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PERF: optimize conversion from boolean Arrow array to masked BooleanArray #41051

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Merged
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Apr 21, 2021
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PERF: optimize conversion from boolean Arrow array to masked BooleanArray #41051

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PERF: optimize conversion from boolean Arrow array to masked BooleanA…
jorisvandenbossche Apr 20, 2021
4b8bfd3
check type and error + test
jorisvandenbossche Apr 20, 2021
6c17271
add whatsnew note
jorisvandenbossche Apr 21, 2021
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v1.3.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -624,6 +624,7 @@ Performance improvements
- Performance improvement in :class:`Styler` where render times are more than 50% reduced (:issue:`39972` :issue:`39952`)
- Performance improvement in :meth:`core.window.ewm.ExponentialMovingWindow.mean` with ``times`` (:issue:`39784`)
- Performance improvement in :meth:`.GroupBy.apply` when requiring the python fallback implementation (:issue:`40176`)
- Performance improvement in the conversion of pyarrow boolean array to a pandas nullable boolean array (:issue:`41051`)
- Performance improvement for concatenation of data with type :class:`CategoricalDtype` (:issue:`40193`)

.. ---------------------------------------------------------------------------
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18 changes: 16 additions & 2 deletions pandas/core/arrays/boolean.py
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Expand Up @@ -114,6 +114,9 @@ def __from_arrow__(
"""
import pyarrow

if array.type != pyarrow.bool_():
raise TypeError(f"Expected array of boolean type, got {array.type} instead")

if isinstance(array, pyarrow.Array):
chunks = [array]
else:
Expand All @@ -122,8 +125,19 @@ def __from_arrow__(

results = []
for arr in chunks:
# TODO should optimize this without going through object array
bool_arr = BooleanArray._from_sequence(np.array(arr))
buflist = arr.buffers()
data = pyarrow.BooleanArray.from_buffers(
arr.type, len(arr), [None, buflist[1]], offset=arr.offset
).to_numpy(zero_copy_only=False)
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@simonjayhawkins simonjayhawkins Apr 20, 2021 •
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related to #41018 (comment)

if we do pass an incompatible type we now get a less helpful message

>>> import pandas as pd
>>> from pandas.core.arrays.string_arrow import ArrowStringDtype
>>> 
>>> s2 = pd.Series(["a", None, "1"], dtype="arrow_string")
>>> 
>>> arr = s2.values._data
>>> 
>>> arr
<pyarrow.lib.ChunkedArray object at 0x7f50a262ba90>
[
  [
    "a",
    null,
    "1"
  ]
]
>>> 
>>> pd.BooleanDtype().__from_arrow__(arr)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/simon/pandas/pandas/core/arrays/boolean.py", line 126, in __from_arrow__
    data = pyarrow.BooleanArray.from_buffers(
  File "pyarrow/array.pxi", line 947, in pyarrow.lib.Array.from_buffers
ValueError: Type's expected number of buffers (3) did not match the passed number (2).

on master

TypeError: Need to pass bool-like values

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if we do pass an incompatible type we now get a less helpful message

Generally we should never do that ourselves, though.
It's possible to get that in a conversion of arrow -> pandas if you have outdated metadata:

>>> df = pd.DataFrame({'a': pd.array([True, False])})
>>> table = pa.table(df)
>>> new_table = table.cast(pa.schema([('a', pa.int8())], metadata=table.schema.metadata))
>>> new_table.to_pandas()
...
~/scipy/repos/pandas-build-arrow/pandas/core/arrays/boolean.py in __from_arrow__(self, array)
    135                 mask = np.zeros(len(arr), dtype=bool)
    136 
--> 137             bool_arr = BooleanArray(data, mask)
    138             results.append(bool_arr)
    139 

~/scipy/repos/pandas-build-arrow/pandas/core/arrays/boolean.py in __init__(self, values, mask, copy)
    289     def __init__(self, values: np.ndarray, mask: np.ndarray, copy: bool = False):
    290         if not (isinstance(values, np.ndarray) and values.dtype == np.bool_):
--> 291             raise TypeError(
    292                 "values should be boolean numpy array. Use "
    293                 "the 'pd.array' function instead"

TypeError: values should be boolean numpy array. Use the 'pd.array' function instead

>>> new_table = table.cast(pa.schema([('a', pa.string())], metadata=table.schema.metadata))
>>> new_table.to_pandas()
...
~/scipy/repos/pandas-build-arrow/pandas/core/arrays/boolean.py in __from_arrow__(self, array)
    124         for arr in chunks:
    125             buflist = arr.buffers()
--> 126             data = pyarrow.BooleanArray.from_buffers(
    127                 arr.type, len(arr), [None, buflist[1]], offset=arr.offset
    128             ).to_numpy(zero_copy_only=False)

~/scipy/repos/arrow/python/pyarrow/array.pxi in pyarrow.lib.Array.from_buffers()

ValueError: Type's expected number of buffers (3) did not match the passed number (2).

And I think this is maybe something to fix on the pyarrow side (it should ignore the metadata on error).

Now, it's also easy to add a arr.type check to ensure it is boolean arrow type, so will do that.

if arr.null_count != 0:
mask = pyarrow.BooleanArray.from_buffers(
arr.type, len(arr), [None, buflist[0]], offset=arr.offset
).to_numpy(zero_copy_only=False)
mask = ~mask
else:
mask = np.zeros(len(arr), dtype=bool)

bool_arr = BooleanArray(data, mask)
results.append(bool_arr)

return BooleanArray._concat_same_type(results)
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31 changes: 29 additions & 2 deletions pandas/tests/arrays/masked/test_arrow_compat.py
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Expand Up @@ -55,12 +55,39 @@ def test_arrow_from_arrow_uint():


@td.skip_if_no("pyarrow", min_version="0.16.0")
def test_arrow_sliced():
def test_arrow_sliced(data):
# https://github.com/pandas-dev/pandas/issues/38525
import pyarrow as pa

df = pd.DataFrame({"a": pd.array([0, None, 2, 3, None], dtype="Int64")})
df = pd.DataFrame({"a": data})
table = pa.table(df)
result = table.slice(2, None).to_pandas()
expected = df.iloc[2:].reset_index(drop=True)
tm.assert_frame_equal(result, expected)

# no missing values
df2 = df.fillna(data[0])
table = pa.table(df2)
result = table.slice(2, None).to_pandas()
expected = df2.iloc[2:].reset_index(drop=True)
tm.assert_frame_equal(result, expected)


@td.skip_if_no("pyarrow", min_version="0.16.0")
def test_from_arrow_type_error(request, data):
# ensure that __from_arrow__ returns a TypeError when getting a wrong
# array type
import pyarrow as pa

if data.dtype != "boolean":
# TODO numeric dtypes cast any incoming array to the correct dtype
# instead of erroring
request.node.add_marker(
pytest.mark.xfail(reason="numeric dtypes don't error but cast")
)

arr = pa.array(data).cast("string")
with pytest.raises(TypeError, match=None):
# we don't test the exact error message, only the fact that it raises
# a TypeError is relevant
data.dtype.__from_arrow__(arr)