np.arange() for range arrays in NumPy - Step-by-Step Execution

Concept Flow - np.arange() for range arrays

Start with parameters: start, stop, step

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Calculate values from start to stop (exclusive) by step

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Create array with these values

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Return array

np.arange() creates an array of numbers starting from 'start' up to but not including 'stop', increasing by 'step'.

Execution Sample

NumPy

import numpy as np
arr = np.arange(2, 10, 2)
print(arr)

Creates an array starting at 2, up to 10 (exclusive), stepping by 2.

Execution Table

Step	Current Value	Condition (value < stop)	Action	Array State
1	2	2 < 10: True	Add 2 to array	[2]
2	4	4 < 10: True	Add 4 to array	[2, 4]
3	6	6 < 10: True	Add 6 to array	[2, 4, 6]
4	8	8 < 10: True	Add 8 to array	[2, 4, 6, 8]
5	10	10 < 10: False	Stop adding values	[2, 4, 6, 8]

💡 Value reached 10 which is not less than stop (10), so loop stops.

Variable Tracker

Variable	Start	After 1	After 2	After 3	After 4	Final
value	2	4	6	8	10	10
array	[]	[2]	[2, 4]	[2, 4, 6]	[2, 4, 6, 8]	[2, 4, 6, 8]

Key Moments - 3 Insights

Why does np.arange() stop before reaching the stop value?

np.arange() includes values starting from 'start' but stops before 'stop' because the condition is value < stop, not <= stop. See execution_table row 5 where 10 < 10 is False.

What happens if the step is negative?

If step is negative, np.arange() counts down from start to stop (exclusive), adding values while value > stop. The direction depends on step sign.

Can np.arange() include the stop value?

Usually no, because np.arange() excludes stop. To include stop, you can adjust stop or use np.linspace().

Visual Quiz - 3 Questions

Look at the execution_table, what is the array state after step 3?

A [2, 4]

B [2, 4, 6]

C [2, 4, 6, 8]

D [2]

💡 Hint

Check the 'Array State' column in execution_table row 3.

At which step does the condition become false and stop adding values?

A Step 5

B Step 3

C Step 4

D Step 2

💡 Hint

Look at the 'Condition' column in execution_table row 5.

If we change step to 3, how would the array change after step 3?

A [2, 6, 9]

B [2, 4, 6]

C [2, 5, 8]

D [2, 3, 6]

💡 Hint

Calculate values starting at 2, adding 3 each time, less than 10.

Concept Snapshot

np.arange(start, stop, step) creates an array from start up to but not including stop, stepping by step.
- Default start is 0, default step is 1.
- Values are added while value < stop (if step > 0).
- For negative step, values added while value > stop.
- Stop value is excluded.
- Useful for creating ranges of numbers quickly.

Full Transcript

np.arange() is a function in numpy that creates arrays of numbers starting from a given start value, increasing by a step size, and stopping before a stop value. It works like a number line segment generator. For example, np.arange(2, 10, 2) creates [2, 4, 6, 8]. The function checks at each step if the current value is less than the stop value (for positive steps) and adds it to the array if true. When the value reaches or passes the stop, it stops adding. This behavior is shown step-by-step in the execution table. Beginners often wonder why the stop value is not included; this is because the condition is strictly less than stop. Changing the step size changes the values generated. np.arange() is a simple and fast way to create numeric ranges for data science tasks.