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Fix test failures / warnings for pandas 0.24 #2720

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Jan 27, 2019
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Fix test failures / warnings for pandas 0.24 #2720

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710b55f
Fix test failures / warnings for pandas 0.24
shoyer Jan 27, 2019
3705ff9
Brief doc note
shoyer Jan 27, 2019
6c1c5d3
Comment on name order
shoyer Jan 27, 2019
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5 changes: 3 additions & 2 deletions xarray/core/coordinates.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
from collections import Mapping, OrderedDict
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thanks for catching this.

import collections.abc
from collections import OrderedDict
from contextlib import contextmanager

import pandas as pd
Expand All @@ -14,7 +15,7 @@
_THIS_ARRAY = ReprObject('<this-array>')


class AbstractCoordinates(Mapping):
class AbstractCoordinates(collections.abc.Mapping):
def __getitem__(self, key):
raise NotImplementedError

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15 changes: 10 additions & 5 deletions xarray/core/dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -130,7 +130,7 @@ def merge_indexes(
if isinstance(var_names, str):
var_names = [var_names]

names, labels, levels = [], [], [] # type: (list, list, list)
names, codes, levels = [], [], [] # type: (list, list, list)
current_index_variable = variables.get(dim)

for n in var_names:
Expand All @@ -144,13 +144,18 @@ def merge_indexes(
if current_index_variable is not None and append:
current_index = current_index_variable.to_index()
if isinstance(current_index, pd.MultiIndex):
try:
current_codes = current_index.codes
except AttributeError:
# fpr pandas<0.24
current_codes = current_index.labels
names.extend(current_index.names)
labels.extend(current_index.labels)
codes.extend(current_codes)
levels.extend(current_index.levels)
else:
names.append('%s_level_0' % dim)
cat = pd.Categorical(current_index.values, ordered=True)
labels.append(cat.codes)
codes.append(cat.codes)
levels.append(cat.categories)

if not len(names) and len(var_names) == 1:
Expand All @@ -161,10 +166,10 @@ def merge_indexes(
names.append(n)
var = variables[n]
cat = pd.Categorical(var.values, ordered=True)
labels.append(cat.codes)
codes.append(cat.codes)
levels.append(cat.categories)

idx = pd.MultiIndex(labels=labels, levels=levels, names=names)
idx = pd.MultiIndex(levels, codes, names=names)

vars_to_replace[dim] = IndexVariable(dim, idx)
vars_to_remove.extend(var_names)
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25 changes: 15 additions & 10 deletions xarray/tests/test_coding_times.py
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Expand Up @@ -533,17 +533,22 @@ def test_infer_cftime_datetime_units(calendar, date_args, expected):

@pytest.mark.parametrize(
['timedeltas', 'units', 'numbers'],
[('1D', 'days', np.int64(1)),
(['1D', '2D', '3D'], 'days', np.array([1, 2, 3], 'int64')),
('1h', 'hours', np.int64(1)),
('1ms', 'milliseconds', np.int64(1)),
('1us', 'microseconds', np.int64(1)),
(['NaT', '0s', '1s'], None, [np.nan, 0, 1]),
(['30m', '60m'], 'hours', [0.5, 1.0]),
(np.timedelta64('NaT', 'ns'), 'days', np.nan),
(['NaT', 'NaT'], 'days', [np.nan, np.nan])])
[
('1D', 'days', np.int64(1)),
(['1D', '2D', '3D'], 'days', np.array([1, 2, 3], 'int64')),
('1h', 'hours', np.int64(1)),
('1ms', 'milliseconds', np.int64(1)),
('1us', 'microseconds', np.int64(1)),
(['NaT', '0s', '1s'], None, [np.nan, 0, 1]),
(['30m', '60m'], 'hours', [0.5, 1.0]),
('NaT', 'days', np.nan),
(['NaT', 'NaT'], 'days', [np.nan, np.nan]),
])
def test_cf_timedelta(timedeltas, units, numbers):
timedeltas = pd.to_timedelta(timedeltas, box=False)
if timedeltas == 'NaT':
timedeltas = np.timedelta64('NaT', 'ns')
else:
timedeltas = pd.to_timedelta(timedeltas, box=False)
numbers = np.array(numbers)

expected = numbers
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33 changes: 10 additions & 23 deletions xarray/tests/test_dataarray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -122,21 +122,12 @@ def test_struct_array_dims(self):
"""
# GH837, GH861
# checking array subraction when dims are the same
p_data = np.array([('John', 180), ('Stacy', 150), ('Dick', 200)],
p_data = np.array([('Abe', 180), ('Stacy', 150), ('Dick', 200)],
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dtype=[('name', '|S256'), ('height', object)])

p_data_1 = np.array([('John', 180), ('Stacy', 150), ('Dick', 200)],
dtype=[('name', '|S256'), ('height', object)])

p_data_2 = np.array([('John', 180), ('Dick', 200)],
dtype=[('name', '|S256'), ('height', object)])

weights_0 = DataArray([80, 56, 120], dims=['participant'],
coords={'participant': p_data})

weights_1 = DataArray([81, 52, 115], dims=['participant'],
coords={'participant': p_data_1})

coords={'participant': p_data})
actual = weights_1 - weights_0

expected = DataArray([1, -4, -5], dims=['participant'],
Expand All @@ -145,31 +136,27 @@ def test_struct_array_dims(self):
assert_identical(actual, expected)

# checking array subraction when dims are not the same
p_data_1 = np.array([('John', 180), ('Stacy', 151), ('Dick', 200)],
dtype=[('name', '|S256'), ('height', object)])

p_data_alt = np.array([('Abe', 180), ('Stacy', 151), ('Dick', 200)],
dtype=[('name', '|S256'), ('height', object)])
weights_1 = DataArray([81, 52, 115], dims=['participant'],
coords={'participant': p_data_1})

coords={'participant': p_data_alt})
actual = weights_1 - weights_0

expected = DataArray([1, -5], dims=['participant'],
coords={'participant': p_data_2})
coords={'participant': p_data[[0, 2]]})

assert_identical(actual, expected)

# checking array subraction when dims are not the same and one
# is np.nan
p_data_1 = np.array([('John', 180), ('Stacy', np.nan), ('Dick', 200)],
dtype=[('name', '|S256'), ('height', object)])

p_data_nan = np.array([('Abe', 180), ('Stacy', np.nan), ('Dick', 200)],
dtype=[('name', '|S256'), ('height', object)])
weights_1 = DataArray([81, 52, 115], dims=['participant'],
coords={'participant': p_data_1})

coords={'participant': p_data_nan})
actual = weights_1 - weights_0

expected = DataArray([1, -5], dims=['participant'],
coords={'participant': p_data_2})
coords={'participant': p_data[[0, 2]]})

assert_identical(actual, expected)

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8 changes: 6 additions & 2 deletions xarray/tests/test_utils.py
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Expand Up @@ -74,7 +74,9 @@ def test_multiindex_from_product_levels():
result = utils.multiindex_from_product_levels(
[pd.Index(['b', 'a']), pd.Index([1, 3, 2])])
np.testing.assert_array_equal(
result.labels, [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
# compat for pandas < 0.24
result.codes if hasattr(result, 'codes') else result.labels,
[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
np.testing.assert_array_equal(result.levels[0], ['b', 'a'])
np.testing.assert_array_equal(result.levels[1], [1, 3, 2])

Expand All @@ -86,7 +88,9 @@ def test_multiindex_from_product_levels_non_unique():
result = utils.multiindex_from_product_levels(
[pd.Index(['b', 'a']), pd.Index([1, 1, 2])])
np.testing.assert_array_equal(
result.labels, [[0, 0, 0, 1, 1, 1], [0, 0, 1, 0, 0, 1]])
# compat for pandas < 0.24
result.codes if hasattr(result, 'codes') else result.labels,
[[0, 0, 0, 1, 1, 1], [0, 0, 1, 0, 0, 1]])
np.testing.assert_array_equal(result.levels[0], ['b', 'a'])
np.testing.assert_array_equal(result.levels[1], [1, 2])

Expand Down