# pandas.DataFrame.dropna

`DataFrame.``dropna`(axis=0, how='any', thresh=None, subset=None, inplace=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.

  > Deprecated since version 0.23.0: Pass tuple or list to drop on multiple axes. 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.

  subset : array-like, 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

  > If True, do operation inplace and return None.

  Returns:

  DataFrame

  > DataFrame with NA entries dropped from it.

  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', 'born'])
             name        toy       born
      1    Batman  Batmobile 1940-04-25

  Keep the DataFrame with valid entries in the same variable.

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

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