np.save() and np.load() for binary in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.save() do in NumPy?

np.save() saves a NumPy array to a binary file on your disk. This file stores the array data efficiently so you can load it later.

beginner

How do you load a NumPy array saved with np.save()?

You use np.load() with the filename to read the binary file and get back the original NumPy array.

intermediate

Why use binary files with np.save() instead of text files?

Binary files are faster to read/write and use less space. They keep the exact data format without converting to text, which can lose precision or be slower.

beginner

What file extension does np.save() add by default?

It adds .npy to the filename automatically if you don't include it.

intermediate

Can np.load() load files saved by np.save() from different Python sessions or machines?

Yes, as long as the file is not corrupted and the NumPy versions are compatible, np.load() can load the saved binary array across sessions or machines.

What is the default file extension added by np.save()?

A .csv

B .txt

C .npy

D .bin

np.save() automatically adds the .npy extension to the saved file.

Which function do you use to read a binary file saved by np.save()?

A np.load()

B np.read()

C np.open()

D np.import()

np.load() is used to load arrays saved in binary format by np.save().

Why is saving arrays in binary format better than saving as text?

A Binary files convert data to strings

B Binary files are slower to read

C Binary files are human-readable

D Binary files save space and keep exact data

Binary files save space and keep the exact data without converting it to text, making them faster and more precise.

If you save an array with np.save('data'), what is the actual filename?

A data.npy

B data

C data.txt

D data.bin

np.save() adds .npy automatically, so the file is data.npy.

Can np.load() open files saved by np.save() on another computer?

A Only if saved as CSV

B Yes, if the file is intact and NumPy versions are compatible

C Only if the file is converted to text first

D No, only on the same computer

np.load() can load saved binary files across machines if the file is not corrupted and NumPy versions match.

Explain how to save and load a NumPy array using np.save() and np.load().

Think about saving data to disk and reading it back exactly.

Why is it better to save NumPy arrays in binary format rather than as text files?

Consider speed, size, and data accuracy.

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