PySpark makes it 100x times faster than Pandas for large datasets. Pandas DataFrames are incapable of constructing a scalable application, - pyspark python
In recent years, we have seen an increase in data, and as a result, it increases computational time and memory. Therefore, tools like Pandas which works sequentially, fail to achieve the result in the required time in large datasets.
Some packages, like Dask, Swift, Ray, etc., can parallelize the panda's operation. Parallelizing the panda’s processes causes a lot of speed up. Still, there are specific memory limitations caused by your system as Pandas load your data frame into the memory, even if it is not required at that particular instance. This can be a massive problem in the case of desktop systems, as we need to keep the UI live.
We need a framework to solve the above problems and achieve parallelization under the given memory limitations.solves some of these problems under some given thresholds. In addition, it can perform faster processing speeds. It uses a concept called lazy evaluation , which solves some of the limitations caused by memory.
For the initial phase, up to 20 GB, they have the same slope, but as file size increased, Pandas goes out of memory, and PySpark was able to complete the job successfully.Therefore, for small datasets of 10–12 GB, you can prefer Pandas over PySpark due to the same runtime and less complexity, and above that, you have to work using PySpark.Everything good at something lacks behind in the other areas. This is also the case with PySpark.
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