Detect missing values.
Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values.
Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.
Examples
Show which entries in a DataFrame are 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.isna()
age born name toy
0 False True False True
1 False False False False
2 True False False False
Show which entries in a Series are NA.
>>> ser = pd.Series([5, 6, np.nan]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64
>>> ser.isna() 0 False 1 False 2 True dtype: bool
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