packages/vaex-core/vaex/dataframe.py
def take(self, indices):
        '''Returns a DataFrame containing only rows indexed by indices

        {note_copy}

        Example:

        >>> import vaex, numpy as np
        >>> df = vaex.from_arrays(s=np.array(['a', 'b', 'c', 'd']), x=np.arange(1,5))
        >>> df.take([0,2])
         #  s      x
         0  a      1
         1  c      3

        :param indices: sequence (list or numpy array) with row numbers
        :return: DataFrame which is a shallow copy of the original data.
        :rtype: DataFrame
        '''
        df = self.copy()
        # if the columns in ds already have a ColumnIndex
        # we could do, direct_indices = df.column['bla'].indices[indices]
        # which should be shared among multiple ColumnIndex'es, so we store
        # them in this dict
        direct_indices_map = {}
        indices = np.array(indices)
        for name in df:
            column = df.columns.get(name)
            if column is not None:
                # we optimize this somewhere, so we don't do multiple
                # levels of indirection
                if isinstance(column, ColumnIndexed):
                    # TODO: think about what happpens when the indices are masked.. ?
                    if id(column.indices) not in direct_indices_map:
                        direct_indices = column.indices[indices]
                        direct_indices_map[id(column.indices)] = direct_indices
                    else:
                        direct_indices = direct_indices_map[id(column.indices)]
                    df.columns[name] = ColumnIndexed(column.df, direct_indices, column.name)
                else:
                    df.columns[name] = ColumnIndexed(self, indices, name)
        df._length_original = len(indices)
        df._length_unfiltered = df._length_original
        df.set_selection(None, name=FILTER_SELECTION_NAME)
        return df
def take(self, indices):
        '''Returns a DataFrame containing only rows indexed by indices

        {note_copy}

        Example:

        >>> import vaex, numpy as np
        >>> df = vaex.from_arrays(s=np.array(['a', 'b', 'c', 'd']), x=np.arange(1,5))
        >>> df.take([0,2])
         #  s      x
         0  a      1
         1  c      3

        :param indices: sequence (list or numpy array) with row numbers
        :return: DataFrame which is a shallow copy of the original data.
        :rtype: DataFrame
        '''
        df = self.copy()
        # if the columns in ds already have a ColumnIndex
        # we could do, direct_indices = df.column['bla'].indices[indices]
        # which should be shared among multiple ColumnIndex'es, so we store
        # them in this dict
        direct_indices_map = {}
        indices = np.array(indices)
        for name in df:
            column = df.columns.get(name)
            if column is not None:
                # we optimize this somewhere, so we don't do multiple
                # levels of indirection
                if isinstance(column, ColumnIndexed):
                    # TODO: think about what happpens when the indices are masked.. ?
                    if id(column.indices) not in direct_indices_map:
                        direct_indices = column.indices[indices]
                        direct_indices_map[id(column.indices)] = direct_indices
                    else:
                        direct_indices = direct_indices_map[id(column.indices)]
                    df.columns[name] = ColumnIndexed(column.df, direct_indices, column.name)
                else:
                    df.columns[name] = ColumnIndexed(self, indices, name)
        df._length_original = len(indices)
        df._length_unfiltered = df._length_original
        df.set_selection(None, name=FILTER_SELECTION_NAME)
        return df
def take ( self , indices ) : df = self . copy ( ) # if the columns in ds already have a ColumnIndex # we could do, direct_indices = df.column['bla'].indices[indices] # which should be shared among multiple ColumnIndex'es, so we store # them in this dict direct_indices_map = { } indices = np . array ( indices ) for name in df : column = df . columns . get ( name ) if column is not None : # we optimize this somewhere, so we don't do multiple # levels of indirection if isinstance ( column , ColumnIndexed ) : # TODO: think about what happpens when the indices are masked.. ? if id ( column . indices ) not in direct_indices_map : direct_indices = column . indices [ indices ] direct_indices_map [ id ( column . indices ) ] = direct_indices else : direct_indices = direct_indices_map [ id ( column . indices ) ] df . columns [ name ] = ColumnIndexed ( column . df , direct_indices , column . name ) else : df . columns [ name ] = ColumnIndexed ( self , indices , name ) df . _length_original = len ( indices ) df . _length_unfiltered = df . _length_original df . set_selection ( None , name = FILTER_SELECTION_NAME ) return df
Returns a DataFrame containing only rows indexed by indices

        {note_copy}

        Example:

        >>> import vaex, numpy as np
        >>> df = vaex.from_arrays(s=np.array(['a', 'b', 'c', 'd']), x=np.arange(1,5))
        >>> df.take([0,2])
         #  s      x
         0  a      1
         1  c      3

        :param indices: sequence (list or numpy array) with row numbers
        :return: DataFrame which is a shallow copy of the original data.
        :rtype: DataFrame
Returns a DataFrame containing only rows indexed by indices