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# pandas.DataFrame.isnull

DataFrame.isnull()

  DataFrame.isnull is an alias for DataFrame.isna.

  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.

  Returns:
    Series/DataFrame

      Mask of bool values for each element in Series/DataFrame that indicates whether an element is an NA value.

  See also

  `Series.isnull`

    Alias of isna.

  `DataFrame.isnull`

    Alias of isna.

  `Series.notna`

    Boolean inverse of isna.

  `DataFrame.notna`

    Boolean inverse of isna.

  `Series.dropna`

    Omit axes labels with missing values.

  `DataFrame.dropna`

    Omit axes labels with missing values.

  `isna`

    Top-level isna.

  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        NaN
      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