Sorting along axes in NumPy

Introduction

Sorting data helps us organize numbers or values in order. Sorting along axes means arranging data row-wise or column-wise in arrays.

You want to find the smallest or largest values in each row or column of a table.

You need to organize data in a spreadsheet by rows or columns for easier reading.

You want to prepare data for analysis by sorting each feature separately.

You want to compare sorted values across rows or columns.

You want to quickly find median or quartiles along rows or columns.

Syntax

NumPy

numpy.sort(a, axis=-1, kind='quicksort', order=None)

a is the input array to sort.

axis decides which direction to sort: 0 for rows, 1 for columns, -1 for last axis (default).

Examples

This sorts each row individually in ascending order.

NumPy

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

This sorts each column individually in ascending order.

NumPy

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

Sample Program

This program creates a 2D array and sorts it first by rows, then by columns, showing how sorting along different axes changes the order.

NumPy

import numpy as np

# Create a 2D array
arr = np.array([[10, 3, 5], [7, 8, 2]])

# Sort along rows (axis=1)
sorted_rows = np.sort(arr, axis=1)
print('Sorted along rows:')
print(sorted_rows)

# Sort along columns (axis=0)
sorted_cols = np.sort(arr, axis=0)
print('Sorted along columns:')
print(sorted_cols)

Important Notes

Sorting does not change the original array unless you assign the result back.

Use axis=0 to sort each row, axis=1 to sort each column.

Sorting is always in ascending order by default.

Summary

Sorting along axes organizes data row-wise or column-wise.

Use numpy.sort with axis parameter to control direction.

Sorting helps in data analysis by arranging values clearly.

Practice

1. What does the axis parameter control in numpy.sort?

easy

A. It decides whether to sort rows or columns in an array.

B. It sets the sorting algorithm type.

C. It changes the data type of the array before sorting.

D. It specifies the order of sorting (ascending or descending).

Solution

1. Step 1: Understand the role of axis in sorting

   The axis parameter tells numpy which direction to sort: 0 means sort each column, 1 means sort each row.

2. Step 2: Differentiate from other parameters

   Sorting algorithm type and order are controlled by other parameters, not axis.

3. Final Answer:

   It decides whether to sort rows or columns in an array. -> Option A

4. Quick Check:

   axis controls direction = A [OK]

Hint: Remember axis=0 sorts columns, axis=1 sorts rows [OK]

Common Mistakes:

• Confusing axis with sorting order
• Thinking axis changes data type
• Assuming axis sets sorting algorithm

2. Which of the following is the correct syntax to sort a 2D numpy array arr along rows?

easy

A. numpy.sort(arr, axis=0)

B. numpy.sort(arr, axis=None)

C. numpy.sort(arr, axis=1)

D. numpy.sort(arr, axis=2)

Solution

1. Step 1: Identify axis for sorting rows

   In a 2D array, axis=1 means sorting each row individually.

2. Step 2: Check other options for validity

   Axis=0 sorts columns, axis=None flattens the array into 1D before sorting, axis=2 is invalid for 2D arrays.

3. Final Answer:

   numpy.sort(arr, axis=1) -> Option C

4. Quick Check:

   axis=1 sorts rows = B [OK]

Hint: Use axis=1 to sort rows in 2D arrays [OK]

Common Mistakes:

• Using axis=0 to sort rows
• Using invalid axis like 2 for 2D arrays
• Using axis=None which flattens the array

3. What is the output of the following code?

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

medium

A. [[1 2 3]
[4 5 6]]

B. [[3 1 2]
[6 4 5]] sorted by columns

C. [[3 1 2]
[6 4 5]] (unchanged)

D. [[3 1 2]
[6 4 5]]

Solution

1. Step 1: Understand sorting along axis=0

   Sorting with axis=0 sorts each column independently in ascending order.

2. Step 2: Sort each column of the array

   Columns: [3,6] -> [3,6], [1,4] -> [1,4], [2,5] -> [2,5]. Since columns are already sorted, array remains the same.

3. Final Answer:

   [[3 1 2] [6 4 5]] sorted by columns -> Option B

4. Quick Check:

   axis=0 sorts columns = A [OK]

Hint: axis=0 sorts columns top to bottom [OK]

Common Mistakes:

• Assuming sorting rearranges rows
• Confusing axis=0 with axis=1
• Expecting full array sort instead of column-wise

4. The code below throws an error. What is the problem?

import numpy as np
arr = np.array([[1, 3], [2, 4]])
sorted_arr = np.sort(arr, axis=2)
print(sorted_arr)

medium

A. Axis 2 does not exist for a 2D array.

B. The array contains non-numeric data.

C. The sort function requires axis to be None.

D. The array must be 1D to sort.

Solution

1. Step 1: Check array dimensions

   The array is 2D with shape (2,2), so valid axes are 0 and 1 only.

2. Step 2: Validate axis parameter

   Using axis=2 is invalid and causes an IndexError because axis 2 does not exist.

3. Final Answer:

   Axis 2 does not exist for a 2D array. -> Option A

4. Quick Check:

   Axis must be within array dimensions = C [OK]

Hint: Axis must be less than array dimensions [OK]

Common Mistakes:

• Using axis out of range
• Assuming sort only works on 1D arrays
• Confusing axis with array shape

5. Given a 3D numpy array arr with shape (2, 2, 3), how would you sort the array along the last axis for each 2D slice?

hard

A. np.sort(arr, axis=-2)

B. np.sort(arr, axis=1)

C. np.sort(arr, axis=0)

D. np.sort(arr, axis=2)

Solution

1. Step 1: Identify the last axis in a 3D array

   For shape (2, 2, 3), axes are 0, 1, 2. The last axis is 2.

2. Step 2: Use axis=2 to sort along the last axis

   Sorting with axis=2 sorts each 2D slice along the last dimension (length 3).

3. Final Answer:

   np.sort(arr, axis=2) -> Option D

4. Quick Check:

   Last axis is 2, so axis=2 sorts last dimension [OK]

Hint: Use axis=-1 or axis=2 for last axis in 3D arrays [OK]

Common Mistakes:

• Using axis=0 or 1 instead of last axis
• Confusing negative axis indexing
• Not matching axis to array shape