np.array() from Python lists in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.array() with nested lists

What is the output of this code?

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

NumPy

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

A (2, 2)

B (4,)

C (1, 4)

D (2,)

💡 Hint

Think about how many rows and columns the nested list has.

The nested list has 2 lists each with 2 elements, so the shape is (2, 2).

❓ data_output

intermediate

Data type of np.array from mixed Python lists

What is the data type of the numpy array created by this code?

import numpy as np
arr = np.array([1, 2.5, 3])
print(arr.dtype)

NumPy

import numpy as np
arr = np.array([1, 2.5, 3])
print(arr.dtype)

A bool

B int64

C float64

D object

💡 Hint

Numpy converts all elements to the same type, choosing the one that can hold all values.

Since 2.5 is a float, numpy converts all elements to float64 to hold decimal values.

🔧 Debug

advanced

Error when creating np.array from irregular nested lists

What error does this code raise?

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

NumPy

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

A ValueError: setting an array element with a sequence

B SyntaxError

C TypeError: unhashable type: 'list'

D Visible array with shape (2,)

💡 Hint

Check how numpy handles nested lists with different lengths.

Numpy creates a 1D array of lists (dtype=object) when nested lists have different lengths.

🚀 Application

advanced

Creating a 3D numpy array from Python lists

Which option creates a numpy array with shape (2, 2, 2)?

A np.array([[[1,2],[3,4]], [[5,6],[7,8]]])

B np.array([[1,2],[3,4],[5,6],[7,8]])

C np.array([1,2,3,4,5,6,7,8])

D np.array([[1,2,3],[4,5,6]])

💡 Hint

Count the number of nested brackets and elements per level.

Option A has two blocks, each with two rows and two columns, making shape (2, 2, 2).

🧠 Conceptual

expert

Why does np.array() sometimes create dtype=object?

Why does numpy create an array with dtype=object when given certain Python lists?

A Because the Python list contains only integers.

B Because the nested lists have different lengths or types, so numpy cannot create a uniform numeric array.

C Because numpy always defaults to dtype=object for all Python lists.

D Because numpy does not support nested lists.

💡 Hint

Think about numpy's need for uniform data types and shapes.

Numpy requires arrays to have uniform shape and type; irregular nested lists force dtype=object.

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()