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.

Practice

1. What does np.arange(3) produce?

easy

A. [0, 1, 2]

B. [1, 2, 3]

C. [0, 1, 2, 3]

D. [3]

Solution

1. Step 1: Understand default start value

   If only one number is given, np.arange starts from 0 by default.

2. Step 2: Identify stop value exclusion

   The stop value 3 is excluded, so the array includes 0, 1, 2.

3. Final Answer:

   [0, 1, 2] -> Option A

4. Quick Check:

   Start=0, stop=3 excluded, so [0,1,2] [OK]

Hint: Remember: stop value is NOT included in output [OK]

Common Mistakes:

• Including the stop value in the array
• Starting from 1 instead of 0
• Confusing step with stop

2. Which of these is the correct syntax to create an array from 2 to 6 (excluding 6) with step 1 using np.arange()?

easy

A. np.arange(6, 2, 1)

B. np.arange(2, 6, 0)

C. np.arange(2, 7)

D. np.arange(2, 6, 1)

Solution

1. Step 1: Check start, stop, and step order

   Start should be less than stop for positive step; step cannot be zero.

2. Step 2: Validate each option

   np.arange(2, 6, 1) uses start=2, stop=6, step=1 correctly. np.arange(6, 2, 1) reverses start and stop. np.arange(2, 7) sets stop=7, producing [2 3 4 5 6] which includes 6. np.arange(2, 6, 0) has step=0 which is invalid.

3. Final Answer:

   np.arange(2, 6, 1) -> Option D

4. Quick Check:

   Start=2, stop=6, step=1 is correct syntax [OK]

Hint: Step must not be zero; start < stop for positive step [OK]

Common Mistakes:

• Using zero as step value
• Reversing start and stop values
• Using wrong stop value

3. What is the output of this code?

import numpy as np
arr = np.arange(1, 10, 3)
print(arr)

medium

A. [1 4 7]

B. [1 3 6 9]

C. [1 2 3 4 5 6 7 8 9]

D. [3 6 9]

Solution

1. Step 1: Understand parameters of np.arange

   Start=1, stop=10 (excluded), step=3 means numbers start at 1 and increase by 3 until less than 10.

2. Step 2: Calculate values

   Values are 1, 4 (1+3), 7 (4+3). Next would be 10 which is excluded.

3. Final Answer:

   [1 4 7] -> Option A

4. Quick Check:

   Start=1, step=3, stop=10 excluded gives [1,4,7] [OK]

Hint: Add step repeatedly until reaching stop (excluded) [OK]

Common Mistakes:

• Including stop value 10
• Adding step incorrectly
• Confusing step with stop

4. What is wrong with this code?

import numpy as np
arr = np.arange(5, 2, 1)
print(arr)

medium

A. It raises a ValueError

B. It produces an empty array

C. It produces [5, 4, 3]

D. It produces [2, 3, 4, 5]

Solution

1. Step 1: Analyze start, stop, and step values

   Start=5, stop=2, step=1 means counting up from 5 to less than 2, which is impossible.

2. Step 2: Understand np.arange behavior

   Since step is positive but start > stop, no values satisfy condition, so result is empty array.

3. Final Answer:

   It produces an empty array -> Option B

4. Quick Check:

   Positive step with start > stop gives empty array [OK]

Hint: Positive step needs start < stop to produce values [OK]

Common Mistakes:

• Expecting reversed array without negative step
• Thinking it raises error
• Confusing start and stop order

5. You want to create an array starting at 10, decreasing by 2, stopping before 0. Which np.arange() call is correct?

hard

A. np.arange(0, 10, -2)

B. np.arange(10, 0, 2)

C. np.arange(10, 0, -2)

D. np.arange(0, 10, 2)

Solution

1. Step 1: Identify start, stop, and step for decreasing sequence

   Start=10, stop=0 (excluded), step must be negative to decrease.

2. Step 2: Check each option

   np.arange(10, 0, -2) uses step=-2 correctly. np.arange(10, 0, 2) has positive step, so no values. Options C and D start at 0, which is not desired.

3. Final Answer:

   np.arange(10, 0, -2) -> Option C

4. Quick Check:

   Decreasing sequence needs negative step [OK]

Hint: Use negative step to count down in np.arange [OK]

Common Mistakes:

• Using positive step for decreasing sequence
• Swapping start and stop values
• Starting at stop value instead of start