Memory Error in Python

When a code is executed in Python, and the system runs out of memory, then a memory error is encountered in the Python’s terminal. This error occurs when a lot of data is executed in the Python script or when the Python script creates an excess number of objects. The error indicates that our memory of RAM is smaller than the memory of the executing code. This write-up will explore the possible reasons for the “MemoryError” in Python and provide the solution.

So let’s begin!

How to Fix MemoryError in Python?

This section enlists the possible reasons and the best solutions to fix the MemoryError in Python. Let’s head over to them one by one.

Reason 1: Memory Error Due to 32-Bit Python

One of the main reasons for memory errors in Python is a 32-Bit Python installation. If your computer is 64-bit and you are using a 32-bit Python installation, then the memory error will occur most of the time. A 32-bit Python uses approximately 4GB of RAM, and if the computer operating system is 32-bit, then the 4GB memory will be shrunk more.

Solution: Install 64-bit Python Installation

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If you have a 64-bit system, it is always recommended to use a 64-bit Python installation to fix all limitations and errors. Python 64-bit operates with all its functions and provides extra speed for executing programs. The following link leads you to the downloads page of Python, where you can get your desired version of Python:

https://www.python.org/

Reason 2: Use of Large Dataset

In Python, we have to deal with a large number of data sets, especially when we are doing any machine learning project or utilizing any Python algorithms. The data set of these algorithms may be so large that if all the data is called directly into memory at a time and starts operating on it then our Python script memory reaches the memory limit.

Solution: Use Generator Function

To overcome this problem, the data needs to be executed step by step or with the help of the Generator Function. Most of the Python libraries like Tensorflow, Keras, etc have a specific Generator function that will execute the data in the form of chunks.

Let’s understand the simple generator function by an example:

Code

def gen_funct():
    n = 1
    yield n
    n += 1
    yield n
    n += 1
    yield n

# Using for loop
for item in gen_funct():
    print(item)

In the above code, the generator function has been defined, and the variable value is used to save the sequence of numbers. The value will be incremented one by one and iterated using a for Loop until the end of the list.

Output

Memory error resolve python (3)

The output shows us that every value is executed one by one.

Reason 3: Inappropriate Package Installation

Memory error will also occur when any Python package does not install efficiently. Most of the time, the Python package is installed using the PiP command or the console library function.

Solution: Use Conda Package Installer

To avoid memory errors, a proper package installer is needed that will help us to install all the packages efficiently and correctly in Python. One of the most popular package installers is Conda, which helps install any package without error and with proper memory allocation. To install a “condo” on windows, it is recommended to install a “minicondo” which includes the condo package manager and the latest version of python. The installation link and instructions are in the following link:

https://docs.conda.io/en/latest/miniconda.html

Reason 4: Out Of Memory Error In Python

In modern computers, the hard disk manager will automatically use the available hard disk to store the memory pages that will not take place in RAM. Our system can allocate the RAM and fill it until the disk fills up. So when the Python script is executed, it will show the memory error. It will become worse when the program allocation can split the memory into small chunks, making it useless for new allocation.

Solution: Free Memory Using Garbage Collector Module In Python

A Python package named “gc” is used to resolve memory error problems. Garbage values also sometime will lead us to memory error problems in Python. To avoid this, free all the memory using the collect() function. We need to get rid of garbage data and make the memory free for new programs.

Import gc
gc.collect()

Reason 5: Python Memory Error Due To Low RAM

Low RAM is also considered one of the reasons for memory error because memory error occurs when the program does not have enough memory to execute. The PC having low RAM is also needed to be upgraded so the program can run properly.

Solution: Provide More Memory to Python Tools

The issue can be resolved by providing more memory to the tools and libraries used in Python. The tool or library can be manually configured and will increase the memory because most of the time, the default configuration will limit the memory usage.

In Python, a large amount of data and algorithms of machine learning are handled, so always take care of memory usage to avoid any problems. An open-source tool named weka is used for handling different machine learning algorithms and bigger datasets. In weka, with the help of parameters, the memory size can be increased.

Conclusion

In this post, the causes and solutions of Python memory errors are discussed. A Python memory error occurs due to using “32-Bit Python ” installation, loading large data in memory, inappropriate package installation, and low RAM Computer. The memory error problems have been cleaned with the help of “gc” collect Python, installing packages using the appropriate package installer, allocating more memory to Python tools and libraries, and increasing the memory of the computer system. This post has demonstrated the multiple reasons for the “MemoryError” in Python and also listed the solutions to these reasons.