H G created ARROW-9623:
--------------------------
Summary: Performance difference between pc.multiply vs pd.multiply
Key: ARROW-9623
URL: https://issues.apache.org/jira/browse/ARROW-9623
Project: Apache Arrow
Issue Type: Improvement
Components: Python
Affects Versions: 1.0.0
Environment: Windows
Pyarrow 1.0.0
Reporter: H G
Wanted to report the performance difference observed between Pandas and Pyarrow.
```
import numpy as np
import pandas as pd
import pyarrow as pa
import pyarrow.compute as pc
df = pd.DataFrame(np.random.randn(100000000))
%timeit -n 5 -r 5 df.multiply(df)
table = pa.Table.from_pandas(df)
%timeit -n 5 -r 5 pc.multiply(table[0],table[0])
```
Results:
```
%timeit -n 5 -r 5 df.multiply(df)
374 ms ± 15.9 ms per loop (mean ± std. dev. of 5 runs, 5 loops each)
```
```
%timeit -n 5 -r 5 pc.multiply(table[0],table[0])
698 ms ± 297 ms per loop (mean ± std. dev. of 5 runs, 5 loops each)
```
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