I need to calculate Total Hours and Hours by Status per Week using Python / Pandas GROUP BY. I can get Total Hours by each Week: But I don’t know how to also group by Status, so it will be 2 additional columns (On Status Hours and Off Status Hours) If I add Status column just to the groupby part,
Tag: group-by
How to sum a value based on group?
I am trying to figure out how to sum a value from rank 5 to the LOWEST rank (I.E. 5-1,000) for each geography in my dataframe. However, I am getting the error: ‘DataFrameGroupBy’ object has no attribute ‘iloc’ Am I using iloc incorrectly? Answer IIUC, try:
Using groupby on already grouped data in Pandas
I would like to achieve the result below in Python using Pandas. I tried groupby and sum on the id and Group columns using the below: I got the first two columns, but I’m not sure how to get the third column (Overall_Total). How can I do it? Initial data (before grouping) id Group Time 1 a 2 1 a
Can repeating query be saved?
In my Python / Sqlite program, I am running queries like this So the “basic” query is the same, and the rows Sqlite gathers are the same, but because of the different grouping , I have to run the same query multiple times. I wonder if there is a way to achieve the same output more effectively, ie. run the
Groupby and get the values in one dataframe
I have a dataframe which I need to do groupby by multiple columns and get the items within every group as a row; I need to output a below table after group; I have been looking for the answer but I was not able to find any, I appreciate if you can help we with the code. Answer You could
Pandas: Tidy up groupby aggregation
I really struggle with tidying up the table into a “normal” dataframe again after having aggregated something. I had a table like that (columns): So I calculated average and std of the Result column over multiple runs using that command: The output is a DataFrame like that: It looks a bit like three levels. df.columns outputs the following multiindex: How
Make list after groupby in pandas using apply() function
I have this dataframe: My goal is to keep tracking the values in column2, based on the letters of column1 separated by(:), the output should look like this: What’s the most pythonic way to do this: At the moment I’m able to group by the column 1 and I’m trying to use the apply() function, but I do not know
How to to cumcount in group for specific value?
I have a dataframe: when I do df.groupby(“id”).cumcount() it returns: I want to count only those ones that equal w in column value and it must be in dataframe form: How to do that with cumcount function? Answer Use: Output As an alternative:
How to calculate average percentages of values within group?
I have a dataframe: I want to calculate percentage of each ‘type’ within date group and then average values among all dates. So desired results must be: and then average among all dates. this is the desired final result: How to do that? Answer You can try this: or this: It’s not quite clear by your question
Selecting rows based on condition in python pandas
I have a data-frame with columns as [‘ID’,’Title’,’Category’,’Company’,’Field’] and it has both blank values and at some places missing values are put as N/A. I have to pick the row which has maximum information available. For example one case could be. In this case i want to select the row number 2 as it has maximum information available. I tried