I am trying to sort a pandas dataframe block-wise without changing the order within blocks.The dataframe contains forum posts, timestamps, and thread names. I have already sorted the dataframe such that all posts belonging to the same thread are in the right order using df.sort_values(['thread','timestamp'],inplace=True). I now want to sort...
How could I use a multidimensional Grouper, in this case another dataframe, as a Grouper for another dataframe? Can it be done in one step?
I'm trying to extract grouped row data to use values to plot it with label colors another file.my dataframe is like below.
I have a table with keywords associated with articles, looks like this:I need to get a sort of a pivot table:
I have a df DataFrame like : And I want to group all columns X with X_ for all letters A,B,... (let's say, the group is called X as well), and group as well using COMMON. I would like to apply later function like std() to all the grouped values.
I am having a little problem with producing statistics for my dataframe in pandas. My dataframe looks like this (I omit the index):
How to get last 'n' groups after df.groupby() and combine them as a dataframe. After doing grouped.ngroups i am getting total number of groups 277. I want to combine last 12 groups and generate a dataframe.
I am looking for a more efficient and maintainable way to offset values conditionally by group. Easiest to show an example.
I have a dataset with name(person_name), day and color(shirt_color) as columns Each person wears a shirt with a certain color on a particular day (number of days can be arbitrary)
I have a pandas dataframe that looks as follows:I want to perform an aggregate groupby that adds the lists stored in the Y columns element-wise. Code I have tried: