a": ["red"] * 2 + ["blue"] * 2 + ["black"] * 2, "b": range(6)}
        ... )
        >>> df
               a  b
        0    red  0
        1    red  1
        2   blue  2
        3   blue  3
        4  black  4
        5  black  5

        Select one row at random for each distinct value in column a. The
        `random_state` argument can be used to guarantee reproducibility:

        >>> df.groupby("a").sample(n=1, random_state=1)
               a  b
        4  black  4
        2   blue  2
        1    red  1

        Set `frac` to sample fixed proportions rather than counts:

        >>> df.groupby("a")["b"].sample(frac=0.5, random_state=2)
        5    5
        2    2
        0    0
        Name: b, dtype: int64

        Control sample probabilities within groups by setting weights:

        >>> df.groupby("a").sample(
        ...     n=1,
        ...     weights=[1, 1, 1, 0, 0, 1],
        ...     random_state=1,
        ... )
               a  b
        5  black  5
        2   blue  2
        0    red  0
        NrČ