Tag: Big Data
Replacing NaN (Not a Number) values with a specified value in a column : nanvl
The nanvl function in PySpark is used to replace NaN (Not a Number) values with a specified value in a…
Computing the average value of a numeric column in PySpark
The mean function in PySpark is used to compute the average value of a numeric column. This function is part…
Concatenating two or more maps into a single map : map_concat
The map_concat function in PySpark is designed to concatenate two or more maps into a single map. It merges key-value…
Removing leading spaces (spaces on the left side) from a string in PySpark
PySpark, a leading tool in big data processing, provides several functions for string manipulation, one of which is ltrim. This…
Adding a new column to a DataFrame with a constant value
The lit function in PySpark is a straightforward yet powerful tool for adding constant values as new columns in a…
Finding the position of a substring within a string using PySpark
pyspark.sql.functions.locate PySpark, a tool for handling large-scale data processing, offers a plethora of functions for string manipulation, one of which…
Adding a specified character to the left of a string until it reaches a certain length in PySpark
LPAD, or Left Padding, is a string function in PySpark that adds a specified character to the left of a…
PySpark : Reference a column in a DataFrame – col
In the world of PySpark, efficient data manipulation and transformation are key to handling big data. The col function plays…
Perform ascending sorting of data while placing null values at the end in PySpark
In the realm of big data processing with PySpark, handling null values efficiently during sorting operations is crucial. The asc_nulls_last…
Mastering the Pivot function in PySpark : Rotate data from a long format to a wide format
Understanding pivot in PySpark This article aims to elucidate the concept of pivot, its advantages, and its practical application through…
