np.save() and np.load() for binary in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of loading a saved NumPy array?

Consider the following code that saves and loads a NumPy array using np.save() and np.load(). What will be printed?

NumPy

import numpy as np
arr = np.array([10, 20, 30])
np.save('test_array.npy', arr)
loaded_arr = np.load('test_array.npy')
print(loaded_arr)

A [10, 20, 30]

B [[10 20 30]]

C [10 20 30]

D Error: File not found

💡 Hint

Remember that np.save() stores the array in binary format and np.load() restores it exactly.

The np.save() function saves the array in a binary file. When loaded back with np.load(), it returns the original array with the same shape and values. Printing the array shows it without commas as in option C.

❓ data_output

intermediate

How many elements are in the loaded array?

Given the code below, how many elements does the loaded array contain?

NumPy

import numpy as np
arr = np.arange(12).reshape(3,4)
np.save('matrix.npy', arr)
loaded = np.load('matrix.npy')
print(loaded.size)

A 3

B Error: AttributeError

C 4

D 12

💡 Hint

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

The original array has shape (3,4), so total elements = 3 * 4 = 12. The loaded array keeps the same shape and size.

🔧 Debug

advanced

What error occurs when loading a non-existent file with np.load()?

What error will this code raise?

NumPy

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

A FileNotFoundError

B TypeError

C ValueError

D SyntaxError

💡 Hint

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

Trying to load a file that does not exist raises a FileNotFoundError in Python.

🧠 Conceptual

advanced

Which option correctly describes the file format used by np.save()?

What type of file does np.save() create when saving an array?

A A binary file in NumPy's .npy format

B A text file with comma-separated values

C A JSON file with array data

D An XML file describing the array

💡 Hint

The file is designed for fast loading and saving of NumPy arrays.

np.save() writes the array to a binary file with the .npy extension, which stores metadata and data efficiently.

🚀 Application

expert

What is the shape of the loaded array after saving and loading a 3D array?

Given the code below, what will be the shape of the loaded array?

NumPy

import numpy as np
arr = np.ones((2,3,4))
np.save('3d_array.npy', arr)
loaded_arr = np.load('3d_array.npy')
print(loaded_arr.shape)

A (3, 4, 2)

B (2, 3, 4)

C (24,)

D (2, 12)

💡 Hint

The shape of the array is preserved exactly when saved and loaded with np.save and np.load.

The original array has shape (2, 3, 4). Saving and loading with np.save and np.load keeps the shape unchanged.

Practice

1. What does the np.save() function do in NumPy?

easy

A. Saves a NumPy array to a binary file on disk

B. Loads a NumPy array from a binary file

C. Converts a NumPy array to a list

D. Prints the contents of a NumPy array

Solution

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

   The np.save() function is designed to save a NumPy array to a file in binary format, preserving its data type and shape.

2. Step 2: Differentiate from np.load()

   np.load() is used to load arrays from files, not save them. Other options do not relate to saving files.

3. Final Answer:

   Saves a NumPy array to a binary file on disk -> Option A

4. Quick Check:

   np.save() saves array [OK]

Hint: np.save() writes array to file, np.load() reads it back [OK]

Common Mistakes:

• Confusing np.save() with np.load()
• Thinking np.save() converts array to list
• Assuming np.save() prints array

2. Which of the following is the correct syntax to save a NumPy array arr to a file named data.npy?

easy

A. np.savefile('data.npy', arr)

B. np.save(arr, 'data.npy')

C. np.load('data.npy', arr)

D. np.save('data.npy', arr)

Solution

1. Step 1: Recall np.save() parameter order

   The first argument is the filename (string), the second is the array to save.

2. Step 2: Check other options for correctness

   np.save(arr, 'data.npy') reverses parameters, np.load('data.npy', arr) uses np.load() which loads, not saves, np.savefile('data.npy', arr) uses a non-existent function.

3. Final Answer:

   np.save('data.npy', arr) -> Option D

4. Quick Check:

   Filename first, array second in np.save() [OK]

Hint: np.save(filename, array) always filename first [OK]

Common Mistakes:

• Swapping filename and array arguments
• Using np.load() instead of np.save() to save
• Using wrong function name like np.savefile()

3. What will be the output of the following code?

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

medium

A. [1 2 3]

B. ['1' '2' '3']

C. Error: file not found

D. [[1 2 3]]

Solution

1. Step 1: Save and load the array

   The array [1, 2, 3] is saved to 'file.npy' and then loaded back exactly as it was.

2. Step 2: Understand print output of loaded array

   Printing the loaded array shows the original array as [1 2 3] without quotes or extra brackets.

3. Final Answer:

   [1 2 3] -> Option A

4. Quick Check:

   np.load(np.save()) returns original array [OK]

Hint: np.load(np.save()) returns original array unchanged [OK]

Common Mistakes:

• Expecting string elements instead of integers
• Thinking np.load() returns nested arrays
• Assuming file not found error without saving first

4. What is wrong with this code snippet?

import numpy as np
arr = np.array([4, 5, 6])
np.save('mydata.npy')
loaded = np.load('mydata.npy')
print(loaded)

medium

A. np.save() cannot save integer arrays

B. np.load() should be called before np.save()

C. np.save() is missing the array argument to save

D. The filename should have .txt extension

Solution

1. Step 1: Check np.save() usage

   The np.save() function requires two arguments: filename and array. Here, the array argument is missing.

2. Step 2: Verify other options

   np.load() should be called before np.save() is incorrect because loading happens after saving. The filename should have .txt extension is wrong because .npy is the correct extension. np.save() cannot save integer arrays is false; np.save() can save integer arrays.

3. Final Answer:

   np.save() is missing the array argument to save -> Option C

4. Quick Check:

   np.save() needs filename and array [OK]

Hint: np.save() always needs array argument after filename [OK]

Common Mistakes:

• Forgetting to pass the array to np.save()
• Thinking .txt is needed instead of .npy
• Confusing order of np.save() and np.load()

5. You have saved multiple arrays separately using np.save() as arr1.npy and arr2.npy. How can you load both arrays and combine them into a single 2D array where each original array is a row?

hard

A. Use np.load('arr1.npy', 'arr2.npy') directly

B. Load each with np.load() and use np.vstack([arr1, arr2])

C. Save both arrays in one file using np.save() and then load

D. Load arrays and use np.concatenate(arr1, arr2, axis=1)

Solution

1. Step 1: Load arrays separately

   Since arrays are saved in separate files, load each using np.load() individually.

2. Step 2: Combine arrays as rows

   Use np.vstack([arr1, arr2]) to stack arrays vertically, making each array a row in the new 2D array.

3. Step 3: Check other options

   Use np.load('arr1.npy', 'arr2.npy') directly is invalid syntax, Save both arrays in one file using np.save() and then load is incorrect because np.save() saves one array per file, Load arrays and use np.concatenate(arr1, arr2, axis=1) concatenates along columns which may not work if shapes differ.

4. Final Answer:

   Load each with np.load() and use np.vstack([arr1, arr2]) -> Option B

5. Quick Check:

   Load separately, stack with vstack [OK]

Hint: Load arrays separately, stack rows with np.vstack() [OK]

Common Mistakes:

• Trying to load multiple files in one np.load() call
• Using np.concatenate with wrong axis
• Assuming np.save() can save multiple arrays in one file