# dask.dataframe.DataFrame.dropna

DataFrame.dropna(how=<no_default>, subset=None, thresh=<no_default>)

  Remove missing values.

  This docstring was copied from pandas.core.frame.DataFrame.dropna.

  Some inconsistencies with the Dask version may exist.

  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 (Not supported in Dask)

      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 sequence 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 (Not supported in Dask)

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

    ignore_index bool, default `False` (Not supported in Dask)

      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