cudf.DataFrame.notnull

DataFrame.notnull()

Identify non-missing values.

Return a boolean same-sized object indicating if the values are not <NA>. Non-missing values get mapped to True. <NA> values get mapped to False values. <NA> values include:

• Values where null mask is set.

• NaN in float dtype.

• NaT in datetime64 and timedelta64 types.

Characters such as empty strings '' or inf in case of float are not considered <NA> values.

Returns:

DataFrame/Series/Index

Mask of bool values for each element in the object that indicates whether an element is not an NA value.

Examples

Show which entries in a DataFrame are NA.

>>> import cudf
>>> import numpy as np
>>> import pandas as pd
>>> df = cudf.DataFrame({'age': [5, 6, np.NaN],
...                    'born': [pd.NaT, pd.Timestamp('1939-05-27'),
...                             pd.Timestamp('1940-04-25')],
...                    'name': ['Alfred', 'Batman', ''],
...                    'toy': [None, 'Batmobile', 'Joker']})
>>> df
    age                        born    name        toy
0     5                        <NA>  Alfred       <NA>
1     6  1939-05-27 00:00:00.000000  Batman  Batmobile
2  <NA>  1940-04-25 00:00:00.000000              Joker
>>> df.notna()
     age   born  name    toy
0   True  False  True  False
1   True   True  True   True
2  False   True  True   True

Show which entries in a Series are NA.

>>> ser = cudf.Series([5, 6, np.NaN, np.inf, -np.inf])
>>> ser
0     5.0
1     6.0
2    <NA>
3     Inf
4    -Inf
dtype: float64
>>> ser.notna()
0     True
1     True
2    False
3     True
4     True
dtype: bool

Show which entries in an Index are NA.

>>> idx = cudf.Index([1, 2, None, np.NaN, 0.32, np.inf])
>>> idx
Index([1.0, 2.0, <NA>, <NA>, 0.32, Inf], dtype='float64')
>>> idx.notna()
array([ True,  True, False, False,  True,  True])