bigframes.pandas.DataFrame.isna

DataFrame.isna() → DataFrame

Detect missing (NULL) values.

Return a boolean same-sized object indicating if the values are NA (NULL in BigQuery). NA/NULL values get mapped to True values. Everything else gets mapped to False values.

Note that empty strings '', numpy.inf, and numpy.nan are *not* considered NA values. This NA/NULL logic differs from numpy, but it is the same as BigQuery and the pandas.ArrowDtype.

Examples:

>>> df = bpd.DataFrame(dict(
...         age=pd.Series(pa.array(
...             [5, 6, None, 4],
...             type=pa.int64(),
...         ), dtype=pd.ArrowDtype(pa.int64())),
...         born=pd.to_datetime([pd.NA, "1940-04-25", "1940-04-25", "1941-08-25"]),
...         name=['Alfred', 'Batman', '', 'Plastic Man'],
...         toy=[None, 'Batmobile', 'Joker', 'Play dough'],
...         height=pd.Series(pa.array(
...             [6.1, 5.9, None, np.nan],
...             type=pa.float64(),
...         ), dtype=pd.ArrowDtype(pa.float64())),
... ))
>>> df
    age                 born         name         toy  height
0     5                 <NA>       Alfred        <NA>     6.1
1     6  1940-04-25 00:00:00       Batman   Batmobile     5.9
2  <NA>  1940-04-25 00:00:00                    Joker    <NA>
3     4  1941-08-25 00:00:00  Plastic Man  Play dough     NaN

[4 rows x 5 columns]

Show which entries in a DataFrame are NA (NULL in BigQuery):

>>> df.isna()
     age   born   name    toy  height
0  False   True  False   True   False
1  False  False  False  False   False
2   True  False  False  False    True
3  False  False  False  False   False

[4 rows x 5 columns]

>>> df.isnull()
     age   born   name    toy  height
0  False   True  False   True   False
1  False  False  False  False   False
2   True  False  False  False    True
3  False  False  False  False   False

[4 rows x 5 columns]

Show which entries in a Series are NA (NULL in BigQuery):

>>> ser = bpd.Series(pa.array(
...     [5, None, 6, np.nan, None],
...     type=pa.float64(),
... ), dtype=pd.ArrowDtype(pa.float64()))
>>> ser
0     5.0
1    <NA>
2     6.0
3     NaN
4    <NA>
dtype: Float64

>>> ser.isna()
0    False
1     True
2    False
3    False
4     True
dtype: boolean

>>> ser.isnull()
0    False
1     True
2    False
3    False
4     True
dtype: boolean

Returns:

Mask of bool values for each element that indicates whether an element is an NA value.

Return type:

bigframes.pandas.DataFrame or bigframes.pandas.Series