'col1', 'col2'])
          col1  col2  col3 col4
        1    A     1     1    B
        0    A     2     0    a
        2    B     9     9    c
        5    C     4     3    F
        4    D     7     2    e
        3  NaN     8     4    D

        Sort Descending

        >>> df.sort_values(by='col1', ascending=False)
          col1  col2  col3 col4
        4    D     7     2    e
        5    C     4     3    F
        2    B     9     9    c
        0    A     2     0    a
        1    A     1     1    B
        3  NaN     8     4    D

        Putting NAs first

        >>> df.sort_values(by='col1', ascending=False, na_position='first')
          col1  col2  col3 col4
        3  NaN     8     4    D
        4    D     7     2    e
        5    C     4     3    F
        2    B     9     9    c
        0    A     2     0    a
        1    A     1     1    B

        Sorting with a key function

        >>> df.sort_values(by='col4', key=lambda col: col.str.lower())
           col1  col2  col3 col4
        0    A     2     0    a
        1    A     1     1    B
        2    B     9     9    c
        3  NaN     8     4    D
        4    D     7     2    e
        5    C     4     3    F

        Natural sort with the key argument,
        using the `natsort <https://github.com/SethMMorton/natsort>` package.

        >>> df = pd.DataFrame({
        ...    "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
        ...    "value": [10, 20, 30, 40, 50]
        ... })
        >>> df
            time  value
        0    0hr     10
        1  128hr     20
        2   72hr     30
        3   48hr     40
        4   96hr     50
        >>> from natsort import index_natsorted
        >>> df.sort_values(
        ...     by="time",
        ...     key=lambda x: np.argsort(index_natsorted(df["time"]))
        ... )
            time  value
        0    0hr     10
        3   48hr     40
        2   72hr     30
        4   96hr     50
        1  128hr     20
        r½