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docs: clarify that only NULL values are handled by fillna/isna, not NaN
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‎conftest.py‎

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# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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import pyarrow as pa
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import pytest
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import bigframes._config
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import bigframes.pandas as bpd
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@pytest.fixture(autouse=True)
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def default_doctest_imports(doctest_namespace):
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"""
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Avoid some boilerplate in pandas-inspired tests.
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See: https://docs.pytest.org/en/stable/how-to/doctest.html#doctest-namespace-fixture
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"""
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doctest_namespace["np"] = np
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doctest_namespace["pd"] = pd
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doctest_namespace["pa"] = pa
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doctest_namespace["bpd"] = bpd
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bigframes._config.options.display.progress_bar = None

‎third_party/bigframes_vendored/pandas/core/frame.py‎

Lines changed: 29 additions & 25 deletions
Original file line numberDiff line numberDiff line change
@@ -6978,7 +6978,7 @@ def query(self, expr: str) -> DataFrame | None:
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69796979
def interpolate(self, method: str = "linear"):
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"""
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Fill NaN values using an interpolation method.
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Fill NA (NULL in BigQuery) values using an interpolation method.
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**Examples:**
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@@ -7028,35 +7028,39 @@ def interpolate(self, method: str = "linear"):
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def fillna(self, value):
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"""
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Fill NA/NaN values using the specified method.
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Fill NA (NULL in BigQuery) values using the specified method.
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**Examples:**
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Note that empty strings ``''``, :attr:`numpy.inf`, and
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:attr:`numpy.nan` are ***not*** considered NA values. This NA/NULL
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logic differs from numpy, but it is the same as BigQuery and the
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:class:`pandas.ArrowDtype`.
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>>> import bigframes.pandas as bpd
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>>> bpd.options.display.progress_bar = None
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**Examples:**
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>>> df = bpd.DataFrame([[np.nan, 2, np.nan, 0],
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... [3, 4, np.nan, 1],
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... [np.nan, np.nan, np.nan, np.nan],
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... [np.nan, 3, np.nan, 4]],
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... columns=list("ABCD")).astype("Float64")
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>>> df = bpd.DataFrame(
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... [
7042+
... pa.array([np.nan, 2, None, 0], type=pa.float64()),
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... pa.array([3, np.nan, None, 1], type=pa.float64()),
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... pa.array([None, None, np.nan, None], type=pa.float64()),
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... pa.array([4, 5, None, np.nan], type=pa.float64()),
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... ], columns=list("ABCD"), dtype=pd.ArrowDtype(pa.float64()))
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>>> df
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A B C D
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0 <NA> 2.0 <NA> 0.0
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1 3.0 4.0 <NA> 1.0
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2 <NA> <NA> <NA> <NA>
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3 <NA> 3.0 <NA> 4.0
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A B C D
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0 NaN 2.0 <NA> 0.0
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1 3.0 NaN <NA> 1.0
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2 <NA> <NA> NaN <NA>
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3 4.0 5.0 <NA> NaN
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<BLANKLINE>
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[4 rows x 4 columns]
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Replace all NA elements with 0s.
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Replace all NA (NULL) elements with 0s.
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>>> df.fillna(0)
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A B C D
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0 0.0 2.0 0.0 0.0
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1 3.0 4.0 0.0 1.0
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2 0.0 0.0 0.0 0.0
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3 0.0 3.0 0.0 4.0
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0 NaN 2.0 0.0 0.0
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1 3.0 NaN 0.0 1.0
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2 0.0 0.0 NaN 0.0
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3 4.0 5.0 0.0 NaN
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<BLANKLINE>
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[4 rows x 4 columns]
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@@ -7072,11 +7076,11 @@ def fillna(self, value):
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<BLANKLINE>
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[3 rows x 4 columns]
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>>> df.fillna(df_fill)
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A B C D
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0 0.0 2.0 2.0 0.0
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1 3.0 4.0 6.0 1.0
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2 8.0 9.0 10.0 11.0
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3 <NA> 3.0 <NA> 4.0
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A B C D
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0 NaN 2.0 2.0 0.0
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1 3.0 NaN 6.0 1.0
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2 8.0 9.0 NaN 11.0
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3 4.0 5.0 <NA> NaN
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<BLANKLINE>
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[4 rows x 4 columns]
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‎third_party/bigframes_vendored/pandas/core/generic.py‎

Lines changed: 50 additions & 37 deletions
Original file line numberDiff line numberDiff line change
@@ -816,75 +816,88 @@ def bfill(self, *, limit: Optional[int] = None):
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raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
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818818
def isna(self) -> NDFrame:
819-
"""Detect missing values.
819+
"""Detect missing (NULL) values.
820820
821-
Return a boolean same-sized object indicating if the values are NA.
822-
NA values get mapped to True values. Everything else gets mapped to
823-
False values. Characters such as empty strings ``''`` or
824-
:attr:`numpy.inf` are not considered NA values.
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Return a boolean same-sized object indicating if the values are NA
822+
(NULL in BigQuery). NA/NULL values get mapped to True values.
823+
Everything else gets mapped to False values.
825824
826-
**Examples:**
825+
Note that empty strings ``''``, :attr:`numpy.inf`, and
826+
:attr:`numpy.nan` are ***not*** considered NA values. This NA/NULL
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logic differs from numpy, but it is the same as BigQuery and the
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:class:`pandas.ArrowDtype`.
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>>> import bigframes.pandas as bpd
829-
>>> bpd.options.display.progress_bar = None
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>>> import numpy as np
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**Examples:**
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>>> df = bpd.DataFrame(dict(
833-
... age=[5, 6, np.nan],
834-
... born=[bpd.NA, "1940-04-25", "1940-04-25"],
835-
... name=['Alfred', 'Batman', ''],
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... toy=[None, 'Batmobile', 'Joker'],
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... age=pd.Series(pa.array(
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... [5, 6, None, 4],
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... type=pa.int64(),
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... ), dtype=pd.ArrowDtype(pa.int64())),
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... born=pd.to_datetime([pd.NA, "1940-04-25", "1940-04-25", "1941-08-25"]),
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... name=['Alfred', 'Batman', '', 'Plastic Man'],
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... toy=[None, 'Batmobile', 'Joker', 'Play dough'],
840+
... height=pd.Series(pa.array(
841+
... [6.1, 5.9, None, np.nan],
842+
... type=pa.float64(),
843+
... ), dtype=pd.ArrowDtype(pa.float64())),
837844
... ))
838845
>>> df
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age born name toy
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0 5.0 <NA> Alfred <NA>
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1 6.0 1940-04-25 Batman Batmobile
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2 <NA> 1940-04-25 Joker
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age born name toy height
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0 5 <NA> Alfred <NA> 6.1
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1 6 1940-04-25 00:00:00 Batman Batmobile 5.9
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2 <NA> 1940-04-25 00:00:00 Joker <NA>
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3 4 1941-08-25 00:00:00 Plastic Man Play dough NaN
843851
<BLANKLINE>
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[3 rows x 4 columns]
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[4 rows x 5 columns]
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Show which entries in a DataFrame are NA:
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Show which entries in a DataFrame are NA (NULL in BigQuery):
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848856
>>> df.isna()
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age born name toy
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0 False True False True
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1 False False False False
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2 True False False False
857+
age born name toy height
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0 False True False True False
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1 False False False False False
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2 True False False False True
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3 False False False False False
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<BLANKLINE>
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[3 rows x 4 columns]
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[4 rows x 5 columns]
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>>> df.isnull()
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age born name toy
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0 False True False True
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1 False False False False
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2 True False False False
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age born name toy height
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0 False True False True False
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1 False False False False False
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2 True False False False True
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3 False False False False False
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<BLANKLINE>
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[3 rows x 4 columns]
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[4 rows x 5 columns]
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Show which entries in a Series are NA:
874+
Show which entries in a Series are NA (NULL in BigQuery):
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>>> ser = bpd.Series([5, None, 6, np.nan, bpd.NA])
876+
>>> ser = bpd.Series(pa.array(
877+
... [5, None, 6, np.nan, None],
878+
... type=pa.float64(),
879+
... ), dtype=pd.ArrowDtype(pa.float64()))
867880
>>> ser
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0 5
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0 5.0
869882
1 <NA>
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2 6
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3 <NA>
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2 6.0
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3 NaN
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4 <NA>
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dtype: Int64
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dtype: Float64
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>>> ser.isna()
876889
0 False
877890
1 True
878891
2 False
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3 True
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3 False
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4 True
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dtype: boolean
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>>> ser.isnull()
884897
0 False
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1 True
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2 False
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3 True
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3 False
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4 True
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dtype: boolean
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‎third_party/bigframes_vendored/pandas/core/indexes/base.py‎

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@@ -957,17 +957,23 @@ def value_counts(
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958958
def fillna(self, value) -> Index:
959959
"""
960-
Fill NA/NaN values with the specified value.
960+
Fill NA (NULL in BigQuery) values using the specified method.
961961
962-
**Examples:**
962+
Note that empty strings ``''``, :attr:`numpy.inf`, and
963+
:attr:`numpy.nan` are ***not*** considered NA values. This NA/NULL
964+
logic differs from numpy, but it is the same as BigQuery and the
965+
:class:`pandas.ArrowDtype`.
963966
964-
>>> import bigframes.pandas as bpd
965-
>>> import numpy as np
966-
>>> bpd.options.display.progress_bar = None
967+
**Examples:**
967968
968-
>>> idx = bpd.Index([np.nan, np.nan, 3])
969+
>>> idx = bpd.Index(
970+
... pa.array([None, np.nan, 3, None], type=pa.float64()),
971+
... dtype=pd.ArrowDtype(pa.float64()),
972+
... )
973+
>>> idx
974+
Index([<NA>, nan, 3.0, <NA>], dtype='Float64')
969975
>>> idx.fillna(0)
970-
Index([0.0, 0.0, 3.0], dtype='Float64')
976+
Index([0.0, nan, 3.0, 0.0], dtype='Float64')
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972978
Args:
973979
value (scalar):

‎third_party/bigframes_vendored/pandas/core/series.py‎

Lines changed: 14 additions & 10 deletions
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@@ -2410,26 +2410,30 @@ def fillna(
24102410
value=None,
24112411
) -> Series | None:
24122412
"""
2413-
Fill NA/NaN values using the specified method.
2413+
Fill NA (NULL in BigQuery) values using the specified method.
24142414
2415-
**Examples:**
2415+
Note that empty strings ``''``, :attr:`numpy.inf`, and
2416+
:attr:`numpy.nan` are ***not*** considered NA values. This NA/NULL
2417+
logic differs from numpy, but it is the same as BigQuery and the
2418+
:class:`pandas.ArrowDtype`.
24162419
2417-
>>> import bigframes.pandas as bpd
2418-
>>> import numpy as np
2419-
>>> bpd.options.display.progress_bar = None
2420+
**Examples:**
24202421
2421-
>>> s = bpd.Series([np.nan, 2, np.nan, -1])
2422+
>>> s = bpd.Series(
2423+
... pa.array([np.nan, 2, None, -1], type=pa.float64()),
2424+
... dtype=pd.ArrowDtype(pa.float64()),
2425+
... )
24222426
>>> s
2423-
0 <NA>
2427+
0 NaN
24242428
1 2.0
24252429
2 <NA>
24262430
3 -1.0
24272431
dtype: Float64
24282432
2429-
Replace all NA elements with 0s.
2433+
Replace all NA (NULL) elements with 0s.
24302434
24312435
>>> s.fillna(0)
2432-
0 0.0
2436+
0 NaN
24332437
1 2.0
24342438
2 0.0
24352439
3 -1.0
@@ -4482,7 +4486,7 @@ def update(self, other) -> None:
44824486
2 6
44834487
dtype: Int64
44844488
4485-
If ``other`` contains NaNs the corresponding values are not updated
4489+
If ``other`` contains NA (NULL values) the corresponding values are not updated
44864490
in the original Series.
44874491
44884492
>>> s = bpd.Series([1, 2, 3])

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