在pandas数据帧中的函数，它复制R中的dplyr group_by（多个变量）函数

Question

Consider this case:

Python pandas equvilant to R groupby mutate

In dplyr :

df = df%>% group_by(a,b) %>%  

means first the dataframe is grouped by column a then by b .

In my case I am trying to group my data first by group_name column, then by user_name , then by type_of_work . There are more than three columns (which is why I got confused) but I need data grouped according to these three headers in the same order. I already have an algorithm to work with columns after this stage. I only need an algorithm for creating a dataframe grouped according to these three columns.

It is important in my case that the sequence is preserved like the dplyr function.

Do we have anything similar in pandas data-frame?

Answer 1

Grouped = df.groupby(['a', 'b'])

Read more on "split-apply-combine" strategy in the pandas docs to see how pandas deals with these issues compared to R.

From your comment it seem you want assign the grouped frames. You can either use a groupbyobject through the API, eg grouped.mean(), or you can iterate through the groupby object. You will get name and group in each loop.