I am trying to solve a problem some what very similar to: https://platform.stratascratch.com/coding/10362-top-monthly-sellers?code_type=2

I have a pandas series.groupby objects, call it data. If I print out the elements, it looks like this:

Hope some one can help me with this. I have several dataframes that contains a value a corresponding id and name. For each id I want the max value for all dataframes but I must also now for which name this max value occured.

I’ve spent about a week trying to figure out why this loop isn’t working and I can’t figure it out.

say I have a dataframe df1 with a column “A”. I do group by operation

I’m trying to get different groups from a grouped dataframe in pandas, but I’m getting an error.

I am to apply consecutive filtering and logical indexing to the pandas df to display the number of plays each day for the week for two cities as you can see below.

I have a dataframe which has a key column, some value columns and some timestamp columns. For some keys, there might be multiple rows with differing values in value and timestamp columns.

Assume groups will have more than n memebers, I want to take every nth row from each group. I looked at https://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.core.groupby.GroupBy.nth.html but that only takes one row from each group.

I have a DataFrame of population counts by combination of categorical demographic features and date, with some missing values (consistent across all combos) per date constituting gaps in the data.