src/expr/scalar_variable.rs (29 lines of code) (raw):

// Licensed to the Apache Software Foundation (ASF) under one // or more contributor license agreements. See the NOTICE file // distributed with this work for additional information // regarding copyright ownership. The ASF licenses this file // to you under the Apache License, Version 2.0 (the // "License"); you may not use this file except in compliance // with the License. You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, // software distributed under the License is distributed on an // "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY // KIND, either express or implied. See the License for the // specific language governing permissions and limitations // under the License. use datafusion::arrow::datatypes::DataType; use pyo3::prelude::*; use crate::common::data_type::PyDataType; #[pyclass(name = "ScalarVariable", module = "datafusion.expr", subclass)] #[derive(Clone)] pub struct PyScalarVariable { data_type: DataType, variables: Vec<String>, } impl PyScalarVariable { pub fn new(data_type: &DataType, variables: &[String]) -> Self { Self { data_type: data_type.to_owned(), variables: variables.to_vec(), } } } #[pymethods] impl PyScalarVariable { /// Get the data type fn data_type(&self) -> PyResult<PyDataType> { Ok(self.data_type.clone().into()) } fn variables(&self) -> PyResult<Vec<String>> { Ok(self.variables.clone()) } fn __repr__(&self) -> PyResult<String> { Ok(format!("{}{:?}", self.data_type, self.variables)) } }