NumPy array foundation review in SciPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Understanding NumPy array slicing output

What is the output of this code snippet?

SciPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
result = arr[1:, :2]
print(result)

A

[[4 5]
 [7 8]]

B

[[4 5 6]
 [7 8 9]]

C

[[5 6]
 [8 9]]

D

[[2 3]
 [5 6]]

💡 Hint

Remember that arr[1:, :2] means rows from index 1 to end, columns from index 0 to 1.

The slice arr[1:, :2] selects rows starting from index 1 (second row) to the end, and columns from index 0 up to but not including 2 (first two columns). So it returns [[4,5],[7,8]].

❓ data_output

intermediate

Shape and size of a reshaped NumPy array

Given the code below, what is the shape and size of the array 'reshaped'?

SciPy

import numpy as np
arr = np.arange(12)
reshaped = arr.reshape(3, 4)
print(reshaped.shape, reshaped.size)

A (3, 4) 12

B (12,) 12

C (3, 4) 7

D (4, 3) 12

💡 Hint

The reshape changes the shape but not the total number of elements.

The array is reshaped into 3 rows and 4 columns, so shape is (3,4). The size is total elements, which is 12.

🔧 Debug

advanced

Identify the error in NumPy array creation

What error does this code raise?

SciPy

import numpy as np
arr = np.array([1, 2, 3], dtype='float32', shape=(2,2))

A ValueError: cannot reshape array of size 3 into shape (2,2)

B TypeError: unexpected keyword argument 'shape'

C SyntaxError: invalid syntax

D No error, creates a 2x2 array

💡 Hint

Check the parameters accepted by np.array function.

The np.array function does not accept a 'shape' argument. This causes a TypeError.

🧠 Conceptual

advanced

Broadcasting behavior in NumPy arrays

Which option correctly describes the output shape of the operation below?

SciPy

import numpy as np
a = np.ones((3,1))
b = np.ones((1,4))
c = a + b
print(c.shape)

A (3, 1)

B (1, 1)

C (3, 4)

D (1, 4)

💡 Hint

Broadcasting expands arrays to compatible shapes for element-wise operations.

Array 'a' has shape (3,1), 'b' has shape (1,4). Broadcasting expands them to (3,4) for addition.

🚀 Application

expert

Find the number of unique rows in a 2D NumPy array

Given the array below, how many unique rows does it contain?

SciPy

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

A 2

B 4

C 5

D 3

💡 Hint

np.unique with axis=0 finds unique rows, not elements.

The unique rows are [1,2], [3,4], and [5,6], so total 3 unique rows.