Array creation (array, arange, linspace) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array from a list of numbers.

Data Analysis Python

import numpy as np
arr = np.[1]([1, 2, 3, 4, 5])
print(arr)

Drag options to blanks, or click blank then click option'

Aarray

Barange

Clinspace

Dzeros

Attempts:

3 left

💡 Hint

Common Mistakes

Using arange or linspace instead of array when starting from a list.

Forgetting to import numpy as np.

The array function creates a NumPy array from a Python list.

2fill in blank

medium

Complete the code to create an array of numbers from 0 to 9 using NumPy.

Data Analysis Python

import numpy as np
arr = np.[1](10)
print(arr)

Drag options to blanks, or click blank then click option'

Aarange

Barray

Clinspace

Dzeros

Attempts:

3 left

💡 Hint

Common Mistakes

Using array with a single integer argument instead of arange.

Using linspace which requires start and stop arguments.

The arange function creates an array with evenly spaced values within a given range. Here, it creates numbers from 0 up to (but not including) 10.

3fill in blank

hard

Fix the error in the code to create 5 evenly spaced numbers between 0 and 1.

Data Analysis Python

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

Drag options to blanks, or click blank then click option'

A10

B0

C5

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0 or 1 which results in too few points.

Using 10 which creates more points than needed.

The third argument to linspace is the number of points to generate. To get 5 points between 0 and 1, use 5.

4fill in blank

hard

Fill both blanks to create an array of numbers from 2 to 10 (exclusive) with step size 2.

Data Analysis Python

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

Drag options to blanks, or click blank then click option'

A2

B10

C12

D8

Attempts:

3 left

💡 Hint

Common Mistakes

Using 12 as stop which includes extra numbers.

Using 8 as stop which excludes 8 itself.

arange takes start, stop, and step arguments. To get numbers from 2 up to but not including 10 with step 2, use start=2 and stop=10.

5fill in blank

hard

Fill all three blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 3 characters.

Data Analysis Python

words = ['apple', 'bat', 'carrot', 'dog']
lengths = [1]: [2] for word in words if len(word) [3] 3
print(lengths)

Drag options to blanks, or click blank then click option'

A{word

Blen(word)

C>

D{word: len(word)

Attempts:

3 left

💡 Hint

Common Mistakes

Not using braces to create a dictionary.

Using '<' instead of '>' in the condition.

Not using len(word) for the value.

This is a dictionary comprehension. It creates a dictionary where each key is a word and the value is its length, but only includes words longer than 3 characters.

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