Array creation (array, arange, linspace) in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the numpy.array() function do?

It creates a new array from a list or sequence of numbers. This array can be used for fast math and data operations.

beginner

How does numpy.arange() differ from Python's built-in range()?

numpy.arange() can create arrays with decimal steps, not just whole numbers like range(). It returns a NumPy array instead of a list.

beginner

What is the purpose of numpy.linspace()?

It creates an array of evenly spaced numbers between a start and stop value, including the stop value. You specify how many numbers you want.

intermediate

Example: What does np.arange(1, 5, 0.5) produce?

It creates an array starting at 1, increasing by 0.5, up to but not including 5: [1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5]

intermediate

Why might you choose linspace() over arange()?

Because linspace() lets you specify the exact number of points you want between start and stop, which is helpful for precise spacing. arange() uses step size, which can cause rounding issues.

Which function creates an array with a specific number of evenly spaced points between two values?

A numpy.array()

B numpy.linspace()

C numpy.arange()

D Python range()

numpy.linspace() creates a fixed number of points evenly spaced between start and stop.

What will np.arange(0, 3, 1) output?

A [0, 1, 2]

B [1, 2, 3]

C [0, 1, 2, 3]

D [0, 1, 2, 3, 4]

np.arange() includes start but excludes stop, so it stops before 3.

Which function converts a Python list into a NumPy array?

A numpy.array()

B numpy.arange()

C numpy.tolist()

D numpy.linspace()

numpy.array() takes a list and returns a NumPy array.

If you want 5 numbers evenly spaced from 0 to 1, which function and parameters would you use?

A np.array([0, 0.2, 0.4, 0.6, 0.8])

B np.arange(0, 1, 0.2)

C np.linspace(0, 1, 5)

D np.arange(0, 1, 0.25)

np.linspace(0, 1, 5) creates exactly 5 points evenly spaced between 0 and 1.

What type of object do numpy.array(), arange(), and linspace() return?

A Python list

B Dictionary

C Tuple

D NumPy array

All these functions return a NumPy array, which is optimized for math and data tasks.

Explain how to create a NumPy array from a Python list and why you might want to do this.

Think about turning simple lists into powerful arrays for calculations.

Describe the difference between arange() and linspace() and when to use each.

Focus on how you control the spacing and number of points.

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