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Defer Series.str.get_dummies to pandas.get_dummies #26686

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Defer Series.str.get_dummies to pandas.get_dummies #26686

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d95a42b
Defer Series.str.get_dummies to pandas.get_dummies
joshlk Jun 6, 2019
7ccaf47
Properly handle NaN
joshlk Jun 7, 2019
11e168f
Move imports locally to resolve circular import conflict
joshlk Jun 7, 2019
70e077a
Deal with empty input data
joshlk Jun 7, 2019
a98aa26
Deal with empty input data
Jun 16, 2019
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30 changes: 16 additions & 14 deletions pandas/core/strings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -999,23 +999,25 @@ def str_get_dummies(arr, sep='|'):
1 0 0 0
2 1 0 1
"""
arr = arr.fillna('')
try:
arr = sep + arr + sep
except TypeError:
arr = sep + arr.astype(str) + sep
from pandas.core.reshape.reshape import get_dummies
from pandas import Series

if len(arr) == 0:
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is this case not handled by get_dummies already?

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np.concatenate fails with:

stacked = Series(np.concatenate(arr_split))
ValueError: need at least one array to concatenate

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Is there a nicer way to detect an empty input and output empty results? Should this be handled by pandas.core.strings.StringMethods.get_dummies instead?

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@jreback I have replaced if len(arr) == 0: return empty with Series(np.concatenate(arr_split)) if len(arr) > 0 else Series() which side steps np.concatenate when the input is empty. Is this satisfactory?

empty = np.empty(0, dtype='object')
return empty, empty

arr = arr.fillna('').astype('str')
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Why is this astype needed?

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astype is needed as otherwise Series.str.split fails when the datatype is not a string (e.g. NaN).

One of the example given in the docstring is:

>>> pd.Series(['a|b', np.nan, 'a|c']).str.get_dummies()
a  b  c
0  1  1  0
1  0  0  0
2  1  0  1


arr_split = arr.str.split(sep)

tags = set()
for ts in arr.str.split(sep):
tags.update(ts)
tags = sorted(tags - {""})
stacked = Series(np.concatenate(arr_split))
stacked[stacked == ''] = np.nan
stacked_idx = np.repeat(np.arange(len(arr)), arr_split.str.len())

dummies = np.empty((len(arr), len(tags)), dtype=np.int64)
dummies_stacked = get_dummies(stacked)
dummies = dummies_stacked.groupby(by=stacked_idx).sum()

for i, t in enumerate(tags):
pat = sep + t + sep
dummies[:, i] = lib.map_infer(arr.values, lambda x: pat in x)
return dummies, tags
return dummies.values, dummies.columns.values


def str_join(arr, sep):
Expand Down