ENH: Series.str.get_dummies should defer to pd.get_dummies and pass thru args

[randomgambit]

Hello the Pandas team and thanks for making this package greater day after day.

I was using the str.get_dummies method on a dataframe and I realized that by default the dummies are coded as int64.

This looks to me very inefficient because I ran into a memory error when trying to get dummies for a dataframe with several millions of rows (and about 5k dummies). I had to create the dummies by chunk, and use to_numeric() to coerce to int8.

Would it be possible to natively have the dummies in int8 format so that they take very little space? In that case NaN would be coerced to 0 but that should be fine.

What do you think?
Thanks!

[jreback]

what actually should happen is that .str.get_dummies should just call and defer to pd.get_dummies

which already does all of this:

Signature: pd.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None)
Docstring:
Convert categorical variable into dummy/indicator variables

Parameters
----------
data : array-like, Series, or DataFrame
prefix : string, list of strings, or dict of strings, default None
    String to append DataFrame column names
    Pass a list with length equal to the number of columns
    when calling get_dummies on a DataFrame. Alternatively, `prefix`
    can be a dictionary mapping column names to prefixes.
prefix_sep : string, default '_'
    If appending prefix, separator/delimiter to use. Or pass a
    list or dictionary as with `prefix.`
dummy_na : bool, default False
    Add a column to indicate NaNs, if False NaNs are ignored.
columns : list-like, default None
    Column names in the DataFrame to be encoded.
    If `columns` is None then all the columns with
    `object` or `category` dtype will be converted.
sparse : bool, default False
    Whether the dummy columns should be sparse or not.  Returns
    SparseDataFrame if `data` is a Series or if all columns are included.
    Otherwise returns a DataFrame with some SparseBlocks.
drop_first : bool, default False
    Whether to get k-1 dummies out of k categorical levels by removing the
    first level.

    .. versionadded:: 0.18.0

dtype : dtype, default np.uint8
    Data type for new columns. Only a single dtype is allowed.

    .. versionadded:: 0.23.0

Returns
-------
dummies : DataFrame or SparseDataFrame

[jreback]

want to do a PR, this is pretty straightforward.

[jreback]

pd.get_dummies already does this by default

In [1]: s = Series(list('aabbcdefga'))

In [2]: s.str.get_dummies()
Out[2]: 
   a  b  c  d  e  f  g
0  1  0  0  0  0  0  0
1  1  0  0  0  0  0  0
2  0  1  0  0  0  0  0
3  0  1  0  0  0  0  0
4  0  0  1  0  0  0  0
5  0  0  0  1  0  0  0
6  0  0  0  0  1  0  0
7  0  0  0  0  0  1  0
8  0  0  0  0  0  0  1
9  1  0  0  0  0  0  0

In [3]: pd.get_dummies(s)
Out[3]: 
   a  b  c  d  e  f  g
0  1  0  0  0  0  0  0
1  1  0  0  0  0  0  0
2  0  1  0  0  0  0  0
3  0  1  0  0  0  0  0
4  0  0  1  0  0  0  0
5  0  0  0  1  0  0  0
6  0  0  0  0  1  0  0
7  0  0  0  0  0  1  0
8  0  0  0  0  0  0  1
9  1  0  0  0  0  0  0

In [4]: pd.get_dummies(s).dtypes
Out[4]: 
a    uint8
b    uint8
c    uint8
d    uint8
e    uint8
f    uint8
g    uint8
dtype: object

In [5]: s.str.get_dummies().dtypes
Out[5]: 
a    int64
b    int64
c    int64
d    int64
e    int64
f    int64
g    int64
dtype: object

[randomgambit]

thanks @jreback I think the issue is that the str method allows me to get dummies in the very common situation where the dummies are separated by some separator.

Example

df = pd.DataFrame({'mystring' : ['JEFF;REBACK;PANDAS',
                                 'JEFFERSON;REBACKSON;PANDAS']}) 

df
Out[17]: 
                     mystring
0          JEFF;REBACK;PANDAS
1  JEFFERSON;REBACKSON;PANDAS

Now,

df.mystring.str.get_dummies(sep = ';')
Out[18]: 
   JEFF  JEFFERSON  PANDAS  REBACK  REBACKSON
0     1          0       1       1          0
1     0          1       1       0          1

while pd.get_dummies cant do that.

pd.get_dummies(df,prefix_sep = ';')
Out[19]: 
   mystring;JEFF;REBACK;PANDAS  mystring;JEFFERSON;REBACKSON;PANDAS
0                            1                                    0
1                            0                                    1

Thanks

[jreback]

@randomgambit my point is that this can simply dispatch to the impl of get_dummies.

[johnmalaty]

@randomgambit did you find a solution to that issue as I have the same with my dataset?