def _call_dataframe()

in core/maxframe/dataframe/statistics/quantile.py [0:0]

• 88 lines of code
• 15 McCabe index (conditional complexity)

    def _call_dataframe(self, a, inputs):
        if self.numeric_only:
            empty_df = build_empty_df(a.dtypes)
            dtypes = empty_df._get_numeric_data().dtypes
        else:
            dtypes = a.dtypes
        if isinstance(self.q, TENSOR_TYPE):
            q_val = self.q
            pd_index = pd.Index([], dtype=q_val.dtype)
            name = None
            store_index_value = False
        else:
            q_val = np.asanyarray(self.q)
            if q_val.ndim == 0:
                pd_index = pd.Index(q_val.reshape(1))
            else:
                pd_index = pd.Index(q_val)
            name = self.q if q_val.size == 1 else None
            store_index_value = True
        tokenize_objects = (a, q_val, self.interpolation, type(self).__name__)

        if q_val.ndim == 0 and self.axis == 0:
            index_value = parse_index(dtypes.index, store_data=store_index_value)
            shape = (len(dtypes),)
            # calc dtype
            dtype = self._calc_dtype_on_axis_1(a, dtypes)
            return self.new_series(
                inputs,
                shape=shape,
                dtype=dtype,
                index_value=index_value,
                name=name or dtypes.index.name,
            )
        elif q_val.ndim == 0 and self.axis == 1:
            index_value = a.index_value
            shape = (len(a),)
            # calc dtype
            dt = tensor_quantile(
                empty(a.shape[1], dtype=find_common_type(list(dtypes))),
                self.q,
                interpolation=self.interpolation,
                handle_non_numeric=not self.numeric_only,
            ).dtype
            return self.new_series(
                inputs,
                shape=shape,
                dtype=dt,
                index_value=index_value,
                name=name or index_value.name,
            )
        elif q_val.ndim == 1 and self.axis == 0:
            shape = (len(q_val), len(dtypes))
            index_value = parse_index(
                pd_index, *tokenize_objects, store_data=store_index_value
            )
            dtype_list = []
            for name in dtypes.index:
                dtype_list.append(
                    tensor_quantile(
                        tensor_from_series(a[name]),
                        self.q,
                        interpolation=self.interpolation,
                        handle_non_numeric=not self.numeric_only,
                    ).dtype
                )
            dtypes = pd.Series(dtype_list, index=dtypes.index)
            return self.new_dataframe(
                inputs,
                shape=shape,
                dtypes=dtypes,
                index_value=index_value,
                columns_value=parse_index(dtypes.index, store_data=True),
            )
        else:
            assert q_val.ndim == 1 and self.axis == 1
            shape = (len(q_val), a.shape[0])
            index_value = parse_index(
                pd_index, *tokenize_objects, store_data=store_index_value
            )
            pd_columns = a.index_value.to_pandas()
            dtype_list = np.full(len(pd_columns), self._calc_dtype_on_axis_1(a, dtypes))
            dtypes = pd.Series(dtype_list, index=pd_columns)
            return self.new_dataframe(
                inputs,
                shape=shape,
                dtypes=dtypes,
                index_value=index_value,
                columns_value=parse_index(
                    dtypes.index, store_data=True, key=a.index_value.key
                ),
            )