packages/vaex-core/vaex/dataframe.py
def data(self):
        """Gives direct access to the data as numpy arrays.

        Convenient when working with IPython in combination with small DataFrames, since this gives tab-completion.
        Only real columns (i.e. no virtual) columns can be accessed, for getting the data from virtual columns, use
        DataFrame.evalulate(...).

        Columns can be accesed by there names, which are attributes. The attribues are of type numpy.ndarray.

        Example:

        >>> df = vaex.example()
        >>> r = np.sqrt(df.data.x**2 + df.data.y**2)

        """
        class Datas(object):
            pass

        datas = Datas()
        for name, array in self.columns.items():
            setattr(datas, name, array)
        return datas
def data(self):
        class Datas(object):
            pass

        datas = Datas()
        for name, array in self.columns.items():
            setattr(datas, name, array)
        return datas
def data ( self ) : class Datas ( object ) : pass datas = Datas ( ) for name , array in self . columns . items ( ) : setattr ( datas , name , array ) return datas
Gives direct access to the data as numpy arrays.

        Convenient when working with IPython in combination with small DataFrames, since this gives tab-completion.
        Only real columns (i.e. no virtual) columns can be accessed, for getting the data from virtual columns, use
        DataFrame.evalulate(...).

        Columns can be accesed by there names, which are attributes. The attribues are of type numpy.ndarray.

        Example:

        >>> df = vaex.example()
        >>> r = np.sqrt(df.data.x**2 + df.data.y**2)
Gives direct access to the data as numpy arrays .