def test_mode_array():
# ARROW-9917
arr = pa.array([1, 1, 3, 4, 3, 5], type='int64')
expected = {"mode": 1, "count": 2}
assert pc.mode(arr).as_py() == {"mode": 1, "count": 2}
arr = pa.array([], type='int64')
expected = {"mode": None, "count": None}
assert pc.mode(arr).as_py() == expected
def test_mode_chunked_array():
# ARROW-9917
arr = pa.chunked_array([pa.array([1, 1, 3, 4, 3, 5], type='int64')])
expected = {"mode": 1, "count": 2}
assert pc.mode(arr).as_py() == expected
arr = pa.chunked_array((), type='int64')
expected = {"mode": None, "count": None}
assert arr.num_chunks == 0
assert pc.mode(arr).as_py() == expected
def test_mode_array():
# ARROW-9917
arr = pa.array([1, 1, 3, 4, 3, 5], type='int64')
mode = pc.mode(arr)
assert len(mode) == 1
assert mode[0].as_py() == {"mode": 1, "count": 2}
mode = pc.mode(arr, 2)
assert len(mode) == 2
assert mode[0].as_py() == {"mode": 1, "count": 2}
assert mode[1].as_py() == {"mode": 3, "count": 2}
arr = pa.array([], type='int64')
assert len(pc.mode(arr)) == 0
def test_mode_chunked_array():
# ARROW-9917
arr = pa.chunked_array([pa.array([1, 1, 3, 4, 3, 5], type='int64')])
mode = pc.mode(arr)
assert len(mode) == 1
assert mode[0].as_py() == {"mode": 1, "count": 2}
mode = pc.mode(arr, 2)
assert len(mode) == 2
assert mode[0].as_py() == {"mode": 1, "count": 2}
assert mode[1].as_py() == {"mode": 3, "count": 2}
arr = pa.chunked_array((), type='int64')
assert arr.num_chunks == 0
assert len(pc.mode(arr)) == 0
def _mode(self: ArrowExtensionArrayT,
dropna: bool = True) -> ArrowExtensionArrayT:
"""
Returns the mode(s) of the ExtensionArray.
Always returns `ExtensionArray` even if only one value.
Parameters
----------
dropna : bool, default True
Don't consider counts of NA values.
Not implemented by pyarrow.
Returns
-------
same type as self
Sorted, if possible.
"""
if pa_version_under6p0:
raise NotImplementedError(
"mode only supported for pyarrow version >= 6.0")
modes = pc.mode(self._data, pc.count_distinct(self._data).as_py())
values = modes.field(0)
counts = modes.field(1)
# counts sorted descending i.e counts[0] = max
mask = pc.equal(counts, counts[0])
most_common = values.filter(mask)
return type(self)(most_common)
