Sorting along axes in NumPy - Step-by-Step Execution

Concept Flow - Sorting along axes

Start with 2D array

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Choose axis to sort

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Sort elements along axis

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Return sorted array

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End

We start with a 2D array, pick an axis (0 for columns, 1 for rows), sort elements along that axis, and get a sorted array.

Execution Sample

NumPy

import numpy as np
arr = np.array([[3,1,2],[6,4,5]])
sorted_axis0 = np.sort(arr, axis=0)
sorted_axis1 = np.sort(arr, axis=1)

This code sorts a 2D array first by columns (axis=0) and then by rows (axis=1).

Execution Table

Step	Array State	Axis	Action	Result
1	[[3,1,2],[6,4,5]]	0	Sort each column	Column 1: [3,6] -> [3,6] Column 2: [1,4] -> [1,4] Column 3: [2,5] -> [2,5] Result: [[3,1,2],[6,4,5]]
2	[[3,1,2],[6,4,5]]	0	Sort columns ascending	[[3,1,2],[6,4,5]]
3	[[3,1,2],[6,4,5]]	1	Sort each row	Row 1: [3,1,2] -> [1,2,3] Row 2: [6,4,5] -> [4,5,6] Result: [[1,2,3],[4,5,6]]
4	[[3,1,2],[6,4,5]]	1	Sort rows ascending	[[1,2,3],[4,5,6]]
5	-	-	End	Sorting complete

💡 All rows and columns sorted along specified axes, process ends.

Variable Tracker

Variable	Start	After axis=0 sort	After axis=1 sort	Final
arr	[[3,1,2],[6,4,5]]	[[3,1,2],[6,4,5]]	[[3,1,2],[6,4,5]]	[[3,1,2],[6,4,5]]
sorted_axis0	-	[[3,1,2],[6,4,5]]	-	[[3,1,2],[6,4,5]]
sorted_axis1	-	-	[[1,2,3],[4,5,6]]	[[1,2,3],[4,5,6]]

Key Moments - 3 Insights

Why does sorting along axis=0 sort columns, not rows?

Sorting along axis=0 means sorting down each column because axis=0 refers to the vertical direction. See execution_table rows 1 and 2 where columns are sorted.

Why does sorting along axis=1 sort rows individually?

Axis=1 means sorting along the horizontal direction, so each row is sorted separately. See execution_table row 3 where each row is sorted.

Does the original array change after sorting?

No, np.sort returns a new sorted array and does not modify the original. See variable_tracker where 'arr' stays the same.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, what is the sorted first row?

A [1, 2, 3]

B [3, 1, 2]

C [6, 4, 5]

D [4, 5, 6]

💡 Hint

Check execution_table row 3 under 'Result' for sorted rows.

At which step does sorting along axis=1 complete?

A Step 2

B Step 4

C Step 3

D Step 5

💡 Hint

Look at execution_table rows 3 and 4 for axis=1 sorting progress.

If we change axis=0 to axis=1 in np.sort, what changes in variable_tracker?

A Both sorted_axis0 and sorted_axis1 remain unchanged

B sorted_axis1 will have sorted columns instead of rows

C sorted_axis0 will have sorted rows instead of columns

D Original array 'arr' will be modified

💡 Hint

Refer to variable_tracker and concept_flow about axis meaning.

Concept Snapshot

np.sort(array, axis)
- axis=0 sorts each column (downwards)
- axis=1 sorts each row (across)
- Returns a new sorted array
- Original array stays unchanged
- Useful for sorting multi-dimensional data

Full Transcript

We start with a 2D numpy array. Sorting along axis=0 means sorting each column independently, vertically. Sorting along axis=1 means sorting each row independently, horizontally. The np.sort function returns a new sorted array without changing the original. We saw step-by-step how the array changes after sorting along each axis. This helps organize data by rows or columns as needed.