def test_strptime():
arr = pa.array(["5/1/2020", None, "12/13/1900"])
got = pc.strptime(arr, format='%m/%d/%Y', unit='s')
expected = pa.array([datetime(2020, 5, 1), None, datetime(1900, 12, 13)],
type=pa.timestamp('s'))
assert got == expected
def __init__(self, schema, table):
super(InMemoryColumnarTable, self).__init__(schema)
self.num_rows = table.num_rows
self.columns = table.columns
for idx in range(len(self.columns)):
if self.columns[idx].type.equals(pa.int64()):
self.columns[idx] = self.columns[idx].cast(pa.float64())
if self.columns[idx].type.equals(pa.string()):
if re.match("^[0-9]{4}[\-][0-9]{2}[\-][0-9]{2}$",
self.columns[idx][0].as_py()):
self.columns[idx] = compute.strptime(self.columns[idx],
format="%Y-%m-%d",
unit='us')
self.attr_to_idx = {a.aname: i for i, a in enumerate(self.schema)}
def to_arrow_table(self,
*,
skip_date_conversion: bool = False) -> pa.Table:
"""Export the report data to an Apache Arrow Table.
Keyword Args:
skip_date_conversion:
Whether to skip automatically converting date columns to
the ``timestamp[ns]`` format. Defaults to ``False``.
Returns:
The newly constructed Apache Arrow Table.
.. versionadded:: 3.2.0
.. versionchanged:: 3.6.0
Time series columns are now converted to ``timestamp[ns]``
format instead of ``timestamp[us]`` format.
"""
if analytix.can_use("pyarrow"):
import pyarrow as pa
import pyarrow.compute as pc
else:
raise errors.MissingOptionalComponents("pyarrow")
table = pa.table(list(zip(*self.data["rows"])), names=self.columns)
if not skip_date_conversion:
s = {"day", "month"} & set(table.column_names)
if len(s):
col = next(iter(s))
fmt = {"day": "%Y-%m-%d", "month": "%Y-%m"}[col]
dt_series = pc.strptime(table.column(col),
format=fmt,
unit="ns")
table = table.set_column(0, "day", dt_series)
_log.info(f"Converted {col!r} column to timestamp[ns] format")
return table
