let’s try an example!

We will be using the same runner data from the last exercise, but this time we are going to take the average times per lap rather than per runner. This requires that we use column-major traversal.



You are provided with some runner lap data. Take a look at the loops we’re using to iterate through this 2D array. Replace the incorrect for loop headers to perform column-major traversal. Use the iterators outer and inner for the outer and inner loops.


Enter the missing line of code within the nested for loop to sum up the values for each column in the runner data.

We’ve already created a variable named lapTime that you can use to sum these values.


We’ve given you a variable named averageVal that currently stores 0. Edit that line of code to find the average time of each lap.

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