Python MemoryError — What It Means & How to Fix It

What Does This Error Mean?

A MemoryError means Python tried to allocate more memory than your computer has available. Your program is trying to create or store more data than the system can handle. This often happens when loading a very large file all at once, creating enormous lists, or a bug causes data to grow without stopping.

Affected Models

Python 2.x
Python 3.x
All Python versions

Common Causes

Loading an entire large file into memory at once instead of reading it line by line
Creating a very large list or dictionary, often through a loop that grows without limit
A memory leak — objects are being created and never deleted, gradually consuming all available RAM
Running a 32-bit Python interpreter, which is limited to about 2 GB of memory regardless of your computer's RAM
Processing large images, videos, or scientific datasets without using memory-efficient libraries

How to Fix It

Instead of loading a whole file into memory, read it line by line or in chunks. Use 'with open(file) as f: for line in f:' to process one line at a time.

This works for files of any size because only one line is in memory at a time.
If you are building a large list, ask yourself if you really need all the data in memory at once. Consider using a generator instead, which produces values one at a time.

Change a list comprehension [x for x in data] to a generator expression (x for x in data) — it uses far less memory.
If you are using pandas to read a CSV file, use the chunksize parameter: pd.read_csv('file.csv', chunksize=1000). This reads 1000 rows at a time instead of all at once.

Process each chunk and discard it before loading the next one.
Check if you are running 32-bit Python. On 64-bit Windows, 32-bit Python is limited to about 2 GB of RAM. Install the 64-bit version of Python to access your full system memory.

Run 'python' and look at the first line. It says '32 bit' or '64 bit'. Download 64-bit Python from python.org.
Look for loops that add items to a list or dictionary without ever clearing them. If data grows every iteration and never shrinks, you have a memory leak. Break the loop into batches and process each batch separately.

Also check for global variables that accumulate data across many function calls.

When to Call a Professional

MemoryErrors can usually be fixed by the developer. The key is to process data in smaller chunks instead of loading everything at once. For very large data tasks, consider using libraries like NumPy, pandas with chunking, or streaming APIs.

Frequently Asked Questions

How much memory does Python typically use?

Python itself uses around 30-50 MB just to start. The memory your program uses depends entirely on the data you load. A list of 1 million integers uses about 35 MB. A list of 1 million strings uses much more. Use the tracemalloc module or a tool like memory_profiler to measure your program's actual memory usage.

Can I increase the memory available to Python?

No — Python uses as much RAM as your operating system allows. The real fix is to use less memory by processing data in smaller pieces. For truly massive datasets, consider a database or a distributed computing framework instead of loading everything into Python.

Why does a MemoryError crash my whole program immediately?

Because Python cannot finish the operation that needed the memory. If Python cannot allocate memory for a new object, it has no choice but to raise an error immediately. You can technically catch it with try/except MemoryError, but once you are out of memory, there is very little you can do inside the except block.