Why saving and loading matters in NumPy - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this NumPy save and load code?

Consider this code that saves and loads a NumPy array. What will be printed?

NumPy

import numpy as np
arr = np.array([1, 2, 3])
np.save('temp.npy', arr)
loaded_arr = np.load('temp.npy')
print(loaded_arr)

A Error: File not found

B [[1 2 3]]

C [1, 2, 3]

D [1 2 3]

💡 Hint

Remember that np.save saves the array in binary format and np.load loads it back as the same shape.

np.save stores the array in a binary file preserving its shape and type. np.load reads it back exactly. The print shows the array as [1 2 3] without commas.

❓ data_output

intermediate

How many elements are in the loaded array?

This code saves a 2D array and loads it back. How many elements does the loaded array have?

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6]])
np.save('temp.npy', arr)
loaded_arr = np.load('temp.npy')
print(loaded_arr.size)

A 6

B 3

C Error: AttributeError

D 2

💡 Hint

The size attribute gives total number of elements in the array.

The original array has 3 rows and 2 columns, total 6 elements. np.load returns the same array, so size is 6.

🔧 Debug

advanced

What error does this code raise when loading a non-existent file?

What error will this code produce?

NumPy

import numpy as np
loaded_arr = np.load('missing_file.npy')

A ValueError

B FileNotFoundError

C TypeError

D No error, returns None

💡 Hint

Think about what happens if you try to open a file that does not exist.

np.load tries to open the file and fails because it does not exist, raising FileNotFoundError.

🧠 Conceptual

advanced

Why is saving and loading data important in data science?

Which of these is NOT a reason why saving and loading data matters?

A To avoid repeating expensive computations

B To share data easily between programs or people

C To permanently delete data from memory

D To preserve data state for future use

💡 Hint

Saving and loading helps keep data, not delete it.

Saving and loading is for storing and reusing data, not deleting it. Deleting data is unrelated.

🚀 Application

expert

Which option correctly saves and loads a dictionary using NumPy?

You want to save a Python dictionary with NumPy and load it back. Which code works correctly?

A np.save('dict.npy', my_dict); loaded = np.load('dict.npy', allow_pickle=True).item()

B np.save('dict.npy', my_dict); loaded = np.load('dict.npy')

C np.save('dict.npy', list(my_dict.items())); loaded = dict(np.load('dict.npy'))

D np.save('dict.npy', my_dict); loaded = np.load('dict.npy', allow_pickle=False)

💡 Hint

Dictionaries require pickling to save with NumPy.

NumPy saves dictionaries as pickled objects. To load them back, allow_pickle=True is needed and .item() extracts the dict.