I have a data transformation problem where the original data consists of “blocks” of three rows of data, where the first row denotes a ‘parent’ and the two others are related children. A minimum working example looks like this: In reality, there are up to 15 Providers (so up to 30 columns), but they are not necessary for the example.
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
I have DataFrame in Python Pandas like below: ID U1 U2 U3 CP CH 111 1 1 0 10-20 1 222 1 0 1 10-20 1 333 0 1 0 20-30 0 444 0 1 1 40-50 0 555 1 0 0 10-20 0 And I need to create column with percent of ‘1’ in column ‘CH’ per combination for:
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
I have this table and I need to melt away this table to be like the expected table where I need to get the point name (a and b) from the column name and let the bq and progress columns melt. The expected result is as below: How can do it in python? Answer Try this: Result :
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
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