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We’re super excited to share that Aporia is now the first ML observability offering integration to the Databricks Lakehouse Platform. This partnership means that you can now effortlessly automate your data pipelines, monitor, visualize, and explain your ML models in production. Aporia and Databricks: A Match Made in Data Heaven One key benefit of this […]
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We’re excited 😁 to share that Forbes has named Aporia a Next Billion-Dollar Company. This recognition comes on the heels of our recent $25 million Series A funding and is a huge testament that Aporia’s mission and the need for trust in AI are more relevant than ever. We are very proud to be listed […]
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")