ENH: decode for Categoricals

[fkaufer]

There seems no direct way to return to the original dtype and the documentation recommends: "To get back to the original Series or numpy array, use Series.astype(original_dtype) or np.asarray(categorical)"

That's slow and a decode or decat method would be trivial:

df=pd.DataFrame(np.random.choice(list(u'abcde'), 4e6).reshape(1e6, 4),
    columns=list(u'ABCD'))                                     
for col in df.columns: df[col] = df[col].astype('category')   

%timeit for col in df.columns: df[col].astype('unicode')      
1 loops, best of 3: 1.06 s per loop

%timeit for col in df.columns: cats=df[col].cat.categories; cats[df[col].cat.codes]    
10 loops, best of 3: 33.2 ms per loop   

I was working with ~10 categories (partially longer strings) on a 20 mio rows dataset where the difference was even bigger (unfortunately can't reproduce it with dummy data) and using astype felt rather buggy (minutes) than only a performance issue.

Given the current limitations on exporting categorical data, having a fast decode method would be very convenient. Since category codes are most often strings an optional parameter for direct character set encoding would also be good to have for such a method.

%timeit for col in df.columns: df[col].astype('unicode').str.encode('latin1')  
1 loops, best of 3: 3.95 s per loop
%timeit for col in df.columns: cats=pd.Series(df[col].cat.categories).str.encode('latin1'); cats[df[col].cat.codes]                                                                  
10 loops, best of 3: 74.5 ms per loop   

[jreback]

I agree would be nicer to support this directly (and it is quite easy). here is how:

.astype simple needs to do this (its essentially not-implemented atm, and reverts to object type behavor), which is completely non-performant when you are doing things like this

In [14]: pd.Categorical.from_codes(df['A'].cat.codes,categories=df['A'].cat.categories.astype('unicode'))
Out[14]: 
[b, d, b, a, d, ..., c, a, a, d, a]
Length: 1000000
Categories (5, object): [a, b, c, d, e]

In [15]: %timeit pd.Categorical.from_codes(df['A'].cat.codes,categories=df['A'].cat.categories.astype('unicode'))  
100 loops, best of 3: 3.74 ms per loop

love for you to make a chage to astype to support this, its pretty straightorward

[shoyer]

np.asarray is pretty fast, only slightly slower than doing it manually:

%timeit np.asarray(df['A'].cat.categories[df['A'].cat.codes])
100 loops, best of 3: 9.91 ms per loop
%timeit np.asarray(df['A'])
100 loops, best of 3: 13.3 ms per loop

The slow part is converting even numpy ndarrays from object dtype into back unicode (though I'm not sure why exactly you want that, given that pandas normally uses object arrays for strings):

x = np.asarray(df['A'])
%timeit np.asarray(x, unicode)
10 loops, best of 3: 112 ms per loop

[jreback]

right but if u know you have a categorical then u only need to do str ops on a very small amount (relatively speaking ) amount of data (compared to object arrays)

[shoyer]

@jreback Yep, someone I missed that in your earlier comment.

To complete my earlier benchmarks:

%timeit df['A'].cat.categories.astype(unicode)[df['A'].cat.codes]
100 loops, best of 3: 8.34 ms per loop

[wesm]

I think it would be useful to have an inverse equivalent to factorize and categorical conversions