Memory-mapped arrays for large data in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a memory-mapped array from a file.

NumPy

import numpy as np
mmap_array = np.memmap('data.dat', dtype='float32', mode=[1], shape=(1000, 1000))

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

A'r'

B'w+'

C'r+'

D'c'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'w+' mode when the file does not exist causes an error.

Using 'r+' mode without write permission causes an error.

The mode 'r' opens the file for reading only, which is common for memory-mapped arrays when you don't want to modify the data.

2fill in blank

medium

Complete the code to write data to a new memory-mapped file.

NumPy

import numpy as np
mmap_array = np.memmap('new_data.dat', dtype='int32', mode=[1], shape=(500, 500))
mmap_array[:] = np.arange(250000).reshape(500, 500)
mmap_array.flush()

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

A'r+'

B'r'

C'c'

D'w+'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'r' mode which does not allow writing.

Using 'r+' mode without an existing file.

The mode 'w+' creates a new file or overwrites an existing one and allows reading and writing.

3fill in blank

hard

Fix the error in the code to correctly access the memory-mapped array slice.

NumPy

import numpy as np
mmap_array = np.memmap('data.dat', dtype='float64', mode='r', shape=(100, 100))
slice_data = mmap_array[[1]]

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

A[50:60]

B50:60

C(50:60)

D50, 60

Attempts:

3 left

💡 Hint

Common Mistakes

Using commas inside the brackets which is invalid for slicing.

Adding extra brackets around the slice.

To slice rows 50 to 59, use the slice notation 50:60 without extra brackets or commas.

4fill in blank

hard

Fill both blanks to create a memory-mapped array and then read a specific element.

NumPy

import numpy as np
mmap_array = np.memmap('file.dat', dtype=[1], mode='r', shape=(10, 10))
element = mmap_array[[2]]

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

A'float64'

B5, 5

C7, 3

D'int32'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a tuple for dtype instead of a string.

Using a single index instead of two for a 2D array.

The dtype should be 'float64' to match the data type, and the element at row 5, column 5 is accessed by mmap_array[5, 5].

5fill in blank

hard

Fill all three blanks to create a writable memory-mapped array, modify it, and save changes.

NumPy

import numpy as np
mmap_array = np.memmap('mod_data.dat', dtype=[1], mode=[2], shape=(20, 20))
mmap_array[10, 10] = [3]
mmap_array.flush()

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

A'float32'

B'r+'

C42.0

D'r'

Attempts:

3 left

💡 Hint

Common Mistakes

Using mode 'r' which does not allow writing.

Assigning a value without matching dtype.

Use dtype 'float32' for the array, mode 'r+' to allow writing, and assign 42.0 to the element at position (10, 10).