Combined value_counts()

Rather often, especially when exploring the data or creating reports, one is interested both in pd.Series.value_counts() and `pd.Series.value_counts(normalize=True). I prepared a branch which implements a wrapper which takes care of this (link)

• Does it make sense?
• Is this implementation meeting pandas’ standards?
• Should testing of this simple wrapper be extended?

[drorata]

I guess it is not really worthy. For future reference, I will leave the function I wrote here:

def value_counts_comb(self, sort=True, ascending=False,
                          bins=None, dropna=True):
        """
        A wrapper of value_counts which returns both the counts and the
        normalized view.

        The resulting object will be in descending order so that the
        first element is the most frequently-occurring element.
        Excludes NA values by default.

        Parameters
        ----------
        normalize : boolean, default False
            If True then the object returned will contain the relative
            frequencies of the unique values.
        sort : boolean, default True
            Sort by values
        ascending : boolean, default False
            Sort in ascending order
        bins : integer, optional
            Rather than count values, group them into half-open bins,
            a convenience for pd.cut, only works with numeric data
        dropna : boolean, default True
            Don't include counts of NaN.

        Returns
        -------
        counts : DataFrame
        """
        from pandas.core.algorithms import value_counts
        from pandas.core.reshape.concat import concat
        res_norm = value_counts(self, sort=sort, ascending=ascending,
                      normalize=True, bins=bins, dropna=dropna)
        res_regu = value_counts(self, sort=sort, ascending=ascending,
                      normalize=False, bins=bins, dropna=dropna)
        result = concat([res_norm, res_regu], axis=1, keys=['Ratio', 'Count'])
        return result

[TomAugspurger]

I’m not aware of any pandas-utils-like libraries, perhaps you could start one?

And to be clear, others in the community may disagree with me, and prefer that it is included in pandas. Let’s wait and hear feedback from other maintainers and users.