Speed Up Your Pandas Code with These Memory Optimization Tricks

Описание: Have you ever tried loading a large dataset using Pandas ??

By Default, Pandas loads the entire data into the memory which can often lead to memory insufficiency . Additionally, the transformations on Dataframes usually creates a copy in the memory, thus causing further memory bottleneck

Memory is costly and it is therefore very important to make efficient use of the available resources.

But then is there a way we could deal with this memory issues and load BIG Data using PANDAS ??

Indeed,

Using these 3 tricks, we could make better utilize our memory resources.
In addition, certain structures built around Pandas try to incorporate the capabilities of multithreading
One such popular framework is Dask

[We shall learn more about Dask later and for now read more here: https://docs.dask.org/en/stable/insta...]

In this video,
we shall learn about
Category data type in PANDAS Python
3 Tricks for Pandas Memory Optimization

Find out more about the techniques and a special data type in pandas, the 'category' dtype
doc: https://pandas.pydata.org/pandas-docs...

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