Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values. NA values, such as None or numpy.NaN, get mapped to False values.
Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.
Examples
Show which entries in a DataFrame are not NA.
>>> df = pd.DataFrame(
... dict(
... 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 5.0 NaT Alfred None
1 6.0 1939-05-27 Batman Batmobile
2 NaN 1940-04-25 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 not NA.
>>> ser = pd.Series([5, 6, np.nan]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64
>>> ser.notna() 0 True 1 True 2 False dtype: bool
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