adienes opened a new issue, #51194:
URL: https://github.com/apache/arrow/issues/51194
### Describe the bug, including details regarding any error messages,
version, and platform.
the initial value is documented to be
> the minimum value of input type (so that any other value will replace the
start as the new maximum)
but since it uses `std::numeric_limits<T>::min()`, for floating-point types
like `double`, this actually initializes with the smallest _positive_ value of
the input type. so any input with a prefix of values `<=0` will yield incorrect
results. this also impacts `pandas.cummax()`
GPT-6 Astra helped identify the bug
```python
import pyarrow as pa
import pyarrow.compute as pc
import pandas as pd
values = [-2.5, 2.5]
arrow_result = pc.cumulative_max(pa.array(values,
type=pa.float64())).to_pylist()
pandas_result = pd.Series(values, dtype="float64[pyarrow]").cummax().tolist()
expected = [-2.5, 2.5]
print("expected running maximum:", expected)
print("pyarrow cumulative_max: ", arrow_result)
print("pandas cummax (arrow): ", pandas_result)
print("pandas cummax (numpy): ", pd.Series(values).cummax().tolist())
assert arrow_result == expected, "cumulative_max seeded with the smallest
positive float"
```
```python
>>> expected
[-2.5, 2.5]
>>> arrow_result
[2.2250738585072014e-308, 2.5]
>>> pandas_result
[2.2250738585072014e-308, 2.5]
```
### Component(s)
C++
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