def test_isna(self):
ind = Index([3, 2, -999, 4, -999])
actual = ind.isna()
expected = Index([False, False, True, False, True], np.dtype(np.bool))
assert_index_equal(actual, expected)
def test_op_scalar(self, operation, expected_data, data_f32):
ind = Index(data_f32)
actual = eval('ind {} 2'.format(operation))
expected = Index(expected_data, np.dtype(np.float32))
assert_index_equal(actual, expected)
def test_dropna(self):
ind = Index([3, 2, -999, 4, -999])
actual = ind.dropna()
expected = Index([3, 2, 4], np.dtype(np.int64))
assert_index_equal(actual, expected)
def test_fillna(self):
ind = Index([3, 2, -999, 4, -999])
actual = ind.fillna(15)
expected = Index([3, 2, 15, 4, 15], np.dtype(np.int64))
assert_index_equal(actual, expected)
def test_join(self):
df1 = DataFrame(
OrderedDict(
(('a', np.arange(5)), ('b', np.arange(1, 6,
dtype=np.float64)))),
Index(np.arange(0, 5)))
df2 = DataFrame(
OrderedDict(
(('b', np.arange(3, 6,
dtype=np.float32)), ('c', np.arange(4, 7)))),
Index(np.array(np.array([1, 3, 5]))))
actual = df1.join(df2, lsuffix='_x')
expected = DataFrame(
OrderedDict((('a', np.arange(5)), ('b_x',
np.arange(1,
6,
dtype=np.float64)),
('b',
Series(
np.array([-999., 3., -999., 4., -999.],
dtype=np.float32))),
('c', np.array([-999, 4, -999, 5, -999])))),
Index(np.arange(0, 5), np.dtype(np.int64), 'index'))
assert_dataframe_equal(actual, expected)
def test_op_array(self, operation, expected_data, data_f32, op_array_other):
data = Index(data_f32)
actual = eval('data {} op_array_other'.format(operation))
expected = Index(expected_data, np.dtype(np.float32))
assert_index_equal(actual, expected)
def test_set_multi_index(self, df_small, data_f32, data_i64, data_str):
actual = df_small.set_index(['b', 'c'])
expected = DataFrame({'a': data_f32},
MultiIndex([
Index(data_i64, np.dtype(np.int64), 'b'),
Index(data_str, name='c')
], ['b', 'c']))
assert_dataframe_equal(actual, expected)
def test_slice(self, data_f32, index_i64):
mi = MultiIndex([data_f32, index_i64])
actual = mi[1:3]
expected = MultiIndex([
Index(np.array([2, 3], dtype=np.float32), np.dtype(np.float32)),
Index(np.array([1, 2], dtype=np.int64), np.dtype(np.int64))
])
assert_multiindex_equal(actual, expected)
def test_filter(self, data_f32, index_i64):
mi = MultiIndex([data_f32, index_i64])
actual = mi[Index(np.array([False, True, True, False, False]))]
expected = MultiIndex([
Index(np.array([2, 3], dtype=np.float32), np.dtype(np.float32)),
Index(np.array([1, 2], dtype=np.int64), np.dtype(np.int64))
])
assert_multiindex_equal(actual, expected)
def test_groupby_multi(self, index_i64, series_i64, df_dupl):
actual = df_dupl.groupby(['a', 'b']).min()
expected = DataFrame(
OrderedDict({'c': [1, 2, 4, 5]}),
MultiIndex([
Index(np.array([0, 1, 2, 3], dtype=np.float32),
np.dtype(np.float32), 'a'),
Index([4, 5, 6, 6], np.dtype(np.int64), 'b')
], ['a', 'b']))
assert_dataframe_equal(actual, expected, sort=True)
def test_dropna(self, index_i64):
sr = Series([3, 2, -999, 4, -999], index_i64, dtype=np.dtype(np.int64))
actual = sr.dropna()
expected = Series([3, 2, 4], Index([0, 1, 3]), np.dtype(np.int64))
assert_series_equal(actual, expected)
def test_filter(self, series_i64):
actual = series_i64[series_i64 != 2]
expected = Series(np.array([1, 3, 4, 5]), Index(np.array([0, 2, 3,
4])),
np.dtype(np.int64))
assert_series_equal(actual, expected)
def test_len_lazy(self, data_i64_lazy):
ind = Index(data_i64_lazy, np.dtype(np.int64))
actual = len(ind)
expected = 5
assert actual == expected
def test_from_pandas_index(self, index_i64):
pandas_index = pd.Index([0, 1, 2, 3, 4])
actual = Index.from_pandas(pandas_index)
expected = index_i64
assert_index_equal(actual, expected)
def test_iloc_indices_missing(self, series_f32):
indices = Series(np.array([0, 3, 5]))
actual = series_f32.iloc._iloc_with_missing(indices.weld_expr)
expected = Series(np.array([1, 4, -999], dtype=np.float32),
Index(np.array([0, 3, -999])), np.dtype(np.float32))
assert_series_equal(actual, expected)
def test_iloc_indices(self, series_f32):
indices = Series(np.array([0, 3, 4]))
actual = series_f32.iloc[indices]
expected = Series(np.array([1, 4, 5], dtype=np.float32),
Index(np.array([0, 3, 4])), np.dtype(np.float32))
assert_series_equal(actual, expected)
def test_filter_str(self, series_str):
actual = series_str[series_str != 'Abc']
expected = Series(
np.array(['a', 'goosfraba', ' dC ', 'secrETariat'],
dtype=np.bytes_), Index(np.array([0, 2, 3, 4])),
np.dtype(np.bytes_))
assert_series_equal(actual, expected)
def test_sort_values(self, data_f32, series_i64, data_i64):
data = [np.array([3, 1, 2, 5, 4], dtype=np.float32), series_i64]
df = DataFrame(OrderedDict((('a', data[0]), ('b', data[1]))),
Index(data_i64))
actual = df.sort_values('a')
expected_index = Index(np.array([2, 3, 1, 5, 4]), np.dtype(np.int64))
expected_data = [
Series(data_f32, expected_index, np.dtype(np.float32), 'a'),
Series(np.array([2, 3, 1, 5, 4], dtype=np.int64), expected_index,
np.dtype(np.int64), 'b')
]
expected = DataFrame(
OrderedDict((('a', expected_data[0]), ('b', expected_data[1]))),
expected_index)
assert_dataframe_equal(actual, expected)
def test_agg(self, series_f32):
aggregations = ['max', 'var', 'count', 'mean']
actual = series_f32.agg(aggregations)
expected = Series(np.array([5, 2.5, 5, 3], dtype=np.float64),
Index(np.array(aggregations, dtype=np.bytes_)),
np.dtype(np.float64))
assert_series_equal(actual, expected)
def test_dropna(self):
mi = MultiIndex([[0, -999, 2, -999],
Index([1., -999., -999., 3.],
dtype=np.dtype(np.float64))])
actual = mi.dropna()
expected = MultiIndex([[0], [1.]])
assert_multiindex_equal(actual, expected)
def test_sort_index_index_descending(self, data_f32, series_i64):
data = [data_f32, series_i64]
df = DataFrame(OrderedDict((('a', data[0]), ('b', data[1]))),
Index(np.array([3, 1, 2, 5, 4])))
actual = df.sort_index(ascending=False)
expected_index = Index(np.arange(5, 0, -1), np.dtype(np.int64))
expected_data = [
Series(np.array([4, 5, 1, 3, 2], dtype=np.float32), expected_index,
np.dtype(np.float32), 'a'),
Series(np.array([4, 5, 1, 3, 2], dtype=np.int64), expected_index,
np.dtype(np.int64), 'b')
]
expected = DataFrame(
OrderedDict((('a', expected_data[0]), ('b', expected_data[1]))),
expected_index)
assert_dataframe_equal(actual, expected)
def test_merge_unsorted_unique_single_on_inner(self, df2, data_f32,
index_i64_2):
df1 = DataFrame(
OrderedDict((('a', Series(np.array([3, 2, 0, 4, 1]))),
('b', np.array([4, 3, 1, 5, 2], dtype=np.float32)))),
Index([5, 4, 2, 6, 3]))
actual = df1.merge(df2, on='a')
expected = DataFrame(
OrderedDict(
(('index', np.array([3, 5])), ('b_x',
np.array([2, 4],
dtype=np.float32)),
('d', np.array(['abc', 'def'], dtype=np.dtype('|S4'))),
('b_y', Series(np.arange(3, 5, dtype=np.float32))),
('c', np.arange(4, 6)))),
Index(np.array([1, 3]), np.dtype(np.int64), 'a'))
assert_dataframe_equal(actual, expected)
def test_agg(self, df_small):
aggregations = ['max', 'var', 'count', 'mean']
actual = df_small.agg(aggregations)
expected = DataFrame(
OrderedDict((('a', np.array([5, 2.5, 5, 3], dtype=np.float64)),
('b', np.array([5, 2.5, 5, 3], dtype=np.float64)))),
Index(np.array(aggregations, dtype=np.bytes_),
np.dtype(np.bytes_)))
assert_dataframe_equal(actual, expected)
def test_head(self, df_small):
actual = df_small.head(2)
data = [1, 2]
expected = DataFrame(
OrderedDict(
(('a', np.array(data,
dtype=np.float32)), ('b', np.array(data)),
('c', np.array(['a', 'Abc'], dtype=np.dtype('|S11'))))),
Index(np.array([0, 1])))
assert_dataframe_equal(actual, expected)
def test_tail(self, df_small):
actual = df_small.tail(2)
data = [4, 5]
expected = DataFrame(
OrderedDict(
(('a', np.array(data, dtype=np.float32)),
('b', np.array(data)), ('c',
np.array([' dC ', 'secrETariat'],
dtype=np.dtype('|S11'))))),
Index(np.array([3, 4])))
assert_dataframe_equal(actual, expected)
def test_filter(self, df_small):
actual = df_small[Series(np.array([False, True, True, False, False]))]
data = [2, 3]
expected = DataFrame(
OrderedDict(
(('a', np.array(data,
dtype=np.float32)), ('b', np.array(data)),
('c', np.array(['Abc', 'goosfraba'],
dtype=np.dtype('|S11'))))),
Index(np.array([1, 2])))
assert_dataframe_equal(actual, expected)
def test_dropna(self, index_i64):
df = DataFrame(
OrderedDict((('a', np.array([0, 1, -999, 2, -999],
dtype=np.float32)),
('b', [4, -999, -999, 6, 6]))), index_i64)
actual = df.dropna()
expected = DataFrame(
OrderedDict((('a', np.array([0, 2],
dtype=np.float32)), ('b', [4, 6]))),
Index([0, 3]))
assert_dataframe_equal(actual, expected)
def test_drop_duplicates_subset(self, index_i64):
df = DataFrame(
OrderedDict(
(('a', np.array([0, 1, 1, 2, 3],
dtype=np.float32)), ('b', [4, 5, 5, 6, 6]))),
index_i64)
actual = df.drop_duplicates(subset=['b'])
expected = DataFrame(
OrderedDict((('a', np.array([0, 1, 2],
dtype=np.float32)), ('b', [4, 5, 6]))),
Index([0, 1, 3]))
assert_dataframe_equal(actual, expected, sort=True)
def test_setitem_alignment_needed(self, data_f32, index_i64):
df = DataFrame({'a': data_f32}, index_i64)
df['b'] = Series(np.array([4, 7, 5, 6, 8]),
Index(np.array([0, 3, 1, 2, 5])))
actual = df
expected = DataFrame({
'a': data_f32,
'b': np.array([4, 5, 6, 7, -999])
}, index_i64)
# so is WeldObject which was aligned
assert not isinstance(df['b'].values, np.ndarray)
assert_dataframe_equal(actual, expected)
def test_iloc_indices(self, df_small):
indices = Series(np.array([0, 2, 3]))
actual = df_small.iloc[indices]
data = [1, 3, 4]
expected = DataFrame(
OrderedDict(
(('a', np.array(data,
dtype=np.float32)), ('b', np.array(data)),
('c',
np.array(['a', 'goosfraba', ' dC '],
dtype=np.dtype('|S11'))))),
Index(np.array([0, 2, 3])))
assert_dataframe_equal(actual, expected)