np.arange() for range arrays in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.arange() with float step

What is the output of this code snippet using np.arange()?

NumPy

import numpy as np
result = np.arange(1.0, 2.0, 0.3)
print(result)

A [1. 1.3 1.6 1.9 2.2]

B [1. 1.3 1.6]

C [1. 1.3 1.6 1.9]

D [1. 1.3 1.6 1.9 2.0]

💡 Hint

Remember that np.arange stops before reaching the stop value.

The np.arange() function creates values starting at 1.0, increasing by 0.3, but stops before reaching 2.0. So it includes 1.0, 1.3, 1.6, and 1.9.

❓ data_output

intermediate

Length of array from np.arange() with negative step

How many elements are in the array created by this code?

NumPy

import numpy as np
arr = np.arange(5, 0, -1)
print(len(arr))

A 5

B 4

C 6

D 0

💡 Hint

Count numbers from 5 down to but not including 0, stepping by -1.

np.arange(5, 0, -1) creates the array [5, 4, 3, 2, 1], which has 5 elements. It starts at 5, decrements by 1, and stops before reaching 0.

❓ Predict Output

advanced

Output of np.arange() with integer step and float start

What is the output of this code?

NumPy

import numpy as np
result = np.arange(0.5, 5, 1)
print(result)

A [0.5 1.5 2.5 3.5 4.5]

B [0.5 1.5 2.5 3.5]

C [1.5 2.5 3.5 4.5]

D [0.5 1.5 2.5 3.5 4.5 5.5]

💡 Hint

np.arange includes start and increments by step until reaching or passing stop.

Starting at 0.5, adding 1 each time, the values are 0.5, 1.5, 2.5, 3.5, 4.5. It stops before 5, so 5.5 is not included.

❓ visualization

advanced

Plotting np.arange() output

Which plot correctly shows the points generated by np.arange(0, 3, 0.5)?

NumPy

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 3, 0.5)
plt.scatter(x, [1]*len(x))
plt.xticks(x)
plt.yticks([])
plt.show()

A Points at 0, 0.25, 0.5, 0.75, 1 on x-axis

B Points at 0, 1, 2, 3 on x-axis

C Points at 0.5, 1, 1.5, 2, 2.5, 3 on x-axis

D Points at 0, 0.5, 1, 1.5, 2, 2.5 on x-axis

💡 Hint

Check the start, stop, and step values carefully.

The np.arange(0, 3, 0.5) generates values starting at 0, increasing by 0.5, stopping before 3. So points are at 0, 0.5, 1, 1.5, 2, 2.5.

🧠 Conceptual

expert

Why might np.arange() produce unexpected results with floats?

Why can np.arange() sometimes produce arrays with unexpected last elements when using float steps?

A Because np.arange() always rounds the stop value to the nearest integer

B Because floating point arithmetic can cause rounding errors affecting the stop condition

C Because np.arange() converts all floats to integers internally

D Because np.arange() ignores the step value when it is a float

💡 Hint

Think about how computers store decimal numbers.

Floating point numbers are stored approximately in computers. This can cause small rounding errors. When np.arange() uses these floats, the stop condition may be affected, leading to unexpected last elements.