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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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In this short how-to article, we will learn how to sort DataFrame rows by two or more columns. Rows are sorted by the values in the first column. In the case of equality, the values in the second column are checked, and so on.
The sort_values function is used for sorting DataFrame rows. To sort by multiple columns, column names are written in a list.
df = df.sort_values(by=["A","B"])
By default, the index of the rows prior to sorting are kept, which is not an ideal situation. We can change this behavior by using the ignore_index parameter.
df = df.sort_values(by=["A","B"], ignore_index=True)
The PySpark equivalent of the sort_values function is orderBy. In the case of sorting by multiple columns, we write the column names in a list.
df = df.orderBy(["Date","Team"])