When we run the first line ( x = 1), Python stores integer object 1 in a memory address 140710407579424 on my computer (different from yours). To find it out, we can use a function called id(). Let’s find the memory address on the Heap that the variable x points to. You don’t need to mess with the Heap, but it is better to understand how Python manages the Heap since most of your data is stored in this section of the memory. Python uses a garbage collection algorithm (called Garbage Collector) that keeps the Heap memory clean and removes objects that are not needed anymore. Python interpreter actively allocates and deallocates the memory on the Heap (what C/C++ programmers should do manually!!! Thanks, Python!!!). To store objects, we need memory with dynamic memory allocation (i.e., size of memory and objects can change). So, where are the objects? Are they in Stack memory? No, they are in a different memory called “Heap Memory” (also called the Heap). Python interpreter and OS memory management together take care of this section of memory.Īs you learned, variables (or references in general) only stores memory addresses of objects. As a data scientist and programmer, you don’t have access to Stack memory. Temporary means, as soon as the called function returned its value, the function and the related variable will be removed from the Stack. Static means the size of values stored in the Stack cannot be changed. According to the instruction, the Python interpreter load functions and local variables in the Stack Memory (also called the Stack). The machine follows instructions in the code section. Three important memory sections are:Ĭode (also called Text or Instructions) section of the memory stores code instructions in a form that the machine understands. Heap MemoryĪt the run time, computer memory gets divided into different parts. For example, in our simple example x = 1 the reference x is pointing to a memory address that the integer object 1 is stored. When we defined references in the previous paragraph, I should have told you that a reference does not point to a value in Python but points to the memory address of an object. When we create an object, it will be stored in memory. Lists, dictionaries, tuples, pandas data frames, NumPy arrays are objects. As a Python programmer, you must have heard that “Everything in Python is an object.” An integer number is an object. Now, let's get deeper and introduce objects. In addition to variables, attributes and items are two other popular references in programming. When you define x = 1, x is the variable or reference and 1 is its value (more accurate an integer object). The most famous references in programming are variables. A reference is a name that we use to access a data value (i.e., an object). References are a little bit tricky to explain. When you add two numbers, you are basically applying the add (or sum) method to two values. At the end of this article, you have good basic knowledge of how Python handles memory allocation and deallocation. In this article and an upcoming article, I explain some basic concepts around memory management in Python. As a data scientist, if I knew the basics of Python and memory management, I could prevent it and make much more memory-efficient codes. It made the process super slow (as the disk is much slower in data transmission). About 10% into the process, the dictionary object took all my RAM and started swapping between disk and RAM. I used a python dictionary object to keep track of sequences (i.e., sequences of nucleotides) and store their location in a reference human DNA. But, as soon as we switch to large datasets (big data) or heavy processing projects, basic knowledge about memory management becomes crucial.Īs an example, I was working on a data science project regarding indexing human DNA. As long as we are working on small datasets, ignoring how Python manages memory (i.e., memory allocation and deallocation) does not impact our code performance. After all, Python is the most popular language among data scientists, partly because it automatically handles those details. As data scientists, normally, we don’t pay attention to how Python and the underlying operating system handle memory for our code.
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