Error classes in PySpark#

This is a list of common, named error classes returned by PySpark which are defined at error-conditions.json.

When writing PySpark errors, developers must use an error class from the list. If an appropriate error class is not available, add a new one into the list. For more information, please refer to Contributing Error and Exception.

APPLICATION_NAME_NOT_SET#

An application name must be set in your configuration.

ARGUMENT_REQUIRED#

Argument is required when .

ARROW_LEGACY_IPC_FORMAT#

Arrow legacy IPC format is not supported in PySpark, please unset ARROW_PRE_0_15_IPC_FORMAT.

ATTRIBUTE_NOT_CALLABLE#

Attribute in provided object is not callable.

ATTRIBUTE_NOT_SUPPORTED#

Attribute is not supported.

AXIS_LENGTH_MISMATCH#

Length mismatch: Expected axis has element, new values have elements.

BROADCAST_VARIABLE_NOT_LOADED#

Broadcast variable not loaded.

CALL_BEFORE_INITIALIZE#

Not supported to call before initialize .

CANNOT_ACCEPT_OBJECT_IN_TYPE#

can not accept object in type .

CANNOT_ACCESS_TO_DUNDER#

Dunder(double underscore) attribute is for internal use only.

CANNOT_APPLY_IN_FOR_COLUMN#

Cannot apply ‘in’ operator against a column: please use ‘contains’ in a string column or ‘array_contains’ function for an array column.

CANNOT_BE_EMPTY#

At least one must be specified.

CANNOT_BE_NONE#

Argument cannot be None.

CANNOT_CONFIGURE_SPARK_CONNECT#

Spark Connect server cannot be configured: Existing [], New [].

CANNOT_CONFIGURE_SPARK_CONNECT_MASTER#

Spark Connect server and Spark master cannot be configured together: Spark master [], Spark Connect [].

CANNOT_CONVERT_COLUMN_INTO_BOOL#

Cannot convert column into bool: please use ‘&’ for ‘and’, ‘|’ for ‘or’, ‘~’ for ‘not’ when building DataFrame boolean expressions.

CANNOT_CONVERT_TYPE#

Cannot convert into .

CANNOT_DETERMINE_TYPE#

Some of types cannot be determined after inferring.

CANNOT_GET_BATCH_ID#

Could not get batch id from .

CANNOT_INFER_ARRAY_ELEMENT_TYPE#

Can not infer the element data type, an non-empty list starting with an non-None value is required.

CANNOT_INFER_EMPTY_SCHEMA#

Can not infer schema from an empty dataset.

CANNOT_INFER_SCHEMA_FOR_TYPE#

Can not infer schema for type: .

CANNOT_INFER_TYPE_FOR_FIELD#

Unable to infer the type of the field .

CANNOT_MERGE_TYPE#

Can not merge type and .

CANNOT_OPEN_SOCKET#

Can not open socket: .

CANNOT_PARSE_DATATYPE#

Unable to parse datatype. .

CANNOT_PROVIDE_METADATA#

Metadata can only be provided for a single column.

CANNOT_REGISTER_UDTF#

Cannot register the UDTF ‘’: expected a ‘UserDefinedTableFunction’. Please make sure the UDTF is correctly defined as a class, and then either wrap it in the udtf() function or annotate it with @udtf(…).

CANNOT_SET_TOGETHER#

should not be set together.

CANNOT_SPECIFY_RETURN_TYPE_FOR_UDF#

returnType can not be specified when is a user-defined function, but got .

CANNOT_WITHOUT#

Cannot without .

CLASSIC_OPERATION_NOT_SUPPORTED_ON_DF#

Calling property or member ‘’ is not supported in PySpark Classic, please use Spark Connect instead.

COLLATION_INVALID_PROVIDER#

The value does not represent a correct collation provider. Supported providers are: [].

COLUMN_IN_LIST#

does not allow a Column in a list.

CONNECT_URL_ALREADY_DEFINED#

Only one Spark Connect client URL can be set; however, got a different URL [] from the existing [].

CONNECT_URL_NOT_SET#

Cannot create a Spark Connect session because the Spark Connect remote URL has not been set. Please define the remote URL by setting either the ‘spark.remote’ option or the ‘SPARK_REMOTE’ environment variable.

CONTEXT_ONLY_VALID_ON_DRIVER#

It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transformation. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.

CONTEXT_UNAVAILABLE_FOR_REMOTE_CLIENT#

Remote client cannot create a SparkContext. Create SparkSession instead.

DATA_SOURCE_INVALID_RETURN_TYPE#

Unsupported return type (‘’) from Python data source ‘’. Expected types: .

DATA_SOURCE_RETURN_SCHEMA_MISMATCH#

Return schema mismatch in the result from ‘read’ method. Expected: columns, Found: columns. Make sure the returned values match the required output schema.

DATA_SOURCE_TYPE_MISMATCH#

Expected , but got .

DIFFERENT_PANDAS_DATAFRAME#

DataFrames are not almost equal: Left: Right:

DIFFERENT_PANDAS_INDEX#

Indices are not almost equal: Left: Right:

DIFFERENT_PANDAS_MULTIINDEX#

MultiIndices are not almost equal: Left: Right:

DIFFERENT_PANDAS_SERIES#

Series are not almost equal: Left: Right:

DIFFERENT_ROWS#

DIFFERENT_SCHEMA#

Schemas do not match. — actual +++ expected

DISALLOWED_TYPE_FOR_CONTAINER#

Argument (type: ) should only contain a type in [], got

DUPLICATED_ARTIFACT#

Duplicate Artifact: . Artifacts cannot be overwritten.

DUPLICATED_FIELD_NAME_IN_ARROW_STRUCT#

Duplicated field names in Arrow Struct are not allowed, got

ERROR_OCCURRED_WHILE_CALLING#

An error occurred while calling : .

FIELD_DATA_TYPE_UNACCEPTABLE#

can not accept object in type .

FIELD_DATA_TYPE_UNACCEPTABLE_WITH_NAME#

: can not accept object in type .

FIELD_NOT_NULLABLE#

Field is not nullable, but got None.

FIELD_NOT_NULLABLE_WITH_NAME#

: This field is not nullable, but got None.

FIELD_STRUCT_LENGTH_MISMATCH#

Length of object () does not match with length of fields ().

FIELD_STRUCT_LENGTH_MISMATCH_WITH_NAME#

: Length of object () does not match with length of fields ().

FIELD_TYPE_MISMATCH#

is not an instance of type .

FIELD_TYPE_MISMATCH_WITH_NAME#

: is not an instance of type .

HIGHER_ORDER_FUNCTION_SHOULD_RETURN_COLUMN#

Function should return Column, got .

INCORRECT_CONF_FOR_PROFILE#

spark.python.profile or spark.python.profile.memory configuration

must be set to true to enable Python profile.

INDEX_NOT_POSITIVE#

Index must be positive, got ‘’.

INDEX_OUT_OF_RANGE#

index out of range, got ‘’.

INVALID_ARROW_UDTF_RETURN_TYPE#

The return type of the arrow-optimized Python UDTF should be of type ‘pandas.DataFrame’, but the ‘’ method returned a value of type with value: .

INVALID_BROADCAST_OPERATION#

Broadcast can only be in driver.

INVALID_CALL_ON_UNRESOLVED_OBJECT#

Invalid call to on unresolved object.

INVALID_CONNECT_URL#

Invalid URL for Spark Connect:

INVALID_INTERVAL_CASTING#

Interval to is invalid.

INVALID_ITEM_FOR_CONTAINER#

All items in should be in , got .

INVALID_JSON_DATA_TYPE_FOR_COLLATIONS#

Collations can only be applied to string types, but the JSON data type is .

INVALID_MULTIPLE_ARGUMENT_CONDITIONS#

[{arg_names}] cannot be .

INVALID_NDARRAY_DIMENSION#

NumPy array input should be of dimensions.

INVALID_NUMBER_OF_DATAFRAMES_IN_GROUP#

Invalid number of dataframes in group .

INVALID_PANDAS_UDF#

Invalid function:

INVALID_PANDAS_UDF_TYPE#

should be one of the values from PandasUDFType, got

INVALID_RETURN_TYPE_FOR_ARROW_UDF#

Grouped and Cogrouped map Arrow UDF should return StructType for , got .

INVALID_RETURN_TYPE_FOR_PANDAS_UDF#

Pandas UDF should return StructType for , got .

INVALID_SESSION_UUID_ID#

Parameter value must be a valid UUID format:

INVALID_TIMEOUT_TIMESTAMP#

Timeout timestamp () cannot be earlier than the current watermark ().

INVALID_TYPE#

Argument should not be a .

INVALID_TYPENAME_CALL#

StructField does not have typeName. Use typeName on its type explicitly instead.

INVALID_TYPE_DF_EQUALITY_ARG#

Expected type for but got type .

INVALID_UDF_EVAL_TYPE#

Eval type for UDF must be .

INVALID_UDTF_BOTH_RETURN_TYPE_AND_ANALYZE#

The UDTF ‘’ is invalid. It has both its return type and an ‘analyze’ attribute. Please make it have one of either the return type or the ‘analyze’ static method in ‘’ and try again.

INVALID_UDTF_EVAL_TYPE#

The eval type for the UDTF ‘’ is invalid. It must be one of .

INVALID_UDTF_HANDLER_TYPE#

The UDTF is invalid. The function handler must be a class, but got ‘’. Please provide a class as the function handler.

INVALID_UDTF_NO_EVAL#

The UDTF ‘’ is invalid. It does not implement the required ‘eval’ method. Please implement the ‘eval’ method in ‘’ and try again.

INVALID_UDTF_RETURN_TYPE#

The UDTF ‘’ is invalid. It does not specify its return type or implement the required ‘analyze’ static method. Please specify the return type or implement the ‘analyze’ static method in ‘’ and try again.

INVALID_WHEN_USAGE#

when() can only be applied on a Column previously generated by when() function, and cannot be applied once otherwise() is applied.

INVALID_WINDOW_BOUND_TYPE#

Invalid window bound type: .

JAVA_GATEWAY_EXITED#

Java gateway process exited before sending its port number.

JVM_ATTRIBUTE_NOT_SUPPORTED#

Attribute is not supported in Spark Connect as it depends on the JVM. If you need to use this attribute, do not use Spark Connect when creating your session. Visit https://spark.apache.org/docs/latest/sql-getting-started.html#starting-point-sparksession for creating regular Spark Session in detail.

KEY_NOT_EXISTS#

Key is not exists.

KEY_VALUE_PAIR_REQUIRED#

Key-value pair or a list of pairs is required.

LENGTH_SHOULD_BE_THE_SAME#

and should be of the same length, got and .

MALFORMED_VARIANT#

Variant binary is malformed. Please check the data source is valid.

MASTER_URL_INVALID#

Master must either be yarn or start with spark, k8s, or local.

MASTER_URL_NOT_SET#

A master URL must be set in your configuration.

MEMORY_PROFILE_INVALID_SOURCE#

Memory profiler can only be used on editors with line numbers.

MISSING_LIBRARY_FOR_PROFILER#

Install the ‘memory_profiler’ library in the cluster to enable memory profiling.

MISSING_VALID_PLAN#

Argument to does not contain a valid plan.

MIXED_TYPE_REPLACEMENT#

Mixed type replacements are not supported.

NEGATIVE_VALUE#

Value for must be greater than or equal to 0, got ‘’.

NOT_BOOL#

Argument should be a bool, got .

NOT_BOOL_OR_DICT_OR_FLOAT_OR_INT_OR_LIST_OR_STR_OR_TUPLE#

Argument should be a bool, dict, float, int, str or tuple, got .

NOT_BOOL_OR_DICT_OR_FLOAT_OR_INT_OR_STR#

Argument should be a bool, dict, float, int or str, got .

NOT_BOOL_OR_FLOAT_OR_INT#

Argument should be a bool, float or int, got .

NOT_BOOL_OR_FLOAT_OR_INT_OR_LIST_OR_NONE_OR_STR_OR_TUPLE#

Argument should be a bool, float, int, list, None, str or tuple, got .

NOT_BOOL_OR_FLOAT_OR_INT_OR_STR#

Argument should be a bool, float, int or str, got .

NOT_BOOL_OR_LIST#

Argument should be a bool or list, got .

NOT_BOOL_OR_STR#

Argument should be a bool or str, got .

NOT_CALLABLE#

Argument should be a callable, got .

NOT_COLUMN#

Argument should be a Column, got .

NOT_COLUMN_OR_DATATYPE_OR_STR#

Argument should be a Column, str or DataType, but got .

NOT_COLUMN_OR_FLOAT_OR_INT_OR_LIST_OR_STR#

Argument should be a Column, float, integer, list or string, got .

NOT_COLUMN_OR_INT#

Argument should be a Column or int, got .

NOT_COLUMN_OR_INT_OR_LIST_OR_STR_OR_TUPLE#

Argument should be a Column, int, list, str or tuple, got .

NOT_COLUMN_OR_INT_OR_STR#

Argument should be a Column, int or str, got .

NOT_COLUMN_OR_LIST_OR_STR#

Argument should be a Column, list or str, got .

NOT_COLUMN_OR_STR#

Argument should be a Column or str, got .

NOT_COLUMN_OR_STR_OR_STRUCT#

Argument should be a StructType, Column or str, got .

NOT_DATAFRAME#

Argument should be a DataFrame, got .

NOT_DATATYPE_OR_STR#

Argument should be a DataType or str, got .

NOT_DICT#

Argument should be a dict, got .

NOT_EXPRESSION#

Argument should be an Expression, got .

NOT_FLOAT_OR_INT#

Argument should be a float or int, got .

NOT_FLOAT_OR_INT_OR_LIST_OR_STR#

Argument should be a float, int, list or str, got .

NOT_IMPLEMENTED#

is not implemented.

NOT_INT#

Argument should be an int, got .

NOT_INT_OR_SLICE_OR_STR#

Argument should be an int, slice or str, got .

NOT_IN_BARRIER_STAGE#

It is not in a barrier stage.

NOT_ITERABLE#

is not iterable.

NOT_LIST#

Argument should be a list, got .

NOT_LIST_OF_COLUMN#

Argument should be a list[Column].

NOT_LIST_OF_COLUMN_OR_STR#

Argument should be a list[Column].

NOT_LIST_OF_FLOAT_OR_INT#

Argument should be a list[float, int], got .

NOT_LIST_OF_STR#

Argument should be a list[str], got .

NOT_LIST_OR_NONE_OR_STRUCT#

Argument should be a list, None or StructType, got .

NOT_LIST_OR_STR_OR_TUPLE#

Argument should be a list, str or tuple, got .

NOT_LIST_OR_TUPLE#

Argument should be a list or tuple, got .

NOT_NUMERIC_COLUMNS#

Numeric aggregation function can only be applied on numeric columns, got .

NOT_OBSERVATION_OR_STR#

Argument should be an Observation or str, got .

NOT_SAME_TYPE#

Argument and should be the same type, got and .

NOT_STR#

Argument should be a str, got .

NOT_STRUCT#

Argument should be a struct type, got .

NOT_STR_OR_LIST_OF_RDD#

Argument should be a str or list[RDD], got .

NOT_STR_OR_STRUCT#

Argument should be a str or struct type, got .

NOT_WINDOWSPEC#

Argument should be a WindowSpec, got .

NO_ACTIVE_EXCEPTION#

No active exception.

NO_ACTIVE_OR_DEFAULT_SESSION#

No active or default Spark session found. Please create a new Spark session before running the code.

NO_ACTIVE_SESSION#

No active Spark session found. Please create a new Spark session before running the code.

NO_OBSERVE_BEFORE_GET#

Should observe by calling DataFrame.observe before get.

NO_SCHEMA_AND_DRIVER_DEFAULT_SCHEME#

Only allows to be a path without scheme, and Spark Driver should use the default scheme to determine the destination file system.

ONLY_ALLOWED_FOR_SINGLE_COLUMN#

Argument can only be provided for a single column.

ONLY_ALLOW_SINGLE_TRIGGER#

Only a single trigger is allowed.

ONLY_SUPPORTED_WITH_SPARK_CONNECT#

is only supported with Spark Connect; however, the current Spark session does not use Spark Connect.

PACKAGE_NOT_INSTALLED#

>= must be installed; however, it was not found.

PANDAS_API_ON_SPARK_FAIL_ON_ANSI_MODE#

Pandas API on Spark does not properly work on ANSI mode. Please set a Spark config ‘spark.sql.ansi.enabled’ to false. Alternatively set a pandas-on-spark option ‘compute.fail_on_ansi_mode’ to False to force it to work, although it can cause unexpected behavior.

PANDAS_UDF_OUTPUT_EXCEEDS_INPUT_ROWS#

The Pandas SCALAR_ITER UDF outputs more rows than input rows.

PIPE_FUNCTION_EXITED#

Pipe function exited with error code .

PLOT_INVALID_TYPE_COLUMN#

Column must be one of for plotting, got .

PLOT_NOT_NUMERIC_COLUMN_ARGUMENT#

Argument must be a numerical column for plotting, got .

PYTHON_HASH_SEED_NOT_SET#

Randomness of hash of string should be disabled via PYTHONHASHSEED.

PYTHON_STREAMING_DATA_SOURCE_RUNTIME_ERROR#

Failed when running Python streaming data source:

PYTHON_VERSION_MISMATCH#

Python in worker has different version: than that in driver: , PySpark cannot run with different minor versions. Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.

RDD_TRANSFORM_ONLY_VALID_ON_DRIVER#

It appears that you are attempting to broadcast an RDD or reference an RDD from an action or transformation. RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(lambda x: rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.

READ_ONLY#

is read-only.

RESPONSE_ALREADY_RECEIVED#

OPERATION_NOT_FOUND on the server but responses were already received from it.

RESULT_COLUMNS_MISMATCH_FOR_ARROW_UDF#

Column names of the returned pyarrow.Table do not match specified schema.

RESULT_COLUMNS_MISMATCH_FOR_PANDAS_UDF#

Column names of the returned pandas.DataFrame do not match specified schema.

RESULT_LENGTH_MISMATCH_FOR_PANDAS_UDF#

Number of columns of the returned pandas.DataFrame doesn’t match specified schema. Expected: Actual:

RESULT_LENGTH_MISMATCH_FOR_SCALAR_ITER_PANDAS_UDF#

The length of output in Scalar iterator pandas UDF should be the same with the input’s; however, the length of output was and the length of input was .

RESULT_TYPE_MISMATCH_FOR_ARROW_UDF#

Columns do not match in their data type: .

RETRIES_EXCEEDED#

The maximum number of retries has been exceeded.

REUSE_OBSERVATION#

An Observation can be used with a DataFrame only once.

SCHEMA_MISMATCH_FOR_PANDAS_UDF#

Result vector from pandas_udf was not the required length: expected , got .

SESSION_ALREADY_EXIST#

Cannot start a remote Spark session because there is a regular Spark session already running.

SESSION_NEED_CONN_STR_OR_BUILDER#

Needs either connection string or channelBuilder (mutually exclusive) to create a new SparkSession.

SESSION_NOT_SAME#

Both Datasets must belong to the same SparkSession.

SESSION_OR_CONTEXT_EXISTS#

There should not be an existing Spark Session or Spark Context.

SESSION_OR_CONTEXT_NOT_EXISTS#

SparkContext or SparkSession should be created first.

SLICE_WITH_STEP#

Slice with step is not supported.

STATE_NOT_EXISTS#

State is either not defined or has already been removed.

STOP_ITERATION_OCCURRED#

Caught StopIteration thrown from user’s code; failing the task:

STOP_ITERATION_OCCURRED_FROM_SCALAR_ITER_PANDAS_UDF#

pandas iterator UDF should exhaust the input iterator.

STREAMING_CONNECT_SERIALIZATION_ERROR#

Cannot serialize the function . If you accessed the Spark session, or a DataFrame defined outside of the function, or any object that contains a Spark session, please be aware that they are not allowed in Spark Connect. For foreachBatch, please access the Spark session using df.sparkSession, where df is the first parameter in your foreachBatch function. For StreamingQueryListener, please access the Spark session using self.spark. For details please check out the PySpark doc for foreachBatch and StreamingQueryListener.

TEST_CLASS_NOT_COMPILED#

doesn’t exist. Spark sql test classes are not compiled.

TOO_MANY_VALUES#

Expected values for , got .

TYPE_HINT_SHOULD_BE_SPECIFIED#

Type hints for should be specified; however, got .

UDF_RETURN_TYPE#

Return type of the user-defined function should be , but is .

UDTF_ARROW_TYPE_CAST_ERROR#

Cannot convert the output value of the column ‘’ with type ‘’ to the specified return type of the column: ‘’. Please check if the data types match and try again.

UDTF_CONSTRUCTOR_INVALID_IMPLEMENTS_ANALYZE_METHOD#

Failed to evaluate the user-defined table function ‘’ because its constructor is invalid: the function implements the ‘analyze’ method, but its constructor has more than two arguments (including the ‘self’ reference). Please update the table function so that its constructor accepts exactly one ‘self’ argument, or one ‘self’ argument plus another argument for the result of the ‘analyze’ method, and try the query again.

UDTF_CONSTRUCTOR_INVALID_NO_ANALYZE_METHOD#

Failed to evaluate the user-defined table function ‘’ because its constructor is invalid: the function does not implement the ‘analyze’ method, and its constructor has more than one argument (including the ‘self’ reference). Please update the table function so that its constructor accepts exactly one ‘self’ argument, and try the query again.

UDTF_EVAL_METHOD_ARGUMENTS_DO_NOT_MATCH_SIGNATURE#

Failed to evaluate the user-defined table function ‘’ because the function arguments did not match the expected signature of the ‘eval’ method (). Please update the query so that this table function call provides arguments matching the expected signature, or else update the table function so that its ‘eval’ method accepts the provided arguments, and then try the query again.

UDTF_EXEC_ERROR#

User defined table function encountered an error in the ‘’ method:

UDTF_INVALID_OUTPUT_ROW_TYPE#

The type of an individual output row in the ‘’ method of the UDTF is invalid. Each row should be a tuple, list, or dict, but got ‘’. Please make sure that the output rows are of the correct type.

UDTF_RETURN_NOT_ITERABLE#

The return value of the ‘’ method of the UDTF is invalid. It should be an iterable (e.g., generator or list), but got ‘’. Please make sure that the UDTF returns one of these types.

UDTF_RETURN_SCHEMA_MISMATCH#

The number of columns in the result does not match the specified schema. Expected column count: , Actual column count: . Please make sure the values returned by the ‘’ method have the same number of columns as specified in the output schema.

UDTF_RETURN_TYPE_MISMATCH#

Mismatch in return type for the UDTF ‘’. Expected a ‘StructType’, but got ‘’. Please ensure the return type is a correctly formatted StructType.

UDTF_SERIALIZATION_ERROR#

Cannot serialize the UDTF ‘’:

UNEXPECTED_RESPONSE_FROM_SERVER#

Unexpected response from iterator server.

UNEXPECTED_TUPLE_WITH_STRUCT#

Unexpected tuple with StructType.

UNKNOWN_EXPLAIN_MODE#

Unknown explain mode: ‘’. Accepted explain modes are ‘simple’, ‘extended’, ‘codegen’, ‘cost’, ‘formatted’.

UNKNOWN_INTERRUPT_TYPE#

Unknown interrupt type: ‘’. Accepted interrupt types are ‘all’.

UNKNOWN_RESPONSE#

Unknown response: .

UNKNOWN_VALUE_FOR#

Unknown value for .

UNSUPPORTED_DATA_TYPE#

Unsupported DataType .

UNSUPPORTED_DATA_TYPE_FOR_ARROW#

Single data type is not supported with Arrow.

UNSUPPORTED_DATA_TYPE_FOR_ARROW_CONVERSION#

is not supported in conversion to Arrow.

UNSUPPORTED_DATA_TYPE_FOR_ARROW_VERSION#

is only supported with pyarrow 2.0.0 and above.

UNSUPPORTED_JOIN_TYPE#

Unsupported join type: ‘’. Supported join types include: .

UNSUPPORTED_LITERAL#

Unsupported Literal ‘’.

UNSUPPORTED_LOCAL_CONNECTION_STRING#

Creating new SparkSessions with local connection string is not supported.

UNSUPPORTED_NUMPY_ARRAY_SCALAR#

The type of array scalar ‘’ is not supported.

UNSUPPORTED_OPERATION#

is not supported.

UNSUPPORTED_PACKAGE_VERSION#

>= must be installed; however, your version is .

UNSUPPORTED_PARAM_TYPE_FOR_HIGHER_ORDER_FUNCTION#

Function should use only POSITIONAL or POSITIONAL OR KEYWORD arguments.

UNSUPPORTED_PIE_PLOT_PARAM#

Pie plot requires either a y column or subplots=True.

UNSUPPORTED_PLOT_BACKEND#

is not supported, it should be one of the values from

UNSUPPORTED_PLOT_BACKEND_PARAM#

does not support set to , it should be one of the values from

UNSUPPORTED_SIGNATURE#

Unsupported signature: .

UNSUPPORTED_WITH_ARROW_OPTIMIZATION#

is not supported with Arrow optimization enabled in Python UDFs. Disable ‘spark.sql.execution.pythonUDF.arrow.enabled’ to workaround.

VALUE_ALLOWED#

Value for does not allow .

VALUE_NOT_ACCESSIBLE#

Value cannot be accessed inside tasks.

VALUE_NOT_ALLOWED#

Value for has to be amongst the following values: .

VALUE_NOT_ANY_OR_ALL#

Value for must be ‘any’ or ‘all’, got ‘’.

VALUE_NOT_BETWEEN#

Value for must be between and .

VALUE_NOT_NON_EMPTY_STR#

Value for must be a non-empty string, got ‘’.

VALUE_NOT_PEARSON#

Value for only supports the ‘pearson’, got ‘’.

VALUE_NOT_PLAIN_COLUMN_REFERENCE#

Value in should be a plain column reference such as df.col or col(‘column’).

VALUE_NOT_POSITIVE#

Value for must be positive, got ‘’.

VALUE_NOT_TRUE#

Value for must be True, got ‘’.

VALUE_OUT_OF_BOUNDS#

Value for must be between and (inclusive), got

WRONG_NUM_ARGS_FOR_HIGHER_ORDER_FUNCTION#

Function should take between 1 and 3 arguments, but the provided function takes .

WRONG_NUM_COLUMNS#

Function should take at least columns.

ZERO_INDEX#

Index must be non-zero.

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