def pandas(self, row_attr: str = None, selector: Union[List, Tuple, np.ndarray, slice] = None, columns: List[str] = None) -> pd.DataFrame:
if columns is None:<EOL><INDENT>columns = [x for x in self.ca.keys()]<EOL><DEDENT>data: Dict[str, np.ndarray] = {}<EOL>for col in columns:<EOL><INDENT>vals = self.ca[col]<EOL>if vals.ndim >= <NUM_LIT:2>:<EOL><INDENT>for i in range(vals.ndim):<EOL><INDENT>data[col + f"<STR_LIT>"] = vals[:, <NUM_LIT:0>]<EOL><DEDENT><DEDENT>else:<EOL><INDENT>data[col] = self.ca[col]<EOL><DEDENT><DEDENT>if row_attr is not None:  <EOL><INDENT>if selector is None:  <EOL><INDENT>names = self.ra[row_attr]<EOL>vals = self[:, :]<EOL>for ix, name in enumerate(names):<EOL><INDENT>data[name] = vals[ix, :][<NUM_LIT:0>]<EOL><DEDENT><DEDENT>else:  <EOL><INDENT>if type(selector) is slice:  <EOL><INDENT>names = self.ra[row_attr][selector]<EOL>vals = self[selector, :]<EOL>for ix, name in enumerate(names):<EOL><INDENT>data[name] = vals[ix, :][<NUM_LIT:0>]<EOL><DEDENT><DEDENT>elif all([type(s) is str for s in selector]):  <EOL><INDENT>names = self.ra[row_attr][np.in1d(self.ra[row_attr], selector)]<EOL>for name in names:<EOL><INDENT>vals = self[self.ra[row_attr] == name, :][<NUM_LIT:0>]<EOL>data[name] = vals<EOL><DEDENT><DEDENT>else:  <EOL><INDENT>raise ValueError("<STR_LIT>")<EOL><DEDENT><DEDENT><DEDENT>return pd.DataFrame(data)<EOL>
Create a Pandas DataFrame corresponding to (selected parts of) the Loom file.

Args:
        row_attr:	Name of the row attribute to use for selecting rows to include (or None to omit row data)
        selector:	A list, a tuple, a numpy.ndarray or a slice; used to select rows (or None to include all rows)
        columns:	A list of column attributes to include, or None to include all

Returns:
        Pandas DataFrame

Remarks:
        The method returns a Pandas DataFrame with one column per row of the Loom file (i.e. transposed), which is usually
        what is required for plotting and statistical analysis. By default, all column attributes and no rows are included.
        To include row data, provide a ``row_attr`` and a ``selector``. The selector is matched against values of the given
        row attribute, and matching rows are included.

Examples:
        .. highlight:: python
        .. code-block:: python

                import loompy
                with loompy.connect("mydata.loom") as ds:
                        # Include all column attributes, and rows where attribute "Gene" matches one of the given genes
                        df1 = ds.pandas("Gene", ["Actb", "Npy", "Vip", "Pvalb"])
                        # Include the top 100 rows and name them after values of the "Gene" attribute
                        df2 = ds.pandas("Gene", :100)
                        # Include the entire dataset, and name the rows after values of the "Accession" attribute
                        df3 = ds.pandas("Accession")