Accessing Output Datasets

To access the data in Python's output ports, use dedicated objects: OutputTable and OutputTables[]:

  • Use the N index of a corresponding Output port N to distinguish data sources: OutputTables[N]
  • The first output data port, present by default, has index 0, not displayed in the port's name. (I.e., the port name shown is just Output dataset.) To access data in this default port, use a special object: OutputTable

OutputTable properties

Columns

Columns

Contains the read-only iterable collection of columns, implements the Mapping and Sequence protocols, and returns a value of type ColumnsClass. Elements can be accessed via bracket notation [] by names and by indexes. If the Allow creating output columns in script checkbox is marked, the collection elements are of type ConfigurableOutputColumnClass; otherwise, they are OutputColumnClass. Both of these types are inherited from ColumnClass and implement the Sequence protocol (see Full API Description).

ColumnCount

ColumnCount

Contains the read-only count of columns in the output dataset. Returns a value of type int.

RowCount

RowCount

Contains the read-only count of rows in the output dataset. Returns a value of type int.

OutputTable methods

Get

Get(row, col)

  • row: The row index. Accepts a value of type int.
  • col: The column index or name. Accepts values of type int or str.

The method returns the value of the specified column in the specified row. The returned value can be of the following types: bool, int, float, str, datetime.datetime, or None.

IsNull

IsNull(row, col)

  • row: The row index. Accepts a value of type int.
  • col: The column index or name. Accepts values of type int or str.

The method returns a boolean value true if the column in the specified row has a missing value. Otherwise, it returns false.

GetColumn

GetColumn(col)

  • col: The column index or name. Accepts values of type int or str. If the Allow creating output columns in script checkbox is marked, a value of type ConfigurableOutputColumnClass is returned; otherwise, OutputColumnClass is returned. Both of these types are inherited from ColumnClass and implement the Sequence protocol (see Full API Description).
Append

Append()

This method adds a new row to the output dataset. It has no arguments.

Set

Set(col, value)

  • col: The column index or name. Accepts values of type int or str.
  • value: The value of the following types: bool, int, float, str, datetime.datetime, or None.

This method sets the value of the specified column in the row added by the Append() method.

AssignColumns

AssignColumns(array)

  • array: An iterable object containing elements of type ColumnInfo (see Full API Description). This method creates columns for the output dataset from a collection of ColumnInfo elements.
AddColumn

AddColumn(ColumnInfo, Name, DisplayName, DataType, DataKind, DefaultUsageType)

Accepts arguments by keyword:

  • ColumnInfo: A value of type ColumnInfo (see Full API Description). Optional argument.
  • Name: The column name, a value of type str. Optional argument.
  • DisplayName: The column label, a value of type str. Optional argument.
  • DataType: The column data type, a value of type int (see Enumerations). Optional argument.
  • DataKind: The column's data kind, a value of type int (see Enumerations). Optional argument.
  • DefaultUsageType: Default usage of the column, a value of type int (see Enumerations). Optional argument.

This method inserts a column at the end of the output set's column list. It returns a value of type OutputColumnClass (see Full API Description).

InsertColumn

InsertColumn(Index, ColumnInfo, Name, DisplayName, DataType, DataKind, DefaultUsageType)

Accepts arguments by keywords:

  • Index: The index of the column in the column collection. Accepts a value of type int.
  • ColumnInfo: A value of type ColumnInfo (see Full API Description). Optional argument.
  • Name: The column name, a value of type str. Optional argument.
  • DisplayName: The column label, a value of type str. Optional argument.
  • DataType: The column's data type, a value of type int (see Enumerations). Optional argument.
  • DataKind: The column's data kind, a value of type int (see Enumerations). Optional argument.
  • DefaultUsageType: Default usage of the column, a value of type int (see Enumerations). Optional argument.

This method inserts a column at the specified index in the output dataset. It returns a value of type OutputColumnClass (see Full API Description).

DeleteColumn

DeleteColumn(col)

  • col: The column's index or name. Accepts values of type int or str.

This method deletes a column by its name or index.

ClearColumns

ClearColumns()

This method clears the column list. It has no arguments.

Using the builtin_pandas_utils module

If the Allow creating output columns in script option is enabled, the following methods are available:

prepare_compatible_table

prepare_compatible_table(table, dataframe, with_index)

This method sets the field structure of an OutputTable from a pandas.DataFrame. Arguments:

  • table: A reference to the output OutputTableClass dataset.
  • dataframe: A reference to the pandas.DataFrame whose structure is used to create the output dataset columns.
  • with_index: If this argument is True, the pandas.DataFrame indexes are included in the structure of the output dataset. This is an optional argument. The default value is False.
fill_table

fill_table(table, dataframe, with_index)

This method writes data from a pandas.DataFrame to an OutputTable. Arguments:

  • table: A reference to the output dataset. Accepts a value of type OutputTableClass.
  • dataframe: A reference to the pandas.DataFrame.

  • with_index: If this argument is True, the pandas.DataFrame indexes are exported to the output dataset. This is an optional argument. The default value is False.

You can find an example of using builtin_pandas_utils module below (see Example 2).

Examples

Example 1 

from builtin_data import InputTable, OutputTable, DataType, DataKind, UsageType
import datetime

#Copying columns from the input dataset
OutputTable.AssignColumns(InputTable.Columns)
#Deleting a column by index
OutputTable.DeleteColumn(0)
#Deleting a column by name
OutputTable.DeleteColumn("Test1")
#Deleting the entire list of columns
OutputTable.ClearColumns()
#Adding a column to the end of the output dataset's column list
OutputTable.AddColumn(Name="COL0",
                      DisplayName="Date/Time",
                      DataType=DataType.DateTime,
                      DataKind=DataKind.Continuous,
                      DefaultUsageType=UsageType.Active)
#Inserting a column at a specified index in the output dataset's column list
OutputTable.InsertColumn(Index=0,
                         Name="COL1",
                         DisplayName="Flag",
                         DataType=DataType.Boolean)
#Getting a reference to a column by name
COL0 = OutputTable.GetColumn("COL0")
COL1 = OutputTable.GetColumn("COL1")
#Outputting the column property values
print("Index: ", COL1.Index)
print("Name: ", COL1.Name)
print("DisplayName: ", COL1.DisplayName)
print("DataType: ", COL1.DataType)
print("DataKind: ", COL1.DataKind)
print("DefaultUsageType: ", COL1.DefaultUsageType)

#Adding a row to the output dataset
OutputTable.Append()
#The current Date/Time is written to the field with index 0
OutputTable.Columns[1].Set(datetime.datetime.now())
#The value "true" is written to the field with index 1
OutputTable.GetColumn(0).Set(True)
#Copying the values of the first row to the second
OutputTable.Append()
for i in range(OutputTable.ColumnCount):
    value = OutputTable.Get(0, i)
    OutputTable.Set(i, value)

#Checking that the value in the row with index 0 in the column with index 1 is not defined
print(OutputTable.IsNull(0, 1))
print(OutputTable.Get(0, 1) is None)

print("RowCount = ", OutputTable.RowCount)
#Output: RowCount =  2

Example 2

Using the builtin_pandas_utils module.

from builtin_data import InputTable, OutputTable, ConfigurableOutputTableClass
from builtin_pandas_utils import to_data_frame, prepare_compatible_table, fill_table

#The input port is optional and may not contain data
if InputTable:
    #Create a pd.DataFrame from the input dataset
    input_frame = to_data_frame(InputTable)
    #Grouping input_frame
    output_frame = input_frame.groupby(["Class"]).sum()
    #If the "Allow creating output columns in script" option is enabled,
    #the structure of the output dataset can be prepared from a pd.DataFrame
    assert isinstance(OutputTable, ConfigurableOutputTableClass)
    #Defining the structure of the output dataset
    prepare_compatible_table(OutputTable, output_frame, with_index=True)
    #Filling the output dataset
    fill_table(OutputTable, output_frame, with_index=True)

Read on: Python: Enumerations

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