In this tutorial, we will go over 6 examples to demonstrate PySpark version of Pandas on typical data analysis and manipulation tasks. PySpark is a Python API for Spark
Pandas is one of the predominant tools for manipulating and analyzing structured data. It provides numerous functions and methods to play around with tabular data.
However, Pandas may not be your best friend as the data size gets larger. When working with large-scale data, it becomes necessary to distribute both data and computations which cannot be achieved with Pandas.
A highly popular option for such tasks is Spark, which is an analytics engine used for large-scale data processing. It lets you spread both data and computations over clusters to achieve a substantial performance increase.
It has become extremely easy to collect and store data so we are likely to have huge amounts of data when working on a real life problem. Thus, distributed engines like Spark are becoming the predominant tools in the data science ecosystem.
PySpark is a Python API for Spark. It combines the simplicity of Python with the high performance of Spark. In this article, we will go over 6 examples to demonstrate PySpark version of Pandas on typical data analysis and manipulation tasks.
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