Skip to main content
`Ctrl`+`K`

# pandas.Series.dropna

Series.dropna(*, axis=0, inplace=False, how=None, ignore_index=False)

  Return a new Series with missing values removed.

  See the User Guide for more on which values are considered missing, and how to work with missing data.

  Parameters:
    axis{0 or ‘index’}

      Unused. Parameter needed for compatibility with DataFrame.

    inplace bool, default False

      If True, do operation inplace and return None.

    how str, optional

      Not in use. Kept for compatibility.

    ignore_index bool, default `False`

      If `True`, the resulting axis will be labeled 0, 1, …, n - 1.

      Added in version 2.0.0.

  Returns:
    Series or None

      Series with NA entries dropped from it or None if `inplace=True`.

  See also

  `Series.isna`

    Indicate missing values.

  `Series.notna`

    Indicate existing (non-missing) values.

  `Series.fillna`

    Replace missing values.

  `DataFrame.dropna`

    Drop rows or columns which contain NA values.

  `Index.dropna`

    Drop missing indices.

  Examples

      >>> ser = pd.Series([1.0, 2.0, np.nan])
      >>> ser
      0    1.0
      1    2.0
      2    NaN
      dtype: float64

  Drop NA values from a Series.

      >>> ser.dropna()
      0    1.0
      1    2.0
      dtype: float64

  Empty strings are not considered NA values. `None` is considered an NA value.

      >>> ser = pd.Series([np.nan, 2, pd.NaT, "", None, "I stay"])
      >>> ser
      0       NaN
      1         2
      2       NaT
      3
      4      None
      5    I stay
      dtype: object
      >>> ser.dropna()
      1         2
      3
      5    I stay
      dtype: object