Memory-mapped files with np.memmap in NumPy - Mini Project: Build & Apply

Memory-mapped files with np.memmap

📖 Scenario: You are working with a large dataset of numbers that cannot fit entirely into your computer's memory. To handle this, you decide to use memory-mapped files, which allow you to work with data stored on disk as if it were in memory.

🎯 Goal: Learn how to create a memory-mapped file using np.memmap, write data to it, and read data back efficiently without loading the entire file into memory.

📋 What You'll Learn

Create a numpy memmap file with specific shape and data type

Write data to the memmap file

Read data from the memmap file using slicing

Print the read data to verify correctness

💡 Why This Matters

🌍 Real World

Memory-mapped files are used when working with very large datasets that do not fit into memory, such as large images, scientific data, or logs.

💼 Career

Data scientists and engineers use memory-mapped files to efficiently process big data without running out of memory, improving performance and scalability.

1

Create a memory-mapped file

Create a memory-mapped file called data_memmap using np.memmap with filename 'data.dat', data type float32, mode 'w+' (write plus read), and shape (5, 5).

NumPy

# Create a memory-mapped file called data_memmap
# Your code here

Hint

Use np.memmap with the correct filename, dtype, mode, and shape parameters.

2

Write data to the memory-mapped file

Assign the numpy array np.arange(25, dtype='float32').reshape(5, 5) to the memory-mapped file data_memmap to fill it with values from 0 to 24.

NumPy

import numpy as np

data_memmap = np.memmap('data.dat', dtype='float32', mode='w+', shape=(5, 5))
# Write values 0 to 24 reshaped to (5,5) into data_memmap
# Your code here

Hint

Use slicing data_memmap[:] to assign the new array.

3

Read a slice from the memory-mapped file

Create a variable called slice_data that reads the first 3 rows and 2 columns from data_memmap using slicing.

NumPy

import numpy as np

data_memmap = np.memmap('data.dat', dtype='float32', mode='w+', shape=(5, 5))
data_memmap[:] = np.arange(25, dtype='float32').reshape(5, 5)
# Read the first 3 rows and 2 columns into slice_data
# Your code here

Hint

Use slicing with [:3, :2] to get the first 3 rows and 2 columns.

4

Print the sliced data

Print the variable slice_data to display the selected part of the memory-mapped file.

NumPy

import numpy as np

data_memmap = np.memmap('data.dat', dtype='float32', mode='w+', shape=(5, 5))
data_memmap[:] = np.arange(25, dtype='float32').reshape(5, 5)
slice_data = data_memmap[:3, :2]
# Print slice_data
# Your code here

Hint

Use print(slice_data) to show the data.

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