packages/vaex-core/vaex/dataframe.py
def fillna(self, value, fill_nan=True, fill_masked=True, column_names=None, prefix='__original_', inplace=False):
        '''Return a DataFrame, where missing values/NaN are filled with 'value'.

        The original columns will be renamed, and by default they will be hidden columns. No data is lost.

        {note_copy}

        {note_filter}

        Example:

        >>> import vaex
        >>> import numpy as np
        >>> x = np.array([3, 1, np.nan, 10, np.nan])
        >>> df = vaex.from_arrays(x=x)
        >>> df_filled = df.fillna(value=-1, column_names=['x'])
        >>> df_filled
          #    x
          0    3
          1    1
          2   -1
          3   10
          4   -1

        :param float value: The value to use for filling nan or masked values.
        :param bool fill_na: If True, fill np.nan values with `value`.
        :param bool fill_masked: If True, fill masked values with `values`.
        :param list column_names: List of column names in which to fill missing values.
        :param str prefix: The prefix to give the original columns.
        :param inplace: {inplace}
        '''
        df = self.trim(inplace=inplace)
        column_names = column_names or list(self)
        for name in column_names:
            column = df.columns.get(name)
            if column is not None:
                new_name = df.rename_column(name, prefix + name)
                expr = df[new_name]
                df[name] = df.func.fillna(expr, value, fill_nan=fill_nan, fill_masked=fill_masked)
            else:
                df[name] = df.func.fillna(df[name], value, fill_nan=fill_nan, fill_masked=fill_masked)
        return df
def fillna(self, value, fill_nan=True, fill_masked=True, column_names=None, prefix='__original_', inplace=False):
        '''Return a DataFrame, where missing values/NaN are filled with 'value'.

        The original columns will be renamed, and by default they will be hidden columns. No data is lost.

        {note_copy}

        {note_filter}

        Example:

        >>> import vaex
        >>> import numpy as np
        >>> x = np.array([3, 1, np.nan, 10, np.nan])
        >>> df = vaex.from_arrays(x=x)
        >>> df_filled = df.fillna(value=-1, column_names=['x'])
        >>> df_filled
          #    x
          0    3
          1    1
          2   -1
          3   10
          4   -1

        :param float value: The value to use for filling nan or masked values.
        :param bool fill_na: If True, fill np.nan values with `value`.
        :param bool fill_masked: If True, fill masked values with `values`.
        :param list column_names: List of column names in which to fill missing values.
        :param str prefix: The prefix to give the original columns.
        :param inplace: {inplace}
        '''
        df = self.trim(inplace=inplace)
        column_names = column_names or list(self)
        for name in column_names:
            column = df.columns.get(name)
            if column is not None:
                new_name = df.rename_column(name, prefix + name)
                expr = df[new_name]
                df[name] = df.func.fillna(expr, value, fill_nan=fill_nan, fill_masked=fill_masked)
            else:
                df[name] = df.func.fillna(df[name], value, fill_nan=fill_nan, fill_masked=fill_masked)
        return df
def fillna ( self , value , fill_nan = True , fill_masked = True , column_names = None , prefix = '__original_' , inplace = False ) : df = self . trim ( inplace = inplace ) column_names = column_names or list ( self ) for name in column_names : column = df . columns . get ( name ) if column is not None : new_name = df . rename_column ( name , prefix + name ) expr = df [ new_name ] df [ name ] = df . func . fillna ( expr , value , fill_nan = fill_nan , fill_masked = fill_masked ) else : df [ name ] = df . func . fillna ( df [ name ] , value , fill_nan = fill_nan , fill_masked = fill_masked ) return df
Return a DataFrame, where missing values/NaN are filled with 'value'.

        The original columns will be renamed, and by default they will be hidden columns. No data is lost.

        {note_copy}

        {note_filter}

        Example:

        >>> import vaex
        >>> import numpy as np
        >>> x = np.array([3, 1, np.nan, 10, np.nan])
        >>> df = vaex.from_arrays(x=x)
        >>> df_filled = df.fillna(value=-1, column_names=['x'])
        >>> df_filled
          #    x
          0    3
          1    1
          2   -1
          3   10
          4   -1

        :param float value: The value to use for filling nan or masked values.
        :param bool fill_na: If True, fill np.nan values with `value`.
        :param bool fill_masked: If True, fill masked values with `values`.
        :param list column_names: List of column names in which to fill missing values.
        :param str prefix: The prefix to give the original columns.
        :param inplace: {inplace}
Return a DataFrame where missing values / NaN are filled with value .