np.array() from Python lists in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array from a Python list.

NumPy

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

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

Aarray

Blist

Carray_list

Dtoarray

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' instead of 'array' causes an error because 'list' is not a NumPy function.

Trying to use 'toarray' which is not a valid NumPy function.

The np.array() function converts a Python list into a NumPy array.

2fill in blank

medium

Complete the code to create a 2D NumPy array from a list of lists.

NumPy

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

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

Amatrix

Blist

Carray

Dtolist

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'matrix' which is deprecated in NumPy.

Using 'tolist' which converts arrays back to lists.

Using np.array() on a list of lists creates a 2D NumPy array. The shape shows its dimensions.

3fill in blank

hard

Fix the error in the code to convert a list to a NumPy array.

NumPy

import numpy as np
my_list = [5, 6, 7]
arr = np.[1](my_list)
print(arr)

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

Aarray

BArray

Carr

Dtolist

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Array' with uppercase 'A' causes an AttributeError.

Using 'tolist' which is a method to convert arrays back to lists.

The correct function is np.array() with a lowercase 'a'. Python is case-sensitive.

4fill in blank

hard

Fill both blanks to create a NumPy array and check its data type.

NumPy

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

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

Aarray

Bshape

Cdtype

Dtolist

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'shape' instead of 'dtype' to check data type.

Using 'tolist' which converts arrays back to lists.

np.array() creates the array, and arr.dtype shows the data type of elements.

5fill in blank

hard

Fill all three blanks to create a 2D array, get its shape, and convert it back to a list.

NumPy

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

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

Aarray

Bshape

Ctolist

Ddtype

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dtype' instead of 'shape' to get dimensions.

Forgetting parentheses when calling 'tolist()'.

np.array() creates the array, arr.shape shows its dimensions, and arr.tolist() converts it back to a Python list.

Practice

1. What does np.array() do when given a Python list?

easy

A. It converts the list into a numpy array for easier math operations.

B. It changes the list into a Python dictionary.

C. It sorts the list in ascending order.

D. It removes duplicates from the list.

Solution

1. Step 1: Understand the purpose of np.array()

   np.array() is designed to convert Python lists into numpy arrays.

2. Step 2: Recognize the benefit of numpy arrays

   Numpy arrays allow fast, element-wise math operations that lists do not support.

3. Final Answer:

   It converts the list into a numpy array for easier math operations. -> Option A

4. Quick Check:

   np.array() converts lists to arrays [OK]

Hint: np.array() turns lists into arrays for math [OK]

Common Mistakes:

• Thinking np.array() sorts the list
• Confusing arrays with dictionaries
• Assuming it removes duplicates

2. Which of the following is the correct syntax to create a numpy array from a Python list [1, 2, 3]?

easy

A. np.array{1, 2, 3}

B. np.array[[1, 2, 3]]

C. np.array(1, 2, 3)

D. np.array([1, 2, 3])

Solution

1. Step 1: Recall correct function call syntax

   np.array() requires parentheses and a list inside, like np.array([1, 2, 3]).

2. Step 2: Identify syntax errors in other options

   np.array{1, 2, 3} uses curly braces which is invalid; C passes multiple arguments instead of one list; D uses double brackets incorrectly.

3. Final Answer:

   np.array([1, 2, 3]) -> Option D

4. Quick Check:

   np.array() needs a list inside parentheses [OK]

Hint: Use parentheses and a list inside for np.array() [OK]

Common Mistakes:

• Using curly braces instead of parentheses
• Passing multiple arguments instead of a single list
• Using double brackets incorrectly

3. What will be the output of this code?

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

medium

A. (1, 4)

B. (4,)

C. (2, 2)

D. Error

Solution

1. Step 1: Understand the input list shape

   The list has 2 sublists, each with 2 elements, so shape is 2 rows and 2 columns.

2. Step 2: Check numpy array shape property

   np.array converts nested lists into 2D arrays with shape (2, 2).

3. Final Answer:

   (2, 2) -> Option C

4. Quick Check:

   Nested list shape = (2, 2) [OK]

Hint: Shape matches nested list dimensions [OK]

Common Mistakes:

• Thinking shape is (4,)
• Assuming shape is (1, 4)
• Expecting an error for nested lists

4. What is wrong with this code snippet?

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

medium

A. The list contains integers which are not allowed.

B. np.array() does not take a second positional argument like 5.

C. The print statement is missing parentheses.

D. The list must be nested to convert to an array.

Solution

1. Step 1: Check np.array() function signature

   np.array() takes the data as the first argument; a second positional argument like 5 is invalid.

2. Step 2: Identify the error caused by extra argument

   Passing 5 as a second positional argument causes a TypeError.

3. Final Answer:

   np.array() does not take a second positional argument like 5. -> Option B

4. Quick Check:

   Extra positional arguments cause error [OK]

Hint: np.array() takes one main argument: the list [OK]

Common Mistakes:

• Thinking list must be nested
• Forgetting print() needs parentheses (Python 3)
• Believing integers are not allowed in arrays

5. You have a list of lists representing daily temperatures for a week:

temps = [[20, 22, 19], [21, 23, 20], [19, 21, 18], [22, 24, 21], [20, 22, 19], [18, 20, 17], [21, 23, 20]]

How can you convert this into a numpy array and find the average temperature for each day?

hard

A. import numpy as np
temps_arr = np.array(temps)
daily_avg = temps_arr.mean(axis=1)
print(daily_avg)

B. import numpy as np
temps_arr = np.array(temps)
daily_avg = temps_arr.mean(axis=0)
print(daily_avg)

C. import numpy as np
daily_avg = np.array(temps).sum()
print(daily_avg)

D. import numpy as np
temps_arr = np.array(temps)
daily_avg = temps_arr.max(axis=1)
print(daily_avg)

Solution

1. Step 1: Convert list of lists to numpy array

   Use np.array(temps) to create a 2D array where each row is a day.

2. Step 2: Calculate average temperature per day

   Use mean(axis=1) to compute average across columns (hours) for each row (day).

3. Final Answer:

   import numpy as np temps_arr = np.array(temps) daily_avg = temps_arr.mean(axis=1) print(daily_avg) -> Option A

4. Quick Check:

   mean(axis=1) averages rows (days) [OK]

Hint: Use mean(axis=1) to average rows (days) [OK]

Common Mistakes:

• Using mean(axis=0) which averages columns (hours)
• Using sum() without axis for average
• Using max() instead of mean()