The most advanced ML Observability product in the market
Building an ML platform is nothing like putting together Ikea furniture; obviously, Ikea is way more difficult. However, they both, similarly, include many different parts that help create value when put together. As every organization sets out on a unique path to building its own machine learning platform, taking on the project of building a […]
Start integrating our products and tools.
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 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"])