Sorting along axes in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of sorting a 2D array along axis 0

What is the output of the following code that sorts a 2D numpy array along axis 0?

NumPy

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

A

[[1 0 2]
 [2 1 3]
 [3 5 4]]

B

]]4 5 3[ 
]3 1 2[ 
]2 0 1[[

C

[1 0 2]
 [2 1 3]
 [3 5 4]]

D

[[1 0 2]
 [2 1 3]
 [3 4 5]]

💡 Hint

Remember axis=0 sorts each column independently.

Sorting along axis 0 sorts each column independently, so the smallest values in each column come first.

❓ Predict Output

intermediate

Sorting a 3D array along axis 2

What is the output of sorting the following 3D numpy array along axis 2?

NumPy

import numpy as np
arr = np.array([[[3, 1, 2], [1, 5, 4]], [[2, 0, 3], [7, 6, 8]]])
sorted_arr = np.sort(arr, axis=2)
print(sorted_arr)

A

[[[1 2 3]
  [1 4 5]]

 [[0 2 3]
  [6 7 8]]]

B

[[1 2 3]
  [1 4 5]]

 [[0 2 3]
  [6 7 8]]]

C

]]]8 7 6[  
]3 2 0[[ 

]]5 4 1[  
]3 2 1[[[

D

[[[1 2 3]
  [1 4 5]]

 [[0 2 3]
  [6 7 8]]

💡 Hint

Sorting along axis 2 sorts the innermost arrays.

Axis 2 is the last axis, so sorting arr along axis 2 sorts each innermost 1D array.

🔧 Debug

advanced

Identify the error in sorting with an invalid axis

What error does the following code raise when trying to sort a 2D numpy array along axis 3?

NumPy

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

A TypeError: axis must be an integer

B ValueError: could not broadcast input array from shape (3,3) into shape (2,3)

C AxisError: axis 3 is out of bounds for array of dimension 2

D SyntaxError: invalid syntax

💡 Hint

Check the number of dimensions of the array and the axis parameter.

The array has 2 dimensions (axes 0 and 1). Axis 3 does not exist, so numpy raises an AxisError.

❓ data_output

advanced

Number of items after sorting a 2D array along axis 1

After sorting the following 2D numpy array along axis 1, how many items are in the resulting array?

NumPy

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

A 2

B 6

C 9

D 3

💡 Hint

The size attribute gives total number of elements in the array.

The original array has shape (2,3), so total elements = 2*3 = 6. Sorting does not change size.

🚀 Application

expert

Sorting a DataFrame column using numpy sort along axis

Given a pandas DataFrame with multiple columns, which numpy sort call correctly sorts the values of the 'Age' column only, without changing the DataFrame shape?

NumPy

import pandas as pd
import numpy as np
df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 20]})
# Which numpy sort call sorts the 'Age' column values correctly?

A np.sort(df.values, axis=1)

B np.sort(df.values, axis=0)

C np.sort(df['Age'], axis=1)

D np.sort(df['Age'].values, axis=0)

💡 Hint

The 'Age' column is a 1D array; axis=0 sorts it correctly.

df['Age'].values is a 1D array. Sorting along axis=0 sorts the column values. Other options sort the whole DataFrame or use invalid axes.

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