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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 […]
In this short how-to article, we will learn how to rename a column in Pandas and PySpark DataFrames.
The rename function can be used for renaming the columns.
# Rename one columns df = df.rename(columns={"date": "purchase_date"}) # Rename multiple columns df = df.rename(columns={"date": "purchase_date", "qty": "quantity"})
Or, using the inplace parameter:
df.rename(columns={"date": "purchase_date"}, inplace=True)
The withColumnRenamed function is used for renaming columns in a PySpark DataFrame.
# Rename one column df = df.withColumnRenamed("date", "purchase_date") # Multiple columns df = df.withColumnRenamed("date", "purchase_date").withColumnRenamed("qty", "quantity")
