def test_strings(self):
values = ["foo", "bar", "baz"]
to_match = ["bar", "foo", "qux", "foo", "bar", "baz", "qux"]
result = algos.match(to_match, values)
expected = np.array([1, 0, -1, 0, 1, 2, -1])
self.assert_numpy_array_equal(result, expected)
result = Series(algos.match(to_match, values, np.nan))
expected = Series(np.array([1, 0, np.nan, 0, 1, 2, np.nan]))
tm.assert_series_equal(result, expected)
def test_strings(self):
values = ['foo', 'bar', 'baz']
to_match = ['bar', 'foo', 'qux', 'foo', 'bar', 'baz', 'qux']
result = algos.match(to_match, values)
expected = np.array([1, 0, -1, 0, 1, 2, -1])
self.assert_(np.array_equal(result, expected))
result = Series(algos.match(to_match, values, np.nan))
expected = Series(np.array([1, 0, np.nan, 0, 1, 2, np.nan]))
tm.assert_series_equal(result,expected)
def test_strings(self):
values = ["foo", "bar", "baz"]
to_match = ["bar", "foo", "qux", "foo", "bar", "baz", "qux"]
result = algos.match(to_match, values)
expected = np.array([1, 0, -1, 0, 1, 2, -1])
self.assert_(np.array_equal(result, expected))
def test_ints(self):
values = np.array([0, 2, 1])
to_match = np.array([0, 1, 2, 2, 0, 1, 3, 0])
result = algos.match(to_match, values)
expected = np.array([0, 2, 1, 1, 0, 2, -1, 0])
self.assert_(np.array_equal(result, expected))
def match(*args, **kwargs):
import warnings
warnings.warn("pd.match() is deprecated and will be removed "
"in a future version",
FutureWarning, stacklevel=2)
from pandas.core.algorithms import match
return match(*args, **kwargs)
def test_ints(self):
values = np.array([0, 2, 1])
to_match = np.array([0, 1, 2, 2, 0, 1, 3, 0])
result = algos.match(to_match, values)
expected = np.array([0, 2, 1, 1, 0, 2, -1, 0])
self.assert_(np.array_equal(result, expected))
result = Series(algos.match(to_match, values, np.nan))
expected = Series(np.array([0, 2, 1, 1, 0, 2, np.nan, 0]))
tm.assert_series_equal(result,expected)
s = pd.Series(np.arange(5),dtype=np.float32)
result = algos.match(s, [2,4])
expected = np.array([-1, -1, 0, -1, 1])
self.assert_(np.array_equal(result, expected))
result = Series(algos.match(s, [2,4], np.nan))
expected = Series(np.array([np.nan, np.nan, 0, np.nan, 1]))
tm.assert_series_equal(result,expected)
def test_ints(self):
values = np.array([0, 2, 1])
to_match = np.array([0, 1, 2, 2, 0, 1, 3, 0])
result = algos.match(to_match, values)
expected = np.array([0, 2, 1, 1, 0, 2, -1, 0])
self.assert_numpy_array_equal(result, expected)
result = Series(algos.match(to_match, values, np.nan))
expected = Series(np.array([0, 2, 1, 1, 0, 2, np.nan, 0]))
tm.assert_series_equal(result, expected)
s = pd.Series(np.arange(5), dtype=np.float32)
result = algos.match(s, [2, 4])
expected = np.array([-1, -1, 0, -1, 1])
self.assert_numpy_array_equal(result, expected)
result = Series(algos.match(s, [2, 4], np.nan))
expected = Series(np.array([np.nan, np.nan, 0, np.nan, 1]))
tm.assert_series_equal(result, expected)
def _read_panel_table(self, group, where=None):
table = getattr(group, 'table')
fields = table._v_attrs.fields
# create the selection
sel = Selection(table, where, table._v_attrs.index_kind)
sel.select()
fields = table._v_attrs.fields
columns = _maybe_convert(sel.values['column'],
table._v_attrs.columns_kind)
index = _maybe_convert(sel.values['index'], table._v_attrs.index_kind)
values = sel.values['values']
major = Factor.from_array(index)
minor = Factor.from_array(columns)
J, K = len(major.levels), len(minor.levels)
key = major.labels * K + minor.labels
if len(unique(key)) == len(key):
sorter, _ = lib.groupsort_indexer(com._ensure_int64(key), J * K)
sorter = com._ensure_platform_int(sorter)
# the data need to be sorted
sorted_values = values.take(sorter, axis=0)
major_labels = major.labels.take(sorter)
minor_labels = minor.labels.take(sorter)
block = block2d_to_block3d(sorted_values, fields, (J, K),
major_labels, minor_labels)
mgr = BlockManager([block],
[block.ref_items, major.levels, minor.levels])
wp = Panel(mgr)
else:
if not self._quiet: # pragma: no cover
print(
'Duplicate entries in table, taking most recently '
'appended')
# reconstruct
long_index = MultiIndex.from_arrays([index, columns])
lp = DataFrame(values, index=long_index, columns=fields)
# need a better algorithm
tuple_index = long_index._tuple_index
unique_tuples = lib.fast_unique(tuple_index)
unique_tuples = _asarray_tuplesafe(unique_tuples)
indexer = match(unique_tuples, tuple_index)
indexer = com._ensure_platform_int(indexer)
new_index = long_index.take(indexer)
new_values = lp.values.take(indexer, axis=0)
lp = DataFrame(new_values, index=new_index, columns=lp.columns)
wp = lp.to_panel()
if sel.column_filter:
new_minor = sorted(set(wp.minor_axis) & sel.column_filter)
wp = wp.reindex(minor=new_minor)
return wp
def _read_panel_table(self, group, where=None):
table = getattr(group, 'table')
fields = table._v_attrs.fields
# create the selection
sel = Selection(table, where, table._v_attrs.index_kind)
sel.select()
fields = table._v_attrs.fields
columns = _maybe_convert(sel.values['column'],
table._v_attrs.columns_kind)
index = _maybe_convert(sel.values['index'],
table._v_attrs.index_kind)
values = sel.values['values']
major = Factor(index)
minor = Factor(columns)
J, K = len(major.levels), len(minor.levels)
key = major.labels * K + minor.labels
if len(unique(key)) == len(key):
sorter, _ = lib.groupsort_indexer(key, J * K)
# the data need to be sorted
sorted_values = values.take(sorter, axis=0)
major_labels = major.labels.take(sorter)
minor_labels = minor.labels.take(sorter)
block = block2d_to_block3d(sorted_values, fields, (J, K),
major_labels, minor_labels)
mgr = BlockManager([block], [block.items,
major.levels, minor.levels])
wp = Panel(mgr)
else:
if not self._quiet: # pragma: no cover
print ('Duplicate entries in table, taking most recently '
'appended')
# reconstruct
long_index = MultiIndex.from_arrays([index, columns])
lp = DataFrame(values, index=long_index, columns=fields)
# need a better algorithm
tuple_index = long_index.get_tuple_index()
unique_tuples = lib.fast_unique(tuple_index)
unique_tuples = _asarray_tuplesafe(unique_tuples)
indexer = match(unique_tuples, tuple_index)
new_index = long_index.take(indexer)
new_values = lp.values.take(indexer, axis=0)
lp = DataFrame(new_values, index=new_index, columns=lp.columns)
wp = lp.to_panel()
if sel.column_filter:
new_minor = sorted(set(wp.minor_axis) & sel.column_filter)
wp = wp.reindex(minor=new_minor)
return wp
