cudf.DataFrame.notnull#

DataFrame.notnull()[source]#

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])