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BUG: need better inference for path in Series construction (GH9456) #9924

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BUG: need better inference for path in Series construction (GH9456) #9924

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f883b79
TST: Adding test for bug GH 9456
patrickfournier Apr 17, 2015
a934792
BUG: GH 9456 Fixed Series.__init__ to better handle dict data
patrickfournier Apr 17, 2015
04eeabe
Merge branch 'master' into issue-9456
patrickfournier May 31, 2015
c59c4fe
BUG: GH 9456 Fixed Series.__init__ to better handle dict data
patrickfournier May 31, 2015
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34 changes: 21 additions & 13 deletions pandas/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,7 +99,8 @@ class Series(base.IndexOpsMixin, generic.NDFrame):
Parameters
----------
data : array-like, dict, or scalar value
Contains data stored in Series
Contains data stored in Series. If a dict and no index is provided,
an attempt will be made to sort the dict.
index : array-like or Index (1d)
Values must be unique and hashable, same length as data. Index
object (or other iterable of same length as data) Will default to
Expand Down Expand Up @@ -131,6 +132,7 @@ def __init__(self, data=None, index=None, dtype=None, name=None,

else:

original_index = index
if index is not None:
index = _ensure_index(index)

Expand Down Expand Up @@ -162,21 +164,27 @@ def __init__(self, data=None, index=None, dtype=None, name=None,
elif isinstance(data, dict):
if index is None:
if isinstance(data, OrderedDict):
index = Index(data)
original_index = data.keys()
else:
index = Index(_try_sort(data))
original_index = _try_sort(data)

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why is it necessary to use a new variable here. This original code looks fine.

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Since we cannot always reorder the dict using the Index (because of incompatible data types), I keep the original index to use it if I cannot use the Index.

index = Index(original_index)
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I wanted to use _ensure_index, but it does not create a MultiIndex when the dict keys are tuples. Bug?


try:
if isinstance(index, DatetimeIndex):
# coerce back to datetime objects for lookup
data = lib.fast_multiget(data, index.astype('O'),
default=np.nan)
elif isinstance(index, PeriodIndex):
data = [data.get(i, nan) for i in index]
# lib.fast_multiget raises TypeError if type(data) != dict

if lib.infer_dtype(data) == lib.infer_dtype(index.values):
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Here I try to avoid a call to np.array() by checking if we can access data values using index.values. However I checked the code for infer_dtype and it may be quite complex. Maybe we should just go straight to the else?

data = lib.fast_multiget(data, index.values, default=np.nan)
else:
data = lib.fast_multiget(data, index.values,
default=np.nan)
except TypeError:
data = [data.get(i, nan) for i in index]
if isinstance(original_index, PeriodIndex):
data = [data.get(i, np.nan) for i in original_index]
else:
# np.array(['z', ('a', 'b')]) raises ValueError;
# this may happens with MultiIndex.
data = lib.fast_multiget(data, np.array(original_index),
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If lib.fast_multiget supported list as its second parameter, we could save the call to np.array.

default=np.nan)
except (TypeError, ValueError) as e:
data = [data.get(i, np.nan) for i in index]

elif isinstance(data, SingleBlockManager):
if index is None:
Expand Down
43 changes: 43 additions & 0 deletions pandas/tests/test_series.py
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Expand Up @@ -915,6 +915,49 @@ def test_constructor_dtype_datetime64(self):
dr = date_range('20130101',periods=3,tz='US/Eastern')
self.assertTrue(str(Series(dr).iloc[0].tz) == 'US/Eastern')

# GH 9456
d = {np.datetime64('2015-01-07T02:00:00.000000000+0200'): 4.2,
np.datetime64('2015-01-09T02:00:00.000000000+0200'): 4.0,
np.datetime64('2015-01-08T02:00:00.000000000+0200'): 3.9,
np.datetime64('2015-01-12T02:00:00.000000000+0200'): 3.5}
keys = list()
vals = list()
for k in sorted(d.keys()):
vals.append(d[k])
keys.append(k)
expected = Series(vals, keys)
expected_unsorted = Series(d.values(), d.keys())

s = Series(d)
assert_series_equal(s, expected)

s = Series(d, d.keys())
assert_series_equal(s, expected_unsorted)

d = {datetime(2015,1,7): 4.2,
datetime(2015,1,9): 4.0,
datetime(2015,1,8): 3.9,
datetime(2015,1,12): 3.5}
expected_unsorted = Series(d.values(), d.keys())

s = Series(d)
assert_series_equal(s, expected)

s = Series(d, d.keys())
assert_series_equal(s, expected_unsorted)

d = {datetime(2015,1,7): 4.2,
np.datetime64('2015-01-09T02:00:00.000000000+0200'): 4.0,
'20150108': 3.9,
np.datetime64('2015-01-12T02:00:00.000000000+0200'): 3.5}
expected_unsorted = Series(d.values(), d.keys())

s = Series(d)
assert_series_equal(s, expected_unsorted)

s = Series(d, d.keys())
assert_series_equal(s, expected_unsorted)

def test_constructor_periodindex(self):
# GH7932
# converting a PeriodIndex when put in a Series
Expand Down