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.

## Number of unique pairs within one column – pandas

I am having a little problem with producing statistics for my dataframe in pandas. My dataframe looks like this (I omit the index):

## pandas – how to get last n groups of a groupby object and combine them as a dataframe

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.

## Conditionally offseting values by group with Pandas

I am looking for a more efficient and maintainable way to offset values conditionally by group. Easiest to show an example.

## Faster alternative to perform pandas groupby operation

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)

## pandas groupby aggregate element-wise list addition

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:

## Sum a column by ID, but skip the first instance?

I have a dataframe like the following. I would essentially like to do an operation like df.groupby('ID').sum() to get the sum of the Variable column, but I need to skip the first period observed for a particular ID. So, for ID=1, I am dropping the observation at period 1, but...

## How do I sum a column by ID, but skip the first instance? (Pandas)

## Pandas dataframe to dict of dict

Given the following pandas data frame:I want to get a dictionary of dictionary.But I managed to create the following only:

## Groupby on pandas dataframe and concatenate strings with comma based on the frequency of values in a column

I have a Pandas DataFrame with the following structure.I would like to groupby on the meet_id column and concatenate the label column in such a way that the label with higher frequency for that group is left untouched, while the second most frequent label will have the first label concatenated,...