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.