Why saving and loading matters in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to save a NumPy array to a file.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
np.save('my_array.npy', [1])

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

Aarray

Barr

Cnp.array

Dmy_array

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the wrong variable name.

Trying to save the function instead of the array.

The np.save function saves the array passed as the second argument. Here, arr is the array we want to save.

2fill in blank

medium

Complete the code to load a saved NumPy array from a file.

NumPy

import numpy as np
loaded_arr = np.[1]('my_array.npy')

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

Aload

Bsave

Cread

Dopen

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.save instead of np.load.

Using file open functions instead of NumPy's load.

The np.load function loads a saved NumPy array from a file.

3fill in blank

hard

Fix the error in the code to correctly save a NumPy array.

NumPy

import numpy as np
arr = np.array([10, 20, 30])
np.save('data.npy', [1])

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

Aarray

Bnp.array

Carr

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the function np.array instead of the variable.

Passing a variable that does not exist.

The variable holding the array is arr. Passing arr to np.save correctly saves the array.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that saves lengths of words longer than 3 characters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong comparison operator.

Using the word itself instead of its length.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that saves uppercase words with length greater than 3.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
result = { [1]: [2] for word in words if len(word) [3] 3 }

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

Aword.upper()

Bword

C>

Dlen(word)

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping keys and values.

Using wrong comparison operator.

Not converting words to uppercase.

The dictionary comprehension uses the uppercase word as the key (word.upper()) and the original word as the value. It filters words with length greater than 3 using >.

Practice

1. Why is it important to save and load NumPy arrays when working on data science projects?

easy

A. To delete your data after use

B. To make your code run slower

C. To convert arrays into strings automatically

D. To keep your data safe and reuse it without recalculating

Solution

1. Step 1: Understand the purpose of saving data

   Saving data helps keep your work safe so you don't lose it.

2. Step 2: Understand the benefit of loading data

   Loading saved data lets you reuse it without recalculating or reprocessing.

3. Final Answer:

   To keep your data safe and reuse it without recalculating -> Option D

4. Quick Check:

   Saving and loading = reuse data [OK]

Hint: Saving keeps data safe; loading reuses it fast [OK]

Common Mistakes:

• Thinking saving slows down code
• Believing saving deletes data
• Confusing saving with data conversion

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

easy

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

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

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

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

Solution

1. Step 1: Identify the correct function for saving

   Use np.save to save arrays to a file.

2. Step 2: Check the argument order

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

3. Final Answer:

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

4. Quick Check:

   Save syntax = np.save(filename, array) [OK]

Hint: np.save(filename, array) saves data [OK]

Common Mistakes:

• Swapping filename and array arguments
• Using np.load instead of np.save to save
• Confusing save and load functions

3. What will be the output of this code?

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

medium

A. Error: file not found

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

C. [1 2 3]

D. [[1 2 3]]

Solution

1. Step 1: Save the array to a file

   The array [1, 2, 3] is saved to 'temp.npy' using np.save.

2. Step 2: Load the array back and print

   np.load reads the saved file and returns the original array.

3. Final Answer:

   [1 2 3] -> Option C

4. Quick Check:

   Save then load returns original array [OK]

Hint: Load after save returns original array [OK]

Common Mistakes:

• Expecting string output instead of numbers
• Thinking load reads text files
• Assuming nested array output

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. Filename should not have .npy extension

B. np.save is missing the array argument

C. Array must be saved as a list, not np.array

D. np.load cannot read .npy files

Solution

1. Step 1: Check np.save usage

   np.save requires two arguments: filename and array to save.

2. Step 2: Identify missing argument

   The code calls np.save with only filename, missing the array argument.

3. Final Answer:

   np.save is missing the array argument -> Option B

4. Quick Check:

   np.save needs filename and array [OK]

Hint: np.save needs filename and array [OK]

Common Mistakes:

• Forgetting to pass the array to np.save
• Thinking np.load can't read .npy files
• Believing .npy extension is wrong

5. You have a large NumPy array data that you want to save and share with a colleague. Which approach is best to ensure your colleague can load it exactly as you saved it, and why?

hard

A. Save with np.save and share the .npy file because it preserves array shape and data type

B. Convert array to string and save as .txt because text files are universal

C. Save with np.savez_compressed but rename file to .txt for easy sharing

D. Print array to console and ask colleague to copy-paste it

Solution

1. Step 1: Understand file formats for saving arrays

   .npy files save arrays with shape and data type intact, ideal for sharing.

2. Step 2: Evaluate options for sharing

   Text files lose shape and type info; renaming compressed files confuses loading; copy-paste is error-prone.

3. Final Answer:

   Save with np.save and share the .npy file because it preserves array shape and data type -> Option A

4. Quick Check:

   .npy files keep data exact [OK]

Hint: Use .npy files to keep array exact for sharing [OK]

Common Mistakes:

• Using text files loses array structure
• Renaming compressed files breaks loading
• Copy-pasting arrays causes errors