jorisvandenbossche commented on code in PR #13155:
URL: https://github.com/apache/arrow/pull/13155#discussion_r877040772
##########
python/pyarrow/table.pxi:
##########
@@ -2882,24 +2882,27 @@ cdef class Table(_PandasConvertible):
return pyarrow_wrap_table(result)
- def filter(self, mask, object null_selection_behavior="drop"):
+ def filter(self, mask_or_expr, object null_selection_behavior="drop"):
"""
Select rows from the table.
- See :func:`pyarrow.compute.filter` for full usage.
+ The Table can be filtered based on a mask, which will be passed to
+ :func:`pyarrow.compute.filter` to perform the filtering, or it can
+ be filtered through a boolean :class:`.Expression`
Parameters
----------
- mask : Array or array-like
- The boolean mask to filter the table with.
+ mask_or_expr : Array or array-like or .Expression
+ The boolean mask or the :class:`.Expression` to filter the table
with.
null_selection_behavior
- How nulls in the mask should be handled.
+ How nulls in the mask should be handled, does nothing if
+ an :class:`.Expression` is used.
Review Comment:
Using your example table:
```
In [29]: t.filter(pa.array([True, None, True, False, False, False]))
Out[29]:
pyarrow.Table
rows: int64
----
rows: [[1,3]]
```
vs
```
In [33]: t.filter(pa.array([True, None, True, False, False, False]),
null_selection_behavior="emit_null")
Out[33]:
pyarrow.Table
rows: int64
----
rows: [[1,null,3]]
```
The null is in a place where the original data had a "2"
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