MemoryError: unable to allocate large NumPy array

The error MemoryError: unable to allocate large NumPy array occurs when Python tries to create a NumPy array that is too large for the available system memory.

1. Why Does This Happen?

RAM Limitations: If the requested array exceeds the available RAM, Python raises MemoryError.
Inefficient Data Types: Using high-memory data types like float64 instead of float32 increases memory usage.
No Virtual Memory Available: If swapping (virtual memory) is disabled, large arrays may fail to allocate.

2. Common Causes and Solutions

Cause 1: Requesting an Extremely Large Array

If the requested array size is too large, it may exceed available RAM.

Example (Allocating an Excessive Array)

import numpy as np
arr = np.zeros((100000, 100000), dtype=np.float64)  # Requires ~74.5 GB RAM!

Solution: Use a Smaller Array or Reduce Data Type Size

arr = np.zeros((10000, 10000), dtype=np.float32)  # Uses ~381 MB RAM

Estimate Required Memory:

size_in_bytes = 100000 * 100000 * np.dtype(np.float64).itemsize  # 8 bytes per float64
size_in_gb = size_in_bytes / (1024**3)
print(f"Required Memory: {size_in_gb:.2f} GB")

Cause 2: Using an Inefficient Data Type

Using float64 instead of float32 doubles memory usage.

Example (Using Unnecessary `float64`)

arr = np.ones((50000, 50000), dtype=np.float64)  # ~18.6 GB

Solution: Use a Smaller Data Type

arr = np.ones((50000, 50000), dtype=np.float32)  # ~9.3 GB

Alternative: Use Integer Types Instead of Floats

arr = np.ones((50000, 50000), dtype=np.int8)  # Uses only ~2.3 GB

Cause 3: Not Using Memory-Mapped Files

NumPy supports memory mapping, which allows working with large arrays without loading them fully into RAM.

Solution: Use memmap to Work with Large Arrays

arr = np.memmap('large_array.dat', dtype=np.float32, mode='w+', shape=(100000, 100000))
arr[:] = np.random.rand(100000, 100000)  # Works without consuming all RAM

This stores data on disk, preventing RAM overflow.
Use case: Large datasets in ML, AI, data science.

Cause 4: Lack of Virtual Memory (Swap Space)

When physical RAM is full, systems use virtual memory (swap). If disabled, NumPy may fail to allocate large arrays.

Solution: Enable Swap Space (Linux/macOS/WSL)

sudo fallocate -l 8G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile

This adds 8GB of virtual memory to prevent MemoryErrors.

Windows: Increase Pagefile Size

Go to System Properties → Advanced → Performance
Click Settings → Advanced → Virtual Memory
Increase pagefile size.

Cause 5: Processing Data in Large Chunks

Processing a large dataset all at once can exhaust memory.

Solution: Process Data in Smaller Chunks
Instead of:

data = np.loadtxt('huge_file.csv', delimiter=',')

Use:

import pandas as pd
chunks = pd.read_csv('huge_file.csv', chunksize=10000)
for chunk in chunks:
    process(chunk)

3. Summary of Fixes

Issue	Fix
Allocating an extremely large array	Reduce size or data type precision
Using `float64` unnecessarily	Use `float32` or smaller integers
RAM limits	Use memory-mapped arrays (`memmap`)
No swap space	Enable virtual memory (swap/pagefile)
Processing large data all at once	Use chunk-based processing