This question already has answers here: How do I use Pandas group-by to get the sum? (11 answers) Closed 3 months ago. I find duplicate rows in a dataframe using pd.duplicated() function. I would like to keep the first row, adding values of columns in duplicate rows and then reset index of the duplicate rows. […]

I am trying to switch values between the Range and Unit columns in the dataframe below based on the condition that if Unit contains -, then replace Unit with Range and Range with Unit. To do that, I am creating a unit_backup column so that I don’t lose the original Unit value.

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I am using .size() on a groupby result in order to count how many items are in each group.

What are the reasons behind pandas allowing operations between sets / strings / other non-float types and NaN (yielding NaN), whereas pure Python does not?

I am trying to format the data in a column. It is monetary but I need to match it up with another dataset so I need to format it before it goes into PowerBi.

I have a pandas dataframe and one of the columns ‘sigResponseActions’ has the following data in it

I have a dataframe that lists a users codes

I have the below code: