/
test__pandas_helpers.py
1361 lines (1190 loc) · 49.8 KB
/
test__pandas_helpers.py
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# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import datetime
import decimal
import functools
import operator
import warnings
import mock
import six
try:
import pandas
import pandas.api.types
import pandas.testing
except ImportError: # pragma: NO COVER
pandas = None
try:
import pyarrow
import pyarrow.types
except ImportError: # pragma: NO COVER
# Mock out pyarrow when missing, because methods from pyarrow.types are
# used in test parameterization.
pyarrow = mock.Mock()
import pytest
import pytz
from google import api_core
from google.cloud.bigquery import schema
@pytest.fixture
def module_under_test():
from google.cloud.bigquery import _pandas_helpers
return _pandas_helpers
def is_none(value):
return value is None
def is_datetime(type_):
# See: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#datetime-type
return all_(
pyarrow.types.is_timestamp,
lambda type_: type_.unit == "us",
lambda type_: type_.tz is None,
)(type_)
def is_numeric(type_):
# See: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#numeric-type
return all_(
pyarrow.types.is_decimal,
lambda type_: type_.precision == 38,
lambda type_: type_.scale == 9,
)(type_)
def is_timestamp(type_):
# See: https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#timestamp-type
return all_(
pyarrow.types.is_timestamp,
lambda type_: type_.unit == "us",
lambda type_: type_.tz == "UTC",
)(type_)
def do_all(functions, value):
return all((func(value) for func in functions))
def all_(*functions):
return functools.partial(do_all, functions)
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_is_datetime():
assert is_datetime(pyarrow.timestamp("us", tz=None))
assert not is_datetime(pyarrow.timestamp("ms", tz=None))
assert not is_datetime(pyarrow.timestamp("us", tz="UTC"))
assert not is_datetime(pyarrow.timestamp("ns", tz="UTC"))
assert not is_datetime(pyarrow.string())
def test_do_all():
assert do_all((lambda _: True, lambda _: True), None)
assert not do_all((lambda _: True, lambda _: False), None)
assert not do_all((lambda _: False,), None)
def test_all_():
assert all_(lambda _: True, lambda _: True)(None)
assert not all_(lambda _: True, lambda _: False)(None)
@pytest.mark.parametrize(
"bq_type,bq_mode,is_correct_type",
[
("STRING", "NULLABLE", pyarrow.types.is_string),
("STRING", None, pyarrow.types.is_string),
("string", "NULLABLE", pyarrow.types.is_string),
("StRiNg", "NULLABLE", pyarrow.types.is_string),
("BYTES", "NULLABLE", pyarrow.types.is_binary),
("INTEGER", "NULLABLE", pyarrow.types.is_int64),
("INT64", "NULLABLE", pyarrow.types.is_int64),
("FLOAT", "NULLABLE", pyarrow.types.is_float64),
("FLOAT64", "NULLABLE", pyarrow.types.is_float64),
("NUMERIC", "NULLABLE", is_numeric),
("BOOLEAN", "NULLABLE", pyarrow.types.is_boolean),
("BOOL", "NULLABLE", pyarrow.types.is_boolean),
("TIMESTAMP", "NULLABLE", is_timestamp),
("DATE", "NULLABLE", pyarrow.types.is_date32),
("TIME", "NULLABLE", pyarrow.types.is_time64),
("DATETIME", "NULLABLE", is_datetime),
("GEOGRAPHY", "NULLABLE", pyarrow.types.is_string),
("UNKNOWN_TYPE", "NULLABLE", is_none),
# Use pyarrow.list_(item_type) for repeated (array) fields.
(
"STRING",
"REPEATED",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_string(type_.value_type),
),
),
(
"STRING",
"repeated",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_string(type_.value_type),
),
),
(
"STRING",
"RePeAtEd",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_string(type_.value_type),
),
),
(
"BYTES",
"REPEATED",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_binary(type_.value_type),
),
),
(
"INTEGER",
"REPEATED",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_int64(type_.value_type),
),
),
(
"INT64",
"REPEATED",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_int64(type_.value_type),
),
),
(
"FLOAT",
"REPEATED",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_float64(type_.value_type),
),
),
(
"FLOAT64",
"REPEATED",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_float64(type_.value_type),
),
),
(
"NUMERIC",
"REPEATED",
all_(pyarrow.types.is_list, lambda type_: is_numeric(type_.value_type)),
),
(
"BOOLEAN",
"REPEATED",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_boolean(type_.value_type),
),
),
(
"BOOL",
"REPEATED",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_boolean(type_.value_type),
),
),
(
"TIMESTAMP",
"REPEATED",
all_(pyarrow.types.is_list, lambda type_: is_timestamp(type_.value_type)),
),
(
"DATE",
"REPEATED",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_date32(type_.value_type),
),
),
(
"TIME",
"REPEATED",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_time64(type_.value_type),
),
),
(
"DATETIME",
"REPEATED",
all_(pyarrow.types.is_list, lambda type_: is_datetime(type_.value_type)),
),
(
"GEOGRAPHY",
"REPEATED",
all_(
pyarrow.types.is_list,
lambda type_: pyarrow.types.is_string(type_.value_type),
),
),
("RECORD", "REPEATED", is_none),
("UNKNOWN_TYPE", "REPEATED", is_none),
],
)
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_bq_to_arrow_data_type(module_under_test, bq_type, bq_mode, is_correct_type):
field = schema.SchemaField("ignored_name", bq_type, mode=bq_mode)
actual = module_under_test.bq_to_arrow_data_type(field)
assert is_correct_type(actual)
@pytest.mark.parametrize("bq_type", ["RECORD", "record", "STRUCT", "struct"])
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_bq_to_arrow_data_type_w_struct(module_under_test, bq_type):
fields = (
schema.SchemaField("field01", "STRING"),
schema.SchemaField("field02", "BYTES"),
schema.SchemaField("field03", "INTEGER"),
schema.SchemaField("field04", "INT64"),
schema.SchemaField("field05", "FLOAT"),
schema.SchemaField("field06", "FLOAT64"),
schema.SchemaField("field07", "NUMERIC"),
schema.SchemaField("field08", "BOOLEAN"),
schema.SchemaField("field09", "BOOL"),
schema.SchemaField("field10", "TIMESTAMP"),
schema.SchemaField("field11", "DATE"),
schema.SchemaField("field12", "TIME"),
schema.SchemaField("field13", "DATETIME"),
schema.SchemaField("field14", "GEOGRAPHY"),
)
field = schema.SchemaField("ignored_name", bq_type, mode="NULLABLE", fields=fields)
actual = module_under_test.bq_to_arrow_data_type(field)
expected = pyarrow.struct(
(
pyarrow.field("field01", pyarrow.string()),
pyarrow.field("field02", pyarrow.binary()),
pyarrow.field("field03", pyarrow.int64()),
pyarrow.field("field04", pyarrow.int64()),
pyarrow.field("field05", pyarrow.float64()),
pyarrow.field("field06", pyarrow.float64()),
pyarrow.field("field07", module_under_test.pyarrow_numeric()),
pyarrow.field("field08", pyarrow.bool_()),
pyarrow.field("field09", pyarrow.bool_()),
pyarrow.field("field10", module_under_test.pyarrow_timestamp()),
pyarrow.field("field11", pyarrow.date32()),
pyarrow.field("field12", module_under_test.pyarrow_time()),
pyarrow.field("field13", module_under_test.pyarrow_datetime()),
pyarrow.field("field14", pyarrow.string()),
)
)
assert pyarrow.types.is_struct(actual)
try:
assert actual.num_fields == len(fields)
except AttributeError: # py27
assert actual.num_children == len(fields)
assert actual.equals(expected)
@pytest.mark.parametrize("bq_type", ["RECORD", "record", "STRUCT", "struct"])
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_bq_to_arrow_data_type_w_array_struct(module_under_test, bq_type):
fields = (
schema.SchemaField("field01", "STRING"),
schema.SchemaField("field02", "BYTES"),
schema.SchemaField("field03", "INTEGER"),
schema.SchemaField("field04", "INT64"),
schema.SchemaField("field05", "FLOAT"),
schema.SchemaField("field06", "FLOAT64"),
schema.SchemaField("field07", "NUMERIC"),
schema.SchemaField("field08", "BOOLEAN"),
schema.SchemaField("field09", "BOOL"),
schema.SchemaField("field10", "TIMESTAMP"),
schema.SchemaField("field11", "DATE"),
schema.SchemaField("field12", "TIME"),
schema.SchemaField("field13", "DATETIME"),
schema.SchemaField("field14", "GEOGRAPHY"),
)
field = schema.SchemaField("ignored_name", bq_type, mode="REPEATED", fields=fields)
actual = module_under_test.bq_to_arrow_data_type(field)
expected_value_type = pyarrow.struct(
(
pyarrow.field("field01", pyarrow.string()),
pyarrow.field("field02", pyarrow.binary()),
pyarrow.field("field03", pyarrow.int64()),
pyarrow.field("field04", pyarrow.int64()),
pyarrow.field("field05", pyarrow.float64()),
pyarrow.field("field06", pyarrow.float64()),
pyarrow.field("field07", module_under_test.pyarrow_numeric()),
pyarrow.field("field08", pyarrow.bool_()),
pyarrow.field("field09", pyarrow.bool_()),
pyarrow.field("field10", module_under_test.pyarrow_timestamp()),
pyarrow.field("field11", pyarrow.date32()),
pyarrow.field("field12", module_under_test.pyarrow_time()),
pyarrow.field("field13", module_under_test.pyarrow_datetime()),
pyarrow.field("field14", pyarrow.string()),
)
)
assert pyarrow.types.is_list(actual)
assert pyarrow.types.is_struct(actual.value_type)
try:
assert actual.value_type.num_fields == len(fields)
except AttributeError: # py27
assert actual.value_type.num_children == len(fields)
assert actual.value_type.equals(expected_value_type)
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_bq_to_arrow_data_type_w_struct_unknown_subfield(module_under_test):
fields = (
schema.SchemaField("field1", "STRING"),
schema.SchemaField("field2", "INTEGER"),
# Don't know what to convert UNKNOWN_TYPE to, let type inference work,
# instead.
schema.SchemaField("field3", "UNKNOWN_TYPE"),
)
field = schema.SchemaField("ignored_name", "RECORD", mode="NULLABLE", fields=fields)
with warnings.catch_warnings(record=True) as warned:
actual = module_under_test.bq_to_arrow_data_type(field)
assert actual is None
assert len(warned) == 1
warning = warned[0]
assert "field3" in str(warning)
@pytest.mark.parametrize(
"bq_type,rows",
[
("STRING", ["abc", None, "def", None]),
("BYTES", [b"abc", None, b"def", None]),
("INTEGER", [123, None, 456, None]),
("INT64", [-9223372036854775808, None, 9223372036854775807, 123]),
("FLOAT", [1.25, None, 3.5, None]),
(
"NUMERIC",
[
decimal.Decimal("-99999999999999999999999999999.999999999"),
None,
decimal.Decimal("99999999999999999999999999999.999999999"),
decimal.Decimal("999.123456789"),
],
),
("BOOLEAN", [True, None, False, None]),
("BOOL", [False, None, True, None]),
(
"TIMESTAMP",
[
datetime.datetime(1, 1, 1, 0, 0, 0, tzinfo=pytz.utc),
None,
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999, tzinfo=pytz.utc),
datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=pytz.utc),
],
),
(
"DATE",
[
datetime.date(1, 1, 1),
None,
datetime.date(9999, 12, 31),
datetime.date(1970, 1, 1),
],
),
(
"TIME",
[
datetime.time(0, 0, 0),
None,
datetime.time(23, 59, 59, 999999),
datetime.time(12, 0, 0),
],
),
(
"DATETIME",
[
datetime.datetime(1, 1, 1, 0, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999),
None,
datetime.datetime(1970, 1, 1, 0, 0, 0),
datetime.datetime(1999, 3, 14, 15, 9, 26, 535898),
],
),
(
"GEOGRAPHY",
[
"POINT(30 10)",
None,
"LINESTRING (30 10, 10 30, 40 40)",
"POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))",
],
),
],
)
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_bq_to_arrow_array_w_nullable_scalars(module_under_test, bq_type, rows):
series = pandas.Series(rows, dtype="object")
bq_field = schema.SchemaField("field_name", bq_type)
arrow_array = module_under_test.bq_to_arrow_array(series, bq_field)
roundtrip = arrow_array.to_pylist()
assert rows == roundtrip
@pytest.mark.parametrize(
"bq_type,rows",
[
(
"TIMESTAMP",
[
"1971-09-28T23:59:07+00:00",
"1975-04-09T23:59:02+00:00",
"1979-08-17T23:59:05+00:00",
"NaT",
"1983-05-09T13:00:00+00:00",
],
),
(
"DATETIME",
[
"1971-09-28T23:59:07",
"1975-04-09T23:59:02",
"1979-08-17T23:59:05",
"NaT",
"1983-05-09T13:00:00",
],
),
],
)
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_bq_to_arrow_array_w_pandas_timestamp(module_under_test, bq_type, rows):
rows = [pandas.Timestamp(row) for row in rows]
series = pandas.Series(rows)
bq_field = schema.SchemaField("field_name", bq_type)
arrow_array = module_under_test.bq_to_arrow_array(series, bq_field)
roundtrip = arrow_array.to_pandas()
assert series.equals(roundtrip)
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_bq_to_arrow_array_w_arrays(module_under_test):
rows = [[1, 2, 3], [], [4, 5, 6]]
series = pandas.Series(rows, dtype="object")
bq_field = schema.SchemaField("field_name", "INTEGER", mode="REPEATED")
arrow_array = module_under_test.bq_to_arrow_array(series, bq_field)
roundtrip = arrow_array.to_pylist()
assert rows == roundtrip
@pytest.mark.parametrize("bq_type", ["RECORD", "record", "STRUCT", "struct"])
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_bq_to_arrow_array_w_structs(module_under_test, bq_type):
rows = [
{"int_col": 123, "string_col": "abc"},
None,
{"int_col": 456, "string_col": "def"},
]
series = pandas.Series(rows, dtype="object")
bq_field = schema.SchemaField(
"field_name",
bq_type,
fields=(
schema.SchemaField("int_col", "INTEGER"),
schema.SchemaField("string_col", "STRING"),
),
)
arrow_array = module_under_test.bq_to_arrow_array(series, bq_field)
roundtrip = arrow_array.to_pylist()
assert rows == roundtrip
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_bq_to_arrow_array_w_special_floats(module_under_test):
bq_field = schema.SchemaField("field_name", "FLOAT64")
rows = [float("-inf"), float("nan"), float("inf"), None]
series = pandas.Series(rows, dtype="object")
arrow_array = module_under_test.bq_to_arrow_array(series, bq_field)
roundtrip = arrow_array.to_pylist()
assert len(rows) == len(roundtrip)
assert roundtrip[0] == float("-inf")
# Since we are converting from pandas, NaN is treated as NULL in pyarrow
# due to pandas conventions.
# https://arrow.apache.org/docs/python/data.html#none-values-and-nan-handling
assert roundtrip[1] is None
assert roundtrip[2] == float("inf")
assert roundtrip[3] is None
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_bq_to_arrow_schema_w_unknown_type(module_under_test):
fields = (
schema.SchemaField("field1", "STRING"),
schema.SchemaField("field2", "INTEGER"),
# Don't know what to convert UNKNOWN_TYPE to, let type inference work,
# instead.
schema.SchemaField("field3", "UNKNOWN_TYPE"),
)
with warnings.catch_warnings(record=True) as warned:
actual = module_under_test.bq_to_arrow_schema(fields)
assert actual is None
if six.PY3:
assert len(warned) == 1
warning = warned[0]
assert "field3" in str(warning)
else:
assert len(warned) == 0
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_get_column_or_index_not_found(module_under_test):
dataframe = pandas.DataFrame({"not_the_column_youre_looking_for": [1, 2, 3]})
with pytest.raises(ValueError, match="col_is_missing"):
module_under_test.get_column_or_index(dataframe, "col_is_missing")
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_get_column_or_index_with_multiindex_not_found(module_under_test):
dataframe = pandas.DataFrame(
{"column_name": [1, 2, 3, 4, 5, 6]},
index=pandas.MultiIndex.from_tuples(
[("a", 0), ("a", 1), ("b", 0), ("b", 1), ("c", 0), ("c", 1)]
),
)
with pytest.raises(ValueError, match="not_in_df"):
module_under_test.get_column_or_index(dataframe, "not_in_df")
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_get_column_or_index_with_both_prefers_column(module_under_test):
dataframe = pandas.DataFrame(
{"some_name": [1, 2, 3]}, index=pandas.Index([0, 1, 2], name="some_name")
)
series = module_under_test.get_column_or_index(dataframe, "some_name")
expected = pandas.Series([1, 2, 3], name="some_name")
pandas.testing.assert_series_equal(series, expected)
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_get_column_or_index_with_column(module_under_test):
dataframe = pandas.DataFrame({"column_name": [1, 2, 3], "other_column": [4, 5, 6]})
series = module_under_test.get_column_or_index(dataframe, "column_name")
expected = pandas.Series([1, 2, 3], name="column_name")
pandas.testing.assert_series_equal(series, expected)
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_get_column_or_index_with_named_index(module_under_test):
dataframe = pandas.DataFrame(
{"column_name": [1, 2, 3]}, index=pandas.Index([4, 5, 6], name="index_name")
)
series = module_under_test.get_column_or_index(dataframe, "index_name")
expected = pandas.Series([4, 5, 6], name="index_name")
pandas.testing.assert_series_equal(series, expected)
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_get_column_or_index_with_datetimeindex(module_under_test):
datetimes = [
datetime.datetime(2000, 1, 2, 3, 4, 5, 101),
datetime.datetime(2006, 7, 8, 9, 10, 11, 202),
datetime.datetime(2012, 1, 14, 15, 16, 17, 303),
]
dataframe = pandas.DataFrame(
{"column_name": [1, 2, 3]},
index=pandas.DatetimeIndex(datetimes, name="index_name"),
)
series = module_under_test.get_column_or_index(dataframe, "index_name")
expected = pandas.Series(datetimes, name="index_name")
pandas.testing.assert_series_equal(series, expected)
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_get_column_or_index_with_multiindex(module_under_test):
dataframe = pandas.DataFrame(
{"column_name": [1, 2, 3, 4, 5, 6]},
index=pandas.MultiIndex.from_tuples(
[("a", 0), ("a", 1), ("b", 0), ("b", 1), ("c", 0), ("c", 1)],
names=["letters", "numbers"],
),
)
series = module_under_test.get_column_or_index(dataframe, "letters")
expected = pandas.Series(["a", "a", "b", "b", "c", "c"], name="letters")
pandas.testing.assert_series_equal(series, expected)
series = module_under_test.get_column_or_index(dataframe, "numbers")
expected = pandas.Series([0, 1, 0, 1, 0, 1], name="numbers")
pandas.testing.assert_series_equal(series, expected)
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_list_columns_and_indexes_without_named_index(module_under_test):
df_data = collections.OrderedDict(
[
("a_series", [1, 2, 3, 4]),
("b_series", [0.1, 0.2, 0.3, 0.4]),
("c_series", ["a", "b", "c", "d"]),
]
)
dataframe = pandas.DataFrame(df_data)
columns_and_indexes = module_under_test.list_columns_and_indexes(dataframe)
expected = [
("a_series", pandas.api.types.pandas_dtype("int64")),
("b_series", pandas.api.types.pandas_dtype("float64")),
("c_series", pandas.api.types.pandas_dtype("object")),
]
assert columns_and_indexes == expected
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_list_columns_and_indexes_with_named_index_same_as_column_name(
module_under_test,
):
df_data = collections.OrderedDict(
[
("a_series", [1, 2, 3, 4]),
("b_series", [0.1, 0.2, 0.3, 0.4]),
("c_series", ["a", "b", "c", "d"]),
]
)
dataframe = pandas.DataFrame(
df_data,
# Use same name as an integer column but a different datatype so that
# we can verify that the column is listed but the index isn't.
index=pandas.Index([0.1, 0.2, 0.3, 0.4], name="a_series"),
)
columns_and_indexes = module_under_test.list_columns_and_indexes(dataframe)
expected = [
("a_series", pandas.api.types.pandas_dtype("int64")),
("b_series", pandas.api.types.pandas_dtype("float64")),
("c_series", pandas.api.types.pandas_dtype("object")),
]
assert columns_and_indexes == expected
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_list_columns_and_indexes_with_named_index(module_under_test):
df_data = collections.OrderedDict(
[
("a_series", [1, 2, 3, 4]),
("b_series", [0.1, 0.2, 0.3, 0.4]),
("c_series", ["a", "b", "c", "d"]),
]
)
dataframe = pandas.DataFrame(
df_data, index=pandas.Index([4, 5, 6, 7], name="a_index")
)
columns_and_indexes = module_under_test.list_columns_and_indexes(dataframe)
expected = [
("a_index", pandas.api.types.pandas_dtype("int64")),
("a_series", pandas.api.types.pandas_dtype("int64")),
("b_series", pandas.api.types.pandas_dtype("float64")),
("c_series", pandas.api.types.pandas_dtype("object")),
]
assert columns_and_indexes == expected
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_list_columns_and_indexes_with_multiindex(module_under_test):
df_data = collections.OrderedDict(
[
("a_series", [1, 2, 3, 4]),
("b_series", [0.1, 0.2, 0.3, 0.4]),
("c_series", ["a", "b", "c", "d"]),
]
)
dataframe = pandas.DataFrame(
df_data,
index=pandas.MultiIndex.from_tuples(
[(0, 0, 41), (0, 0, 42), (1, 0, 41), (1, 1, 41)],
names=[
"a_index",
# Use same name as column, but different dtype so we can verify
# the column type is included.
"b_series",
"c_index",
],
),
)
columns_and_indexes = module_under_test.list_columns_and_indexes(dataframe)
expected = [
("a_index", pandas.api.types.pandas_dtype("int64")),
("c_index", pandas.api.types.pandas_dtype("int64")),
("a_series", pandas.api.types.pandas_dtype("int64")),
("b_series", pandas.api.types.pandas_dtype("float64")),
("c_series", pandas.api.types.pandas_dtype("object")),
]
assert columns_and_indexes == expected
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_dataframe_to_bq_schema_dict_sequence(module_under_test):
df_data = collections.OrderedDict(
[
("str_column", [u"hello", u"world"]),
("int_column", [42, 8]),
("bool_column", [True, False]),
]
)
dataframe = pandas.DataFrame(df_data)
dict_schema = [
{"name": "str_column", "type": "STRING", "mode": "NULLABLE"},
{"name": "bool_column", "type": "BOOL", "mode": "REQUIRED"},
]
returned_schema = module_under_test.dataframe_to_bq_schema(dataframe, dict_schema)
expected_schema = (
schema.SchemaField("str_column", "STRING", "NULLABLE"),
schema.SchemaField("int_column", "INTEGER", "NULLABLE"),
schema.SchemaField("bool_column", "BOOL", "REQUIRED"),
)
assert returned_schema == expected_schema
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
@pytest.mark.skipif(not six.PY2, reason="Requires Python 2.7")
def test_dataframe_to_bq_schema_w_struct_raises_py27(module_under_test):
dataframe = pandas.DataFrame(
data=[{"struct_field": {"int_col": 1}}, {"struct_field": {"int_col": 2}}]
)
bq_schema = [
schema.SchemaField(
"struct_field",
field_type="STRUCT",
fields=[schema.SchemaField("int_col", field_type="INT64")],
),
]
with pytest.raises(ValueError) as excinfo:
module_under_test.dataframe_to_bq_schema(dataframe, bq_schema=bq_schema)
assert "struct (record) column types is not supported" in str(excinfo.value)
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_dataframe_to_arrow_with_multiindex(module_under_test):
bq_schema = (
schema.SchemaField("str_index", "STRING"),
# int_index is intentionally omitted, to verify that it's okay to be
# missing indexes from the schema.
schema.SchemaField("dt_index", "DATETIME"),
schema.SchemaField("int_col", "INTEGER"),
schema.SchemaField("nullable_int_col", "INTEGER"),
schema.SchemaField("str_col", "STRING"),
)
df_data = collections.OrderedDict(
[
("int_col", [1, 2, 3, 4, 5, 6]),
("nullable_int_col", [6.0, float("nan"), 7.0, float("nan"), 8.0, 9.0]),
("str_col", ["apple", "banana", "cherry", "durian", "etrog", "fig"]),
]
)
df_index = pandas.MultiIndex.from_tuples(
[
("a", 0, datetime.datetime(1999, 12, 31, 23, 59, 59, 999999)),
("a", 0, datetime.datetime(2000, 1, 1, 0, 0, 0)),
("a", 1, datetime.datetime(1999, 12, 31, 23, 59, 59, 999999)),
("b", 1, datetime.datetime(2000, 1, 1, 0, 0, 0)),
("b", 0, datetime.datetime(1999, 12, 31, 23, 59, 59, 999999)),
("b", 0, datetime.datetime(2000, 1, 1, 0, 0, 0)),
],
names=["str_index", "int_index", "dt_index"],
)
dataframe = pandas.DataFrame(df_data, index=df_index)
arrow_table = module_under_test.dataframe_to_arrow(dataframe, bq_schema)
assert arrow_table.schema.names == [
"str_index",
"dt_index",
"int_col",
"nullable_int_col",
"str_col",
]
arrow_data = arrow_table.to_pydict()
assert arrow_data["str_index"] == ["a", "a", "a", "b", "b", "b"]
expected_dt_index = [
pandas.Timestamp(dt)
for dt in (
datetime.datetime(1999, 12, 31, 23, 59, 59, 999999),
datetime.datetime(2000, 1, 1, 0, 0, 0),
datetime.datetime(1999, 12, 31, 23, 59, 59, 999999),
datetime.datetime(2000, 1, 1, 0, 0, 0),
datetime.datetime(1999, 12, 31, 23, 59, 59, 999999),
datetime.datetime(2000, 1, 1, 0, 0, 0),
)
]
assert arrow_data["dt_index"] == expected_dt_index
assert arrow_data["int_col"] == [1, 2, 3, 4, 5, 6]
assert arrow_data["nullable_int_col"] == [6, None, 7, None, 8, 9]
assert arrow_data["str_col"] == [
"apple",
"banana",
"cherry",
"durian",
"etrog",
"fig",
]
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_dataframe_to_arrow_with_required_fields(module_under_test):
bq_schema = (
schema.SchemaField("field01", "STRING", mode="REQUIRED"),
schema.SchemaField("field02", "BYTES", mode="REQUIRED"),
schema.SchemaField("field03", "INTEGER", mode="REQUIRED"),
schema.SchemaField("field04", "INT64", mode="REQUIRED"),
schema.SchemaField("field05", "FLOAT", mode="REQUIRED"),
schema.SchemaField("field06", "FLOAT64", mode="REQUIRED"),
schema.SchemaField("field07", "NUMERIC", mode="REQUIRED"),
schema.SchemaField("field08", "BOOLEAN", mode="REQUIRED"),
schema.SchemaField("field09", "BOOL", mode="REQUIRED"),
schema.SchemaField("field10", "TIMESTAMP", mode="REQUIRED"),
schema.SchemaField("field11", "DATE", mode="REQUIRED"),
schema.SchemaField("field12", "TIME", mode="REQUIRED"),
schema.SchemaField("field13", "DATETIME", mode="REQUIRED"),
schema.SchemaField("field14", "GEOGRAPHY", mode="REQUIRED"),
)
dataframe = pandas.DataFrame(
{
"field01": ["hello", "world"],
"field02": [b"abd", b"efg"],
"field03": [1, 2],
"field04": [3, 4],
"field05": [1.25, 9.75],
"field06": [-1.75, -3.5],
"field07": [decimal.Decimal("1.2345"), decimal.Decimal("6.7891")],
"field08": [True, False],
"field09": [False, True],
"field10": [
datetime.datetime(1970, 1, 1, 0, 0, 0, tzinfo=pytz.utc),
datetime.datetime(2012, 12, 21, 9, 7, 42, tzinfo=pytz.utc),
],
"field11": [datetime.date(9999, 12, 31), datetime.date(1970, 1, 1)],
"field12": [datetime.time(23, 59, 59, 999999), datetime.time(12, 0, 0)],
"field13": [
datetime.datetime(1970, 1, 1, 0, 0, 0),
datetime.datetime(2012, 12, 21, 9, 7, 42),
],
"field14": [
"POINT(30 10)",
"POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))",
],
}
)
arrow_table = module_under_test.dataframe_to_arrow(dataframe, bq_schema)
arrow_schema = arrow_table.schema
assert len(arrow_schema) == len(bq_schema)
for arrow_field in arrow_schema:
assert not arrow_field.nullable
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_dataframe_to_arrow_with_unknown_type(module_under_test):
bq_schema = (
schema.SchemaField("field00", "UNKNOWN_TYPE"),
schema.SchemaField("field01", "STRING"),
schema.SchemaField("field02", "BYTES"),
schema.SchemaField("field03", "INTEGER"),
)
dataframe = pandas.DataFrame(
{
"field00": ["whoami", "whatami"],
"field01": ["hello", "world"],
"field02": [b"abd", b"efg"],
"field03": [1, 2],
}
)
with warnings.catch_warnings(record=True) as warned:
arrow_table = module_under_test.dataframe_to_arrow(dataframe, bq_schema)
arrow_schema = arrow_table.schema
assert len(warned) == 1
warning = warned[0]
assert "field00" in str(warning)
assert len(arrow_schema) == len(bq_schema)
assert arrow_schema[0].name == "field00"
assert arrow_schema[1].name == "field01"
assert arrow_schema[2].name == "field02"
assert arrow_schema[3].name == "field03"
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_dataframe_to_arrow_dict_sequence_schema(module_under_test):
dict_schema = [
{"name": "field01", "type": "STRING", "mode": "REQUIRED"},
{"name": "field02", "type": "BOOL", "mode": "NULLABLE"},
]
dataframe = pandas.DataFrame(
{"field01": [u"hello", u"world"], "field02": [True, False]}
)
arrow_table = module_under_test.dataframe_to_arrow(dataframe, dict_schema)
arrow_schema = arrow_table.schema
expected_fields = [
pyarrow.field("field01", "string", nullable=False),
pyarrow.field("field02", "bool", nullable=True),
]
assert list(arrow_schema) == expected_fields
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
def test_dataframe_to_parquet_without_pyarrow(module_under_test, monkeypatch):
monkeypatch.setattr(module_under_test, "pyarrow", None)
with pytest.raises(ValueError) as exc_context:
module_under_test.dataframe_to_parquet(pandas.DataFrame(), (), None)
assert "pyarrow is required" in str(exc_context.value)
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_dataframe_to_parquet_w_extra_fields(module_under_test, monkeypatch):
with pytest.raises(ValueError) as exc_context:
module_under_test.dataframe_to_parquet(
pandas.DataFrame(), (schema.SchemaField("not_in_df", "STRING"),), None
)
message = str(exc_context.value)
assert "bq_schema contains fields not present in dataframe" in message
assert "not_in_df" in message
@pytest.mark.skipif(pandas is None, reason="Requires `pandas`")
@pytest.mark.skipif(isinstance(pyarrow, mock.Mock), reason="Requires `pyarrow`")
def test_dataframe_to_parquet_w_missing_fields(module_under_test, monkeypatch):
with pytest.raises(ValueError) as exc_context:
module_under_test.dataframe_to_parquet(
pandas.DataFrame({"not_in_bq": [1, 2, 3]}), (), None
)
message = str(exc_context.value)