API: Add str/dt accessors to categorical

[sinhrks]

Accessors should be enabled depending on categories. Should care CategoricalIndex also.

import pandas as pd
s = pd.Series(['A', 'B'], dtype='category')
s.str
# AttributeError: Can only use .str accessor with string values, which use np.object_ dtype in pandas

s = pd.Series([pd.Timestamp('2011-01-01'), pd.Timestamp('2011-01-01')], dtype='category')
s.dt
# AttributeError: Can only use .dt accessor with datetimelike values

[jreback]

xref to #8627

What you are saying is the should ALSO allow the dtype correct accessor for its values (or categories if its a Categorical), in addition to the .cat if its a categorical. Then I would agree (though I don't think this actually works ATM see the xref issue)

[jankatins]

So, what this should do is simple allow the s.str to succeede when s.cat.categories is of type string and s.dt if s.cat.categories is of type datetime/...?

[jankatins]

One solution (for .dt) is to return a copy of the categorical as datetime object:

    def _make_dt_accessor(self):
        try:
            return maybe_to_datetimelike(self)
        except Exception:
            if is_categorical_dtype(self.dtype):
                try:
                    cat_dtype = self.values.categories.dtype
                    return maybe_to_datetimelike(self.astype(cat_dtype))
                except:
                    pass # raise in next line...
            raise AttributeError("Can only use .dt accessor with datetimelike "
                                 "values")

[jreback]

@JanSchulz not at all.

This needs to operate on the categories, then return a new categorical object that has (transformed values).

In [1]: s = Series(list('aabbc')).astype('category')

In [2]: s.astype(object).str.upper()
Out[2]: 
0    A
1    A
2    B
3    B
4    C
dtype: object

# I still want to type: s.str.upper() though
In [4]: s.cat.rename_categories(s.cat.categories.str.upper())
Out[4]: 
0    A
1    A
2    B
3    B
4    C
dtype: category
Categories (3, object): [A, B, C]

[jankatins]

ok, on it...

[jankatins]

Ok, what should that return:

s = Series(list('aabbc')).astype('category')
s.str.upper()

-> a series of type string/object or a series of type category where the categories are of dtype str (and transformed)? I would go for the first, as it would honor the contract for str, which says it's a string:

from http://pandas.pydata.org/pandas-docs/stable/api.html#string-handling

Series.str can be used to access the values of the series as strings and apply several methods to it. These can be acccessed like Series.str.<function/property>.