How to Build an End-To-End ML Pipeline With Databricks & Aporia
This tutorial will show you how to build a robust end-to-end ML pipeline with Databricks and Aporia. Here’s what you’ll...
There might be some redundant columns in a DataFrame or we might just not need some columns for the task at hand. In this short how-to article, we will learn how to delete a column from Pandas and PySpark DataFrames.
We can use the drop function to delete a column or multiple columns from a DataFrame.
# delete one column
df = df.drop("NO", axis=1)
# delete multiple columns
df = df.drop(["f1", "f2"], axis=1)
In the case of deleting multiple columns, column names need to be written in a list.
PySpark DataFrame has a drop method to delete single or multiple columns.
# delete one column
df = df.drop("NO")
# delete multiple columns
df = df.drop("f1", "f2")
This tutorial will show you how to build a robust end-to-end ML pipeline with Databricks and Aporia. Here’s what you’ll...
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