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# pandas.DataFrame.dropna

DataFrame.dropna(*, axis=0, how=<no_default>, thresh=<no_default>, subset=None, inplace=False, ignore_index=False)

  Remove missing values.

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

  Parameters:
    axis{0 or ‘index’, 1 or ‘columns’}, default 0

      Determine if rows or columns which contain missing values are removed.

      - 0, or ‘index’ : Drop rows which contain missing values.

      - 1, or ‘columns’ : Drop columns which contain missing value.

      Only a single axis is allowed.

    how{‘any’, ‘all’}, default ‘any’

      Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.

      - ‘any’ : If any NA values are present, drop that row or column.

      - ‘all’ : If all values are NA, drop that row or column.

    thresh int, optional

      Require that many non-NA values. Cannot be combined with how.

    subset column label or iterable of labels, optional

      Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include.

    inplace bool, default False

      Whether to modify the DataFrame rather than creating a new one.

    ignore_index bool, default `False`

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

      Added in version 2.0.0.

  Returns:
    DataFrame or None

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

  See also

  `DataFrame.isna`

    Indicate missing values.

  `DataFrame.notna`

    Indicate existing (non-missing) values.

  `DataFrame.fillna`

    Replace missing values.

  `Series.dropna`

    Drop missing values.

  `Index.dropna`

    Drop missing indices.

  Examples

      >>> df = pd.DataFrame(
      ...     {
      ...         "name": ["Alfred", "Batman", "Catwoman"],
      ...         "toy": [np.nan, "Batmobile", "Bullwhip"],
      ...         "born": [pd.NaT, pd.Timestamp("1940-04-25"), pd.NaT],
      ...     }
      ... )
      >>> df
             name        toy       born
      0    Alfred        NaN        NaT
      1    Batman  Batmobile 1940-04-25
      2  Catwoman   Bullwhip        NaT

  Drop the rows where at least one element is missing.

      >>> df.dropna()
           name        toy       born
      1  Batman  Batmobile 1940-04-25

  Drop the columns where at least one element is missing.

      >>> df.dropna(axis="columns")
             name
      0    Alfred
      1    Batman
      2  Catwoman

  Drop the rows where all elements are missing.

      >>> df.dropna(how="all")
             name        toy       born
      0    Alfred        NaN        NaT
      1    Batman  Batmobile 1940-04-25
      2  Catwoman   Bullwhip        NaT

  Keep only the rows with at least 2 non-NA values.

      >>> df.dropna(thresh=2)
             name        toy       born
      1    Batman  Batmobile 1940-04-25
      2  Catwoman   Bullwhip        NaT

  Define in which columns to look for missing values.

      >>> df.dropna(subset=["name", "toy"])
             name        toy       born
      1    Batman  Batmobile 1940-04-25
      2  Catwoman   Bullwhip        NaT