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.