Python Extension Development (Labs)

These classes can be used by developers to implement their own Python nodes for KNIME. For a more detailed description see the Pure Python Node Extensions Guide

Note

Before KNIME AP 4.7, the module used to access KNIME functionality was called knime_extension. This module has been renamed to knime.extension.

Nodes

class knime.extension.PythonNode

Extend this class to provide a pure Python based node extension to KNIME Analytics Platform.

Users can either use the decorators @knext.input_table, @knext.input_binary, @knext.output_table, @knext.output_binary, and @knext.output_view, or populate the input_ports, output_ports, and output_view attributes.

Use the Python logging facilities and its .warning and .error methods to write warnings and errors to the KNIME console. .info and .debug will only show up in the KNIME console if the log level in KNIME is configured to show these.

Example:

import logging
import knime.extension as knext

LOGGER = logging.getLogger(__name__)

category = knext.category("/community", "mycategory", "My Category", "My category described", icon="icons/category.png")

@knext.node(name="Pure Python Node", node_type=knext.NodeType.LEARNER, icon_path="icons/icon.png", category=category)
@knext.input_table(name="Input Data", description="We read data from here")
@knext.output_table(name="Output Data", description="Whatever the node has produced")
class TemplateNode(knext.PythonNode):
    # A Python node has a description.

    def configure(self, configure_context, table_schema):
        LOGGER.info(f"Configuring node")
        return table_schema

    def execute(self, exec_context, table):
        return table

abstract configure(config_context: ConfigurationContext, *inputs)

Configure this Python node.

Parameters:

config_context – The ConfigurationContext providing KNIME utilities during execution
*inputs – Each input table spec or binary port spec will be added as parameter, in the same order that the ports were defined.

Returns:

Either a single spec, or a tuple or list of specs. The number of specs must match the number of defined output ports, and they must be returned in this order. Alternatively, instead of a spec, a knext.Column can be returned (if the spec shall only consist of one column).

Raises:

InvalidParametersError – If the current input parameters do not satisfy this node’s requirements.

abstract execute(exec_context: ExecutionContext, *inputs)

Execute this Python node.

Parameters:

exec_context – The ExecutionContext providing KNIME utilities during execution
*inputs – Each input table or binary port object will be added as parameter, in the same order that the ports were defined. Tables will be provided as a kn.Table, while binary data will be a plain Python bytes object.

Returns:

Either a single output object (table or binary), or a tuple or list of objects. The number of output objects must match the number of defined output ports, and they must be returned in this order. Tables must be provided as a kn.Table or kn.BatchOutputTable, while binary data should be returned as plain Python bytes object.

A node is part of a category:

knime.extension.category(path: str, level_id: str, name: str, description: str, icon: str, after: str = '', locked: bool = True)

Register a new node category.

A node category must only be created once. Use a string encoding the absolute category path to add nodes to an existing category.

Parameters:

path (Union[str, Category]) – The absolute “path” that lead to this category e.g. “/io/read”. The segments are the category level-IDs, separated by a slash (“/”). Categories that contain community nodes should be placed in the “/community” category.
level_id (str) – The identifier of the level which is used as a path-segment and must be unique at the level specified by “path”.
name (str) – The name of this category e.g. “File readers”.
description (str) – A short description of the category.
icon (str) – File path to 16x16 pixel PNG icon for this category. The path must be relative to the root of the extension.
after (str, optional) – Specifies the level-id of the category after which this category should be sorted in. Defaults to “”.
locked (bool, optional) – Set this to False to allow extensions from other vendors to add sub-categories or nodes to this category. Defaults to True.

Returns:

The full path of the category which can be used to create nodes inside this category.

Return type:

str

A node has a type:

class knime.extension.NodeType(value)

Defines the different node types that are available for Python based nodes.

LEARNER = 'Learner': A node learning a model that is typically consumed by a PREDICTOR.

MANIPULATOR = 'Manipulator': A node that manipulates data.

OTHER = 'Other': A node that doesn’t fit one of the other node types.

PREDICTOR = 'Predictor': A node that predicts something typically using a model provided by a LEARNER.

SINK = 'Sink': A node consuming data.

SOURCE = 'Source': A node producing data.

VISUALIZER = 'Visualizer': A node that visualizes data.

A node’s configure method receives a configuration context that lets you interact with KNIME

class knime.extension.ConfigurationContext(java_ctx, flow_variables)

The ConfigurationContext provides utilities to communicate with KNIME during a node’s configure() method.

property flow_variables: Dict[str, Any]

The flow variables coming in from KNIME as a dictionary with string keys. The dictionary can be edited and supports flow variables of the following types:

bool
list(bool)
float
list(float)
int
list(int)
str
list(str)

get_credential_names(): Returns the identifier (flow variable name) for each credential

get_credentials(identifier: str) → Credential

Returns the credentials dataclass for the given identifier.

Parameters:: identifier – the identifier of the credentials to retrieve

set_warning(message: str) → None

Sets a warning on the node.

Parameters:: message – the warning message to display on the node

A node’s execute method receives an execution context that lets you interact with KNIME and e.g. check whether the user has cancelled the execution of your Python node.

class knime.extension.ExecutionContext(java_ctx, flow_variables)

The ExecutionContext provides utilities to communicate with KNIME during a node’s execute() method.

property flow_variables: Dict[str, Any]

The flow variables coming in from KNIME as a dictionary with string keys. The dictionary can be edited and supports flow variables of the following types:

bool
list(bool)
float
list(float)
int
list(int)
str
list(str)

get_credential_names(): Returns the identifier (flow variable name) for each credential

get_credentials(identifier: str) → Credential

Returns the credentials dataclass for the given identifier.

Parameters:: identifier – the identifier of the credentials to retrieve

get_knime_home_dir() → str: Returns the local absolute path to the directory in which KNIME stores its configuration as well as log files.

get_workflow_data_area_dir() → str: Returns the local absolute path to the current workflow’s data area folder. This folder is meant to be part of the workflow, so its contents are included whenever the workflow is shared.

get_workflow_temp_dir() → str

Returns the local absolute path where temporary files for this workflow should be stored. Files created in this folder are not automatically deleted by KNIME.

By default, this folder is located in the operating system’s temporary folder. In that case, the contents will be cleaned by the OS.

is_canceled() → bool: Returns true if this node’s execution has been canceled from KNIME. Nodes can check for this property and return early if the execution does not need to finish. Raising a RuntimeError in that case is encouraged.

set_progress(progress: float, message: str | None = None)

Set the progress of the execution.

Note that the progress that can be set here is 80% of the total progress of a node execution. The first and last 10% are reserved for data transfer and will be set by the framework.

Parameters:

progress – a floating point number between 0.0 and 1.0
message – an optional message to display in KNIME with the progress

set_warning(message: str) → None

Sets a warning on the node.

Parameters:: message – the warning message to display on the node

The dialog creation context is used to create dialogs for the configuration of the node. It can be accessed indirectly, by passing its method’s as arguments to specific parameters (see the example below).

class knime.extension.DialogCreationContext(java_ctx, flow_variables, specs_to_python_converter)

The DialogCreationContext provides utilities to communicate with KNIME during the dialog creation phase. It enables access to the flow variables, the specs of the input tables and the credentials. These can be used to create the dialog elements, by passing the respective method as lambda function to the constructor of the string parameter class. The lambdas will receive the dialog creation context as parameter which should be passed as first parameter to the fully qualified method calls of DialogCreationContext as below:

Example:

class ExampleNode:
    # This dialog element displays a dropdown with all available credentials
    string_param = knext.StringParameter(label="Credential parameter", description="Choices is a callable",
                                 choices=lambda a: knext.DialogCreationContext.get_credential_names(a))

property flow_variables: Dict[str, Any]

The flow variables coming in from KNIME as a dictionary with string keys. The dictionary can be edited and supports flow variables of the following types:

bool
list(bool)
float
list(float)
int
list(int)
str
list(str)

get_credential_names(): Returns the identifier (flow variable name) for each credential

get_credentials(identifier: str) → Credential

Returns the credentials dataclass for the given identifier.

Parameters:: identifier – the identifier of the credentials to retrieve

get_flow_variables()

Returns the flow variables coming in from KNIME as a dictionary with string keys. The dictionary cannot be edited and supports flow variables of the following types:

bool
list(bool)
float
list(float)
int
list(int)
str
list(str)

get_input_specs() → List[PortObjectSpec]: Returns the specs for all input ports of the node.

Decorators

These decorators can be used to easily configure your Python node.

knime.extension.node(name: str, node_type: NodeType, icon_path: str, category: str, after: str | None = None, id: str | None = None, is_deprecated: bool = False) → Callable: Use this decorator to annotate a PythonNode class or function that creates a PythonNode instance that should correspond to a node in KNIME.

knime.extension.input_table(name: str, description: str): Use this decorator to define an input port of type “Table” of a node.

knime.extension.input_binary(name: str, description: str, id: str)

Use this decorator to define a bytes-serialized port object input of a node.

Parameters:

name – The name of the input port
description – A description of the input port.
id – A unique ID identifying the type of the Port. Only Ports with equal ID can be connected in KNIME

knime.extension.input_port(name: str, description: str, port_type: PortType)

Use this decorator to add an input port of the provided type to a node.

Parameters:

name – The name of the input port
description – A description of the input port
port_type – The type of the input port

knime.extension.output_table(name: str, description: str): Use this decorator to define an output port of type “Table” of a node.

knime.extension.output_image(name: str, description: str): Use this decorator to define an output port of type “Image” of a node.

knime.extension.output_binary(name: str, description: str, id: str)

Use this decorator to define a bytes-serialized port object output of a node.

Parameters:

name –
description –
id – A unique ID identifying the type of the Port. Only Ports with equal ID can be connected in KNIME

knime.extension.output_port(name: str, description: str, port_type: PortType)

Use this decorator to add an output port of the provided type to a node.

Parameters:

name – The name of the port
description – Description of what the port is used for
port_type – The type of the port to add

knime.extension.output_view(name: str, description: str, static_resources: str | None = None)

Use this decorator to specify that this node produces a view

Parameters:

name – The name of the view
description – Description of the view
static_resources – The path to a folder of resources that will be available to the HTML page. The path given here must be relative to the root of the extension. The resources can be accessed by the same relative file path (e.g. “{static_resources}/{filename}”).

Parameters

To add parameterization to your nodes, the configuration dialog can be defined and customized. Each parameter can be used in the nodes execution by accessing self.param_name. These parameters can be set up by using the following parameter types. For a more detailed description see Defining the node’s configuration dialog.

class knime.extension.IntParameter(label: str | None = None, description: str | None = None, default_value: int | Callable[[Version], int] = 0, validator: Callable[[int], None] | None = None, min_value: int | None = None, max_value: int | None = None, since_version: Version | str | None = None, is_advanced: bool = False): Parameter class for primitive integer types.

class knime.extension.DoubleParameter(label: str | None = None, description: str | None = None, default_value: float | Callable[[Version], float] = 0.0, validator: Callable[[float], None] | None = None, min_value: float | None = None, max_value: float | None = None, since_version: Version | str | None = None, is_advanced: bool = False): Parameter class for primitive float types.

class knime.extension.BoolParameter(label: str | None = None, description: str | None = None, default_value: bool | Callable[[Version], bool] = False, validator: Callable[[bool], None] | None = None, since_version: Version | str | None = None, is_advanced: bool = False): Parameter class for primitive boolean types.

class knime.extension.StringParameter(label: str | None = None, description: str | None = None, default_value: str | Callable[[Version], str] = '', enum: List[str] | None = None, validator: Callable[[str], None] | None = None, since_version: Version | str | None = None, is_advanced: bool = False, choices: Callable | None = None): Parameter class for primitive string types.

class knime.extension.ColumnParameter(label: str | None = None, description: str | None = None, port_index: int = 0, column_filter: Callable[[Column], bool] | None = None, include_row_key: bool = False, include_none_column: bool = False, since_version: str | None = None, is_advanced: bool = False): Parameter class for single columns.

class knime.extension.MultiColumnParameter(label: str | None = None, description: str | None = None, port_index: int | None = 0, column_filter: Callable[[Column], bool] | None = None, since_version: Version | str | None = None, is_advanced: bool = False): Parameter class for multiple columns.

class knime.extension.ColumnFilterParameter(label: str | None = None, description: str | None = None, port_index: int | None = 0, column_filter: Callable[[Column], bool] | None = None, since_version: Version | str | None = None, is_advanced: bool = False): Parameter class that supports full column filtering for columns.

class knime.extension.ColumnFilterConfig(mode=ColumnFilterMode.MANUAL, pattern_filter: PatternFilterConfig | None = None, type_filter: TypeFilterConfig | None = None, manual_filter: ManualFilterConfig | None = None, included_column_names: List[str] | None = None)

The value of a ColumnFilterParameter is a ColumnFilterConfig instance with a mode as well as configuration for the different modes.

Use the apply method to filter schemas and tables according to this filter config

Example:

@knext.node(
    name="Python Column Filter",
    node_type=knext.NodeType.MANIPULATOR,
    icon_path=...,
    category=...,
)
@knext.input_table("Input Table", "Input table.")
@knext.output_table("Output Table", "Output table.")
class ColumnFilterNode:
    column_filter = knext.ColumnFilterParameter("Column Filter", "Column Filter")

    def configure(self, config_context, input_schema: knext.Schema):
        return self.column_filter.apply(input_schema)

    def execute(self, exec_context, input_table):
        return self.column_filter.apply(input_table)

apply(columnar: _Columnar) → _Columnar: Filter a table schema or a table according to this column filter configuration.

Parameter class for multiple-choice parameter types. Replicates and extends the enum functionality previously implemented as part of StringParameter.

A subclass of EnumParameterOptions should be provided as the enum parameter, which should contain class attributes of the form OPTION_NAME = (OPTION_LABEL, OPTION_DESCRIPTION). The corresponding option attributes can be accessed via MyOptions.OPTION_NAME.name, .label, and .description respectively.

The .name attribute of each option is used as the selection constant, e.g. MyOptions.OPTION_NAME.name == "OPTION_NAME".

Example:

class CoffeeOptions(EnumParameterOptions):
    CLASSIC = ("Classic", "The classic chocolatey taste, with notes of bitterness and wood.")
    FRUITY = ("Fruity", "A fruity taste, with notes of berries and citrus.")
    WATERY = ("Watery", "A watery taste, with notes of water and wetness.")

coffee_selection_param = knext.EnumParameter(
    label="Coffee Selection",
    description="Select the type of coffee you like to drink.",
    default_value=CoffeeOptions.CLASSIC.name,
    enum=CoffeeOptions,
)

class knime.extension.EnumParameterOptions(value)

A helper class for creating EnumParameter options, based on Python’s Enum class.

Developers should subclass this class, and provide enumeration options as class attributes of the subclass, of the form OPTION_NAME = (OPTION_LABEL, OPTION_DESCRIPTION).

Enum option objects can be accessed as attributes of the EnumParameterOptions subclass, e.g. MyEnum.OPTION_NAME. Each option object has the following attributes:

name: the name of the class attribute, e.g. “OPTION_NAME”, which is used as the selection constant;
label: the label of the option, displayed in the configuration dialogue of the node;
description: the description of the option, used along with the label to generate a list of the available options in the Node Description and in the configuration dialogue of the node.

Example:

class CoffeeOptions(EnumParameterOptions):
    CLASSIC = ("Classic", "The classic chocolatey taste, with notes of bitterness and wood.")
    FRUITY = ("Fruity", "A fruity taste, with notes of berries and citrus.")
    WATERY = ("Watery", "A watery taste, with notes of water and wetness.")

classmethod get_all_options(): Returns a list of all options defined in the EnumParameterOptions subclass.

Validation

While each parameter type listed above has default type validation (eg checking if the IntParameter contains only Integers), they also support custom validation via a property-like decorator notation. For instance, this can be used to verify that the parameter value matches a certain criteria (see example below). The validator should be placed below the definition of the corresponding parameter.

Parameter class for primitive integer types.

validator(func)

To be used as a decorator for setting a validator function for a parameter. Note that ‘func’ will be encapsulated in ‘_validator’ and will not be available in the namespace of the class.

Example:

@knext.node(args)
class MyNode:
    num_repetitions = knext.IntParameter(
        label="Number of repetitions",
        description="How often to repeat an action",
        default_value=42
    )
    @num_repetitions.validator
    def validate_reps(value):
        if value > 100:
            raise ValueError("Too many repetitions!")

    def configure(args):
        pass

    def execute(args):
        pass

Parameter Groups

Additionally these parameters can be combined in parameter_groups. These groups are visualized as sections in the configuration dialog. Another benefit of defining parameter groups is the ability to provide group validation. As opposed to only being able to validate a single value when attaching a validator to a parameter, group validators have access to the values of all parameters contained in the group, allowing for more complex validation routines.

knime.extension.parameter_group(label: str, since_version: Version | str | None = None, is_advanced: bool = False)

Decorator for classes implementing parameter groups. Parameter group classes can define parameters and other parameter groups both as class-level attributes and as instance-level attributed inside the __init__ method.

Parameter group classes can set values for their parameters inside the __init__ method during the constructor call (e.g. from the node containing the group, or another group). Note: when declaring the keyword arguments for the __init__ method of your parameter group class, you should refrain from using keywords from the following list of reserved keywords: since_version, is_advanced, and validator. These are used by the wrapper class in order to enable the backend functionality.

Group validators need to raise an exception if a values-based condition is violated, where values is a dictionary of parameter names and values. Group validators can be set using either of the following methods:

By implementing the “validate(self, values)” method inside the class definition of the group.

Example:

def validate(self, values):
    assert values['first_param'] + values['second_param'] < 100

By using the “@group_name.validator” decorator notation inside the class definition of the “parent” of the group. The decorator has an optional ‘override’ parameter, set to True by default, which overrides the “validate” method. If ‘override’ is set to False, the “validate” method, if defined, will be called first.

Example:

@hyperparameters.validator(override=False)
def validate_hyperparams(values):
    assert values['first_param'] + values['second_param'] < 100

Example:

@knext.parameter_group(label="My Settings")
class MySettings:
    name = knext.StringParameter("Name", "The name of the person", "Bario")
    num_repetitions = knext.IntParameter("NumReps", "How often do we repeat?", 1, min_value=1)

    @num_repetitions.validator
    def reps_validator(value):
        if value == 2:
            raise ValueError("I don't like the number 2")

@knext.node(args)
class MyNodeWithSettings:
    settings = MySettings()
    def configure(args):
        pass

    def execute(args):
        pass

Tables

Table and Schema are the two classes that are used to communicate tabular data (Table) during execute, or the table structure (Schema) in configure between Python and KNIME.

class knime.extension.Table

This class serves as public API to create KNIME tables either from pandas or pyarrow. These tables can than be sent back to KNIME. This class has to be instantiated by calling either from_pyarrow() or from_pandas()

Creates a view of this Table by slicing rows and columns. The slicing syntax is similar to that of numpy arrays, but columns can also be addressed as index lists or via a list of column names.

The syntax is [column_slice, row_slice]. Note that this is the exact opposite order than in the deprecated scripting API’s ReadTable.

Parameters:

column_slice – A column index, a column name, a slice object, a list of column indices, or a list of column names.
row_slice – Optional: A slice object describing which rows to use.

Returns:

A _TabularView representing a slice of the original Table

Example:

row_sliced_table = table[:, :100] # Get the first 100 rows
column_sliced_table = table[["name", "age"]] # Get all rows of the columns "name" and "age"
row_and_column_sliced_table = table[1:5, :100] # Get the first 100 rows of columns 1,2,3,4

batches() → Iterator[Table]

Returns a generator over the batches in this table. A batch is part of the table with all columns, but only a subset of the rows. A batch should always fit into memory (max size currently 64mb). The table being passed to execute() is already present in batches, so accessing the data this way is very efficient.

Example:

output_table = BatchOutputTable.create()
for batch in my_table.batches():
    input_batch = batch.to_pandas()
    # process the batch
    output_table.append(Table.from_pandas(input_batch))

static from_pandas(data: pandas.DataFrame, sentinel: str | int | None = None, row_ids: str = 'auto')

Factory method to create a Table given a pandas.DataFrame. The index of the data frame will be used as RowKey by KNIME.

Example:

Table.from_pandas(my_pandas_df, sentinel="min")

Parameters:

data – A pandas.DataFrame
sentinel –
Interpret the following values in integral columns as missing value:
- "min" min int32 or min int64 depending on the type of the column
- "max" max int32 or max int64 depending on the type of the column
- a special integer value that should be interpreted as missing value
row_ids –
Defines what RowID should be used. Must be one of the following values:
- "keep": Keep the DataFrame.index as the RowID. Convert the index to strings if necessary.
- "generate": Generate new RowIDs of the format f"Row{i}" where i is the position of the row (from 0 to length-1).
- "auto": If the DataFrame.index is of type int or unsigned int, use f"Row{n}" where n is the index of the row. Else, use “keep”.

static from_pyarrow(data: pyarrow.Table, sentinel: str | int | None = None, row_ids: str = 'auto')

Factory method to create a Table given a pyarrow.Table.

All batches of the table must have the same number of rows. Only the last batch can have less rows than the other batches.

Example:

Table.from_pyarrow(my_pyarrow_table, sentinel="min")

Parameters:

data – A pyarrow.Table
sentinel –
Interpret the following values in integral columns as missing value:
- "min" min int32 or min int64 depending on the type of the column
- "max" max int32 or max int64 depending on the type of the column
- a special integer value that should be interpreted as missing value
row_ids –
Defines what RowID should be used. Must be one of the following values:
- "keep": Use the first column of the table as RowID. The first column must be of type string.
- "generate": Generate new RowIDs of the format f"Row{i}" where i is the position of the row (from 0 to length-1).
- "auto": Use the first column of the table if it has the name “<RowID>” and is of type string or integer.
  - If the “<RowID>” column is of type string, use it directly
  - If the “<RowID>” column is of an integer type use f"Row{n} where n is the value of the integer column.
  - Generate new RowIDs ("generate") if the first column has another type or name.

remove(slicing: str | int | List[str])

Implements remove method for Columnar data structures. The input can be a column index, a column name or a list of column names.

If the input is a column index, the column with that index will be removed. If it is a column name, then the first column with matching name is removed. Passing a list of column names will filter out all (including duplicate) columns with matching names.

Parameters:

slicing – Can be of type integer representing the index in column_names to remove. Or a list of strings removing every column matching from that list. Or a string of which first occurence is removed from the column_names.

Returns:

A View missing the columns to be removed.

Raises:

ValueError if no matching column is found given a list or str –
IndexError if column is accessed by integer and is out of bounds –
TypeError if the key is neither a integer nor a string or list of strings. –

abstract property schema: Schema: The schema of this table, containing column names, types, and potentially metadata

to_batches() → Iterator[Table]: Alias for Table.batches()

to_pandas(sentinel: str | int | None = None) → pandas.DataFrame

Access this table as a pandas.DataFrame.

Parameters:

sentinel –

Replace missing values in integral columns by the given value, one of:

"min" min int32 or min int64 depending on the type of the column
"max" max int32 or max int64 depending on the type of the column
An integer value that should be inserted for each missing value

to_pyarrow(sentinel: str | int | None = None) → pyarrow.Table

Access this table as a pyarrow.Table.

Parameters:

sentinel –

Replace missing values in integral columns by the given value, one of:

"min" min int32 or min int64 depending on the type of the column
"max" max int32 or max int64 depending on the type of the column
An integer value that should be inserted for each missing value

class knime.extension.BatchOutputTable

An output table generated by combining smaller tables (also called batches).

All batches must have the same number, names and types of columns.

All batches except the last batch must have the same number of rows. The last batch can have less rows than the other batches.

Does not provide means to continue to work with the data but is meant to be used as a return value of a Node’s execute() method.

abstract append(batch: Table | pandas.DataFrame | pyarrow.Table | pyarrow.RecordBatch) → None: Append a batch to this output table. The first batch defines the structure of the table, and all subsequent batches must have the same number of columns, column names and column types.

Note

Keep in mind that the RowID will be handled according to the “row_ids” mode chosen in BatchOutputTable.create.

static create(row_ids: str = 'keep')

Create an empty BatchOutputTable

Parameters:

row_ids –

Defines what RowID should be used. Must be one of the following values:

"keep":
- For appending DataFrames: Keep the DataFrame.index as the RowID. Convert the index to strings if necessary.
- For appending Arrow tables or record batches: Use the first column of the table as RowID. The first column must be of type string.
"generate": Generate new RowIDs of the format f"Row{i}"

static from_batches(generator, row_ids: str = 'generate')

Create output table where each batch is provided by a generator

Parameters:: row_ids – See BatchOutputTable.create.

abstract property num_batches: int: The number of batches written to this output table

class knime.extension.Schema(ktypes: List[KnimeType | Type], names: List[str], metadata: List | None = None)

A schema defines the data types and names of the columns inside a table. Additionally, it can hold metadata for the individual columns.

__getitem__(slicing: slice | List[int] | List[str]) → _ColumnarView

Creates a view of this Table or Schema by slicing columns. The slicing syntax is similar to that of numpy arrays, but columns can also be addressed as index lists or via a list of column names.

Parameters:: column_slice – A column index, a column name, a slice object, a list of column indices, or a list of column names. For single indices, the view will create a “Column” object. For slices or lists of indices, a new Schema will be returned.
Returns:: A _ColumnarView representing a slice of the original Schema or Table.

Examples:

Get columns 1,2,3,4: sliced_schema = schema[1:5]

Get the columns “name” and “age”: sliced_schema = schema[["name", "age"]]

property column_names: List[str]: Return the list of column names

classmethod deserialize(table_schema: dict) → Schema

Construct a Schema from a dict that was retrieved from KNIME in JSON encoded form as the input to a node’s configure() method.

KNIME provides table information with a RowKey column at the beginning, which we drop before returning the created schema.

classmethod from_columns(columns: Sequence[Column] | Column): Create a schema from a single column or a list of columns

classmethod from_types(ktypes: List[KnimeType | Type], names: List[str], metadata: List | None = None): Create a schema from a list of column data types, names and metadata

property num_columns: The number of columns in this schema

remove(slicing: str | int | List[str])

Implements remove method for Columnar data structures. The input can be a column index, a column name or a list of column names.

If the input is a column index, the column with that index will be removed. If it is a column name, then the first column with matching name is removed. Passing a list of column names will filter out all (including duplicate) columns with matching names.

Parameters:

slicing – Can be of type integer representing the index in column_names to remove. Or a list of strings removing every column matching from that list. Or a string of which first occurence is removed from the column_names.

Returns:

A View missing the columns to be removed.

Raises:

ValueError if no matching column is found given a list or str –
IndexError if column is accessed by integer and is out of bounds –
TypeError if the key is neither a integer nor a string or list of strings. –

serialize() → Dict

Convert this Schema into dict which can then be JSON encoded and sent to KNIME as result of a node’s configure() method.

Because KNIME expects a row key column as first column of the schema, but we don’t include this in the KNIME Python table schema, we insert a row key column here.

Raises:: RuntimeError – if duplicate column names are detected

class knime.extension.Column(ktype: KnimeType | Type, name: str, metadata=None)

A column inside a table schema consists of the knime datatype, a column name and optional metadata.

__init__(ktype: KnimeType | Type, name: str, metadata=None)

Construct a Column from type, name and optional metadata.

Parameters:

ktype – The KNIME type of the column or a type which can be converted via knime.api.schema.logical(ktype) to a KNIME type
name – The name of the column. May not be empty.
metadata – Metadata of this column as dictionary

Raises:

TypeError – if the type is no KNIME type or cannot be converted to a KNIME type
ValueError – if the name is empty

Data Types

These are helper functions to create KNIME compatible datatypes. For instance, if a new column is created.

knime.extension.int32(): Create a KNIME integer type with 32 bits

knime.extension.int64(): Create a KNIME integer type with 64 bits

knime.extension.double(): Create a KNIME floating point type with double precision (64 bits)

knime.extension.bool_(): Create a KNIME boolean type

knime.extension.string(dict_encoding_key_type: DictEncodingKeyType | None = None)

Create a KNIME string type.

Parameters:: dict_encoding_key_type – The key type to use for dictionary encoding. If this is None (the default), no dictionary encoding will be used. Dictionary encoding helps to reduce storage space and read/write performance for columns with repeating values such as categorical data.

knime.extension.blob(dict_encoding_key_type: DictEncodingKeyType | None = None)

Create a KNIME blob type for binary data of variable length

Parameters:: dict_encoding_key_type – The key type to use for dictionary encoding. If this is None (the default), no dictionary encoding will be used. Dictionary encoding helps to reduce storage space and read/write performance for columns with repeating values such as categorical data.

knime.extension.list_(inner_type: KnimeType)

Create a KNIME type that is a list of the given inner types

Parameters:: inner_type – The type of the elements in the list. Must be a KnimeType

knime.extension.struct(*inner_types)

Create a KNIME structured data type where each given argument represents a field of the struct.

Parameters:: inner_types – The argument list of this method defines the fields in this structured data type. Each inner type must be a KNIME type

knime.extension.logical(value_type) → LogicalType

Create a KNIME logical data type of the given Python value type.

Parameters:: value_type – The type of the values inside this column. A knime.api.types.PythonValueFactory must be registered for this type.
Raises:: TypeError – if no PythonValueFactory has been registered for this value type with knime.api.types.register_python_value_factory

Views

knime.scripting.io.view(obj) → NodeView

Create an NodeView for the given object.

This method tries to find out the best option to display the given object. First, the method checks if a special view implementation (listed below) exists for the given object. Next, IPython _repr_html_, _repr_svg_, _repr_png_, or _repr_jpeg_ are used.

Special view implementations:

HTML: The obj must be of type str and start with “<!DOCTYPE html>”. The document must be self-contained and must not reference external resources. Links to external resources will be opened in an external browser.
SVG: The obj must be of type str and contain a valid SVG
PNG: The obj must be of type bytes and contain a PNG image file
JPEG: The obj must be of type bytes and contain a JPEG image file
Matplotlib: The obj must be a matplotlib.figure.Figure
Plotly: The obj must be a plotly.graph_objects.Figure

Parameters:: obj – The object which should be displayed
Raises:: ValueError – If no view could be created for the given object

knime.scripting.io.view_matplotlib(fig=None, format='png') → NodeView

Create a view showing the given matplotlib figure.

The figure is displayed by exporting it as an SVG. If no figure is given the current active figure is displayed. Note that the figure is closed and should not be used after calling this method.

Parameters:

fig – A matplotlib.figure.Figure which should be displayed.
format – Format of the view inside the HTML document. Either “png” or “svg”.

Raises:

ImportError – If matplotlib is not available.
TypeError – If the figure is not a matplotlib figure.

knime.scripting.io.view_seaborn() → NodeView

Create a view showing the current active seaborn figure.

This fuction just calls view_matplotlib() because seaborn plots are just matplotlib figures under the hood.

Raises:: ImportError – If matplotlib is not available.

knime.scripting.io.view_plotly(fig) → NodeView

Create a view showing the given plotly figure.

The figure is displayed by exporting it as an HTML document.

To be able to synchronize the selection between the view and other KNIME views the customdata of the figure traces must be set to the RowID.

Example:

fig = px.scatter(df, x="my_x_col", y="my_y_col", color="my_label_col",
                 custom_data=[df.index])
node_view = view_plotly(fig)

Parameters:

fig – A plotly.graph_objects.Figure object which should be displayed.

Raises:

ImportError – If plotly is not available.
TypeError – If the figure is not a plotly figure.

knime.scripting.io.view_html(html: str, svg_or_png: str | bytes | None = None, render_fn: Callable[[], str | bytes] | None = None) → NodeView

Create a NodeView that displays the given HTML document.

The document must be self-contained and must not reference external resources. Links to external resources will be opened in an external browser.

Parameters:

html – A string containing the HTML document.
svg_or_png – A rendered representation of the HTML page. Either a string containing an SVG or a bytes object containing an PNG image
render_fn – A callable that returns an SVG or PNG representation of the page

knime.scripting.io.view_svg(svg: str) → NodeView

Create a NodeView that displays the given SVG.

Parameters:: svg – A string containing the SVG.

knime.scripting.io.view_png(png: bytes) → NodeView

Create a NodeView that displays the given PNG image.

Parameters:: png – The bytes of the PNG image

knime.scripting.io.view_jpeg(jpeg: bytes) → NodeView

Create a NodeView that displays the given JPEG image.

Parameters:: jpeg – The bytes of the JPEG image

knime.scripting.io.view_ipy_repr(obj) → NodeView

Create a NodeView by using the IPython _repr_*_ function of the object.

Tries to use * _repr_html_ * _repr_svg_ * _repr_png_ * _repr_jpeg_ in this order.

Parameters:: obj – The object which should be displayed
Raises:: ValueError – If no view could be created for the given object

class knime.scripting.io.NodeView(html: str, svg_or_png: str | bytes | None = None, render_fn: Callable[[], str | bytes] | None = None)

A view of a KNIME node that can be displayed for the user.

Do not create a NodeView directly but use the utility functions view, view_html, view_svg, view_png, and view_jpeg.

Port Objects

Port Object Specs

class knime.extension.PortObjectSpec

Base protocol for port object specs.

A PortObjectSpec must support conversion from/to a dictionary which is then encoded as JSON and sent to/from KNIME.

class knime.extension.BinaryPortObjectSpec(id: str)

Port object spec for simple binary port objects.

BinaryPortObjectSpecs have an ID that is used to ensure that only ports with equal ID can be connected.

class knime.extension.ImagePortObjectSpec(format: str | Enum)

Port object spec for image port objects.

ImagePortObjectSpec objects require the format specified via knext.ImageFormat.PNG or knext.ImageFormat.SVG.

Custom Port Object Types

class knime.extension.PortObject(spec: PortObjectSpec)

Base class for custom port objects. The must have a corresponding PortObjectSpec and support serialization from and to bytes.

abstract classmethod deserialize(spec: PortObjectSpec, storage: bytes) → PortObject: Creates the port object from its spec and storage.

abstract serialize() → bytes: Serializes the object to bytes.

property spec: PortObjectSpec: Provides access to the spec of the PortObject.

class knime.extension.ConnectionPortObject(spec: PortObjectSpec)

Connection port objects are a special type of port objects which support dealing with non-serializable objects such as database connections or web sessions.

Connection port objects are passed downstream by ensuring that the same Python process is used to execute subsequent nodes. ConnectionPortObjects must provide the data in the to_connection_data and create new instances from the same data in from_connection_data. A reference to the data Python object is maintained and handed to downstream nodes. So the data does not need to be serializable/picklable.

abstract classmethod from_connection_data(spec: PortObjectSpec, data: Any) → ConnectionPortObject

Construct a ConnectionPortObject from spec and data. The data is the data that has been returned by the to_connection_data method of the ConnectionPortObject by the upstream node.

The data should not be tempered with, as it is a Python object that is handed to all nodes using this ConnectionPortObject.

property spec: PortObjectSpec: Provides access to the spec of the PortObject.

abstract to_connection_data() → Any: Provide the data that makes up this ConnectionPortObject such that it can be used by downstream nodes in the from_connection_data method.