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Tag: melt

Chain df.str.split() in pandas dataframe

Edit: 2022NOV21 How do we chain df.col.str.split() since this returns the split columns if expand = True I am trying to split a column after performing .melt(). If I use assign I end up using the original column and the melted column actually does not even exist. Answer Using expand converts it into a DataFrame, which you do not really

How do I melt a pandas dataframe?

On the pandas tag, I often see users asking questions about melting dataframes in pandas. I am gonna attempt a cannonical Q&A (self-answer) with this topic. I am gonna clarify: What is melt? How do I use melt? When do I use melt? I see some hotter questions about melt, like: Convert columns into rows with Pandas : This one

Pandas ‘partial melt’ or ‘group melt’

I have a DataFrame like this and I want to transform it into something like this This is an unpivot / melt problem, but I don’t know of any way to melt by keeping these groups intact. I know I can create projections across the original dataframe and then concat those but I’m wondering if I’m missing some common melt

PySpark Dataframe melt columns into rows

As the subject describes, I have a PySpark Dataframe that I need to melt three columns into rows. Each column essentially represents a single fact in a category. The ultimate goal is to aggregate the data into a single total per category. There are tens of millions of rows in this dataframe, so I need a way to do the