Is there a more idiomatic way to select rows from a PyArrow table based on contents of a column?

问题

I have a large PyArrow table with one column called index that I would like to use to partition the table; each separate value of index represents a different quantity in the table.

Is there an idiomatic way to select rows from a PyArrow table based on contents of a column?

Here's an example table:

import pyarrow as pa
import pyarrow.parquet as pq
import pandas as pd
import numpy as np

# Example table for data schema
irow = np.arange(2**20)
dt = 17
df0 = pd.DataFrame({'timestamp': np.array((irow//2)*dt, dtype=np.int64),
                   'index':     np.array(irow%2, dtype=np.int16),
                   'value':     np.array(irow*0, dtype=np.int32)},
                   columns=['timestamp','index','value'])
ii = df0['index'] == 0
df0.loc[ii,'value'] = irow[ii]//2
ii = df0['index'] == 1
df0.loc[ii,'value'] = (np.sin(df0.loc[ii,'timestamp']*0.01)*10000).astype(np.int32)
table0 = pa.Table.from_pandas(df0)
print(df0)

# prints the following:
         timestamp  index   value
0                0      0       0
1                0      1       0
2               17      0       1
3               17      1    1691
4               34      0       2
...            ...    ...     ...
1048571    8912845      1    9945
1048572    8912862      0  524286
1048573    8912862      1    9978
1048574    8912879      0  524287
1048575    8912879      1    9723

[1048576 rows x 3 columns]

It's very easy to do this selection in Pandas:

print(df0[df0['index']==1])

# prints the following
         timestamp  index  value
1                0      1      0
3               17      1   1691
5               34      1   3334
7               51      1   4881
9               68      1   6287
...            ...    ...    ...
1048567    8912811      1   9028
1048569    8912828      1   9625
1048571    8912845      1   9945
1048573    8912862      1   9978
1048575    8912879      1   9723

[524288 rows x 3 columns]

But for PyArrow I have to do some shuffling around between PyArrow and either numpy or pandas:

value_index = table0.column('index').to_numpy()
# get values of the index column, convert to numpy format
row_indices = np.nonzero(value_index==1)[0]
# find matches and get their indices
selected_table = table0.take(pa.array(row_indices))
# use take() with those indices
v = selected_table.column('value')
print(v.to_numpy())

# which prints
[   0 1691 3334 ... 9945 9978 9723]

Is there a more straightforward way?

回答1:

A conversion to numpy is not needed to do a boolean filter operation. You can use the equal and filter functions from the pyarrow.compute module for this:

import pyarrow.compute as pc

value_index = table0.column('index')
row_mask = pc.equal(value_index, pa.scalar(1, value_index.type))
selected_table = table0.filter(row_mask)

来源：https://stackoverflow.com/questions/64578761/is-there-a-more-idiomatic-way-to-select-rows-from-a-pyarrow-table-based-on-conte

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