Understanding Series.transform(func[, axis])

Series.transform(func[, axis])In this article, we’ll explore the Series.transform(func[, axis]) function, shedding light on its capabilities through comprehensive examples and outputs.Understanding Series.transform(func[, axis]): The Series.transform(func[, axis]) function in Pandas API on Spark is tailored to invoke func, yielding a Series with transformed values akin to the original Series. It...

Series.aggregate(func) : Pandas API on Spark

In this article, we will explore the Series.aggregate(func) function, which enables users to aggregate data using one or more operations over a specified axis in a Spark DataFrame. Through comprehensive examples and outputs, we’ll unravel the versatility and power of this function.Understanding Series.aggregate(func): The Series.aggregate(func) function in Pandas is designed to apply one or more...

Series.agg(func) : Pandas API on Spark

The integration of Pandas API in Spark bridges the gap between these two ecosystems, allowing users familiar with Pandas to leverage their knowledge in a distributed computing environment. In this article, we will delve into the Series.agg(func) function, which enables us to aggregate data using one or more operations over a specified axis, demonstrating its usage with examples and outputs.Understanding...

Apply custom functions to each element of a Series in PySpark:Series.apply()

PySpark-Pandas Series.apply() apply() function, which allows users to apply custom functions to each element of a Series. In this article, we’ll explore the capabilities of Series.apply() through a practical example and delve into its significance in data transformation tasks.Significance of Series.apply():Flexibility: apply() allows users to define and apply any custom function to each element,...