Array creation (array, arange, linspace) in Data Analysis Python - Step-by-Step Execution

Concept Flow - Array creation (array, arange, linspace)

Start

↓

Choose method

↓

array()

↓

Create array

↓

Output numpy array

↓

End

You start by choosing one of three methods to create a numpy array: array() for fixed lists, arange() for ranges with steps, or linspace() for evenly spaced numbers. Each creates an array output.

Execution Sample

Data Analysis Python

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.arange(0, 5, 2)
arr3 = np.linspace(0, 1, 5)

This code creates three arrays: a fixed list, a range with step 2, and 5 evenly spaced numbers between 0 and 1.

Execution Table

Step	Action	Input Parameters	Resulting Array	Notes
1	Call np.array()	[1, 2, 3]	[1 2 3]	Creates array from list
2	Call np.arange()	start=0, stop=5, step=2	[0 2 4]	Creates array from range with step
3	Call np.linspace()	start=0, stop=1, num=5	[0. 0.25 0.5 0.75 1. ]	Creates array with evenly spaced values
4	End	-	-	All arrays created successfully

💡 All arrays created; no errors; execution ends.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3
arr1	undefined	[1 2 3]	[1 2 3]	[1 2 3]
arr2	undefined	undefined	[0 2 4]	[0 2 4]
arr3	undefined	undefined	undefined	[0. 0.25 0.5 0.75 1. ]

Key Moments - 3 Insights

Why does np.arange(0, 5, 2) not include 5 in the output?

Because np.arange stops before the stop value; it includes numbers less than 5 but not equal to 5, as shown in step 2 of the execution table.

How is np.linspace different from np.arange when creating arrays?

np.linspace creates a fixed number of evenly spaced values between start and stop (inclusive), while np.arange creates values with a fixed step size but may not include the stop value, as seen in steps 2 and 3.

What type of input does np.array() accept?

np.array() accepts a list or sequence of numbers and creates an array exactly matching those values, as shown in step 1.

Visual Quiz - 3 Questions

Look at the execution table, what is the value of arr2 after step 2?

A [0 2 4 6]

B [0 2 4]

C [0 1 2 3 4]

D [1 2 3]

💡 Hint

Check the 'Resulting Array' column for step 2 in the execution table.

At which step does the array contain evenly spaced decimal values?

A Step 3

B Step 1

C Step 2

D Step 4

💡 Hint

Look for decimal numbers in the 'Resulting Array' column.

If np.arange(0, 6, 2) was used instead of np.arange(0, 5, 2), what would be the last element in arr2?

A 6

B 5

C 4

D 8

💡 Hint

np.arange stops before the stop value, so it will not include 6.

Concept Snapshot

Array creation methods in numpy:
- np.array(list): creates array from given list
- np.arange(start, stop, step): creates array from start to stop (exclusive) with step
- np.linspace(start, stop, num): creates num evenly spaced values from start to stop (inclusive)
Use these to quickly build arrays for data analysis.

Full Transcript

This lesson shows how to create arrays in numpy using three methods: array(), arange(), and linspace(). The array() method takes a list and makes an array with the same values. The arange() method creates numbers from start up to but not including stop, stepping by a given amount. The linspace() method creates a set number of evenly spaced values between start and stop, including both ends. We traced code that creates three arrays using these methods and saw their outputs. We also answered common questions about why arange excludes the stop value and how linspace differs by spacing values evenly. This helps beginners understand how to build arrays for data work.

Practice

1. What does the function np.arange(1, 5) produce in Python's NumPy?

easy

A. An array with values [1, 2, 3, 4, 5]

B. An array with values [1, 3, 5]

C. An array with values [0, 1, 2, 3, 4]

D. An array with values [1, 2, 3, 4]

Solution

1. Step 1: Understand np.arange parameters

   np.arange(start, stop) creates values from start up to but not including stop.

2. Step 2: Apply to np.arange(1, 5)

   Values start at 1 and go up to 4 (5 is excluded), so the array is [1, 2, 3, 4].

3. Final Answer:

   An array with values [1, 2, 3, 4] -> Option D

4. Quick Check:

   arange excludes stop = [1, 2, 3, 4] [OK]

Hint: Remember arange excludes the stop value [OK]

Common Mistakes:

• Including the stop value in the output
• Starting from 0 instead of the given start
• Confusing arange with linspace

2. Which of the following is the correct syntax to create a NumPy array from the list [10, 20, 30]?

easy

A. np.array([10, 20, 30])

B. np.linspace(10, 20, 30)

C. np.arange([10, 20, 30])

D. np.array(10, 20, 30)

Solution

1. Step 1: Recall np.array syntax

   np.array() takes a single list or iterable as input to create an array.

2. Step 2: Check each option

   np.array([10, 20, 30]) correctly passes a list inside brackets. np.array(10, 20, 30) passes separate arguments, which is invalid. np.arange([10, 20, 30]) uses arange incorrectly. np.linspace(10, 20, 30) uses linspace which is for ranges, not lists.

3. Final Answer:

   np.array([10, 20, 30]) -> Option A

4. Quick Check:

   np.array needs a list inside brackets [OK]

Hint: Use brackets [] inside np.array to pass a list [OK]

Common Mistakes:

• Passing multiple arguments instead of a list
• Using arange or linspace to convert lists
• Forgetting brackets around the list

3. What is the output of the following code?

import numpy as np
arr = np.linspace(0, 1, 5)
print(arr)

medium

A. [0. 0.5 1. ]

B. [0. 0.2 0.4 0.6 0.8]

C. [0. 0.25 0.5 0.75 1. ]

D. [0. 0.1 0.2 0.3 0.4]

Solution

1. Step 1: Understand np.linspace parameters

   np.linspace(start, stop, num) creates num evenly spaced points from start to stop inclusive.

2. Step 2: Apply to np.linspace(0, 1, 5)

   It creates 5 points between 0 and 1: 0.0, 0.25, 0.5, 0.75, 1.0.

3. Final Answer:

   [0. 0.25 0.5 0.75 1. ] -> Option C

4. Quick Check:

   linspace includes stop and divides evenly [OK]

Hint: linspace includes stop and divides into equal parts [OK]

Common Mistakes:

• Confusing linspace with arange step behavior
• Expecting stop to be excluded
• Wrong number of points generated

4. Identify the error in the following code snippet:

import numpy as np
arr = np.arange(0, 10, 3.5)
print(arr)

medium

A. arange does not accept float step values

B. The code runs correctly and prints [0. 3.5 7.]

C. arange requires integer start and stop values

D. Missing import statement for numpy

Solution

1. Step 1: Check np.arange with float steps

   NumPy's arange supports float step values, so 3.5 is valid.

2. Step 2: Predict output of np.arange(0, 10, 3.5)

   Values start at 0, then 3.5, then 7.0. Next would be 10.5 which exceeds stop 10, so stops.

3. Final Answer:

   The code runs correctly and prints [0. 3.5 7.] -> Option B

4. Quick Check:

   arange accepts float steps and excludes stop [OK]

Hint: arange allows float steps, output excludes stop [OK]

Common Mistakes:

• Assuming arange only works with integers
• Expecting stop value to be included
• Forgetting to import numpy

5. You want to create an array of exactly 4 numbers starting at 2 and ending at 5, including both ends. Which code snippet correctly does this?

hard

A. np.linspace(2, 5, 4)

B. np.arange(2, 5, 1)

C. np.arange(2, 6, 2)

D. np.linspace(2, 5, 3)

Solution

1. Step 1: Understand the requirement

   We need 4 numbers starting at 2 and ending at 5, including both ends.

2. Step 2: Check np.arange options

   np.arange(2, 5, 1) creates [2, 3, 4], only 3 numbers and excludes 5. np.arange(2, 6, 2) creates [2, 4], wrong number of elements and values.

3. Step 3: Check np.linspace options

   np.linspace(2, 5, 4) creates 4 evenly spaced numbers including 2 and 5: [2.0, 3.0, 4.0, 5.0]. np.linspace(2, 5, 3) creates only 3 numbers.

4. Final Answer:

   np.linspace(2, 5, 4) -> Option A

5. Quick Check:

   linspace fixes number of points including ends [OK]

Hint: Use linspace to fix number of points including start and stop [OK]

Common Mistakes:

• Using arange which excludes stop and depends on step size
• Choosing wrong number of points in linspace
• Confusing arange and linspace behavior