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Fix: Add compat code for pd.Categorical in pandas>=0.15 #47

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Fix: Add compat code for pd.Categorical in pandas>=0.15 #47

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57 changes: 47 additions & 10 deletions patsy/categorical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,8 +42,22 @@
safe_scalar_isnan,
iterable)



# Only pandas can have categorical dtypes, so this can always return False
# until overwritten by the proper pandas version
is_categorical_dtype = lambda x: False

if have_pandas:
import pandas
try:
from pandas.core.common import is_categorical_dtype as is_cat
is_categorical_dtype = is_cat
except ImportError:
# this is only available in pandas >0.15, everything lower has no categorical dtype,
# so the above implementation is right there too...
# FIXME: remove try...except when only pandas version >0.15 are allowed
pass

# Objects of this type will always be treated as categorical, with the
# specified levels and contrast (if given).
Expand Down Expand Up @@ -171,7 +185,13 @@ def sniff(self, data):
if have_pandas_categorical and isinstance(data, pandas.Categorical):
# pandas.Categorical has its own NA detection, so don't try to
# second-guess it.
self._levels = tuple(data.levels)
self._levels = tuple(data.categories)
return True
if hasattr(data, "dtype"): print(data.dtype)
print(pandas.__version__)
if hasattr(data, "dtype") and is_categorical_dtype(data):
# A Series(Categorical(...)) in pandas >= 0.15
self._levels = tuple(data.values.levels)
return True
if isinstance(data, _CategoricalBox):
if data.levels is not None:
Expand Down Expand Up @@ -221,9 +241,9 @@ def t(NA_types, datas, exp_finish_fast, exp_levels, exp_contrast=None):
t([], [pandas.Categorical.from_array([1, 2, None])],
True, (1, 2))
# check order preservation
t([], [pandas.Categorical([1, 0], ["a", "b"])],
t([], [pandas.Categorical.from_codes([1, 0], ["a", "b"])],
True, ("a", "b"))
t([], [pandas.Categorical([1, 0], ["b", "a"])],
t([], [pandas.Categorical.from_codes([1, 0], ["b", "a"])],
True, ("b", "a"))
# check that if someone sticks a .contrast field onto a Categorical
# object, we pick it up:
Expand Down Expand Up @@ -287,14 +307,31 @@ def categorical_to_int(data, levels, NA_action, origin=None):
# In this function, missing values are always mapped to -1

if have_pandas_categorical and isinstance(data, pandas.Categorical):
data_levels_tuple = tuple(data.levels)
# compat code for levels -> categories change in pandas 0.15
# FIXME: use data.categories directly when we don't want to support pandas < 0.15
lvls = data.categories if hasattr(data, "categories") else data.levels
data_levels_tuple = tuple(lvls)
if not data_levels_tuple == levels:
raise PatsyError("mismatching levels: expected %r, got %r"
% (levels, data_levels_tuple), origin)
# pandas.Categorical also uses -1 to indicate NA, and we don't try to
# second-guess its NA detection, so we can just pass it back.
return data.labels

# NOTE: this keeps "NA as level" if there are any but assumes that the user did that
# on purpose!
# Compat code for the labels -> codes change in pandas 0.15
# FIXME: use data.codes directly when we don't want to support pandas < 0.15
if hasattr(data, "codes"):
return data.codes
else:
return data.labels
if hasattr(data, "dtype") and is_categorical_dtype(data):
# This is Series(Categorical(...)) in pandas >= 0.15
# newer pandas has 'categories' insteadt of 'levels'
data_levels_tuple = tuple(data.values.categories)
if not data_levels_tuple == levels:
raise PatsyError("mismatching levels: expected %r, got %r"
% (levels, data_levels_tuple), origin)
return pandas.Series(data.values.codes, index=data.index)
if isinstance(data, _CategoricalBox):
if data.levels is not None and tuple(data.levels) != levels:
raise PatsyError("mismatching levels: expected %r, got %r"
Expand Down Expand Up @@ -358,22 +395,22 @@ def test_categorical_to_int():
categorical_to_int,
pandas.DataFrame({10: s}), ("a", "b", "c"), NAAction())
if have_pandas_categorical:
cat = pandas.Categorical([1, 0, -1], ("a", "b"))
cat = pandas.Categorical.from_codes([1, 0, -1], ("a", "b"))
conv = categorical_to_int(cat, ("a", "b"), NAAction())
assert np.all(conv == [1, 0, -1])
# Trust pandas NA marking
cat2 = pandas.Categorical([1, 0, -1], ("a", "None"))
cat2 = pandas.Categorical.from_codes([1, 0, -1], ("a", "None"))
conv2 = categorical_to_int(cat, ("a", "b"), NAAction(NA_types=["None"]))
assert np.all(conv2 == [1, 0, -1])
# But levels must match
assert_raises(PatsyError,
categorical_to_int,
pandas.Categorical([1, 0], ("a", "b")),
pandas.Categorical.from_codes([1, 0], ("a", "b")),
("a", "c"),
NAAction())
assert_raises(PatsyError,
categorical_to_int,
pandas.Categorical([1, 0], ("a", "b")),
pandas.Categorical.from_codes([1, 0], ("a", "b")),
("b", "a"),
NAAction())

Expand Down