I have a Pandas DataFrame with columns like this:

`col1 col2 col3 col4 col5`

`a a1 foo1 foo2 foo3`

`b b1 foo4 foo5 foo6`

`c c1 foo7 foo8 foo9`

`a a2 foo10 foo11 foo12`

`a a3 foo13 foo14 foo15`

`b b2 foo16 foo17 foo18`

I would like to sort the rows (the entire rows) of this dataframe based on descending frequency of values in `col1` and then for other columns in the row, get one of the rows that have that value in their column (similar to SQL group by). How can I do that in Pandas? I believe this is some combination of groupby and sort\_values, but I'm not exactly sure how to do it.

For the above example, `a` is the most frequent value in `col1`, followed by `b` and `c`. So I would like the first row of the resulting dataframe to be one of the rows with `col1` value being `a`. The next row should be one of the two rows with value `b`. And the last row is the only row having value `c`.

So this is one answer:

`col1 col2 col3 col4 col5`

`a a1 foo1 foo2 foo3`

`b b1 foo4 foo5 foo6`

`c c1 foo7 foo8 foo9`

but so is this one:

`col1 col2 col3 col4 col5`

`a a3 foo13 foo14 foo15`

`b b1 foo4 foo5 foo6`

`c c1 foo7 foo8 foo9`

And this one:

`col1 col2 col3 col4 col5`

`a a2 foo10 foo11 foo12`

`b b2 foo16 foo17 foo18`

`c c1 foo7 foo8 foo9`

Any of these is fine as the result. To be more clear, mixing of values from different rows is not allowed. A row must be returned exactly as is.