kosiew commented on issue #1190:
URL:
https://github.com/apache/datafusion-python/issues/1190#issuecomment-3117895992
hi @l1t1
Can you try
```python
import pyarrow as pa
from datafusion import Accumulator, SessionContext, udaf
# Define a user-defined aggregation function (UDAF)
class MyAccumulator(Accumulator):
"""
Interface of a user-defined accumulation.
"""
def __init__(self) -> None:
self._sum = pa.scalar(0.0)
def update(self, values: pa.Array) -> None:
# not nice since pyarrow scalars can't be summed yet. This breaks on
`None`
self._sum = pa.scalar(self._sum.as_py() +
pa.compute.sum(values).as_py())
def merge(self, states: list[pa.Array]) -> None:
# not nice since pyarrow scalars can't be summed yet. This breaks on
`None`
self._sum = pa.scalar(self._sum.as_py() +
pa.compute.sum(states[0]).as_py())
def state(self) -> list[pa.Scalar]:
return [self._sum]
def evaluate(self) -> pa.Scalar:
return self._sum
my_udaf = udaf(
MyAccumulator,
pa.float64(),
pa.float64(),
[pa.float64()],
"stable",
# This will be the name of the UDAF in SQL
# If not specified it will by default the same as accumulator class name
name="my_accumulator",
)
# Create a context
ctx = SessionContext()
# Create a datafusion DataFrame from a Python dictionary
source_df = ctx.from_pydict({"a": [1, 1, 3], "b": [4, 5, 6]}, name="t")
# Dataframe:
# +---+---+
# | a | b |
# +---+---+
# | 1 | 4 |
# | 1 | 5 |
# | 3 | 6 |
# +---+---+
# Register UDF for use in SQL
ctx.register_udaf(my_udaf)
# Query the DataFrame using SQL
result_df = ctx.sql(
"select a, my_accumulator(b) as b_aggregated from t group by a order by
a"
)
# Dataframe:
# +---+--------------+
# | a | b_aggregated |
# +---+--------------+
# | 1 | 9 |
# | 3 | 6 |
# +---+--------------+
result_dict = result_df.to_pydict()
print("Result:", result_dict)
assert result_dict["a"] == [1, 3]
assert result_dict["b_aggregated"] == [9.0, 6.0]
print("Test passed successfully!")
```
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