| # |
| # 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. |
| # |
| |
| """ |
| Exceptions/Errors used in pandas-on-Spark. |
| """ |
| from typing import Optional |
| |
| |
| class DataError(Exception): |
| pass |
| |
| |
| class SparkPandasIndexingError(Exception): |
| pass |
| |
| |
| def code_change_hint(pandas_function: Optional[str], spark_target_function: Optional[str]) -> str: |
| if pandas_function is not None and spark_target_function is not None: |
| return "You are trying to use pandas function {}, use spark function {}".format( |
| pandas_function, spark_target_function |
| ) |
| elif pandas_function is not None and spark_target_function is None: |
| return ( |
| "You are trying to use pandas function {}, checkout the spark " |
| "user guide to find a relevant function" |
| ).format(pandas_function) |
| elif pandas_function is None and spark_target_function is not None: |
| return "Use spark function {}".format(spark_target_function) |
| else: # both none |
| return "Checkout the spark user guide to find a relevant function" |
| |
| |
| class SparkPandasNotImplementedError(NotImplementedError): |
| def __init__( |
| self, |
| pandas_function: Optional[str] = None, |
| spark_target_function: Optional[str] = None, |
| description: str = "", |
| ): |
| self.pandas_source = pandas_function |
| self.spark_target = spark_target_function |
| hint = code_change_hint(pandas_function, spark_target_function) |
| if len(description) > 0: |
| description += " " + hint |
| else: |
| description = hint |
| super().__init__(description) |
| |
| |
| class PandasNotImplementedError(NotImplementedError): |
| def __init__( |
| self, |
| class_name: str, |
| method_name: Optional[str] = None, |
| arg_name: Optional[str] = None, |
| property_name: Optional[str] = None, |
| deprecated: bool = False, |
| reason: str = "", |
| ): |
| assert (method_name is None) != (property_name is None) |
| self.class_name = class_name |
| self.method_name = method_name |
| self.arg_name = arg_name |
| if method_name is not None: |
| if arg_name is not None: |
| msg = "The method `{0}.{1}()` does not support `{2}` parameter. {3}".format( |
| class_name, method_name, arg_name, reason |
| ) |
| else: |
| if deprecated: |
| msg = ( |
| "The method `{0}.{1}()` is deprecated in pandas and will therefore " |
| + "not be supported in pandas-on-Spark. {2}" |
| ).format(class_name, method_name, reason) |
| else: |
| if reason == "": |
| reason = " yet." |
| else: |
| reason = ". " + reason |
| msg = "The method `{0}.{1}()` is not implemented{2}".format( |
| class_name, method_name, reason |
| ) |
| else: |
| if deprecated: |
| msg = ( |
| "The property `{0}.{1}()` is deprecated in pandas and will therefore " |
| + "not be supported in pandas-on-Spark. {2}" |
| ).format(class_name, property_name, reason) |
| else: |
| if reason == "": |
| reason = " yet." |
| else: |
| reason = ". " + reason |
| msg = "The property `{0}.{1}()` is not implemented{2}".format( |
| class_name, property_name, reason |
| ) |
| super().__init__(msg) |
| |
| |
| def _test() -> None: |
| import os |
| import doctest |
| import sys |
| from pyspark.sql import SparkSession |
| import pyspark.pandas.exceptions |
| |
| os.chdir(os.environ["SPARK_HOME"]) |
| |
| globs = pyspark.pandas.exceptions.__dict__.copy() |
| globs["ps"] = pyspark.pandas |
| spark = ( |
| SparkSession.builder.master("local[4]") |
| .appName("pyspark.pandas.exceptions tests") |
| .getOrCreate() |
| ) |
| (failure_count, test_count) = doctest.testmod( |
| pyspark.pandas.exceptions, |
| globs=globs, |
| optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE, |
| ) |
| spark.stop() |
| if failure_count: |
| sys.exit(-1) |
| |
| |
| if __name__ == "__main__": |
| _test() |