Python is an interactive and object-oriented programming language that supports multiple programming models and strives to make all models accessible and functional. Python language is easy to learn for a beginner. In Python, there are several types of errors that can occur in the Python programming language. This article is to highlight one of the errors in Python called Memory Error and we will discuss its definition, its types, and how to avoid or fix the Python memory error. Let’s start with its definition.
Python MemoryError
Python memory error is an error that occurs when Random Access Memory, also known as RAM, cannot support the code when executed because the code that is running needs more than the existing RAM. The error often occurs when a large amount of data is loaded into memory and when a program runs out of memory while processing the data. Simply put, this error occurs when you run out of RAM to run the code.
This error often occurs when a large amount of data is loaded into memory and the program runs out of memory while processing the data.
Memory Error Types
There are numerous types of memory errors in Python programming. One of the errors may also occur if you have a large RAM size that is capable of handling the massive datasets. Another type occurs when RAM capacity is exceeded due to the data stored on the system hard drive.
The following are some types of memory errors:
Unexpected Memory Error in Python
This error occurs when the system has RAM space, but the software that you are using filled all the virtual address space it has available. This error occurs when using a 32-bit Python installation because it only has access to 4GB of RAM. If the system that you are using is 32-bit only, the available memory will be reduced. Although 4 GB of RAM is enough in most cases, Python is still a programming language that has more general-purpose languages. Python programming language is used in many important fields such as data science, application development, artificial intelligence, and also graphical user interface (GUI).
Memory Error Because of Incorrect Installation of Python and a Dataset
The data file which is loaded into the memory can be small or large depending on the type of program that you want to run. However, if a large amount of dataset is loaded into the memory to run the calculations on, store the data or run the patches on, memory can quickly run out.
Another memory error can occur if you install the wrong Python package. For example, if you manually installed the Python version 2.7 and other necessary programs on Windows, you may experience a memory error due to an incorrect installation package.
Out of Memory Error
A computer can usually distribute the memory until it is full, and the memory manager uses the available hard disk space to store the memory files or documents if they cannot fit into the RAM. This then leads to an out of memory error and you can view it by going into the system properties and then selecting the performance options where you will find a backup option that leads you to view the virtual memory in Windows.
In the following section, we will provide you with some examples of memory errors and the widely used solutions for you to follow.
Example:
Let’s start with an interesting code to see this error in work. Here, we begin with an empty array named “abc” and add the strings to it using the nested arrays. In this example, three levels of nested arrays with 20,000 iterations for each are used. This indicates the string “Once again!” appears 2,000,000,000 times in the array “abc” at the end of the program.
for m in range(20000):
for n in range(20000):
for k in range(20000):
abc.append("Once again!")
Here, you can see that the previous code results in a memory error and the reason is quite obvious.
How to Avoid Memory Error
There are several solutions for memory errors, and you need the right one to fix them. The unexpected memory error can be solved if you install the 64bit version of Python instead of the 32-bit version because it will have much more storage and RAM even though there is more memory usage. A dataset error should use the dedicated generator methods and classes that will be discussed.
Following are some solutions that can be used to avoid the error:
Free Memory in Python
By getting rid of unnecessary and unused data by deleting or freeing it, the memory will have more space. We can also store only the relevant data using generators or using a batch technique to split the large datasets into smaller datasets. To do this, we can use the following code:
This program, known as the garbage collector (gc), is employed to release the memory. We can release the memory by eliminating the unreferenced objects using the import garbage collector and gc.collect() line.
Limit CPU Usage
Here, the resource module can be used to limit the memory usage of programs or the CPU so that a memory error does not occur. This can be done with the following code:
Here, we import the libraries of the signal as “seg”, resource as “res”, and “os”. To check if the time limit is exceeded, we use the code “check_time_exceed(singo, frame)” and print time’s up. To set up the resource limit, we use the code of “set_runtime(seconds)”.
Restrict Memory Usage
To limit the memory usage, the following code limits the total address space. Here, we import the resource module and define a function named “memory_size” in which we provide the option to set a limit.
def memory_size(max_size):
soft, hard = res.getrlimit(res.RLIMIT_AS)
res.setrlimit(res.RLIMIT_AS, (max_size, hard))
Allocate More Memory
Memory error can also be avoided if more memory can be allocated to Python’s memory settings by reconfiguring the memory to allocate more RAM.
Choose a Smaller Data Set or Use a Computer with More Memory
Using the progressive data loading techniques to randomly sample the data after the first 100, 1000, or 100,000 rows to solve the problem. We can also avoid the memory error if we have access to a larger computer with more memory.
numpy.random.uniform(low=1,high=20,size=(50000,200000))
Here, we can see that if we use the “NumPy” operations, it produces random numbers between 50,000 to 200000 with the lowest number being 1 and the highest being 20. The size mentioned in the code is so massive that a memory error occurs.
Therefore, it is better to use smaller datasets or use a computer with higher memory. For example, if we use 1000 and 20000 instead of 50000 and 200000, the code will not show the error.
numpy.random.uniform(low=1,high=20,size=(1000,20000))
Conclusion
In this article, we talked about memory errors. Our main contribution is that the memory error is the use of RAM in our computer, where many operations take place. We also learned different methods that can be used to deal with a Python memory error. This article is a simple guide that introduces what a memory error is, what its types are, and how we can resolve or avoid it.
