Recoding as numerical categories with multiple columns

[lesshaste]

Code Sample, a copy-pastable example if possible

This SO questions asks the simple question of how to recode strings in a data frame as numerical categories http://stackoverflow.com/questions/39475187/how-to-speed-up-recoding-into-integers .

The pandas solution x = df.apply(lambda x: x.astype('category').cat.codes) Is by far the fastest. However it doesn't give a consistent answer if the data frame has more than one column.

E.g.

g,k
a,h
c,i
j,e
d,i
i,h
b,b
d,d
i,a
d,h

gets recoded to:

0 1
0 4 6
1 0 4
2 2 5
3 6 3
4 3 5
5 5 4
6 1 1
7 3 2
8 5 0
9 3 4

Notice that 'd' is mapped to 3 in the first column but 2 in the second.

It would be great if pandas could do this recoding consistently.

Expected Output

output of pd.show_versions()

[jorisvandenbossche]

@lesshaste The fact that df.apply(lambda x: x.astype('category').cat.codes) does this column by column is expected.
But see #12860 for some discussion on how to be able to do this on multiple columns at once (using the same categories for all columns).

The workaround listed over there is:

uniques = np.sort(pd.unique(df.values.ravel()))
df.apply(lambda x: x.astype('category', categories=uniques))

[lesshaste]

That is very nice and I had no idea you could do that. Thank you.