[
https://issues.apache.org/jira/browse/ARROW-9623?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
H G updated ARROW-9623:
-----------------------
Description:
Wanted to report the performance difference observed between Pandas and Pyarrow.
{code:java}
// 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])
{code}
Results:
{code:java}
%timeit -n 5 -r 5 df.multiply(df)
374 ms ± 15.9 ms per loop (mean ± std. dev. of 5 runs, 5 loops each)``{code}
{code:java}
%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){code}
was:
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)
```
Summary: [Python] Performance difference between pc.multiply vs
pd.multiply (was: Performance difference between pc.multiply vs pd.multiply)
> [Python] 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
> Priority: Minor
>
> Wanted to report the performance difference observed between Pandas and
> Pyarrow.
>
> {code:java}
> // 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])
> {code}
> Results:
> {code:java}
> %timeit -n 5 -r 5 df.multiply(df)
> 374 ms ± 15.9 ms per loop (mean ± std. dev. of 5 runs, 5 loops each)``{code}
>
> {code:java}
> %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){code}
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