np.arange() for range arrays in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.arange() do in NumPy?

np.arange() creates an array with evenly spaced values within a specified range, similar to Python's built-in range() but returns a NumPy array.

beginner

How do you create an array from 0 to 9 using np.arange()?

Use np.arange(10). It creates values starting at 0 up to but not including 10.

beginner

What parameters does np.arange() accept?

np.arange(start, stop, step) where:

• start: where to begin (default 0)
• stop: where to stop (not included)
• step: gap between values (default 1)

intermediate

Can np.arange() create arrays with decimal steps?

Yes! For example, np.arange(0, 1, 0.2) creates [0. , 0.2, 0.4, 0.6, 0.8].

intermediate

What happens if the step parameter is negative in np.arange()?

The array counts downwards. For example, np.arange(5, 0, -1) creates [5, 4, 3, 2, 1].

What is the output of np.arange(3)?

A [0, 1, 2, 3]

B [1, 2, 3]

C [0, 1, 2]

D [3]

np.arange(3) starts at 0 and goes up to but not including 3, so it returns [0, 1, 2].

Which parameter in np.arange(start, stop, step) is required?

A start

B stop

C step

D None, all are optional

The stop parameter is required if start is given. If only one argument is given, it is treated as stop with start=0.

What does np.arange(1, 5, 2) produce?

A [1, 3]

B [1, 2, 3, 4]

C [1, 3, 5]

D [2, 4]

Starts at 1, steps by 2, stops before 5, so values are [1, 3].

Can np.arange() generate an empty array?

A No, it always returns values

B Only if step is negative

C Only if step is zero

D Yes, if start >= stop

If the start value is greater than or equal to stop with a positive step, the result is an empty array.

What is the difference between np.arange() and Python's range()?

A <code>np.arange()</code> returns a NumPy array, <code>range()</code> returns a range object

B No difference

C <code>range()</code> supports decimal steps

D <code>np.arange()</code> only works with integers

np.arange() returns a NumPy array and supports decimal steps; range() returns a range object and only supports integers.

Explain how to use np.arange() to create an array of numbers from 5 to 15 with a step of 3.

Remember the stop value is not included in the output.

Describe how np.arange() handles decimal steps and why it might be useful.

Think about creating sequences with fractions or floats.

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