Considerations for PySpark to pandas conversion

This section will introduce pandas, demonstrate the differences between pandas and PySpark, and the considerations that need to be kept in mind while converting datasets between PySpark and pandas.

Introduction to pandas

pandas is one of the most widely used open source data analysis libraries for Python. It contains a diverse set of utilities for processing, manipulating, cleaning, munging, and wrangling data. pandas is much easier to work with than Pythons lists, dictionaries, and loops. In some ways, pandas is like other statistical data analysis tools such as R or SPSS, which makes it very popular with data science and machine learning enthusiasts.

The primary abstractions of pandas are Series and DataFrames, with the former essentially being a one-dimensional array and the latter a two-dimensional array. One of the fundamental differences between pandas and PySpark is that pandas represents its datasets as one- and two-dimensional...