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).

Practice

1. What is the main advantage of using numpy.memmap for large data files?

easy

A. It allows working with data without loading the entire file into memory.

B. It compresses the data to save disk space.

C. It automatically cleans up unused files.

D. It speeds up CPU calculations by using GPU.

Solution

1. Step 1: Understand memory usage with large files

   Loading large files fully into RAM can cause memory errors or slowdowns.

2. Step 2: Role of numpy.memmap

   Memory-mapped arrays let you access parts of the file on disk as if they were in memory, avoiding full loading.

3. Final Answer:

   It allows working with data without loading the entire file into memory. -> Option A

4. Quick Check:

   Memory-mapping = Partial loading [OK]

Hint: Memory-mapping means partial file loading, not full load [OK]

Common Mistakes:

• Thinking memmap compresses data
• Assuming memmap speeds up CPU/GPU
• Believing memmap auto-deletes files

2. Which of the following is the correct way to create a memory-mapped array from a file named data.dat with shape (100, 100) and dtype float64?

easy

A. np.memmap('data.dat', shape=(100, 100), dtype='int32', mode='w+')

B. np.memmap('data.dat', dtype='float64', mode='r+', shape=(100, 100))

C. np.memmap('data.dat', dtype='float64', shape=(100, 100))

D. np.memmap('data.dat', mode='r', shape=(100, 100), dtype='float32')

Solution

1. Step 1: Check required parameters for memmap

   Must specify filename, dtype, mode, and shape for memmap creation.

2. Step 2: Validate each option

   np.memmap('data.dat', dtype='float64', mode='r+', shape=(100, 100)) correctly uses dtype='float64', mode='r+', and shape=(100, 100). Others have wrong dtype or missing mode.

3. Final Answer:

   np.memmap('data.dat', dtype='float64', mode='r+', shape=(100, 100)) -> Option B

4. Quick Check:

   Correct dtype, mode, shape = np.memmap('data.dat', dtype='float64', mode='r+', shape=(100, 100)) [OK]

Hint: Always specify dtype, mode, and shape explicitly [OK]

Common Mistakes:

• Omitting mode parameter
• Using wrong dtype
• Swapping dtype and shape order

3. Given the code below, what will be the output of print(mm[0, 0])?

import numpy as np
filename = 'test.dat'
mm = np.memmap(filename, dtype='int32', mode='w+', shape=(2, 2))
mm[0, 0] = 10
mm.flush()
mm2 = np.memmap(filename, dtype='int32', mode='r', shape=(2, 2))
print(mm2[0, 0])

medium

A. Raises an error

B. 0

C. 10

D. Uninitialized garbage value

Solution

1. Step 1: Write value and flush to disk

   Value 10 is assigned to mm[0,0] and flushed, saving to file.

2. Step 2: Read from file with new memmap

   mm2 reads the same file in read mode, so mm2[0,0] is 10.

3. Final Answer:

   10 -> Option C

4. Quick Check:

   Flush saves data, read memmap shows 10 [OK]

Hint: Flush saves changes; reading memmap shows updated value [OK]

Common Mistakes:

• Forgetting to flush before reading
• Expecting zero without initialization
• Confusing write and read modes

4. What is the error in the following code snippet?

import numpy as np
filename = 'data.dat'
mm = np.memmap(filename, dtype='float64', mode='r+', shape=(3, 3))
mm[0, 0] = 5.0
mm.flush()

Assume data.dat does not exist before running this code.

medium

A. No error; code runs fine.

B. TypeError due to wrong dtype.

C. ValueError because shape is incorrect.

D. FileNotFoundError because mode 'r+' requires existing file.

Solution

1. Step 1: Understand mode 'r+'

   Mode 'r+' opens file for reading and writing but requires the file to exist.

2. Step 2: Check file existence

   Since 'data.dat' does not exist, opening with 'r+' raises FileNotFoundError.

3. Final Answer:

   FileNotFoundError because mode 'r+' requires existing file. -> Option D

4. Quick Check:

   Mode 'r+' needs existing file [OK]

Hint: Mode 'r+' needs existing file; else FileNotFoundError [OK]

Common Mistakes:

• Assuming 'r+' creates file
• Confusing 'r+' with 'w+'
• Ignoring file existence requirement

5. You have a large dataset stored in a binary file large_data.dat with shape (10000, 10000) and dtype float32. You want to increment all values by 1 without loading the entire file into memory. Which approach using numpy.memmap is best?

hard

A. Create a memmap with mode 'r+', loop over rows, increment each row, flush after each.

B. Load entire file with np.fromfile, add 1, then save back.

C. Open memmap with mode 'w+', increment only first 100 elements, flush once.

D. Use memmap with mode 'r', add 1 to the array, then flush.

Solution

1. Step 1: Avoid loading entire file

   Loading full 10000x10000 array uses too much RAM, so avoid np.fromfile full load.

2. Step 2: Use memmap with mode 'r+' and process in chunks

   Memmap with 'r+' allows read/write access. Looping over rows increments data without full load. Flushing saves changes.

3. Step 3: Evaluate other options

   Load entire file with np.fromfile, add 1, then save back. loads full file (bad for memory). Open memmap with mode 'w+', increment only first 100 elements, flush once. only increments part of data. Use memmap with mode 'r', add 1 to the array, then flush. uses 'r' mode (read-only), so cannot modify.

4. Final Answer:

   Create a memmap with mode 'r+', loop over rows, increment each row, flush after each. -> Option A

5. Quick Check:

   Memmap + chunk update + flush = Create a memmap with mode 'r+', loop over rows, increment each row, flush after each. [OK]

Hint: Use memmap with 'r+' and update in chunks to save memory [OK]

Common Mistakes:

• Loading entire file into memory
• Using read-only mode for writing
• Modifying only part of data