def test_join_multiindex(self):
df1 = pdw.DataFrame(
{'col1': np.arange(8)},
pdw.MultiIndex.from_product(
[np.array([1, 2]),
np.array([3, 4]),
np.array([5, 6])], ['i1', 'i2', 'i3']))
df2 = pdw.DataFrame(
{'col2': np.arange(12)},
pdw.MultiIndex.from_product(
[np.array([1, 2, 3]),
np.array([3, 5]),
np.array([5, 6])], ['i1', 'i2', 'i3']))
result = df1.merge(df2)
expected_result = pdw.DataFrame(
{
'col1': np.array([0, 1, 4, 5]),
'col2': np.array([0, 1, 4, 5])
},
pdw.MultiIndex(
[np.array([1, 2]),
np.array([3, 4]),
np.array([5, 6])], [
np.array([0, 0, 1, 1]),
np.array([0, 0, 0, 0]),
np.array([0, 1, 0, 1])
], ['i1', 'i2', 'i3']))
test_equal_multiindex(expected_result.index, result.index)
test_equal_series(expected_result['col1'], result['col1'])
test_equal_series(expected_result['col2'], result['col2'])
def test_groupby_multiple_columns_sum(self):
df = pdw.DataFrame(
{
'col1': np.array([1, 1, 2, 3, 3], dtype=np.int32),
'col2': np.array([3, 4, 5, 5, 6], dtype=np.int64),
'col3': np.array([5., 6., 7., 7., 7.], dtype=np.float32)
},
pdw.MultiIndex([
np.array([1, 2, 3], dtype=np.int32),
np.array([5., 6., 7.], dtype=np.float32)
], [
np.array([0, 0, 1, 2, 2], dtype=np.int64),
np.array([0, 1, 2, 2, 2], dtype=np.int64)
], ['i32', 'f32']))
result = df.groupby(['col1', 'col3']).sum()
expected_result = pdw.DataFrame(
{'col2': np.array([3, 4, 5, 11], dtype=np.int64)},
pdw.MultiIndex([
np.array([1, 2, 3], dtype=np.int32),
np.array([5., 6., 7.], dtype=np.float32)
], [
np.array([0, 0, 1, 2], dtype=np.int64),
np.array([0, 1, 2, 2], dtype=np.int64)
], ['col1', 'col3']))
# TODO: test equal 1d index method (both rangeindex and index should work)
# assume correct index values but in different order; just check the values
levels_result = [
np.sort(level.evaluate()) for level in result.index.levels
]
labels_result = [
np.sort(label.evaluate()) for label in result.index.labels
]
levels_expected = [
np.sort(level) for level in expected_result.index.levels
]
labels_expected = [
np.sort(label) for label in expected_result.index.labels
]
np.testing.assert_array_equal(result.index.names,
expected_result.index.names)
for i in range(2):
np.testing.assert_array_equal(levels_result[i], levels_expected[i])
np.testing.assert_array_equal(labels_result[i], labels_expected[i])
# assume correct values but in different order; just check the values
np.testing.assert_array_equal(
np.sort(expected_result['col2'].evaluate().data),
np.sort(result['col2'].evaluate().data))
def setUp(self):
data = {
'col1': np.array([1, 2, 3, 4]),
'col2': np.array([5., 6., 7., 8.])
}
index = pdw.MultiIndex.from_product(
[np.array([1, 2]), np.array([3, 4])], ['a', 'b'])
self.df = pdw.DataFrame(data, index)
def test_join_1d_index(self):
df1 = pdw.DataFrame({'col1': np.array([1, 2, 3, 4, 5])},
pdw.Index(np.array([1, 3, 4, 5, 6]),
np.dtype(np.int64)))
df2 = pdw.DataFrame({'col2': np.array([1, 2, 3])},
pdw.Index(np.array([2, 3, 5]), np.dtype(np.int64)))
result = df1.merge(df2)
expected_result = pdw.DataFrame(
{
'col1': np.array([2, 4]),
'col2': np.array([2, 3])
}, pdw.Index(np.array([3, 5]), np.dtype(np.int64)))
np.testing.assert_array_equal(
evaluate_if_necessary(expected_result.index),
evaluate_if_necessary(result.index))
test_equal_series(expected_result['col1'], result['col1'])
test_equal_series(expected_result['col2'], result['col2'])
def test_drop_list(self):
data = {}
index = pdw.MultiIndex.from_product(
[np.array([1, 2]), np.array([3, 4])], ['a', 'b'])
expected_result = pdw.DataFrame(data, index)
result = self.df.drop(['col1', 'col2'])
self.assertListEqual(expected_result.data.keys(), result.data.keys())
test_equal_multiindex(expected_result.index, result.index)
def test_element_wise_operation(self):
expected_data = {
'col1': np.array([2, 4, 6, 8]),
'col2': np.array([10, 12, 14, 16])
}
expected_index = pdw.MultiIndex.from_product(
[np.array([1, 2]), np.array([3, 4])], ['a', 'b'])
expected_result = pdw.DataFrame(expected_data, expected_index)
data = {'col1': np.array([1, 2, 3, 4]), 'col2': np.array([5, 6, 7, 8])}
index = pdw.MultiIndex.from_product(
[np.array([1, 2]), np.array([3, 4])], ['a', 'b'])
result = pdw.DataFrame(data, index) * 2
np.testing.assert_array_equal(
evaluate_if_necessary(expected_result['col1']),
evaluate_if_necessary(result['col1']))
np.testing.assert_array_equal(
evaluate_if_necessary(expected_result['col2']),
evaluate_if_necessary(result['col2']))
test_equal_multiindex(expected_result.index, result.index)
def test_drop_str(self):
data = {'col2': np.array([5., 6., 7., 8.])}
index = pdw.MultiIndex.from_product(
[np.array([1, 2]), np.array([3, 4])], ['a', 'b'])
expected_result = pdw.DataFrame(data, index)
result = self.df.drop('col1')
self.assertListEqual(expected_result.data.keys(), result.data.keys())
np.testing.assert_array_equal(
evaluate_if_necessary(expected_result['col2']),
evaluate_if_necessary(result['col2']))
test_equal_multiindex(expected_result.index, result.index)
def test_groupby_single_column_sum(self):
df = pdw.DataFrame(
{
'col1': np.array([1, 1, 2, 3, 3], dtype=np.int32),
'col2': np.array([3, 4, 5, 5, 6], dtype=np.int64),
'col3': np.array([5., 6., 7., 7., 7.], dtype=np.float32)
},
pdw.MultiIndex([
np.array([1, 2, 3], dtype=np.int32),
np.array([5., 6., 7.], dtype=np.float32)
], [
np.array([0, 0, 1, 2, 2], dtype=np.int64),
np.array([0, 1, 2, 2, 2], dtype=np.int64)
], ['i32', 'f32']))
result = df.groupby('col1').sum()
expected_result = pdw.DataFrame(
{
'col2': np.array([7, 5, 11], dtype=np.int64),
'col3': np.array([11., 7., 14.], dtype=np.float32)
},
pdw.Index(np.array([1, 2, 3], dtype=np.int32), np.dtype('int32'),
'col1'))
# TODO: test equal 1d index method (both rangeindex and index should work)
np.testing.assert_array_equal(
np.sort(evaluate_if_necessary(expected_result.index)),
np.sort(evaluate_if_necessary(result.index)))
# assume correct values but in different order; just check the values
np.testing.assert_array_equal(
np.sort(expected_result['col2'].evaluate().data),
np.sort(result['col2'].evaluate().data))
np.testing.assert_array_equal(
np.sort(expected_result['col3'].evaluate().data),
np.sort(result['col3'].evaluate().data))
def test_agg(self):
expected_result = pdw.DataFrame(
{
'col1': np.array([1, 4], dtype=np.float64),
'col2': np.array([5, 8], dtype=np.float64)
},
pdw.Index(np.array(['min', 'max'], dtype=np.dtype('str')),
np.dtype('str')))
result = self.df.agg(['min', 'max'])
np.testing.assert_array_equal(
evaluate_if_necessary(expected_result.index),
evaluate_if_necessary(result.index))
test_equal_series(expected_result['col1'], result['col1'])
test_equal_series(expected_result['col2'], result['col2'])
def test_getitem_series(self):
data = {'col1': np.array([1, 2]), 'col2': np.array([5., 6.])}
index = pdw.MultiIndex(
[np.array([1, 2]), np.array([3, 4])],
[np.array([0, 0]), np.array([0, 1])], ['a', 'b'])
expected_result = pdw.DataFrame(data, index)
result = self.df[self.df['col1'] < 3]
np.testing.assert_array_equal(
evaluate_if_necessary(expected_result['col1']),
evaluate_if_necessary(result['col1']))
np.testing.assert_array_equal(
evaluate_if_necessary(expected_result['col2']),
evaluate_if_necessary(result['col2']))
test_equal_multiindex(expected_result.index, result.index)
def test_reset_index(self):
result = self.df.reset_index()
expected_result = pdw.DataFrame(
{
'col1': np.array([1, 2, 3, 4]),
'col2': np.array([5., 6., 7., 8.]),
'a': np.array([1, 1, 2, 2]),
'b': np.array([3, 4, 3, 4])
}, pdw.RangeIndex(0, 4, 1))
np.testing.assert_array_equal(
evaluate_if_necessary(expected_result.index),
evaluate_if_necessary(result.index))
test_equal_series(expected_result['col1'], result['col1'])
test_equal_series(expected_result['col2'], result['col2'])
test_equal_series(expected_result['a'], result['a'])
test_equal_series(expected_result['b'], result['b'])
def test_read_netcdf4(self):
data = {
'tg':
np.array([
-99.99, 10., 10.099999, -99.99, -99.99, 10.2, -99.99, -99.99,
-99.99, 10.3, 10.4, 10.5, 10.599999, 10.7, 10.8, 10.9, -99.99,
-99.99, -99.99, -99.99, 11., 11., 11., 11., -99.99, -99.99,
-99.99, -99.99, 12., 13.
],
dtype=np.float32),
'tg_ext':
np.array([
-9999, 1000., 1010., -9999, -9999, 1020., -9999, -9999, -9999,
1030., 10401., 10502., 10603., 10704., 10805., 10906., -9999,
-9999, -9999, -9999, 11001., 11002., 11003., 11004., -9999,
-9999, -9999, -9999, 12005., 13006.
],
dtype=np.float32)
}
index = pdw.MultiIndex.from_product([
np.array([25.5, 26.], dtype=np.float32),
np.array([10., 11., 12.], dtype=np.float32),
np.array([
str(date(1950, 1, 1)),
str(date(1950, 1, 2)),
str(date(1950, 1, 3)),
str(date(1950, 1, 4)),
str(date(1950, 1, 5))
])
], ['longitude', 'latitude', 'time'])
expected_result = pdw.DataFrame(data, index)
result = pdw.read_netcdf4(ParserTests.PATH_EXT)
self.assertListEqual(expected_result.data.keys(), result.data.keys())
np.testing.assert_array_equal(
expected_result.data['tg'],
result.data['tg'].evaluate(verbose=False))
np.testing.assert_array_equal(
expected_result.data['tg_ext'],
result.data['tg_ext'].evaluate(verbose=False))
test_equal_multiindex(expected_result.index, result.index)
def test_getitem_list(self):
data = {
'col1': np.array([1, 2, 3, 4]),
'col2': np.array([5., 6., 7., 8.])
}
index = pdw.MultiIndex.from_product(
[np.array([1, 2]), np.array([3, 4])], ['a', 'b'])
expected_result = pdw.DataFrame(data, index)
result = self.df[['col1', 'col2']]
np.testing.assert_array_equal(
evaluate_if_necessary(expected_result['col1']),
evaluate_if_necessary(result['col1']))
np.testing.assert_array_equal(
evaluate_if_necessary(expected_result['col2']),
evaluate_if_necessary(result['col2']))
test_equal_multiindex(expected_result.index, result.index)
def test_describe(self):
# reversed because of dict and not OrderedDict
expected_result = pdw.DataFrame(
{
'col1': np.array([1, 4, 2.5, 1.29089], np.float64),
'col2': np.array([5, 8, 6.5, 1.29099], np.float64)
},
pdw.Index(np.array(['min', 'max', 'mean', 'std'], dtype=np.str),
np.dtype(np.str), "Index"))
result = self.df.describe(['min', 'max', 'mean', 'std']).evaluate()
np.testing.assert_array_equal(
evaluate_if_necessary(expected_result.index),
evaluate_if_necessary(result.index))
test_equal_series(expected_result['col1'].evaluate(),
result['col1'].evaluate())
test_equal_series(expected_result['col2'].evaluate(),
result['col2'].evaluate())