np.sort() for sorting arrays in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.sort() on a 1D array

What is the output of this code?

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

NumPy

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

A [5 4 3 1 1]

B [1 1 3 4 5]

C [3 1 4 1 5]

D [1 3 4 5]

💡 Hint

np.sort() returns a sorted copy of the array in ascending order.

np.sort() sorts the array elements in ascending order and returns a new array. The original array remains unchanged.

❓ data_output

intermediate

Sorting a 2D array along axis 0

Given this 2D array, what is the output of np.sort(arr, axis=0)?

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

NumPy

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

A

[[3 4]
 [1 2]]

B

[[3 2]
 [1 4]]

C

[[1 2]
 [3 4]]

D

[[1 4]
 [3 2]]

💡 Hint

Sorting along axis 0 sorts each column independently.

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

🔧 Debug

advanced

Identify the error in using np.sort()

What error does this code raise?

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

NumPy

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

A AxisError: axis 1 is out of bounds for array of dimension 1

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C ValueError: could not broadcast input array from shape (3,) into shape (1,)

D No error, prints [1 3 4]

💡 Hint

Check the dimension of the array and the axis parameter.

The array is 1D, so axis=1 is invalid. This causes an AxisError.

🚀 Application

advanced

Using np.sort() to sort structured arrays

Given this structured array, what is the output of np.sort(arr, order='age')?

import numpy as np
arr = np.array([(1, 'Alice', 25), (2, 'Bob', 20), (3, 'Carol', 30)],
               dtype=[('id', 'i4'), ('name', 'U10'), ('age', 'i4')])
sorted_arr = np.sort(arr, order='age')
print(sorted_arr['name'])

NumPy

import numpy as np
arr = np.array([(1, 'Alice', 25), (2, 'Bob', 20), (3, 'Carol', 30)],
               dtype=[('id', 'i4'), ('name', 'U10'), ('age', 'i4')])
sorted_arr = np.sort(arr, order='age')
print(sorted_arr['name'])

A ['Bob' 'Alice' 'Carol']

B ['Alice' 'Bob' 'Carol']

C ['Carol' 'Alice' 'Bob']

D ['Bob' 'Carol' 'Alice']

💡 Hint

Sorting by 'age' orders the records by the age field ascending.

np.sort with order='age' sorts the structured array by the age field ascending, so Bob (20), Alice (25), Carol (30).

🧠 Conceptual

expert

Effect of np.sort() on original array

Which statement about np.sort() is true?

A np.sort() only works on 1D arrays.

B np.sort() sorts the array in place and returns None.

C np.sort() modifies the original array and returns the sorted array.

D np.sort() returns a sorted copy and does not change the original array.

💡 Hint

Think about whether np.sort() changes the original array or not.

np.sort() returns a new sorted array and leaves the original array unchanged. To sort in place, use arr.sort().