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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def dropna(self, axis=0, how='any', thresh=None, subset=None, inplace=False):
 """ Remove missing values. See the :ref:`User Guide <missing_data>` 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:: 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 -------- 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. name toy born 1 Batman Batmobile 1940-04-25 Drop the columns where at least one element is missing. name 0 Alfred 1 Batman 2 Catwoman Drop the rows where all elements are missing. 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. name toy born 1 Batman Batmobile 1940-04-25 2 Catwoman Bullwhip NaT Define in which columns to look for missing values. name toy born 1 Batman Batmobile 1940-04-25 Keep the DataFrame with valid entries in the same variable. name toy born 1 Batman Batmobile 1940-04-25 """

        inplace = validate_bool_kwarg(inplace, 'inplace')
        if isinstance(axis, (tuple, list)):
            # GH20987
            msg = ("supplying multiple axes to axis is deprecated and "
                   "will be removed in a future version.")
            warnings.warn(msg, FutureWarning, stacklevel=2)

            result = self
            for ax in axis:
                result = result.dropna(how=how, thresh=thresh, subset=subset,
                                       axis=ax)
        else:
            axis = self._get_axis_number(axis)
            agg_axis = 1 - axis

            agg_obj = self
            if subset is not None:
                ax = self._get_axis(agg_axis)
                indices = ax.get_indexer_for(subset)
                check = indices == -1
                if check.any():
                    raise KeyError(list(np.compress(check, subset)))
                agg_obj = self.take(indices, axis=agg_axis)

            count = agg_obj.count(axis=agg_axis)

            if thresh is not None:
                mask = count >= thresh
            elif how == 'any':
                mask = count == len(agg_obj._get_axis(agg_axis))
            elif how == 'all':
                mask = count > 0
            else:
                if how is not None:
                    raise ValueError('invalid how option: {h}'.format(h=how))
                else:
                    raise TypeError('must specify how or thresh')

            result = self.loc(axis=axis)[mask]

        if inplace:
            self._update_inplace(result)
        else:
            return result