Memory-mapped files with np.memmap in NumPy

Introduction

Memory-mapped files let you work with big data stored on disk as if it were in memory. This helps when your data is too large to fit in your computer's RAM.

You want to process a large dataset that does not fit into your computer's memory.

You want to read or write parts of a large binary file without loading the whole file.

You want to share data between different programs without copying it all into memory.

You want faster access to large arrays stored on disk without loading them fully.

Syntax

NumPy

np.memmap(filename, dtype='float32', mode='r+', offset=0, shape=None, order='C')

filename is the path to the binary file on disk.

mode controls read/write access: 'r' for read-only, 'r+' for read-write, 'w+' to create or overwrite.

Examples

This opens a 1000x1000 float32 array stored in 'data.dat' for reading.

NumPy

import numpy as np

# Open existing file for reading
mmap_array = np.memmap('data.dat', dtype='float32', mode='r', shape=(1000, 1000))

This creates a new file 'new_data.dat', writes numbers 0 to 249999, and saves it to disk.

NumPy

import numpy as np

# Create a new memmap file and write data
mmap_array = np.memmap('new_data.dat', dtype='int32', mode='w+', shape=(500, 500))
mmap_array[:] = np.arange(250000).reshape(500, 500)
mmap_array.flush()

Sample Program

This program creates a memory-mapped file, writes a 3x4 array to it, saves it, then reads it back and prints the array.

NumPy

import numpy as np

# Create a memmap file with shape (3, 4) and int32 data
filename = 'example.dat'
mmap_array = np.memmap(filename, dtype='int32', mode='w+', shape=(3, 4))

# Fill the array with values
mmap_array[:] = np.array([[1, 2, 3, 4],
                          [5, 6, 7, 8],
                          [9, 10, 11, 12]])

# Save changes to disk
mmap_array.flush()

# Open the same file for reading
mmap_read = np.memmap(filename, dtype='int32', mode='r', shape=(3, 4))

# Print the data read from the file
print(mmap_read)

Important Notes

Always call flush() to save changes from memory to disk.

Memory-mapped files work best with binary data, not text files.

Be careful with the shape and dtype to match the file's data layout.

Summary

Memory-mapped files let you handle large data on disk like arrays in memory.

Use np.memmap to create or open these files with control over reading and writing.

This helps save memory and speeds up working with big datasets.

Practice

1. What is the main benefit of using np.memmap in data science?

easy

A. It allows working with large arrays stored on disk without loading all data into memory.

B. It automatically speeds up all calculations by using GPU acceleration.

C. It compresses data files to save disk space.

D. It converts arrays into Python lists for easier manipulation.

Solution

1. Step 1: Understand what np.memmap does

   np.memmap creates an array-like object that accesses data stored on disk instead of loading it fully into memory.

2. Step 2: Identify the main advantage

   This allows handling very large datasets without using large amounts of RAM, which is the main benefit.

3. Final Answer:

   It allows working with large arrays stored on disk without loading all data into memory. -> Option A

4. Quick Check:

   Memory-mapped files save RAM by accessing disk data [OK]

Hint: Remember: memmap works with disk data like memory arrays [OK]

Common Mistakes:

• Thinking memmap compresses data
• Assuming memmap loads all data into RAM
• Confusing memmap with GPU acceleration

2. Which of the following is the correct way to create a new memory-mapped file with np.memmap of shape (100, 100) and dtype float32?

easy

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

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

C. np.memmap('data.dat', dtype='float32', mode='rw', shape=(100, 100))

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

Solution

1. Step 1: Check the mode for creating a new file

   Mode 'w+' creates a new file or overwrites existing one for reading and writing.

2. Step 2: Verify dtype and shape parameters

   The dtype should be 'float32' and shape (100, 100) as given.

3. Final Answer:

   np.memmap('data.dat', dtype='float32', mode='w+', shape=(100, 100)) -> Option D

4. Quick Check:

   Use mode='w+' to create new memmap files [OK]

Hint: Use mode='w+' to create or overwrite memmap files [OK]

Common Mistakes:

• Using mode='r' when creating a new file
• Using incorrect dtype like float64 instead of float32
• Using invalid mode 'rw' which does not exist

3. What will be the output of this code snippet?

import numpy as np
filename = 'test.dat'
# Create memmap file
fp = np.memmap(filename, dtype='int32', mode='w+', shape=(3,3))
fp[:] = np.arange(9).reshape(3,3)
fp.flush()
# Open memmap file in read mode
fp2 = np.memmap(filename, dtype='int32', mode='r', shape=(3,3))
print(fp2[1,2])

medium

A. 6

B. 5

C. 7

D. 8

Solution

1. Step 1: Understand the array content

   np.arange(9).reshape(3,3) creates a 3x3 array: [[0,1,2],[3,4,5],[6,7,8]]

2. Step 2: Identify the value at position [1,2]

   Row 1, column 2 is the third element in second row, which is 5.

3. Final Answer:

   5 -> Option B

4. Quick Check:

   Index [1,2] in arange(9).reshape(3,3) = 5 [OK]

Hint: Remember zero-based indexing for rows and columns [OK]

Common Mistakes:

• Confusing row and column indices
• Forgetting zero-based indexing
• Assuming flush() changes data values

4. Identify the error in this code snippet that tries to open a memmap file:

import numpy as np
filename = 'data.dat'
# Attempt to open memmap file
fp = np.memmap(filename, dtype='float64', mode='r+', shape=(10,10))
print(fp[0,0])

medium

A. File 'data.dat' does not exist, so mode 'r+' causes an error.

B. dtype 'float64' is not supported by np.memmap.

C. Shape parameter must be omitted when opening existing memmap files.

D. Mode 'r+' is read-only and cannot write to file.

Solution

1. Step 1: Understand mode 'r+'

   Mode 'r+' opens an existing file for reading and writing. If file does not exist, it raises an error.

2. Step 2: Check file existence

   If 'data.dat' does not exist, this code will raise a FileNotFoundError.

3. Final Answer:

   File 'data.dat' does not exist, so mode 'r+' causes an error. -> Option A

4. Quick Check:

   Mode 'r+' requires existing file [OK]

Hint: Use mode='w+' to create files, 'r+' needs existing file [OK]

Common Mistakes:

• Assuming 'r+' creates new files
• Thinking dtype 'float64' is invalid
• Believing shape must be omitted always

5. You have a very large dataset stored in a binary file 'large_data.dat' with shape (10000, 10000) and dtype float64. You want to compute the mean of the first column without loading the entire file into memory. Which approach using np.memmap is best?

hard

A. Open the file with mode='w+' and overwrite data before computing mean.

B. Load the entire file into a numpy array and then compute the mean of the first column.

C. Open the file with mode='r' and read only the first column slice to compute the mean.

D. Use np.memmap with mode='c' and compute mean on the whole array.

Solution

1. Step 1: Understand memory constraints

   The dataset is very large (10000x10000), so loading all data into memory is inefficient.

2. Step 2: Use memmap to read only needed data

   Opening with mode='r' allows read-only access. Slicing the first column reads only that part from disk, saving memory.

3. Step 3: Avoid unnecessary writes or full reads

   Mode 'w+' overwrites data, which is not desired. Mode 'c' is copy-on-write and still loads data. Loading full array wastes memory.

4. Final Answer:

   Open the file with mode='r' and read only the first column slice to compute the mean. -> Option C

5. Quick Check:

   Read-only memmap + slice = efficient mean calculation [OK]

Hint: Read only needed slices with mode='r' to save memory [OK]

Common Mistakes:

• Loading entire large file into memory
• Using mode='w+' which overwrites data
• Not slicing and reading whole array unnecessarily